Intelligent LED illumination energy-saving optimization method and system based on AI algorithm

Abstract

The invention discloses an intelligent LED illumination energy-saving optimization method and system based on an AI algorithm, and particularly relates to the field of intelligent LED illumination energy-saving optimization, and the method comprises a data batch division step, an LED data collection step, an LED data analysis step, an LED data reanalysis step, an LED data comprehensive evaluation step and a real-time early warning feedback step. The LED data acquisition step comprises a lamp structural mechanical data acquisition link and a lamp electro-optical data acquisition link and is used for acquiring target data in real time; the LED data analysis step comprises a vibration data analysis node, a connection data analysis node, an acoustic data analysis node, an electromagnetic field data analysis node, a photoelectric data analysis node and an optical feedback data node, and is used for analyzing the collected target data; according to the invention, the data of the intelligent LED lighting system is divided into the data batches, so that the pertinence and effectiveness of the lighting data are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent LED lighting energy-saving optimization, and more specifically, to an intelligent LED lighting energy-saving optimization method and system based on AI algorithm. Background Art

[0002] With the rapid development of intelligent technology and the widespread popularization of the concept of energy conservation and emission reduction, the optimization of LED lighting energy conservation has become increasingly important in the field of architectural lighting, and the requirements for its energy-saving effect and the accuracy of intelligent control have become increasingly stringent. The intelligent LED lighting energy-saving optimization method and system based on AI algorithm have brought new challenges and opportunities to the design and management of intelligent building lighting due to its key role in reducing lighting energy consumption and improving lighting comfort. Efficient energy-saving optimization can ensure the stable operation of the lighting system, reduce energy waste, and meet the needs of green and low-carbon buildings.

[0003] Existing intelligent LED lighting energy-saving optimization mainly includes lighting environment monitoring, data transmission and storage, AI algorithm analysis, and lighting strategy formulation. Through the monitoring of the lighting environment, various types of sensors and monitoring equipment are set up in various areas of the building, such as corridors, offices, and conference rooms, to collect data such as light intensity, personnel activities, time information, and natural light changes in the lighting environment, so as to achieve extensive monitoring of the current lighting situation; through the transmission and storage of data, the collected data is processed using wireless transmission technology and cloud storage platforms to ensure the stability and integrity of the data during transmission and storage; through the analysis of AI algorithms, based on the functions of the lighting area, personnel usage habits, and the characteristics of lighting needs in different time periods, specific AI algorithms are used to conduct in-depth analysis of the data, and possible lighting energy-saving optimization models and strategy frameworks are constructed to provide a basis for determining lighting strategies; through the formulation of lighting strategies, managers can intuitively understand the relevant information on lighting optimization and the response of the lighting system, realize real-time adjustment and optimization of lighting strategies, and timely revise lighting plans to improve the scientificity and adaptability of lighting energy-saving optimization.

[0004] However, this optimization still has some shortcomings in practical applications. For example, in terms of lighting environment monitoring, due to the limitations of sensor layout and monitoring accuracy, there may be some missing or errors in the collection of important lighting data, resulting in incomplete collected data, which has an adverse effect on subsequent AI algorithm analysis; in terms of lighting strategy formulation, facing complex building functional layout and changing usage scenarios, it is easy to cause the problem of unreasonable and incomplete data analysis results, which may cause managers to be unable to quickly and accurately determine lighting strategies, reducing the effect of smart LED lighting energy-saving optimization.

[0005] Therefore, there is an urgent need to provide an intelligent LED lighting energy-saving optimization method and system based on AI algorithm to solve the problems of incomplete data collection and inaccurate analysis results during optimization of existing intelligent LED lighting energy-saving, and further improve the energy saving and sustainability of the operation of the intelligent lighting system. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent LED lighting energy-saving optimization method and system based on an AI algorithm, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above object, the present invention provides the following technical solution: an intelligent LED lighting energy-saving optimization method based on AI algorithm, comprising: S1, data batch division step: used to determine the data to be collected as target data, divide the target data into different collection batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; S2, LED data collection step: including lamp structure mechanics data collection link and lamp electro-optical data collection link, used to collect target data in real time; the lamp structure mechanics data collection link is used to collect vibration data, connection data and acoustic data; the lamp electro-optical data collection link is used to collect electromagnetic field data, photoelectric data and optical feedback data; S3, LED data analysis step: including vibration data analysis node, connection data analysis node, acoustic data analysis node, electromagnetic field data analysis node, photoelectric data analysis node and optical feedback data node, used to analyze the collected target data; S4, LED data re-analysis step: including the lamp structure mechanics data analysis link and the lamp electro-optical data analysis link, used to re-analyze the results of the LED data analysis step; S5, LED data comprehensive evaluation step: including intelligent LED lighting energy-saving data analysis step, used to comprehensively evaluate the results calculated in the LED data reanalysis step; S6, real-time warning feedback step: used to establish a preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, judge the value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index according to the preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0008] Preferably, the vibration data includes the working vibration frequency Fv of the lamp, the peak amplitude Pa of the lamp and the vibration direction concentration Dc of the lamp; the connection data includes the wear depth Cd of the connecting parts, the residual value Rp of the lamp connection preload and the offset Fr of the lamp connection resonance frequency; the acoustic data includes the power spectrum density Hf of the lamp in the Hertz frequency band, the change rate of the lamp noise sound intensity Gn and the Hertz fluctuation Fh of the lamp; the electromagnetic field data includes the electric field and magnetic field coupling Mc inside the lamp, the power slope Sp of the lamp coupling field and the spatial unevenness Sc of the lamp coupling field; the photoelectric data includes the response time Pr of the photocurrent of the lamp, the saturation amount Ps of the photocurrent of the lamp and the color temperature gradient Gt of the photocurrent of the lamp; the optical feedback data includes the peak value Fp of the light feedback voltage fluctuation of the lamp, the modulation amplitude Ma of the light feedback current of the lamp and the electrical recovery time Er of the light feedback of the lamp.

[0009] Preferably, the vibration data analysis node is used to establish a vibration data calculation model, and the vibration data collected in the data collection step is imported into the vibration data calculation model to obtain the vibration energy consumption influence coefficient value. The vibration data calculation model is specifically expressed as: , Among them, α i Indicates the vibration energy consumption influence coefficient value calculated for the i-th time, Fv i represents the working vibration frequency of the lamp collected for the i-th time, Pa i Indicates the peak value of the lamp amplitude collected for the i-th time, Dc i Indicates the concentration of the lamp vibration direction collected for the i-th time.

[0010] Preferably, the connection data analysis node is used to establish a connection data calculation model, import the connection data collected in the data collection step into the connection data calculation model, and obtain a connection stability energy saving coefficient value. The connection data calculation model is specifically expressed as: , Among them, β i Represents the connection stability energy saving coefficient value calculated for the i-th time, Cd i represents the wear depth of the connecting component collected at the i-th time, Rp i Indicates the residual value of the preload force of the lamp connection collected for the i-th time, Fr i Indicates the offset of the lamp connection resonance frequency collected for the i-th time.

[0011] Preferably, the acoustic data analysis node is used to establish an acoustic data calculation model, and the acoustic data collected in the data collection step is imported into the acoustic data calculation model to obtain the noise energy saving impact coefficient value. The acoustic data calculation model is specifically expressed as: , Among them, χi Indicates the noise energy saving impact coefficient value calculated for the i-th time, Hf i Gn represents the power spectrum density of the lamp Hertz band collected for the i-th time, i Indicates the rate of change of the noise intensity of the lamp collected for the i-th time, Fh i Indicates the Hertz fluctuation of the lamp collected for the i-th time.

[0012] Preferably, the electromagnetic field data analysis node is used to establish an electromagnetic field data calculation model, and the electromagnetic field data collected in the data collection step is imported into the electromagnetic field data calculation model to obtain the electromagnetic coupling energy-saving efficacy coefficient value. The electromagnetic field data calculation model is specifically expressed as: , Among them, ∂ i Indicates the electromagnetic coupling energy-saving efficiency coefficient value calculated for the i-th time, Mc i Sp represents the electric and magnetic field coupling of the lamp collected for the i-th time. i Sc represents the slope of the lamp coupling field power collected for the i-th time. i Represents the spatial unevenness of the lamp coupling field collected for the i-th time.

[0013] Preferably, the photoelectric data analysis node is used to establish a photoelectric data calculation model, and the photoelectric data collected in the data collection step is imported into the photoelectric data calculation model to obtain a photoelectric conversion energy-saving benefit coefficient value. The photoelectric data calculation model is specifically expressed as: , Among them, γ i represents the energy-saving benefit coefficient of photoelectric conversion calculated for the i-th time, Pr i Indicates the duration of the photocurrent response of the illuminant collected for the i-th time, Ps i Indicates the saturation amount of the light current collected for the i-th time, Gt i represents the color temperature gradient of the lamp photocurrent collected for the i-th time, Represents the standard deviation of the color temperature gradient of the lamp photocurrent, Represents the average color temperature gradient of the lamp photocurrent.

[0014] Preferably, the optical feedback data analysis node is used to establish an optical feedback data calculation model, and the optical feedback data collected in the data collection step is imported into the optical feedback data calculation model to obtain the optical feedback energy saving loss coefficient value, and the optical feedback data calculation model is specifically expressed as: , Among them, ℓ i represents the optical feedback energy saving loss coefficient value calculated for the i-th time, Fp i Indicates the peak value of the light feedback voltage fluctuation of the lamp collected for the i-th time, Mai represents the modulation amplitude of the light feedback current of the lamp collected for the i-th time, Er i Indicates the electrical recovery time of the light feedback of the lamp collected for the i-th time.

[0015] Preferably, the lamp structure mechanics data analysis link is used to establish a lamp structure mechanics data calculation model, and the vibration energy consumption influence coefficient value, connection stability energy saving coefficient value and noise energy saving influence coefficient value calculated in the LED data analysis step are introduced into the lamp structure mechanics data calculation model to obtain a structural mechanics energy saving comprehensive evaluation index value. The lamp structure mechanics data calculation model is specifically expressed as: , Among them, ES i represents the comprehensive evaluation index value of structural mechanics energy saving calculated for the i-th time, α i represents the vibration energy consumption influence coefficient value calculated for the i-th time, β i represents the connection stability energy saving coefficient value calculated for the i-th time, χ i Represents the noise energy-saving impact coefficient value calculated for the i-th time.

[0016] Preferably, the lamp electro-optical data analysis link is used to establish a lamp electro-optical data analysis calculation model, and the electromagnetic coupling energy-saving efficacy coefficient value, photoelectric conversion energy-saving benefit coefficient value and optical feedback energy-saving loss coefficient value calculated in the LED data analysis step are introduced into the lamp electro-optical data analysis calculation model to obtain the electromagnetic photoelectric energy-saving comprehensive evaluation index value. The lamp electro-optical data analysis calculation model is specifically expressed as: , Among them, EP i represents the electromagnetic and photoelectric energy-saving comprehensive evaluation index value calculated for the i-th time, ∂ i represents the electromagnetic coupling energy-saving efficiency coefficient value calculated for the i-th time, γ i represents the energy-saving efficiency coefficient of photoelectric conversion calculated for the i-th time, ℓ i Represents the optical feedback energy saving loss coefficient value calculated for the i-th time.

[0017] Preferably, the intelligent LED lighting energy-saving data analysis link is used to establish an intelligent LED lighting energy-saving data calculation model, and the structural mechanics energy-saving comprehensive evaluation index value and the electromagnetic photoelectric energy-saving comprehensive evaluation index value calculated in the LED data reanalysis step are introduced into the intelligent LED lighting energy-saving data calculation model to obtain the intelligent LED lighting energy-saving optimization comprehensive evaluation index value. The intelligent LED lighting energy-saving data calculation model is specifically expressed as: , Among them, A represents the calculated comprehensive evaluation index value of intelligent LED lighting energy saving optimization, ES irepresents the comprehensive evaluation index value of structural mechanics energy saving calculated for the i-th time, EP i Represents the electromagnetic and photoelectric energy-saving comprehensive evaluation index value calculated for the i-th time.

[0018] Preferably, an intelligent LED lighting energy-saving optimization system based on AI algorithm comprises: Data batch division module: used to determine the data to be collected as target data, divide the target data into different collection batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; LED data acquisition module: including a lamp structural mechanics data acquisition unit and a lamp electro-optical data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the LED data analysis module; the lamp structural mechanics data acquisition unit is used to acquire vibration data, connection data and acoustic data; the lamp electro-optical data acquisition unit is used to acquire electromagnetic field data, photoelectric data and optical feedback data; LED data analysis module: including vibration data analysis node, connection data analysis node, acoustic data analysis node, electromagnetic field data analysis node, photoelectric data analysis node and optical feedback data node, used to analyze the collected target data and transmit the analysis results to the LED data re-analysis module; LED data re-analysis module: including a lamp structure mechanics data analysis unit and a lamp electro-optical data analysis unit, used to re-analyze the results of the LED data analysis module and transmit the re-analysis results to the LED data comprehensive evaluation module; LED data comprehensive evaluation module: including an intelligent LED lighting energy-saving data analysis unit, which is used to comprehensively evaluate the results calculated in the LED data reanalysis module and transmit the evaluation results to the real-time early warning feedback module; Real-time early warning feedback module: used to establish a preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, judge the value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index according to the preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0019] Technical effects and advantages of the present invention: The present invention effectively improves the pertinence and effectiveness of lighting data by dividing the data of the intelligent LED lighting system into various data batches. By collecting vibration data, connection data, acoustic data, electromagnetic field data, photoelectric data, and optical feedback data in multiple dimensions, this multi-faceted data collection mode has greatly broken through the limitations of the single traditional lighting energy-saving optimization data, and provides a rich and reliable information basis for subsequent precise scientific optimization; the present invention deeply interprets the collected data through data analysis, and then calculates the vibration energy consumption influence coefficient value, connection stability energy-saving coefficient value, noise energy-saving influence coefficient value, electromagnetic coupling energy-saving efficacy coefficient value, photoelectric conversion energy-saving benefit coefficient value, optical feedback energy-saving loss coefficient value, structural mechanics energy-saving comprehensive evaluation index value, and electromagnetic photoelectric energy-saving comprehensive evaluation index value. The index value clearly points out the factors that may affect the lighting energy-saving optimization effect; the present invention obtains the comprehensive evaluation index value of the energy-saving optimization of the entire intelligent LED lighting system by comprehensively considering and organically integrating the data analysis results, which significantly improves the scientificity and feasibility of the optimization plan; through real-time monitoring and continuous follow-up, once lighting abnormalities or energy consumption deviations are detected, early warning information is immediately issued, and relevant staff can timely understand the status and potential problem points of the lighting system, and quickly take measures to adjust and improve lighting parameters and energy-saving strategies, so as to provide a solid guarantee for achieving high-quality intelligent LED lighting energy-saving optimization based on AI algorithms and sustainable energy conservation. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 It is a schematic diagram of the structure of the method of the present invention.

[0020] Figure 2 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] As attached Figure 1 An intelligent LED lighting energy-saving optimization method based on an AI algorithm is shown, comprising: S1, data batch division step: used to determine the data to be collected as target data, divide the target data into different collection batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; What needs to be specifically explained in this embodiment is that the method of dividing by equal time is divided according to the characteristics of each target data. According to the real-time requirements of each target data, it is collected once at equal intervals and divided into different collection batches.

[0023] S2, LED data collection steps: including lamp structure mechanics data collection link and lamp electro-optical data collection link, used to collect target data in real time; the lamp structure mechanics data collection link is used to collect vibration data, connection data and acoustic data; the lamp electro-optical data collection link is used to collect electromagnetic field data, photoelectric data and optical feedback data.

[0024] In this embodiment, it is specifically necessary to explain that: the vibration data includes the working vibration frequency Fv of the lamp, the peak amplitude Pa of the lamp and the concentration Dc of the vibration direction of the lamp; the connection data includes the wear depth Cd of the connection component, the residual value Rp of the preload force of the lamp connection and the offset Fr of the resonance frequency of the lamp connection; the acoustic data includes the power spectrum density Hf of the lamp in the Hertz frequency band, the change rate of the noise sound intensity of the lamp Gn and the Hertz fluctuation of the lamp Fh; the electromagnetic field data includes the electric field and magnetic field coupling Mc of the lamp, the power slope Sp of the coupling field of the lamp and the spatial unevenness Sc of the coupling field of the lamp; the photoelectric data includes the response time Pr of the photocurrent of the lamp, the saturation amount Ps of the photocurrent of the lamp and the color temperature gradient Gt of the photocurrent of the lamp; the optical feedback data includes the peak value Fp of the light feedback voltage fluctuation of the lamp, the modulation amplitude Ma of the light feedback current of the lamp and the electrical recovery time Er of the light feedback of the lamp.

[0025] The working vibration frequency of the lamp: abnormal vibration frequency may indicate problems with the internal components of the lamp, such as loose or unbalanced components. When this happens, the motor and other operating parts of the lamp may consume more electricity due to increased friction or reduced working efficiency. For example, if the speed of the cooling fan is unstable due to abnormal vibration frequency, it will affect the heat dissipation effect, causing the internal temperature of the lamp to rise, thereby reducing the working efficiency of the LED chip, and converting more electrical energy into heat rather than light energy, increasing energy consumption. By monitoring the vibration frequency, these problems can be discovered and solved in a timely manner, ensuring that the lamp works in a normal state, reducing unnecessary power loss, and thus achieving energy saving; the collection method is: a high-precision piezoelectric acceleration sensor is attached to multiple key positions such as the top and side of the lamp housing, and its measurement accuracy must reach 0.01 Hz, and the frequency response range covers the vibration frequency range that the lamp may generate (such as 0-500 Hz). After the lamp is turned on and continues to work normally for 30 minutes, when the internal temperature, current and other parameters of the lamp are stable, data collection begins. The data acquisition system collects sensor signals at a sampling rate of 100 times per second for 5 minutes. After the acquisition is completed, the collected time domain signal is processed by fast Fourier transform through professional vibration analysis software, and it is converted into a frequency domain signal to obtain the spectrum diagram of the vibration frequency. The main vibration frequency components and their corresponding amplitudes are determined from the spectrum diagram, and the stable working vibration frequency is screened out and recorded.

[0026] The peak amplitude of the lamp directly reflects the intensity of the lamp vibration. A large vibration amplitude will cause serious damage to the electrical connection and optical structure inside the lamp. In terms of electricity, vibration may loosen the circuit welding points and cause friction between the wires and the terminals, resulting in a gradual increase in contact resistance. According to Joule's law, when the contact resistance increases, more heat will be generated when the current passes through. This part of heat is not only a waste of energy, but may also further accelerate the aging of the circuit, and even cause short circuit failures, reduce the reliability and safety of the lamp, and also increase the power consumption of the lamp due to resistance loss. In terms of optics, a large vibration amplitude may cause the optical components such as lenses and reflectors to shift or deform, change the propagation path and distribution characteristics of light, and reduce the uniformity and efficiency of lighting. In order to compensate for the decline in lighting effect, it is often necessary to increase the power of the lamp, thereby increasing energy consumption; the collection method is: laser displacement sensors are evenly arranged around the lamp and on the top surface. The measurement accuracy of the sensor should reach 0.01 mm, and the measurement range is set according to the expected vibration amplitude of the lamp. During the normal operation of the lamp, the sensor monitors the vibration displacement of each point on the lamp surface in real time, and the data acquisition card collects sensor data at a frequency of 20 times per second. Every 10 minutes, the data acquisition system analyzes the collected displacement data, finds the maximum displacement value measured by each sensor in the time period, and then compares these maximum displacement values, takes the maximum value as the maximum vibration amplitude of the lamp in the period and records it. Through long-term monitoring and recording, the changing trend of the maximum vibration amplitude of the lamp can be analyzed, and potential problems can be discovered in time.

[0027] The vibration direction concentration of the lamp reflects the distribution of the vibration energy of the lamp in different directions. When the mechanical structure of the lamp is reasonably designed, correctly installed and all components are working normally, the vibration is mainly concentrated in a specific direction, such as the rotation axis direction of the cooling fan or the rotation direction of the motor inside the lamp. A higher vibration direction concentration indicates that the mechanical structure inside the lamp is evenly stressed, the components cooperate well, the mechanical loss is relatively small, and the electrical energy can be more effectively converted into light energy and the energy required to maintain stable mechanical movement. On the contrary, if the vibration direction concentration is low, it means that there are problems such as irregular external force interference, loose components or structural deformation, which will cause unnecessary vibration of the lamp in multiple directions, increase the friction and collision opportunities between the components, increase mechanical losses, and increase power consumption. In addition, the dispersed vibration direction may also affect the stability of the lamp, have adverse effects on the optical system and electrical system, and reduce the overall performance and energy saving effect of the lamp; the collection method is: install three-axis acceleration sensors in the three mutually perpendicular coordinate axis directions of the lamp, the measurement accuracy of the sensor must reach 0.001g, and the frequency response range meets the requirements of the vibration characteristic analysis of the lamp. During the normal operation of the lamp, the sensor synchronously collects vibration acceleration data in three directions. The data acquisition system collects data at a sampling frequency of 50 times per second for 1 hour. After the acquisition is completed, the collected time domain acceleration data is transmitted to the data analysis software. The software first performs square and square root operations on the acceleration data in each direction to obtain the time domain data of the combined acceleration, and then performs a fast Fourier transform on the combined acceleration data to obtain the frequency domain data of the combined acceleration. Calculate the ratio of the root mean square value of the acceleration components in the three directions under each frequency component to the root mean square value of the combined acceleration to obtain the proportion of vibration energy in each direction to the total vibration energy. Sum the energy ratios in the main vibration directions to obtain the vibration direction concentration, and record the relevant data.

[0028] The wear depth of the connecting parts directly reflects the degree of wear of the connecting parts during long-term use. During the operation of the lamp, due to the influence of factors such as vibration, temperature change, and mechanical stress, micro-motion wear will inevitably occur between the connecting parts (such as the threaded surface of the screw and nut, the occlusal surface of the buckle, etc.). As the wear depth increases, the matching accuracy of the connecting parts gradually decreases, and the stability of the connection is seriously threatened. Loose connections will lead to poor electrical contact, increase contact resistance, consume more electricity when current passes, generate heat, and reduce the energy efficiency of the lamp. At the same time, the wear of the connecting parts may also affect the mechanical structure stability of the lamp, and then have an adverse effect on the optical system, making the lighting effect worse. In order to make up for the lack of lighting effect, it is often necessary to increase the power of the lamp, further increasing energy consumption. By regularly monitoring the wear depth of the connecting parts of the lamp and replacing the severely worn parts in time, the mechanical connection stability of the lamp can be effectively maintained, the power loss caused by the wear of the connecting parts can be reduced, and the energy-saving performance and service life of the lamp can be improved; the collection method is: using an ultra-depth of field microscope to regularly inspect the key connecting parts of the lamp and measure the wear depth. The resolution of the ultra-depth microscope should reach 0.1 microns, which can clearly observe the microscopic morphology of the surface of the connecting parts. Carefully disassemble the lamp, take out the connecting parts to be tested, place them on the stage of the ultra-depth microscope, and ensure that the surface of the parts is clean. Adjust the focus, lighting and other parameters of the microscope to obtain a clear microscopic image of the contact surface of the connecting parts. Use the image analysis software of the microscope to select multiple representative wear areas in the image, measure the vertical distance from the original surface of the part to the lowest point of wear, and obtain the wear depth value. Record the wear depth value of each test area, and calculate the average value as the wear depth result of the connection part for this test. Store the wear depth data together with the relevant information of the connection parts in the database to track and analyze the wear of the connection parts for a long time. According to the change trend of the wear depth, predict the remaining service life of the connection parts, make a replacement plan in advance, and ensure the normal operation and energy saving effect of the lamp.

[0029] The residual value of the preload force of the lamp connection: In the lamp, the preload force of the key connection parts (such as the screw connection between the lamp holder and the heat sink, the snap connection between the lampshade and the lamp body, etc.) plays a vital role in maintaining the structural integrity and stability of the entire lamp. Sufficient preload force can ensure that the connection parts fit tightly, reduce relative movement and friction, and enable the electric energy to be efficiently converted into light energy without generating additional energy loss due to problems in the connection parts. When the preload force gradually decreases with the use time of the lamp, the connection parts will be at risk of loosening. Once loose, the contact area between the connection parts decreases and the contact resistance increases. According to Ohm's law, when the current remains unchanged, the increase in contact resistance will cause the connection parts to consume more electric energy and generate heat, which not only wastes energy, but may also further accelerate the aging and damage of the connection parts. In addition, the loose connection may also affect the optical system of the lamp, change the propagation path of the light, and reduce the lighting effect. In order to achieve the expected lighting requirements, it may be necessary to increase the power of the lamp, thereby increasing energy consumption. Therefore, real-time monitoring of the residual value of the preload force of the key connection of the lamp, timely detection of insufficient preload force and taking tightening measures can effectively ensure the energy-saving performance and normal operation of the lamp; the collection method is: for the main key connection parts of the lamp, a special micro strain gauge preload force sensor is installed near the screw or buckle of each connection part. The sensor needs to be calibrated before installation to ensure that the measurement accuracy is within ±0.1 Newton. Before the lamp is assembled and powered on for the first time, a special preload force measuring instrument is used to measure the initial preload force of each sensor and record the initial preload force value. During the normal operation of the lamp, the data acquisition system is connected to the preload force sensor through a wire, and the measurement data of each sensor is regularly collected to obtain the current preload force value. The initial preload force value is subtracted from the current preload force value to obtain the preload force loss value, and then the percentage of the residual preload force value to the initial preload force value is calculated. The data acquisition system records the residual preload force value and related data (such as the connection part number, collection time, etc.) in the database so that maintenance personnel can check the changes in the preload force of each connection part at any time.

[0030] The lamp connection resonance frequency offset: Under normal circumstances, when the lamp is subjected to external excitation (such as vibration, electromagnetic force generated by current changes, etc.), if the excitation frequency is close to the mechanical connection resonance frequency of the lamp, resonance will be triggered. Resonance will cause the vibration amplitude of the lamp to increase sharply, causing serious damage to the mechanical structure, electrical system and optical system of the lamp. For example, resonance may loosen the connection of the electrical circuit, increase the contact resistance, and lead to increased power loss; cause the optical components to be displaced or damaged, and reduce the lighting effect. By monitoring the mechanical connection resonance frequency offset of the lamp, changes in the mechanical structure of the lamp can be discovered in time, the occurrence of resonance can be avoided, the normal operation of the lamp can be guaranteed, and energy waste and lamp damage caused by resonance can be reduced; the collection method is: using vibration excitation equipment and acceleration sensors, after the first installation, the mechanical connection structure of the lamp is subjected to a frequency sweep test to determine the initial resonance frequency, and the test is repeated regularly and compared with the initial value to obtain the offset.

[0031] The Hertz band power spectrum density of the lamp: The noise spectrum of lamp operation is like a stethoscope for the internal health of the lamp. Different lamp components will generate noise with specific frequency ranges and characteristics during operation. For example, when the transformer in the power module is working, due to the magnetostrictive effect of the iron core, a low-frequency humming sound of a specific frequency will be generated; the high-speed rotation of the blades of the cooling fan and the interaction with the air will generate a high-frequency whistling sound. By analyzing the Hertz band power spectrum density in the noise spectrum, the noise generated by each component and its intensity changes can be accurately identified. When a component fails or its performance degrades, the frequency and intensity of the noise it generates will change. For example, if the transformer winding is short-circuited or the iron core is loose, the frequency of the low-frequency noise will drift and the intensity will increase; if the cooling fan blades are worn or unbalanced, the spectrum of the high-frequency noise will become messy and the power spectrum density will increase. Monitoring the noise spectrum can detect these potential problems in a timely manner and avoid energy waste caused by component failure. Because faulty components often consume more electricity or affect the overall energy conversion efficiency of the lamp. For example, a power module failure may make the output voltage unstable, causing the LED chip to fail to emit light in the best working state, thereby reducing the luminous efficiency and increasing energy consumption. In addition, noise itself is also a form of energy transmission. Reducing unnecessary noise generation can also help improve the energy-saving effect of lamps. The collection method is: place a high-precision acoustic spectrum analyzer 1 meter in front of the lamp, and set a sound-absorbing cover around the analyzer to reduce environmental noise interference. The frequency resolution of the acoustic spectrum analyzer must reach 1 Hz, and the measurement range covers the noise frequency range that may be generated by the lamp. When the lamp is operating normally, start the spectrum analyzer for noise collection. The collection time is set to 30 seconds and the sampling frequency is 1000 times per second. After the collection is completed, the spectrum analyzer performs a fast Fourier transform on the collected time domain noise signal to obtain the frequency domain signal of the noise, and then calculates the power spectrum density of each Hertz frequency band, and displays and records the noise spectrum data in decibels / Hertz. In order to have a more comprehensive understanding of the noise characteristics of the lamp, the above acquisition process can be repeated under different operating conditions of the lamp (such as different brightness settings, different heat dissipation loads, etc.) to establish a lamp operating noise spectrum database for comparative analysis and fault diagnosis.

[0032] The lamp noise intensity change rate: The lamp noise intensity change rate is a dynamic indicator of the lamp operation stability. Under normal circumstances, the noise intensity of the lamp during operation should remain relatively stable, and its change rate is extremely small. However, when an emergency occurs inside the lamp, such as a sudden breakage of a fan blade, a stuck motor bearing, or a short circuit in an electrical component, the noise intensity will change rapidly. By real-time monitoring of the lamp noise intensity change rate, these abnormal changes can be captured in time, so that the problem can be quickly located and solved. Because these internal faults often lead to a decrease in the working efficiency of the lamp, the proportion of electrical energy converted into light energy is reduced, and more electrical energy is wasted on additional energy loss caused by the fault. For example, a fan failure will cause poor heat dissipation and increase the temperature of the LED chip. According to the temperature characteristics of the LED, its luminous efficiency will decrease with the increase in temperature. In order to maintain the lighting brightness, the lamp will need to consume more electrical energy. Timely discovery and repair of the fault source can restore the lamp to normal working state, reduce energy waste caused by the fault, and improve the energy-saving effect of the lamp; the collection method is: four sound intensity sensors of the same model are evenly arranged around the lamp, located in the front, back, left and right directions of the lamp, and 1 meter away from the lamp. The measurement accuracy of the sound intensity sensor should reach 0.1 decibel, and the measurement range should meet the requirements for measuring the noise intensity of the lamp. The sensor is connected to the data acquisition terminal through a data cable, and the data acquisition terminal is set to collect sound intensity data every 10 seconds. After the lamp is turned on and works normally, the data acquisition system starts to continuously collect sound intensity data. Whenever new sound intensity data is collected, the difference with the last collected data is calculated, and the difference is divided by the time interval between the two collections (10 seconds, converted to minutes is 1 / 6 minutes), and the noise intensity change rate (dB / minute) is obtained.

[0033] The Hertz fluctuation of the lamp: The characteristic noise frequency of the lamp during normal operation is related to the working state of key components. If these characteristic frequencies fluctuate greatly, it means that the corresponding components may be loose, worn, or have electrical parameter changes. For example, a change in the characteristic frequency of a motor may mean that the motor speed is unstable or the mechanical load changes, which will affect the overall operating efficiency of the lamp and increase energy consumption. Monitoring the stability of the characteristic noise frequency can help to detect component problems in a timely manner, optimize the operating state of the lamp, and reduce energy loss; the collection method is as follows: First, when the lamp is first installed and operates normally, use an acoustic spectrum analyzer to collect and analyze the noise of the lamp in accordance with the above-mentioned method of collecting the operating noise spectrum of the lamp, determine the characteristic noise frequency of the lamp and its corresponding power spectrum density, and record it as the baseline data. After each collection is completed, the current noise spectrum is compared with the baseline data, and the position of the characteristic noise frequency in the current spectrum is found through a specific frequency matching algorithm, and the Hertz fluctuation is calculated.

[0034] The amount of electric field and magnetic field coupling inside the lamp: If the amount of electric field and magnetic field coupling inside the lamp is too high, it will cause electromagnetic interference and affect the normal operation of the electronic components inside the lamp. For example, electromagnetic interference may affect the normal signal transmission of the LED drive circuit, so that the drive circuit cannot accurately control the current and voltage of the LED chip, resulting in the LED chip not being able to emit light in the optimal working state, and the luminous efficiency is reduced. According to the relationship between luminous efficiency and energy consumption, in order to achieve the expected lighting brightness, more electric energy needs to be consumed. By controlling the amount of electric field and magnetic field coupling inside the lamp, electromagnetic interference can be reduced, the normal operation of the electronic components inside the lamp can be ensured, and the electric energy-light energy conversion efficiency of the lamp can be improved, thereby achieving energy saving; the collection method is: using an electromagnetic coupling tester, placing the probe at key locations such as the power module and LED chip inside the lamp, measuring the electric field strength (mV / m) and the magnetic field strength (A / m) at rated power, and calculating the product of the two to obtain the coupling amount.

[0035] The lamp coupling field power slope: The lamp coupling field power slope reflects the law of the coupling field changing with the lamp power. During the lamp power adjustment process, if the coupling field power slope is unreasonable, it may cause the internal electromagnetic environment of the lamp to deteriorate, resulting in electromagnetic interference. For example, when the lamp is adjusted from low power to high power, if the coupling field changes too much, it may interfere with the LED drive circuit, so that the LED chip cannot respond to the power change in a timely and effective manner, resulting in a decrease in luminous efficiency during the transition stage and more power consumption. By mastering the lamp coupling field power slope and reasonably designing the power adjustment strategy, it is possible to reduce energy waste caused by electromagnetic factors and achieve energy saving; the collection method is: adjust the lamp power supply, measure the electric field and magnetic field coupling at different power values, and calculate the ratio of the coupling change to the power change to obtain the power slope.

[0036] The spatial unevenness of the lamp coupling field: The spatial unevenness of the lamp coupling field reflects the spatial uniformity of the electric field and magnetic field coupling field inside the lamp. The uneven coupling field will cause strong electromagnetic interference to the local area inside the lamp. In these disturbed areas, electronic components may malfunction, such as capacitor breakdown or abnormal conduction of transistors. These problems will cause the circuit performance of the lamp to deteriorate and the power loss to increase. For example, when the capacitor breaks down due to electromagnetic interference, the power factor of the circuit will decrease. According to the power formula, the power loss will increase when the voltage and current remain unchanged. By optimizing the spatial unevenness of the lamp coupling field, local electromagnetic interference can be reduced and the energy efficiency of the lamp can be improved; the collection method is: construct a three-dimensional coordinate grid inside the lamp, place an electromagnetic coupling tester probe at each grid node, measure the coupling field strength at each point and calculate the average value, and then calculate the ratio of the root mean square value of the deviation of each point from the average value to the average value to obtain the unevenness.

[0037] The photocurrent response time of the lamp: The photocurrent response time of the lamp determines the response speed of the lamp to changes in the lighting environment. In an intelligent lighting system, when the ambient light changes, the lamp needs to quickly adjust the lighting state. If the photocurrent response time of the lamp is too long, the lamp cannot adjust the brightness according to the light changes in time, and may appear too bright or too dark. For example, in a scene with natural light supplementary lighting, if the lamp cannot reduce the brightness in time, it will waste electricity. By shortening the photocurrent response time of the lamp, the lamp can adapt to changes in the lighting environment more flexibly, avoid unnecessary energy consumption, and achieve energy saving; the collection method is: use a pulse light generator to emit a short pulse of light to illuminate the lamp, and use a high-precision current probe and an oscilloscope to measure the time interval from the start of the light pulse to the photocurrent reaching a stable value, which is the photocurrent response time of the lamp.

[0038] The photocurrent saturation of the lamp: The photocurrent saturation of the lamp determines the critical current value at which the photogenerated current reaches the maximum value and no longer increases with the increase of light intensity when the light intensity of the lamp continues to increase. If the lamp operates in a state exceeding the photocurrent saturation, the LED chip will generate severe heat due to excessive current and the luminous efficiency will decrease. According to the relationship between luminous efficiency and energy consumption, at this time, more electrical energy is converted into heat, wasting energy. Therefore, understanding the photocurrent saturation of the lamp helps to reasonably control the working state of the lamp, avoid the lamp from running in an inefficient and high-consumption state, and achieve energy saving; the collection method is: gradually increase the light intensity irradiating the lamp, and measure the photogenerated current at the same time. When the light intensity continues to increase and the current no longer rises significantly, the current value recorded is the saturation value.

[0039] The color temperature gradient of the photocurrent of the lamp: The color temperature gradient of the photocurrent of the lamp reflects the law of the change of photocurrent with color temperature. In actual lighting applications, different lighting scenes may require light of different color temperatures. By mastering the color temperature gradient of the photocurrent of the lamp, the current of the lamp can be accurately controlled according to the color temperature required for actual lighting. For example, in a scene where warm light is required, the current can be adjusted according to this gradient relationship, so that the lamp can provide light of suitable color temperature while avoiding energy waste caused by unreasonable current setting. Reasonable color temperature adjustment can improve the comfort and visual effect of lighting, and achieve energy saving while meeting lighting needs; the collection method is: set the color temperature adjustment function of the lamp to work at different color temperature values, measure the corresponding photogenerated current, and calculate the ratio of photocurrent change to color temperature change to obtain the color temperature gradient.

[0040] The peak value of the light feedback voltage fluctuation of the lamp: When the peak-to-peak value of the light feedback voltage fluctuation of the lamp is too large, it will affect the electrical energy stability of the lamp. The LED driving circuit of the lamp may not work properly under unstable voltage, resulting in the LED chip not being able to emit light under stable voltage and reducing the luminous efficiency. According to the relationship between luminous efficiency and energy consumption, in order to maintain the lighting brightness, more electrical energy needs to be consumed. For example, voltage fluctuations may cause the luminous intensity of the LED chip to flicker, which not only affects the lighting effect, but also increases the power loss due to frequent current changes. By controlling the peak-to-peak value of the light feedback voltage fluctuation of the lamp, it can be ensured that the lamp works under a stable voltage, improve the energy utilization efficiency of the lamp, and achieve energy saving; the collection method is: install voltage sensors at the power input port and key electrical nodes of the lamp, and the lamp works normally and is in different light reflection environments. The difference between the maximum and minimum values ​​of the voltage fluctuation caused by light feedback is measured as the peak value of the light feedback voltage fluctuation of the lamp.

[0041] The modulation amplitude of the light feedback current of the lamp; if the modulation amplitude of the light feedback current of the lamp is too large, the lamp will work unstably and flicker. This unstable working state will cause the luminous efficiency of the lamp to decrease, because the luminous performance of the LED chip cannot be fully exerted when working under an unstable current. According to the power formula, when the voltage remains unchanged, the instability of the current will increase the energy consumption. By monitoring and controlling the modulation amplitude of the light feedback current of the lamp, it can be ensured that the lamp works under a stable current, reduce energy waste, and achieve energy saving; the collection method is: measure the working current of the lamp through a current sensor, and compare the difference between the maximum and minimum current values ​​when there is no obvious light feedback and when there is light feedback with the ratio of the current average value when there is no feedback to obtain the modulation amplitude.

[0042] The electrical recovery time of the light feedback of the lamp: The electrical recovery time of the light feedback of the lamp reflects the ability of the lamp to resist the interference of light feedback. When the light feedback disappears, if the time required for the electrical parameters of the lamp to return to the normal state is too long, the lamp is susceptible to the long-term influence of the light feedback in a complex lighting environment, causing the electrical parameters to be in an unstable state for a long time. In this case, the LED chip cannot emit light efficiently, which will increase energy consumption. By shortening the electrical recovery time of the light feedback of the lamp, the lamp can be restored to a normal working state faster, the lighting efficiency can be improved, and energy saving can be achieved; the collection method is: after artificially creating a short light feedback, the time for the electrical parameters to return to the normal fluctuation range monitored by the voltage and current sensors is the electrical recovery time of the light feedback of the lamp.

[0043] S3, LED data analysis steps: including vibration data analysis node, connection data analysis node, acoustic data analysis node, electromagnetic field data analysis node, photoelectric data analysis node and optical feedback data node, which are used to analyze the collected target data.

[0044] In this embodiment, it is specifically required to be explained that: the vibration data analysis node is used to establish a vibration data calculation model, and the vibration data collected in the data collection step is imported into the vibration data calculation model to obtain the vibration energy consumption influence coefficient value. The vibration data calculation model is specifically expressed as: , Among them, α i Indicates the vibration energy consumption influence coefficient value calculated for the i-th time, Fv i represents the working vibration frequency of the lamp collected for the i-th time, Pa i Indicates the peak value of the lamp amplitude collected for the i-th time, Dc i Indicates the concentration of the lamp vibration direction collected for the i-th time.

[0045] In this embodiment, it should be specifically explained that: the connection data analysis node is used to establish a connection data calculation model, and the connection data collected in the data collection step is imported into the connection data calculation model to obtain a connection stability energy saving coefficient value. The connection data calculation model is specifically expressed as: , Among them, β i Represents the connection stability energy saving coefficient value calculated for the i-th time, Cd i represents the wear depth of the connecting component collected at the i-th time, Rp i Indicates the residual value of the preload force of the lamp connection collected for the i-th time, Fr i Indicates the offset of the lamp connection resonance frequency collected for the i-th time.

[0046] In this embodiment, it is specifically required to be explained that: the acoustic data analysis node is used to establish an acoustic data calculation model, and the acoustic data collected in the data collection step is imported into the acoustic data calculation model to obtain the noise energy saving impact coefficient value. The acoustic data calculation model is specifically expressed as: , Among them, χ i Indicates the noise energy saving impact coefficient value calculated for the i-th time, Hf i Gn represents the power spectrum density of the lamp Hertz band collected for the i-th time, i Indicates the rate of change of the noise intensity of the lamp collected for the i-th time, Fh i Indicates the Hertz fluctuation of the lamp collected for the i-th time.

[0047] In this embodiment, it is specifically required to be explained that: the electromagnetic field data analysis node is used to establish an electromagnetic field data calculation model, and the electromagnetic field data collected in the data collection step is imported into the electromagnetic field data calculation model to obtain the electromagnetic coupling energy-saving efficacy coefficient value. The electromagnetic field data calculation model is specifically expressed as: , Among them, ∂ i Indicates the electromagnetic coupling energy-saving efficiency coefficient value calculated for the i-th time, Mc i Sp represents the electric and magnetic field coupling of the lamp collected for the i-th time. i Sc represents the slope of the lamp coupling field power collected for the i-th time. i Represents the spatial unevenness of the lamp coupling field collected for the i-th time.

[0048] In this embodiment, it is specifically required to be explained that: the photoelectric data analysis node is used to establish a photoelectric data calculation model, and the photoelectric data collected in the data collection step is imported into the photoelectric data calculation model to obtain the photoelectric conversion energy saving benefit coefficient value. The photoelectric data calculation model is specifically expressed as: , Among them, γ i represents the energy-saving benefit coefficient of photoelectric conversion calculated for the i-th time, Pr i Indicates the duration of the photocurrent response of the illuminant collected for the i-th time, Ps i Indicates the saturation amount of the light current collected for the i-th time, Gt i represents the color temperature gradient of the lamp photocurrent collected for the i-th time, Represents the standard deviation of the color temperature gradient of the lamp photocurrent, Represents the average color temperature gradient of the lamp photocurrent.

[0049] In this embodiment, it is specifically required to be explained that: the optical feedback data analysis node is used to establish an optical feedback data calculation model, and the optical feedback data collected in the data collection step is imported into the optical feedback data calculation model to obtain the optical feedback energy saving loss coefficient value. The optical feedback data calculation model is specifically expressed as: , Among them, ℓ i represents the optical feedback energy saving loss coefficient value calculated for the i-th time, Fp i Indicates the peak value of the light feedback voltage fluctuation of the lamp collected for the i-th time, Ma i represents the modulation amplitude of the light feedback current of the lamp collected for the i-th time, Er i Indicates the electrical recovery time of the light feedback of the lamp collected for the i-th time.

[0050] S4, LED data re-analysis step: including the lamp structure mechanics data analysis link and the lamp electro-optical data analysis link, which is used to re-analyze the results of the LED data analysis step.

[0051] In this embodiment, it is specifically required to be explained that: the lamp structure mechanics data analysis link is used to establish a lamp structure mechanics data calculation model, and the vibration energy consumption influence coefficient value, the connection stability energy saving coefficient value and the noise energy saving influence coefficient value calculated in the LED data analysis step are introduced into the lamp structure mechanics data calculation model to obtain a structural mechanics energy saving comprehensive evaluation index value. The lamp structure mechanics data calculation model is specifically expressed as: , Among them, ES i represents the comprehensive evaluation index value of structural mechanics energy saving calculated for the i-th time, α i represents the vibration energy consumption influence coefficient value calculated for the i-th time, β i represents the connection stability energy saving coefficient value calculated for the i-th time, χ i Represents the noise energy-saving impact coefficient value calculated for the i-th time.

[0052] In this embodiment, it is specifically required to be explained that: the lamp electro-optical data analysis link is used to establish a lamp electro-optical data analysis calculation model, and the electromagnetic coupling energy-saving efficacy coefficient value, the photoelectric conversion energy-saving benefit coefficient value and the optical feedback energy-saving loss coefficient value calculated in the LED data analysis step are introduced into the lamp electro-optical data analysis calculation model to obtain the electromagnetic photoelectric energy-saving comprehensive evaluation index value. The lamp electro-optical data analysis calculation model is specifically expressed as: , Among them, EP i represents the electromagnetic and photoelectric energy-saving comprehensive evaluation index value calculated for the i-th time, ∂ i represents the electromagnetic coupling energy-saving efficiency coefficient value calculated for the i-th time, γ i represents the energy-saving efficiency coefficient of photoelectric conversion calculated for the i-th time, ℓ i Represents the optical feedback energy saving loss coefficient value calculated for the i-th time.

[0053] S5, LED data comprehensive evaluation step: including intelligent LED lighting energy-saving data analysis link, used to comprehensively evaluate the results calculated in the LED data reanalysis step.

[0054] In this embodiment, it is necessary to specifically explain that: the intelligent LED lighting energy-saving data analysis link is used to establish an intelligent LED lighting energy-saving data calculation model, and the structural mechanics energy-saving comprehensive evaluation index value and the electromagnetic photoelectric energy-saving comprehensive evaluation index value calculated in the LED data reanalysis step are introduced into the intelligent LED lighting energy-saving data calculation model to obtain the intelligent LED lighting energy-saving optimization comprehensive evaluation index value. The intelligent LED lighting energy-saving data calculation model is specifically expressed as: , Among them, A represents the calculated comprehensive evaluation index value of intelligent LED lighting energy-saving optimization, ES i represents the comprehensive evaluation index value of structural mechanics energy-saving for the i-th calculation, EP i represents the comprehensive evaluation index value of electromagnetic and optoelectronic energy-saving for the i-th calculation.

[0055] S6. Real-time warning and feedback step: used to establish a preset value of the comprehensive evaluation index of intelligent LED lighting energy-saving optimization, judge the comprehensive evaluation index value of intelligent LED lighting energy-saving optimization according to the preset value, and send corresponding signals according to the judgment results.

[0056] In this embodiment, specifically, it should be noted that: the preset value of the comprehensive evaluation index of intelligent LED lighting energy-saving optimization is marked as A def , when A def < A, a normal signal is sent, and this signal indicates that the preset value of the comprehensive evaluation index of intelligent LED lighting energy-saving optimization is less than the comprehensive evaluation index value of intelligent LED lighting energy-saving optimization, indicating that the intelligent LED lighting energy-saving optimization status is good; when A def >= A def , a warning signal is sent to relevant technical management personnel, and this signal indicates that the preset value of the comprehensive evaluation index of intelligent LED lighting energy-saving optimization is greater than or equal to the comprehensive evaluation index value of intelligent LED lighting energy-saving optimization, indicating that the intelligent LED lighting energy-saving optimization status is poor and relevant technical management personnel need to make adjustments.

[0057] Based on the above scheme and appendix Figure 2 , this application embodiment also discloses an intelligent LED lighting energy-saving optimization system based on the AI algorithm, including: Data batch division module: used to determine the data to be collected as target data, divide the target data into different collection batches in an equal-time division manner, and mark them as 1, 2,..., n in sequence; LED data collection module: including a lamp structure mechanics data collection unit and a lamp electro-optical data collection unit, used to collect the target data in real time and transmit the collected data to the LED data analysis module; the lamp structure mechanics data collection unit is used to collect vibration data, connection data, and acoustic data; the lamp electro-optical data collection unit is used to collect electromagnetic field data, optoelectronic data, and optical feedback data; LED data analysis module: including a vibration data analysis node, a connection data analysis node, an acoustic data analysis node, an electromagnetic field data analysis node, an optoelectronic data analysis node, and an optical feedback data node, used to analyze the collected target data and transmit the analysis results to the LED data re-analysis module; LED data re-analysis module: including a lamp structure mechanics data analysis unit and a lamp electro-optical data analysis unit, used to re-analyze the results of the LED data analysis module and transmit the re-analysis results to the LED data comprehensive evaluation module; LED data comprehensive evaluation module: including an intelligent LED lighting energy-saving data analysis unit, which is used to comprehensively evaluate the results calculated in the LED data reanalysis module and transmit the evaluation results to the real-time early warning feedback module; Real-time early warning feedback module: used to establish a preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, judge the value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index according to the preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0058] The present invention effectively improves the pertinence and effectiveness of lighting data by dividing the data of the intelligent LED lighting system into various data batches. By collecting vibration data, connection data, acoustic data, electromagnetic field data, photoelectric data and optical feedback data in multiple dimensions, this multi-faceted data collection mode has greatly broken through the limitation of single data of traditional lighting energy-saving optimization, and provided a rich and reliable information basis for subsequent precise and scientific optimization. Through data analysis, the collected data is deeply interpreted, and then the vibration energy consumption influence coefficient value, connection stability energy-saving coefficient value, noise energy-saving influence coefficient value, electromagnetic coupling energy-saving efficacy coefficient value, photoelectric conversion energy-saving benefit coefficient value, optical feedback energy-saving loss coefficient value, structural mechanics energy-saving comprehensive evaluation index value and electromagnetic photoelectric energy-saving comprehensive evaluation index value are calculated, and the factors that may affect the lighting energy-saving optimization effect are clearly pointed out. Through comprehensive consideration and organic integration of the data analysis results, the comprehensive evaluation index value of the energy-saving optimization of the entire intelligent LED lighting system is obtained, which significantly improves the scientificity and feasibility of the optimization plan. Through real-time monitoring and continuous follow-up, once the lighting abnormality or energy consumption deviation is detected, the early warning information is immediately issued, and the relevant staff can timely understand the status and potential problem points of the lighting system, and quickly take measures to adjust and improve the lighting parameters and energy-saving strategies, so as to provide a solid guarantee for the realization of high-quality AI algorithm-based intelligent LED lighting energy-saving optimization and sustainable energy conservation.

[0059] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent LED lighting energy-saving optimization method based on AI algorithm, characterized in that: include: S1, data batch division step: used to determine the data to be collected as target data, divide the target data into different collection batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; S2, LED data collection step: including the lamp structure mechanics data collection link and the lamp electro-optical data collection link, which is used to collect the target data in real time; The lamp structure mechanics data collection link is used to collect vibration data, connection data and acoustic data; The lamp electro-optical data collection link is used to collect electromagnetic field data, photoelectric data and optical feedback data; S3, LED data analysis step: including vibration data analysis node, connection data analysis node, acoustic data analysis node, electromagnetic field data analysis node, photoelectric data analysis node and optical feedback data node, used to analyze the collected target data; S4, LED data re-analysis step: including the lamp structure mechanics data analysis link and the lamp electro-optical data analysis link, used to re-analyze the results of the LED data analysis step; S5, LED data comprehensive evaluation step: including intelligent LED lighting energy-saving data analysis step, used to comprehensively evaluate the results calculated in the LED data reanalysis step; S6, real-time warning feedback step: used to establish a preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, judge the value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index according to the preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, and send a corresponding signal according to the judgment result.

2. According to claim 1, the intelligent LED lighting energy-saving optimization method based on AI algorithm is characterized in that: The vibration data includes the working vibration frequency Fv of the lamp, the peak amplitude Pa of the lamp and the concentration Dc of the vibration direction of the lamp; the connection data includes the wear depth Cd of the connecting parts, the residual value Rp of the preload force of the lamp connection and the offset Fr of the resonance frequency of the lamp connection; the acoustic data includes the power spectrum density Hf of the lamp in the Hertz frequency band, the change rate of the noise intensity of the lamp Gn and the Hertz fluctuation of the lamp Fh; the electromagnetic field data includes the electric field and magnetic field coupling Mc inside the lamp, the power slope Sp of the coupling field of the lamp and the spatial unevenness Sc of the coupling field of the lamp; the photoelectric data includes the response time Pr of the photocurrent of the lamp, the saturation amount Ps of the photocurrent of the lamp and the color temperature gradient Gt of the photocurrent of the lamp; the optical feedback data includes the peak value Fp of the light feedback voltage fluctuation of the lamp, the modulation amplitude Ma of the light feedback current of the lamp and the electrical recovery time Er of the light feedback of the lamp.

3. According to the AI ​​algorithm-based intelligent LED lighting energy-saving optimization method of claim 1, it is characterized by: The vibration data analysis node is used to establish a vibration data calculation model, import the vibration data collected in the data collection step into the vibration data calculation model, and obtain the vibration energy consumption influence coefficient value. The vibration data calculation model is specifically expressed as: ， Among them, α i Indicates the vibration energy consumption influence coefficient value calculated for the i-th time, Fv i represents the working vibration frequency of the lamp collected for the i-th time, Pa i Indicates the peak value of the lamp amplitude collected for the i-th time, Dc i Indicates the concentration of the lamp vibration direction collected for the i-th time.

4. According to claim 1, the intelligent LED lighting energy-saving optimization method based on AI algorithm is characterized in that: The connection data analysis node is used to establish a connection data calculation model, import the connection data collected in the data collection step into the connection data calculation model, and obtain the connection stability energy saving coefficient value. The connection data calculation model is specifically expressed as: ， Among them, β i Represents the connection stability energy saving coefficient value calculated for the i-th time, Cd i represents the wear depth of the connecting component collected at the i-th time, Rp i Indicates the residual value of the preload force of the lamp connection collected for the i-th time, Fr i Indicates the offset of the lamp connection resonance frequency collected for the i-th time.

5. The intelligent LED lighting energy-saving optimization method based on AI algorithm according to claim 1 is characterized in that: The acoustic data analysis node is used to establish an acoustic data calculation model, import the acoustic data collected in the data collection step into the acoustic data calculation model, and obtain the noise energy saving impact coefficient value. The acoustic data calculation model is specifically expressed as: ， Among them, χ i Indicates the noise energy saving impact coefficient value calculated for the i-th time, Hf i Gn represents the power spectrum density of the lamp Hertz band collected for the i-th time, i Indicates the rate of change of the noise intensity of the lamp collected for the i-th time, Fh i Indicates the Hertz fluctuation of the lamp collected for the i-th time.

6. The intelligent LED lighting energy-saving optimization method based on AI algorithm according to claim 1 is characterized in that: The electromagnetic field data analysis node is used to establish an electromagnetic field data calculation model, import the electromagnetic field data collected in the data collection step into the electromagnetic field data calculation model, and obtain the electromagnetic coupling energy-saving efficacy coefficient value. The electromagnetic field data calculation model is specifically expressed as: ， Among them, ∂ i Indicates the electromagnetic coupling energy-saving efficiency coefficient value calculated for the i-th time, Mc i Sp represents the electric and magnetic field coupling of the lamp collected for the i-th time. i Indicates the slope of the lamp coupling field power collected for the i-th time, Sc i Represents the spatial unevenness of the lamp coupling field collected for the i-th time.

7. The intelligent LED lighting energy-saving optimization method based on AI algorithm according to claim 1 is characterized in that: The photoelectric data analysis node is used to establish a photoelectric data calculation model, import the photoelectric data collected in the data collection step into the photoelectric data calculation model, and obtain the photoelectric conversion energy-saving benefit coefficient value. The photoelectric data calculation model is specifically expressed as: ， Among them, γ i represents the energy-saving benefit coefficient of photoelectric conversion calculated for the i-th time, Pr i Indicates the duration of the photocurrent response of the illuminant collected for the i-th time, Ps i Indicates the saturation amount of the light current collected for the i-th time, Gt i represents the color temperature gradient of the lamp photocurrent collected for the i-th time, Represents the standard deviation of the color temperature gradient of the lamp photocurrent, Represents the average value of the color temperature gradient of the lamp photocurrent.

8. The intelligent LED lighting energy-saving optimization method based on AI algorithm according to claim 1 is characterized in that: The optical feedback data analysis node is used to establish an optical feedback data calculation model, import the optical feedback data collected in the data collection step into the optical feedback data calculation model, and obtain the optical feedback energy saving loss coefficient value. The optical feedback data calculation model is specifically expressed as: ， Among them, ℓ i represents the optical feedback energy saving loss coefficient value calculated for the i-th time, Fp i Indicates the peak value of the light feedback voltage fluctuation of the lamp collected for the i-th time, Ma i represents the modulation amplitude of the light feedback current of the lamp collected for the i-th time, Er i Indicates the electrical recovery time of the light feedback of the lamp collected for the i-th time.

9. The intelligent LED lighting energy-saving optimization method based on AI algorithm according to claim 1 is characterized in that: The lamp structure mechanics data analysis link is used to establish a lamp structure mechanics data calculation model, and the vibration energy consumption influence coefficient value, connection stability energy saving coefficient value and noise energy saving influence coefficient value calculated in the LED data analysis step are introduced into the lamp structure mechanics data calculation model to obtain a structural mechanics energy saving comprehensive evaluation index value. The lamp structure mechanics data calculation model is specifically expressed as: ， Among them, ES i represents the comprehensive evaluation index value of structural mechanics energy saving calculated for the i-th time, α i represents the vibration energy consumption influence coefficient value calculated for the i-th time, β i represents the connection stability energy saving coefficient value calculated for the i-th time, χ i Represents the noise energy saving impact coefficient value calculated for the i-th time.

10. An intelligent LED lighting energy-saving optimization system based on AI algorithm, used to implement the intelligent LED lighting energy-saving optimization method based on AI algorithm as described in any one of claims 1 and 9 above, characterized in that: include: Data batch division module: used to determine the data to be collected as target data, divide the target data into different collection batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; LED data acquisition module: including the lamp structure mechanics data acquisition unit and the lamp electro-optical data acquisition unit, used to collect target data in real time and transmit the collected data to the LED data analysis module; The lamp structure mechanics data acquisition unit is used to collect vibration data, connection data and acoustic data; The lamp electro-optical data acquisition unit is used to collect electromagnetic field data, photoelectric data and optical feedback data; LED data analysis module: including vibration data analysis node, connection data analysis node, acoustic data analysis node, electromagnetic field data analysis node, photoelectric data analysis node and optical feedback data node, used to analyze the collected target data and transmit the analysis results to the LED data re-analysis module; LED data re-analysis module: including a lamp structure mechanics data analysis unit and a lamp electro-optical data analysis unit, used to re-analyze the results of the LED data analysis module and transmit the re-analysis results to the LED data comprehensive evaluation module; LED data comprehensive evaluation module: including an intelligent LED lighting energy-saving data analysis unit, which is used to comprehensively evaluate the results calculated in the LED data reanalysis module and transmit the evaluation results to the real-time early warning feedback module; Real-time early warning feedback module: used to establish a preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, judge the value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index according to the preset value of the intelligent LED lighting energy-saving optimization comprehensive evaluation index, and send a corresponding signal according to the judgment result.

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