Light effect inspection method for field arrangement of LED (light-emitting diode) landscape lamp

Abstract

The invention relates to the technical field of landscape lamp verification, and discloses an LED landscape lamp field arrangement lighting effect verification method, which comprises the following steps of collecting lamp parameter data of LED landscape lamp field arrangement and field environment optical parameter data; performing lighting effect simulation analysis processing based on the lamp parameter data and the field environment optical parameter data to generate simulated lighting effect distribution data; by collecting environmental optical parameters in real time and integrating lighting effect simulation analysis, the influence of environmental light change on lighting effect comparison can be dynamically detected, meanwhile, environmental fluctuation interference is automatically eliminated by adopting environmental light compensation correction processing, the consistency deviation of simulation data and actual measurement data can be reduced, the accuracy and reliability of lighting effect comparison can be ensured, and the accuracy and reliability of lighting effect comparison can be improved. And inspection errors caused by environmental factors are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of landscape lamp verification, in particular to a light efficiency inspection method for on-site arrangement of LED landscape lamps. BACKGROUND

[0002] Landscape lighting refers to an outdoor lighting engineering that has both lighting function and artistic decoration and environment beautification function. At present, the existing landscape lamp mainly consists of a lamp pole, a lamp and a light source, realizes night landscape lighting through control of a power supply, the LED landscape lamp is a decorative lighting lamp using LED as a light source, has ornamental and lighting properties, is more energy-saving than a traditional light source, has a longer service life, and is widely used in public places such as urban streets, parks, squares, shopping malls and communities.

[0003] At present, in the light efficiency inspection process of on-site arrangement of LED landscape lamps, there are various variable disturbances, when light efficiency simulation and actual measurement comparison are performed, the detection equipment cannot identify the influence of environmental light changes on light efficiency parameters in real time, when the on-site environmental light intensity and color temperature fluctuate, the consistency deviation of simulation data and actual measurement data is large, and the accuracy of light efficiency comparison cannot be ensured.

[0004] Therefore, the present application provides a light efficiency inspection method for on-site arrangement of LED landscape lamps to solve the above problems. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a light efficiency inspection method for on-site arrangement of LED landscape lamps, which solves the problem of large consistency deviation of simulation data and actual measurement data in the background art, and cannot ensure the accuracy of light efficiency comparison.

[0006] To achieve the above purpose, the present application provides the following technical solutions: a light efficiency inspection method for on-site arrangement of LED landscape lamps, the method comprising the following steps:

[0007] S1, collecting lamp parameter data and on-site environmental optical parameter data of on-site arrangement of LED landscape lamps;

[0008] S2, performing light efficiency simulation analysis and processing based on the lamp parameter data and the on-site environmental optical parameter data, and generating simulation light efficiency distribution data;

[0009] S3, performing on-site actual light efficiency measurement processing in the on-site arrangement of the LED landscape lamp, collecting actual light efficiency parameters of the LED landscape lamp through a light efficiency detection device, and generating actual light efficiency measurement data;

[0010] S4, performing light efficiency consistency comparison processing according to the simulation light efficiency distribution data and the actual light efficiency measurement data, and generating light efficiency difference evaluation data;

[0011] S5, performing light effect eligibility determination processing based on the light effect difference evaluation data, determining whether the light effect of the LED landscape lamp arrangement meets the requirements according to a preset light effect standard threshold, and generating light effect inspection result data;

[0012] S6, when it is determined that the light effect does not meet the requirements based on the light effect inspection result data, performing light effect optimization adjustment processing, and generating light effect adjustment suggestion data;

[0013] S7, generating a light effect inspection report according to the light effect inspection result data and the light effect adjustment suggestion data, and outputting to a user terminal.

[0014] Preferably, the parameter data collected in S1 includes the following steps:

[0015] S11, collecting lamp parameter data of the LED landscape lamp by a portable data collection device, including power parameter, color temperature parameter, beam angle parameter and installation position coordinate parameter of the lamp;

[0016] S12, collecting on-site environmental optical parameter data by an environmental optical sensor, including environmental illumination parameter, environmental chrominance parameter and background reflectivity parameter;

[0017] S13, storing the collected lamp parameter data and on-site environmental optical parameter data to a light effect inspection database, and performing data format standardization processing to generate standardized lamp parameter data and standardized environmental optical parameter data.

[0018] Preferably, the light effect simulation analysis processing in S2 includes the following steps:

[0019] S21, obtaining the standardized lamp parameter data and the standardized environmental optical parameter data;

[0020] S22, constructing a three-dimensional light effect simulation model of the on-site arrangement based on a ray tracing algorithm, inputting the standardized lamp parameter data into the three-dimensional light effect simulation model for light effect simulation calculation, and generating initial simulation light effect data;

[0021] S23, combining the standardized environmental optical parameter data to perform environmental light compensation correction processing on the initial simulation light effect data, generating corrected simulation light effect distribution data, the simulation light effect distribution data including light intensity distribution map, illumination distribution map and chrominance distribution map.

[0022] Preferably, the on-site actual light effect measurement processing in S3 includes the following steps:

[0023] S31, using a high-precision light effect detection device to measure the actual light effect on the site of the LED landscape lamp arrangement, the light effect detection device including an illuminometer, a colorimeter and a spectrometer;

[0024] S32, arranging a plurality of measurement points according to a preset measurement grid distribution scheme, collecting actual illuminance values, actual color coordinate values and actual spectral power distribution data of each measurement point;

[0025] S33, performing data fusion processing on the collected actual illuminance values, actual color coordinate values and actual spectral power distribution data to generate actual light efficiency measurement data, which is stored in a matrix form.

[0026] Preferably, the light efficiency consistency comparison processing in S4 includes the following steps:

[0027] S41, acquiring the simulated light efficiency distribution data and the actual light efficiency measurement data;

[0028] S42, using image registration technology to perform spatial alignment processing on the light intensity distribution map in the simulated light efficiency distribution data and the actual illuminance distribution map in the actual light efficiency measurement data, to generate aligned simulated light efficiency data and actual light efficiency data;

[0029] S43, calculating the difference values between the aligned simulated light efficiency data and the actual light efficiency data, including illuminance difference values, chrominance difference values and uniformity difference values, to generate light efficiency difference evaluation data.

[0030] Preferably, the light efficiency qualification determination processing in S5 includes the following steps:

[0031] S51, acquiring the light efficiency difference evaluation data and a preset light efficiency standard threshold value, the preset light efficiency standard threshold value including a maximum allowed illuminance deviation, a maximum allowed chrominance deviation and a minimum uniformity requirement;

[0032] S52, comparing the illuminance difference values, chrominance difference values and uniformity difference values in the light efficiency difference evaluation data with the preset light efficiency standard threshold value;

[0033] S53, when all difference values are lower than the preset light efficiency standard threshold value, determining that the light efficiency meets the requirements, and generating qualified light efficiency inspection result data;

[0034] S54, when any difference value exceeds the preset light efficiency standard threshold value, determining that the light efficiency does not meet the requirements, and generating unqualified light efficiency inspection result data.

[0035] Preferably, the light efficiency optimization adjustment processing in S6 includes the following steps:

[0036] S61, when unqualified light efficiency inspection result data is generated, analyzing the main difference sources in the light efficiency difference evaluation data to identify the key parameters that cause the unqualified light efficiency;

[0037] S62, generating a light effect adjustment scheme based on the artificial intelligence optimization algorithm, including adjusting the installation angle of the lamp, replacing the type of the lamp, and modifying the arrangement density;

[0038] S63, simulating and verifying according to the light effect adjustment scheme to generate optimized simulation light effect data;

[0039] S64, comparing the optimized simulation light effect data with the actual light effect measurement data again until the light effect difference value meets the preset standard to generate light effect adjustment suggestion data.

[0040] Preferably, the generation of the light effect inspection report in S7 includes the following steps:

[0041] S71, combining the light effect inspection result data and the light effect adjustment suggestion data into light effect inspection summary data;

[0042] S72, converting the light effect inspection summary data into a structured light effect inspection report including a text description, a data table, and a light effect distribution chart by using a report generation algorithm;

[0043] S73, transmitting the light effect inspection report to a user terminal including a mobile device and a computer system through a wireless communication module.

[0044] Preferably, the method further includes a real-time light effect data monitoring step after generating the light effect inspection report:

[0045] S91, deploying an Internet of Things sensor network at the site of the LED landscape lamp arrangement to collect real-time light effect parameter data;

[0046] S92, dynamically comparing the real-time light effect parameter data with the light effect inspection result data to generate light effect change trend data;

[0047] S93, automatically triggering a re-inspection process when the light effect change trend data indicates that the light effect degradation exceeds a warning threshold.

[0048] Preferably, the method further includes a light effect inspection calibration step after the real-time light effect data monitoring step:

[0049] S101, periodically calibrating the light effect detection device using a standard light effect source to generate device calibration data;

[0050] S102, correcting the actual light effect measurement data based on the device calibration data to generate corrected actual light effect measurement data;

[0051] S103, inputting the corrected actual light effect measurement data into the subsequent light effect inspection process for continuous light effect inspection.

[0052] Compared with the prior art, the present invention provides a method for testing the luminous efficacy of LED landscape lights in on-site deployment, which has the following beneficial effects:

[0053] 1. In this invention, by collecting environmental optical parameters in real time and integrating luminous efficacy simulation analysis, the influence of ambient light changes on luminous efficacy comparison can be dynamically detected. At the same time, ambient light compensation and correction processing is used to automatically eliminate environmental fluctuation interference, which can reduce the consistency deviation between simulated data and actual measurement data, ensure the accuracy and reliability of luminous efficacy comparison, and avoid inspection errors caused by environmental factors.

[0054] 2. In this invention, by intelligently matching the optimal calibration algorithm type and dynamically adjusting the luminous efficacy judgment strategy based on artificial intelligence optimization algorithm, it is possible to identify algorithm mismatch problems in real time and automatically optimize algorithm selection during the luminous efficacy qualification judgment process. This can reduce judgment deviation caused by improper algorithm selection and improve the accuracy and adaptability of luminous efficacy test results.

[0055] 3. In this invention, multi-region spatial alignment and graded light effect difference assessment are achieved through image registration technology. This can automatically identify uneven light effect distribution in the on-site layout area, calculate local light effect difference values ​​in real time for different areas, avoid ignoring local abnormal areas, and improve the comprehensiveness and detection effect of light effect inspection. Attached Figure Description

[0056] Figure 1 This is a flowchart of a method for testing the luminous efficacy of LED landscape lights in on-site arrangement, as described in this invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] For specific implementation examples, please refer to: Figure 1 A method for testing the luminous efficacy of LED landscape lights in on-site deployment, the method comprising the following steps:

[0059] S1. Collect lighting parameter data and on-site environmental optical parameter data of the LED landscape lighting installation;

[0060] S2. Based on the luminaire parameter data and the on-site environmental optical parameter data, perform luminous efficacy simulation analysis and generate simulated luminous efficacy distribution data;

[0061] S3. In the on-site arrangement of LED landscape lights, conduct on-site actual luminous efficacy measurement and processing, collect the actual luminous efficacy parameters of LED landscape lights through luminous efficacy detection equipment, and generate actual luminous efficacy measurement data;

[0062] S4. Based on the simulated luminous efficacy distribution data and the actual luminous efficacy measurement data, perform luminous efficacy consistency comparison processing to generate luminous efficacy difference assessment data;

[0063] S5. Based on the light efficacy difference assessment data, perform light efficacy qualification judgment processing, and judge whether the light efficacy of the LED landscape light arrangement meets the requirements according to the preset light efficacy standard threshold, and generate light efficacy test result data.

[0064] S6. When the light effect is determined to be non-compliant based on the light effect test results, light effect optimization and adjustment processing is performed to generate light effect adjustment suggestion data.

[0065] S7. Generate a luminous efficacy test report based on the luminous efficacy test results and luminous efficacy adjustment suggestions, and output it to the user terminal.

[0066] The parameter data acquisition in S1 includes the following steps:

[0067] S11. Collect the lighting parameter data of the LED landscape lights using a portable data acquisition device, including the power parameters, color temperature parameters, beam angle parameters, and installation position coordinate parameters of the lights.

[0068] S12. Collect on-site environmental optical parameter data through environmental optical sensors, including ambient illuminance parameters, ambient chromaticity parameters, and background reflectivity parameters;

[0069] S13. Store the collected luminaire parameter data and on-site environmental optical parameter data into the luminous efficacy testing database, and perform data format standardization processing to generate standardized luminaire parameter data and standardized environmental optical parameter data, including the following steps:

[0070] S131. Check for missing values ​​in the luminaire parameter data and the on-site environmental optical parameter data, and fill in the missing data using the mean interpolation method.

[0071] S132. Standardize data units: convert power parameters to watts, color temperature parameters to Kelvin, and illuminance parameters to lux. The standardized formula is:

[0072] ;

[0073] in The power value is the standardized value. This is the original power value. The power average. The standard deviation of power;

[0074] S133. Apply the Z-score standardization method to scale the data to zero mean and unit variance to generate standardized luminaire parameter data and standardized environmental optical parameter data.

[0075] The light effect simulation analysis in S2 includes the following steps:

[0076] S21. Obtain standardized luminaire parameter data and standardized environmental optical parameter data;

[0077] S22. Construct a three-dimensional lighting effect simulation model of the site layout based on the ray tracing algorithm, input standardized luminaire parameter data into the three-dimensional lighting effect simulation model for lighting effect simulation calculation, and generate initial simulated lighting effect data, including the following steps:

[0078] S221. Based on the installation position coordinate parameters in the standardized lighting fixture parameter data, establish a three-dimensional spatial coordinate system for the lighting fixture;

[0079] S222. Employ a ray projection method to emit virtual rays from each lighting fixture location, and calculate the ray propagation path in the scene, including reflection, refraction, and absorption effects;

[0080] S223. Based on the interaction results between light and scene objects, accumulate the light intensity contribution value to generate initial light intensity distribution data, wherein the light intensity calculation formula is:

[0081] ;

[0082] in The sum of the total light intensity. For the first The initial intensity of the ray, For the first The absorption coefficient of a single ray of light. For the first The distance a ray of light travels. The total number of light rays, For summation index;

[0083] S224. Map the initial light intensity distribution data to a three-dimensional mesh model to generate a three-dimensional light effect simulation model, wherein the mesh resolution is adjusted according to the preset accuracy parameters;

[0084] S23. Combine standardized environmental optical parameter data with ambient light compensation and correction processing of the initial simulated luminous efficacy data to generate corrected simulated luminous efficacy distribution data. The simulated luminous efficacy distribution data includes a luminous intensity distribution map, an illuminance distribution map, and a chromaticity distribution map, including the following steps:

[0085] S231. Obtain the ambient illuminance parameter and ambient chromaticity parameter from the standardized environmental optical parameter data;

[0086] S232. Calculate the influence factor of ambient light on the simulated lighting effect. A linear weighting method is used to incorporate ambient light parameters into the initial simulated lighting effect data. The correction formula is:

[0087] ;

[0088] in To correct the light intensity, The initial simulated light intensity, For ambient light intensity, Ambient light compensation factor;

[0089] S233. Adjust the brightness compensation value of the luminous efficacy data according to the background reflectivity parameter, and generate the corrected luminous intensity distribution map and illuminance distribution map.

[0090] S234. Perform smoothing filtering on the corrected data to eliminate noise interference and output the final corrected simulated luminous efficacy distribution data. This includes the following steps:

[0091] S2341. Determine the parameter configuration of the smoothing filtering algorithm, including the filter window size and the filter type. The filter window size is preset according to the data sampling density, and the filter type is Gaussian filtering.

[0092] S2342. Construct a Gaussian filter kernel function whose weight distribution is controlled by the standard deviation parameter. The value of the standard deviation is positively correlated with the size of the filter window to ensure that the weight distribution conforms to the normal distribution law.

[0093] S2343. Arrange the corrected data in spatial order, and apply a Gaussian filter kernel function to perform neighborhood weighting calculations on each data point. The neighborhood range is defined by the size of the filter window, and the weight allocation follows the Gaussian probability density function. The weight calculation formula is as follows:

[0094] ;

[0095] in The relative position of data points within the neighborhood. The standard deviation parameter, These are weight values;

[0096] S2344. Calculate the signal-to-noise ratio (SNR) of the filtered data. If the SNR is lower than the preset threshold, adjust the standard deviation parameter of the Gaussian filter kernel function and re-execute S2343.

[0097] S2345. Output the simulated light effect distribution data after Gaussian filtering as the final correction result, ensuring smooth data and eliminating noise interference.

[0098] The actual on-site luminous efficacy measurement and processing in S3 includes the following steps:

[0099] S31. Use high-precision luminous efficacy testing equipment to conduct actual luminous efficacy measurements at the LED landscape lighting installation site. The luminous efficacy testing equipment includes an illuminance meter, a colorimeter, and a spectrometer.

[0100] S32. According to the preset measurement grid layout plan, set up multiple measurement points on site, and collect the actual illuminance value, actual color coordinate value, and actual spectral power distribution data of each measurement point, including the following steps:

[0101] S321. Based on the area and shape of the site, divide the site into uniform grid units, with the grid size determined according to the preset density parameters.

[0102] S322. Set a measurement point at the center of each grid cell to ensure coverage of the entire layout area;

[0103] S323. Record the coordinate information of each measurement point and collect data according to the grid order;

[0104] S324. Verify the uniformity of the mesh layout. If blind spots are found, dynamically adjust the mesh density using the following formula:

[0105] ;

[0106] in For grid spacing, For the number of grid cells, The site area;

[0107] S33. The actual illuminance value, actual color coordinate value and actual spectral power distribution data are collected and fused to generate actual luminous efficacy measurement data, which are stored in matrix form.

[0108] The process of comparing and processing light efficacy consistency in S4 includes the following steps:

[0109] S41. Obtain simulated luminous efficacy distribution data and actual luminous efficacy measurement data;

[0110] S42. Using image registration technology, spatially align the light intensity distribution map in the simulated light effect distribution data with the actual illuminance distribution map in the actual light effect measurement data to generate aligned simulated light effect data and actual light effect data, including the following steps:

[0111] S421. Extract feature points from the light intensity distribution map in the simulated light effect distribution data and the actual illuminance distribution map in the actual light effect measurement data, and use the scale-invariant feature transformation algorithm to match key points.

[0112] S422. Calculate the affine transformation matrix based on the matching key points, mapping the simulated light effect data to the coordinate system of the actual measured data. The transformation formula is:

[0113] ;

[0114] in To simulate data coordinate vectors, The transformed coordinate vector Let be the affine transformation matrix. It is a translation vector;

[0115] S423. Resample the transformed data using bilinear interpolation to ensure consistent spatial resolution. This includes the following steps:

[0116] S4231. Determine the coordinates of the resampling points. The point is located in the grid of the transformed coordinate system;

[0117] S4232, Find the four nearest neighbor data points around the resampling point. , , , Illuminance value ,in ;

[0118] S4233. Calculate the illuminance value at the resampling point using the bilinear interpolation formula. :

[0119] ;

[0120] in The illuminance value at the resampling point. For horizontal normalized offset, This is a normalized offset in the vertical direction;

[0121] S4234. Repeat S4231 to S4233 to process all resampled points and output the resampled data to ensure that the spatial resolution is consistent with the actual measurement data.

[0122] S424. Verify the alignment accuracy. If the error exceeds the preset threshold, iteratively execute the feature point matching and transformation steps until the alignment is successful.

[0123] S43. Calculate the difference between the aligned simulated luminous efficacy data and the actual luminous efficacy data, including illuminance difference, chromaticity difference and uniformity difference, and generate luminous efficacy difference assessment data.

[0124] The S5 luminous efficacy compliance assessment process includes the following steps:

[0125] S51. Obtain luminous efficacy difference assessment data and preset luminous efficacy standard thresholds. The preset luminous efficacy standard thresholds include the maximum permissible illuminance deviation, the maximum permissible chromaticity deviation, and the minimum uniformity requirement.

[0126] S52. Compare the illuminance difference value, chromaticity difference value and uniformity difference value in the luminous efficacy difference assessment data with the preset luminous efficacy standard threshold.

[0127] S53. When all difference values ​​are lower than the preset luminous efficacy standard threshold, the luminous efficacy is determined to meet the requirements, and qualified luminous efficacy test result data is generated.

[0128] S54. When any difference value exceeds the preset luminous efficacy standard threshold, the luminous efficacy is determined to be non-compliant, and unqualified luminous efficacy test result data is generated.

[0129] The light effect optimization and adjustment process in S6 includes the following steps:

[0130] S61. When generating unqualified luminous efficacy test result data, analyze the main sources of difference in the luminous efficacy difference assessment data and identify the key parameters that lead to unqualified luminous efficacy.

[0131] S62. Generate a light effect adjustment scheme based on artificial intelligence optimization algorithms, including adjusting the installation angle of the lamps, changing the type of lamps, and modifying the arrangement density, including the following steps:

[0132] S621. Construct an optimization objective function to minimize the illuminance difference and chromaticity difference values ​​in the luminous efficacy difference assessment data. The fitness function formula is:

[0133] ;

[0134] in For fitness value, This represents the difference in illuminance. This represents the color difference value;

[0135] S622. Initialize the population using a genetic algorithm, with each individual representing a light efficiency adjustment scheme;

[0136] S623. Evolve the population through selection, crossover, and mutation operations, and calculate the fitness value of each individual;

[0137] S624. When the fitness value converges and reaches the maximum number of iterations, output the optimal light effect adjustment scheme and perform simulation verification in S63.

[0138] S63. Conduct simulation verification based on the light effect adjustment scheme to generate optimized simulated light effect data, including the following steps:

[0139] S631. Input the lighting effect adjustment scheme into the three-dimensional lighting effect simulation model and rerun the ray tracing algorithm;

[0140] S632. Generate optimized simulated light effect data, including updated light intensity distribution map and illuminance distribution map. The verification formula is:

[0141] ;

[0142] in For the new illuminance difference value, To optimize the back illuminance, This refers to the actual illuminance.

[0143] S633. Compare the optimized simulated luminous efficacy data with the actual luminous efficacy measurement data, and calculate new luminous efficacy difference assessment data.

[0144] S634. If the new difference value meets the preset standard, the light effect adjustment plan is confirmed to be feasible; otherwise, return to S62 to optimize again.

[0145] S64. Compare the optimized simulated light effect data with the actual light effect measurement data again until the light effect difference value meets the preset standard, and generate light effect adjustment suggestion data.

[0146] Generating a luminous efficacy test report in S7 involves the following steps:

[0147] S71. Combine the luminous efficacy test results data and luminous efficacy adjustment suggestion data into luminous efficacy test summary data;

[0148] S72. Use a report generation algorithm to convert the luminous efficacy test summary data into a structured luminous efficacy test report, including text descriptions, data tables, and luminous efficacy distribution charts, including the following steps:

[0149] S721. Analyze the summary data of luminous efficacy test and extract key indicators including illuminance difference value, chromaticity difference value and uniformity difference value;

[0150] S722. Use the template engine to populate key metrics into the preset report template and generate the text description section;

[0151] S723. Convert the data tables and light effect distribution charts into PDF format, and dynamically generate visualization content through the chart rendering library;

[0152] S724. Integrate text and charts to output a structured light effect inspection report; the report generation efficiency formula is:

[0153] ;

[0154] in For the report generation time, For the number of data points, , These are empirical constants;

[0155] S73. The light effect test report is transmitted to the user terminal via a wireless communication module. The user terminal includes a mobile device and a computer system.

[0156] The method also includes a real-time monitoring step for luminous efficacy data after generating the luminous efficacy test report:

[0157] S91. Deploy an IoT sensor network at the LED landscape lighting installation site to collect real-time luminous efficacy parameter data during operation, including the following steps:

[0158] S911, Select wireless sensor nodes, each node integrating an illuminance sensor and a colorimeter sensor;

[0159] S912. Based on the site layout, deploy the sensor nodes in a star topology network to ensure communication coverage;

[0160] S913. Configure the sampling frequency and transmission protocol of the sensor node to achieve real-time acquisition of light effect parameter data. The sampling formula is as follows:

[0161] ;

[0162] in Sampling frequency, The sampling interval;

[0163] S914. Periodically calibrate sensor nodes and maintain accuracy using the standard light source in S91;

[0164] S92. Dynamically compare the real-time luminous efficacy parameter data with the luminous efficacy test result data to generate luminous efficacy change trend data.

[0165] S93. When the light effect change trend data indicates that the light effect degradation exceeds the warning threshold, the re-inspection process is automatically triggered.

[0166] The method also includes a luminous efficacy verification and calibration step after the real-time monitoring step of luminous efficacy data:

[0167] S101. Regularly calibrate the luminous efficacy testing equipment using a standard luminous efficacy source to generate equipment calibration data, including the following steps:

[0168] S1011. Place a standard light source in front of the testing equipment. The standard light source emits light with known illuminance and chromaticity values.

[0169] S1012. The operating testing equipment collects data from the standard luminous efficacy source, and the deviation between the measured value and the standard value is calculated. The deviation formula is:

[0170] ;

[0171] in This is a relative deviation. To measure illuminance values, Standard illuminance value;

[0172] S1013. Generate calibration coefficients based on the deviation values ​​and store them as equipment calibration data;

[0173] S1014. Regularly repeat the calibration procedure to ensure the long-term stability of the equipment;

[0174] S102. Correct the actual luminous efficacy measurement data based on the equipment calibration data to generate corrected actual luminous efficacy measurement data;

[0175] S103. Input the corrected actual luminous efficacy measurement data into the subsequent luminous efficacy inspection process for continuous luminous efficacy inspection.

[0176] The operational steps of this method for testing the luminous efficacy of LED landscape lights during on-site deployment are as follows:

[0177] Step 1: Data Acquisition Phase

[0178] This method first collects relevant parameters of the LED landscape lighting arrangement through on-site testing equipment. Specifically, it includes: using portable data acquisition equipment to obtain lighting parameter data, including power, color temperature, beam angle, and installation location coordinates; and simultaneously, collecting on-site environmental optical parameter data, including ambient illuminance, ambient chromaticity, and background reflectivity, through environmental optical sensors. After standardization, these data are stored in a luminous efficacy testing database to provide a basis for subsequent analysis. The data acquisition stage ensures that all parameters accurately reflect the on-site conditions, avoiding errors introduced by missing data or inconsistent formats.

[0179] Step 2: Light Efficacy Simulation and Analysis Stage

[0180] Based on the collected standardized luminaire parameter data and environmental optical parameter data, this method performs luminous efficacy simulation analysis and processing. A three-dimensional luminous efficacy simulation model is constructed through a ray tracing algorithm. The luminaire parameters are input into the model to generate initial simulated luminous efficacy data. Combined with environmental light compensation and correction processing, environmental interference is eliminated, and the corrected simulated luminous efficacy distribution data is output. In this stage, the luminous efficacy performance is predicted through numerical simulation, providing a reference benchmark for actual measurement.

[0181] Step 3: Actual Luminous Efficacy Measurement Stage

[0182] At the LED landscape lighting installation site, this method performs on-site actual luminous efficacy measurement and processing. Using high-precision luminous efficacy detection equipment, according to the preset measurement grid layout scheme, the actual illuminance value, color coordinate value and spectral power distribution data of multiple measurement points are collected. The actual luminous efficacy measurement data is generated through data fusion processing and stored in matrix form.

[0183] Step 4: Comparison and Difference Assessment of Luminous Efficacy

[0184] This method compares simulated luminous efficacy distribution data with actual luminous efficacy measurement data through luminous efficacy consistency comparison processing. It uses image registration technology to spatially align the luminous intensity distribution map and the actual illuminance distribution map, calculates the illuminance difference value, chromaticity difference value and uniformity difference value, and generates luminous efficacy difference assessment data. This stage quantifies the deviation between simulation and reality, providing a basis for qualification judgment.

[0185] Step 5: Conformity Assessment Stage

[0186] Based on luminous efficacy difference assessment data, this method performs luminous efficacy compliance judgment processing. By comparing the difference values ​​with preset luminous efficacy standard thresholds, it determines whether the luminous efficacy meets the requirements. When all difference values ​​are lower than or equal to the threshold, qualified luminous efficacy test result data is generated; otherwise, unqualified result is generated. The judgment process is automated, reducing subjective intervention.

[0187] Step Six: Optimization and Adjustment Phase

[0188] When the light effect is unqualified, the light effect optimization and adjustment process is initiated. The source of the difference is analyzed by artificial intelligence optimization algorithm, a light effect adjustment plan is generated and simulated for verification. The optimized data is compared with the actual data again until the difference value meets the standard. The light effect adjustment suggestion data is output. This stage realizes dynamic correction and improves the adaptability of inspection.

[0189] Step 7: Report Generation and Output Stage

[0190] Finally, this method combines the light effect test results data and light effect adjustment suggestion data into light effect test summary data, converts it into a structured light effect test report through a report generation algorithm, and outputs it to the user terminal via a wireless communication module. The report comprehensively presents the test results and supports decision optimization.

[0191] Step 8: Real-time Monitoring and Calibration Phase

[0192] This method also includes real-time monitoring of luminous efficacy data and luminous efficacy verification and calibration steps. It continuously collects luminous efficacy parameters during operation through an IoT sensor network, dynamically compares trends in luminous efficacy changes, and triggers re-verification in case of anomalies. Simultaneously, it periodically calibrates the testing equipment using a standard luminous efficacy source to ensure long-term accuracy. These additional steps enhance the method's sustainability and reliability.

[0193] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0194] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for testing the luminous efficacy of LED landscape lights in on-site installation, characterized in that: The method includes the following steps: S1. Collect lighting parameter data and on-site environmental optical parameter data of the LED landscape lighting installation; S2. Based on the luminaire parameter data and the on-site environmental optical parameter data, perform luminous efficacy simulation analysis and generate simulated luminous efficacy distribution data; S3. In the on-site arrangement of the LED landscape lights, the actual luminous efficacy is measured and processed on-site. The actual luminous efficacy parameters of the LED landscape lights are collected by the luminous efficacy detection equipment, and the actual luminous efficacy measurement data is generated. S4. Based on the simulated luminous efficacy distribution data and the actual luminous efficacy measurement data, perform a luminous efficacy consistency comparison process to generate luminous efficacy difference evaluation data; S5. Based on the light effect difference assessment data, perform light effect qualification judgment processing, and determine whether the light effect of the LED landscape light arrangement meets the requirements according to the preset light effect standard threshold, and generate light effect inspection result data. S6. When the light effect is determined to be non-compliant based on the light effect test results data, light effect optimization and adjustment processing is performed to generate light effect adjustment suggestion data; S7. Generate a light effect test report based on the light effect test result data and the light effect adjustment suggestion data, and output it to the user terminal.

2. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The parameter data acquisition in S1 includes the following steps: S11. Collect the lighting parameter data of the LED landscape lights using a portable data acquisition device, including the power parameters, color temperature parameters, beam angle parameters, and installation position coordinate parameters of the lights. S12. Collect on-site environmental optical parameter data through environmental optical sensors, including ambient illuminance parameters, ambient chromaticity parameters, and background reflectivity parameters; S13. The collected luminaire parameter data and the on-site environmental optical parameter data are stored in the luminous efficacy test database, and the data format is standardized to generate standardized luminaire parameter data and standardized environmental optical parameter data.

3. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The light effect simulation analysis and processing in S2 includes the following steps: S21. Obtain the standardized lighting parameter data and the standardized environmental optical parameter data; S22. Construct a three-dimensional light effect simulation model of the on-site layout based on the ray tracing algorithm, input the standardized luminaire parameter data into the three-dimensional light effect simulation model to perform light effect simulation calculation, and generate initial simulated light effect data; S23. Combine the standardized environmental optical parameter data to perform ambient light compensation and correction processing on the initial simulated light effect data to generate corrected simulated light effect distribution data, which includes light intensity distribution map, illuminance distribution map and chromaticity distribution map.

4. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The actual on-site light effect measurement and processing in S3 includes the following steps: S31. Use high-precision luminous efficacy testing equipment to conduct actual luminous efficacy measurement at the LED landscape lighting installation site. The luminous efficacy testing equipment includes an illuminance meter, a colorimeter, and a spectrometer. S32. Set up multiple measurement points at the site according to the preset measurement grid layout plan, and collect the actual illuminance value, actual color coordinate value and actual spectral power distribution data of each measurement point; S33. The collected actual illuminance values, actual color coordinate values ​​and actual spectral power distribution data are fused to generate actual luminous efficacy measurement data, which is stored in matrix form.

5. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The light efficacy consistency comparison process in S4 includes the following steps: S41. Obtain the simulated light effect distribution data and the actual light effect measurement data; S42. Using image registration technology, the light intensity distribution map in the simulated light effect distribution data is spatially aligned with the actual illuminance distribution map in the actual light effect measurement data to generate aligned simulated light effect data and actual light effect data. S43. Calculate the difference between the aligned simulated luminous efficacy data and the actual luminous efficacy data, including illuminance difference, chromaticity difference and uniformity difference, and generate luminous efficacy difference assessment data.

6. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The light efficacy qualification determination process in S5 includes the following steps: S51. Obtain the light effect difference evaluation data and the preset light effect standard threshold, wherein the preset light effect standard threshold includes the maximum allowable illuminance deviation, the maximum allowable chromaticity deviation and the minimum uniformity requirement; S52. Compare the illuminance difference value, chromaticity difference value and uniformity difference value in the luminous efficacy difference evaluation data with the preset luminous efficacy standard threshold. S53. When all difference values ​​are lower than the preset light effect standard threshold, the light effect is determined to meet the requirements, and qualified light effect test result data is generated. S54. When any difference value exceeds the preset light effect standard threshold, the light effect is determined to be non-compliant, and unqualified light effect test result data is generated.

7. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The light effect optimization and adjustment process in S6 includes the following steps: S61. When generating unqualified luminous efficacy test result data, analyze the main sources of difference in the luminous efficacy difference assessment data and identify the key parameters that cause the luminous efficacy to be unqualified. S62. Generate a light effect adjustment scheme based on artificial intelligence optimization algorithm, including adjusting the installation angle of the lamps, changing the type of lamps, and modifying the arrangement density; S63. Perform simulation verification according to the light effect adjustment scheme to generate optimized simulated light effect data; S64. Compare the optimized simulated light effect data with the actual light effect measurement data again until the light effect difference value meets the preset standard, and generate light effect adjustment suggestion data.

8. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The process of generating a light efficiency inspection report in S7 includes the following steps: S71. Combine the light effect test result data and the light effect adjustment suggestion data into light effect test summary data; S72. The light effect test summary data is converted into a structured light effect test report using a report generation algorithm, including text description, data tables and light effect distribution charts; S73. The light effect test report is transmitted to the user terminal via a wireless communication module. The user terminal includes a mobile device and a computer system.

9. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The method further includes a real-time monitoring step of luminous efficacy data after generating the luminous efficacy test report: S91. Deploy an IoT sensor network at the LED landscape lighting installation site to collect real-time data on luminous efficacy parameters during operation; S92. Dynamically compare the real-time light effect parameter data with the light effect test result data to generate light effect change trend data; S93. When the light effect change trend data indicates that the light effect degradation exceeds the warning threshold, the re-inspection process is automatically triggered.

10. The method for testing the luminous efficacy of LED landscape lighting on-site according to claim 1, characterized in that: The method further includes a luminous efficacy verification and calibration step after the real-time luminous efficacy data monitoring step: S101. Periodically calibrate the light efficiency detection equipment using a standard light source to generate equipment calibration data; S102. Based on the device calibration data, the actual luminous efficacy measurement data is corrected to generate corrected actual luminous efficacy measurement data; S103. Input the corrected actual luminous efficacy measurement data into the subsequent luminous efficacy inspection process for continuous luminous efficacy inspection.

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