A garden lighting design method

Abstract

The present application relates to the technical field of intelligent manufacturing equipment industry, in particular to a kind of garden lighting design method, comprising the following steps: by collecting garden walk path discrete three-dimensional coordinates, calculate direction vector and label curvature sign, according to curvature difference identification broken point and cut sub-section, according to sign switching mark turning point and reverse decreasing illumination, combined with space distance correction broken transition illumination, according to sight angle screening associated illumination, conversion pulse width modulation electric signal, generate dimming control instruction.In the present application, by collecting entity walk path discrete three-dimensional coordinates form curvature feature sequence, according to curvature difference cut broken path, according to sign switching reverse illumination distribution, combined with space distance correction transition illumination, cooperate with sight angle generation dimming control instruction, make illumination along with path bending, intermittent connection and continuous adaptation with direction of travel, reduce sudden bright and sudden dark, transition imbalance and glare problem, improve light consistency and control efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing equipment industry technology, and in particular to a garden lighting design method. Background Technology

[0002] The field of intelligent manufacturing equipment technology mainly involves the design, manufacturing and application of various types of mechanized, automated and intelligent equipment, including agricultural and horticultural machinery, industrial automation equipment, intelligent machine tools, robot systems, testing and control equipment, and information-based manufacturing systems. Its core aspects cover equipment structure design, power transmission and control, operation and work process optimization, environmental adaptability design, equipment collaboration and integration, etc., and systematically plan and realize the overall performance, operation efficiency and automation level of the equipment.

[0003] One traditional garden lighting design method refers to a design approach used in garden environments to meet lighting needs. It typically employs a combination of manual planning, light demand analysis, light source parameter selection, and physical placement to address technical aspects such as the layout of garden lighting, light source selection and placement, luminaire type and installation location, illuminance distribution and angle adjustment, and wiring and power supply arrangements.

[0004] Traditional garden lighting design typically relies on on-site experience to identify paths, select light sources, and arrange and adjust lamp positions. Pedestrian paths are often treated as continuous lines or fixed areas, making it difficult to extract quantifiable data from the curvature, discontinuity, and line of sight of the actual path. In actual installation, abrupt brightness transitions at turns, harsh illuminance transitions on broken paths, and the coexistence of local glare or dark areas are common. Subsequent adjustments require repeated manual corrections, limiting the matching of light supply and work efficiency. Summary of the Invention

[0005] The objective of this invention is achieved through the following technical measures: A garden lighting design method includes the following steps: S1: Collect the three-dimensional coordinates of discrete points on the walking path of the garden entity, calculate the direction vector sequence for the three-dimensional coordinates of adjacent discrete points, extract the positive and negative directions of adjacent direction vectors in the direction vector sequence, and mark the curvature and sign of the corresponding discrete points to construct a curvature feature sequence. S2: Perform subtraction on the curvature of adjacent discrete points within the curvature feature sequence, mark the discrete points whose difference exceeds a preset curvature difference threshold as continuity breakpoints, and segment the curvature feature sequence to construct a path segment sequence; S3: Based on the path segment sequence, determine the positive and negative sign switching state of the curvature sign identifiers of the current and adjacent discrete points in the independent subsequence, mark the corresponding discrete points as turning point markers, and allocate decreasing illuminance in combination with the independent subsequence. Reverse the order of decreasing illuminance in the path interval after the turning point marker to generate a reverse illuminance sequence. S4: Calculate the spatial distance between the continuity break point and the adjacent associated discrete points based on the three-dimensional coordinates of the discrete points. Extract the illuminance of the associated discrete points in the reverse illuminance sequence and calculate the transition illuminance based on the spatial distance. Rewrite and replace the original illuminance at the continuity break point based on the transition illuminance to construct a global illuminance sequence. S5: Extract discrete point direction vectors from the curvature feature sequence, perform dot product and inverse cosine calculation of the line angle by combining them with the preset standard line angle vector, and associate the discrete points corresponding to the preset line angle threshold with the global illuminance sequence to calculate the illuminance distribution sequence and convert it into a pulse width modulation electrical signal to generate a dimming control command.

[0006] As a further embodiment of the present invention, the curvature feature sequence includes discrete point curvature, direction symbol identifier, and discrete point index; the path segment sequence includes segment boundary identifier, segment length parameter, and segment continuity identifier; the inverted illuminance sequence includes base illuminance, decreasing gradient parameter, and inverted interval identifier; the global illuminance sequence includes transition illuminance, original illuminance mapping, and spatial distance weight; and the dimming control command includes pulse width parameter, signal duty cycle, and output channel identifier.

[0007] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain the three-dimensional coordinates of discrete points on the walking path of the garden entity through a three-dimensional laser scanning device, perform vector difference calculation on the three-dimensional coordinates of adjacent discrete points, subtract the previous coordinate from the next coordinate to obtain the direction vector component set, and serialize and arrange all direction vectors according to the path order to obtain the direction vector sequence. S102: Based on the adjacent direction vectors in the direction vector sequence, perform cross product calculation, extract the sign information in the cross product result, mark positive values ​​as positive directions, mark negative values ​​as negative directions, and mark all adjacent vector pairs with signs to obtain a direction sign sequence. S103: Based on the discrete point positions corresponding to the direction symbol sequence, call the three-dimensional coordinates of the discrete points to perform curvature numerical calculation, use the change in the angle between adjacent vectors as the basis for curvature numerical calculation, and synchronously assign the direction symbols to the corresponding curvature numerical positions to complete point-by-point labeling and integration, and generate a curvature feature sequence.

[0008] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Perform a difference operation on the curvature of adjacent discrete points in the curvature feature sequence, extract the curvature values ​​of each pair of adjacent points and perform item-by-item subtraction to obtain a difference vector, and compare the multiple elements in the difference vector with the preset curvature difference threshold, record the index of the out-of-limit position, and obtain the curvature difference index set. S202: Based on the curvature difference index set, mark the corresponding discrete points, map the index set to the corresponding position in the original curvature feature sequence and add fracture markers to obtain the fracture marker distribution state sequence, and arrange the marker positions in the sequence to obtain the fracture marker sequence; S203: The curvature feature sequence is segmented into intervals according to the fracture identifier sequence, the boundaries of continuous segments are divided according to the identifier position, the discrete points in multiple segments are aggregated into multiple independent fragment sets in the original order, and the fragment sets are organized to generate a path sub-segment sequence.

[0009] As a further aspect of the present invention, the curvature difference threshold is determined by extracting the curvature data of discrete points on the smooth path, calculating the mean and standard deviation of the curvature difference between adjacent points, constructing a statistical benchmark value, calling the sampling resolution and spatial positioning error tolerance parameters of the current three-dimensional laser scanning equipment, and performing linear weighted compensation correction on the statistical benchmark value.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the path segment sequence, determine the consistency between the curvature symbol identifier of the current discrete point and the curvature symbol identifier of the adjacent discrete points within the independent subsequence, encode the discrete points corresponding to the symbol difference positions, and aggregate all symbol switching positions to obtain a curvature symbol switching index set. S302: According to the curvature symbol switching index set, perform state mapping on the corresponding discrete points in the independent subsequence, map the index position to the turning point marker, assign a decreasing illuminance sequence to the discrete points in the independent subsequence, and divide the path segment illuminance sequence according to the spatial coordinates of the turning point marker. S303: Based on the path segmented illuminance sequence, the illuminance values ​​in the path interval after the turning point are reversed in order, the illuminance values ​​in the interval are rearranged in reverse according to the index, and the reversed results of multiple intervals are spliced ​​together to obtain the reversed illuminance sequence.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Calculate the spatial distance between the continuous break point and the adjacent related discrete points based on the three-dimensional coordinates of discrete points. Normalize the ratio of the multiple spatial distance values ​​to the maximum value of the distance set, construct a dimensionless distance sequence, and add a preset non-zero bias term to perform reciprocal transformation operation on each term to obtain the inverse distance weight set. S402: Based on the inverse distance weight set, combined with the index corresponding to the discrete point of the associated location retrieved by the inverse illuminance sequence, the corresponding illuminance is extracted in index order and an illuminance data column is constructed. Based on the inverse distance weight set, a weighted summation operation is performed on the illuminance data column item by item to obtain the transitional illuminance set. S403: Spatial location indexing is performed on the original illuminance of the continuity break point, the elements in the transition illuminance set are mapped to the coordinate position of the associated break point, and the original illuminance values ​​at the continuity break point are overwritten and replaced item by item. The illuminance data is reorganized according to the path starting index order to establish a global illuminance sequence.

[0012] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Extract discrete point direction vectors from the direction vector sequence within the curvature feature sequence, index and arrange the multiple discrete point direction vectors to obtain an initial vector data column, and perform normalized division on the multiple vector components to generate a discrete direction vector set. S502: Call the elements in the discrete direction vector set and perform a dot product summation operation with the preset standard line of sight vector, extract the dot product value and perform an inverse cosine transformation to obtain the line of sight angle, record the associated position index when the line of sight angle value does not reach the preset angle threshold, and obtain the line of sight angle filtering index set. S503: Based on the line-of-sight angle filtering index set, perform position matching and positioning in the global illuminance sequence, extract the illuminance values ​​associated with the positioning position to construct an extracted illuminance data column, multiply and scale it with a scaling factor to obtain an illuminance distribution sequence, and convert it into a pulse width modulation electrical signal to generate a dimming control command.

[0013] As a further aspect of the present invention, the included angle threshold is obtained by extracting the gaze trajectory data of people walking in a garden, filtering the included angle between the gaze point direction and the standard forward direction to construct a sample set, calculating the statistical mean and standard deviation of the included angle values ​​of the sample set, calling the parameter of the difference between the average eye height of people and the height of ground lighting facilities, calculating the compensation angle based on the eye height and the height difference, and summing the compensation angle with the statistical mean to determine the value.

[0014] By adopting the above technical solution, the present invention has the following advantages compared with the prior art: In this invention, discrete three-dimensional coordinates of a physical walking path are collected to form a curvature feature sequence. The curvature difference corresponds to the break position for path segmentation, and the sign switch corresponds to the turning position for illuminance reversal allocation. The transition illuminance is corrected by weighting the spatial distance of the break point. Then, the dimming control command is generated by combining the line of sight angle. The illuminance is continuously adapted to the curvature of the path, the discontinuous connection and the direction of travel, so as to reduce the problems of sudden bright and dark turns, imbalance of break point transition and local glare, and improve the consistency of light supply and the control efficiency after deployment. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the steps of the present invention.

[0016] Figure 2 This is a detailed schematic diagram of S1 of the present invention.

[0017] Figure 3 This is a detailed schematic diagram of S2 in this invention.

[0018] Figure 4 This is a detailed schematic diagram of S3 of the present invention.

[0019] Figure 5 This is a detailed schematic diagram of S4 of the present invention.

[0020] Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0021] Example: Please refer to Figure 1 This invention provides a garden lighting design method, including the following steps: S1: Collect the three-dimensional coordinates of discrete points on the walking path of the garden entity, calculate the direction vector sequence for the three-dimensional coordinates of adjacent discrete points, extract the positive and negative directions of adjacent direction vectors in the direction vector sequence, and label the curvature and sign of the corresponding discrete points to construct the curvature feature sequence. S2: Perform subtraction on the curvature of adjacent discrete points within the curvature feature sequence, mark the discrete points whose difference exceeds the preset curvature difference threshold as continuity breakpoints, and segment the curvature feature sequence to construct a path segment sequence; S3: Based on the path segment sequence, determine the positive and negative sign switching state of the curvature sign identifier of the current and adjacent discrete points in the independent subsequence, mark the corresponding discrete point as the turning point, and combine the independent subsequence to allocate decreasing illuminance. Reverse the order of decreasing illuminance in the path interval after the turning point to generate the reverse illuminance sequence. S4: Calculate the spatial distance between the continuity break point and the adjacent associated discrete points based on the three-dimensional coordinates of the discrete points. Extract the illuminance of the associated discrete points in the reverse illuminance sequence and calculate the transition illuminance based on the spatial distance. Rewrite and replace the original illuminance at the continuity break point based on the transition illuminance to construct the global illuminance sequence. S5: Extract discrete point direction vectors from the curvature feature sequence, perform dot product and inverse cosine calculation of the line angle by combining them with the preset standard line angle vector, and correlate the discrete points that do not reach the preset line angle threshold in the global illuminance sequence to calculate the illuminance distribution sequence and convert it into a pulse width modulation electrical signal to generate dimming control instructions.

[0022] The curvature feature sequence includes discrete point curvature, direction sign identifier, and discrete point index; the path segment sequence includes segment boundary identifier, segment length parameter, and segment continuity identifier; the inverted illuminance sequence includes base illuminance, decreasing gradient parameter, and inverted interval identifier; the global illuminance sequence includes transition illuminance, original illuminance mapping, and spatial distance weight; and the dimming control command includes pulse width parameter, signal duty cycle, and output channel identifier.

[0023] Please see Figure 2 The specific steps of S1 are as follows: S101: Obtain the three-dimensional coordinates of discrete points on the walking path of the garden entity through a three-dimensional laser scanning device, perform vector difference calculation on the three-dimensional coordinates of adjacent discrete points, subtract the previous coordinate from the next coordinate to obtain the direction vector component set, and serialize and arrange all direction vectors according to the path order to obtain the direction vector sequence. The point cloud data file of the garden's actual walking path was directly retrieved from a 3D laser scanner mounted on the bottom of a micro-drone. A local Cartesian coordinate system was established with the center of the main entrance of the garden as the spatial origin. Gaussian filtering and noise reduction were performed on the point cloud data file. By setting the filter window radius parameter to 0.5m, the average elevation value of each scanned node within the window was calculated. Outliers whose elevation differed from the average value by more than 0.2m were removed. Then, path nodes that conformed to ground features were extracted as effective discrete points according to a preset elevation range threshold. In the local Cartesian coordinate system, three consecutive discrete points in the initial segment are extracted for calculation and explanation. They are numbered as node 1, node 2, and node 3. The horizontal coordinate of node 1 is 10.5m, the vertical coordinate is 20.2m, and the elevation is 5.1m. The horizontal coordinate of node 2 is 11.0m, the vertical coordinate is 21.0m, and the elevation is 5.2m. The horizontal coordinate of node 3 is 11.3m, the vertical coordinate is 22.1m, and the elevation is 5.1m. The vector difference operation is performed on the three-dimensional spatial coordinates of adjacent discrete points. The horizontal vector component is obtained by subtracting the horizontal coordinate of the previous discrete point from the horizontal coordinate of the next discrete point. Similarly, the vertical coordinate and elevation coordinate are subtracted accordingly. The coordinate values ​​of node 2 are subtracted from the corresponding coordinate values ​​of node 1. The horizontal vector component of the first direction vector component set is 0.5m, the vertical vector component is 0.8m, and the elevation vector component is 0.1m. The same subtraction logic is used to subtract the corresponding coordinate values ​​of node 2 from the coordinate values ​​of node 3. The horizontal vector component of the second direction vector component set is 0.3m, the vertical vector component is 1.1m, and the elevation vector component is -0.1m.

[0024] Table 1 Spatial coordinate parameters of discrete points along garden paths Node 1 10.5m 20.2m 5.1m Node 2 11.0m 21.0m 5.2m Node 3 11.3m 22.1m 5.1m Table 1 lists the coordinate data details of the discrete points mentioned above. Following the order of the physical extension of the walking path, the direction vector component sets generated from all adjacent discrete points are sequentially arranged to construct and output a sequence of direction vectors with continuous spatial pointing characteristics.

[0025] S102: Based on the adjacent direction vectors in the direction vector sequence, perform cross product operation, extract the sign information in the cross product result, mark positive values ​​as positive direction, mark negative values ​​as negative direction, and mark all adjacent vector pairs with signs to obtain the direction sign sequence; The generated direction vector sequence is retrieved, and consecutive adjacent direction vectors are extracted for computation. Here, the first and second direction vector component sets are extracted as adjacent vector pairs for planar cross product calculation. The lateral and longitudinal vector components of the first and second direction vector component sets are obtained. The lateral and longitudinal vector components of the first and second direction vector component sets are multiplied to obtain the first product parameter. The longitudinal vector component of the first and second direction vector component sets is multiplied to obtain the second product parameter. Finally, the first product parameter is subtracted from the second product parameter to derive the cross product value. Substituting the aforementioned data parameters, the horizontal vector component of the first direction vector component set is 0.5m and the vertical vector component is 0.8m, and the horizontal vector component of the second direction vector component set is 0.3m and the vertical vector component is 1.1m. Performing cross-multiplication logic, multiplying 0.5m and 1.1m yields the first product parameter of 0.55 square meters, and multiplying 0.8m and 0.3m yields the second product parameter of 0.24 square meters. Subtracting 0.24 square meters from 0.55 square meters finally derives the cross-product value of 0.31 square meters. The sign bit information of the cross product value is extracted as the directional determination criterion. Values ​​greater than 0 are defined as positive, and values ​​less than 0 are defined as negative. The current cross product value of 0.31 square meters falls into the positive range, so its sign bit is extracted and marked as a positive direction sign, indicating that the garden path curves to the left at this point. If the cross product value falls into the negative range, it is marked as a negative direction sign, indicating that the path curves to the right. Cross-multiplication and sign bit extraction operations are performed sequentially on all adjacent vector pairs in the sequence, and the generated direction marks are integrated into a direction sign sequence in order.

[0026] S103: Based on the discrete point positions corresponding to the direction symbol sequence, call the three-dimensional coordinates of the discrete points to perform curvature numerical calculation, use the change in the angle between adjacent vectors as the basis for curvature numerical calculation, and synchronously assign the direction symbols to the corresponding curvature numerical positions to complete point-by-point annotation and integration, and generate curvature feature sequence. Based on the spatial positions of the discrete points corresponding to each marker in the direction symbol sequence, the 3D coordinate values ​​of each discrete point in the local spatial coordinate system are used to calculate the curvature characteristics. The change in the angle between adjacent spatial vectors is used as the quantification basis for the curvature parameter. The angle between the first direction vector formed by node 1 and node 2 and the second direction vector formed by node 2 and node 3 is extracted and calculated. The spatial magnitude of these two direction vectors is calculated. The horizontal, vertical, and elevation vector components of the first direction vector are squared, summed, and then squared. The square roots of 0.5m, 0.8m, and 0.1m are obtained as 0.25, 0.64, and 0.01, respectively. The sum is 0.90, and the square root is used to derive the magnitude parameter of the first vector as 0.95m. Similarly, the square roots of 0.3m, 1.1m, and -0.1m of the second direction vector are obtained as 1.31, and the square root is used to derive the magnitude parameter of the second vector as 1.14m. Multiply and sum the corresponding coordinate components of the two direction vectors to obtain the spatial inner product value. Multiplying 0.5m and 0.3m gives 0.15, multiplying 0.8m and 1.1m gives 0.88, and multiplying 0.1m and -0.1m gives -0.01. Summing these three products yields a spatial inner product value of 1.02 square meters. Dividing this spatial inner product value by the product of the first and second vector magnitude parameters (i.e., 1.02 divided by 0.95 multiplied by 1.14, yields 1.08), resulting in a spatial cosine value of 0.94 for the angle between the two direction vectors. Using the inverse cosine function mapping rule, this spatial cosine value is converted into an angle radian parameter, and the angle change is calculated to be 0.35 radians. This 0.35 radians is directly used as the basic curvature feature value of the current intermediate associated point, node 2. Retrieve the positive direction symbol generated for node 2 in the previous steps, and simultaneously combine this positive direction symbol before the curvature feature value to form a curvature feature parameter with directional attributes, namely positive 0.35 radians. Perform angle calculation and symbol addition operations on all discrete points on the garden path one by one, and merge and organize all curvature feature parameters according to the physical order of the nodes to generate a curvature feature sequence.

[0027] Please see Figure 3 The specific steps of S2 are as follows: S201: Perform a difference operation on the curvature of adjacent discrete points within the curvature feature sequence, extract the curvature values ​​of each pair of adjacent points and subtract them one by one to obtain the difference vector, and compare the multiple elements in the difference vector with the preset curvature difference threshold, record the index of the out-of-limit position, and obtain the curvature difference index set. The generated curvature feature sequence is extracted, and the curvature feature values ​​of adjacent discrete points are retrieved to perform a difference operation. Substituting the curvature feature value of node 2 (+0.35 radians) obtained in the previous step, and using the same logic, the curvature feature value of node 3 (+0.38 radians) and the curvature feature value of node 4 (-0.85 radians) are obtained from the sequence. By subtracting the curvature feature value of the previous node from the curvature feature value of the next node and extracting the absolute value of the calculation result, the first curvature difference between node 3 and node 2 is calculated to be 0.03 radians. Similarly, the second curvature difference between node 4 and node 3 is calculated to be 1.23 radians. All the calculated differences are combined in the original sequence order to generate a curvature difference vector. Subsequently, a preset curvature difference threshold is called, and each element in the curvature difference vector is compared to its value. The preset curvature difference threshold is set by statistically analyzing the curvature gradient distribution data of 1000 historical, smooth walking paths in the target garden and extracting the upper limit of the 95% confidence interval of its distribution density. In this scenario, the specific value is 0.50 radians. Substituting the calculated difference elements into the comparison logic, the first curvature difference value of 0.03 radians is less than the 0.50 radian threshold, indicating a continuous curvature change, and is not recorded. The second curvature difference value of 1.23 radians is significantly greater than the 0.50 radian threshold, representing a sharp directional change or abrupt shape change in the path. The original sequence position number corresponding to this difference, i.e., the position number of node 4, is extracted as the out-of-limit position index and recorded. After traversing the entire sequence, all recorded out-of-limit position indices are aggregated to generate a curvature difference index set.

[0028] S202: Mark the corresponding discrete points based on the curvature difference index set, map the index set to the corresponding position in the original curvature feature sequence and add fracture markers to obtain the fracture marker distribution state sequence, and arrange the marker positions in the sequence to obtain the fracture marker sequence; The calculated curvature difference index set is retrieved, and its recorded position number data is mapped back to the original curvature feature sequence one by one to locate the corresponding discrete point nodes. For the mapped discrete points, a state attribute marking operation is performed. The state identifier code parameter for nodes with normal smooth transitions is set to 0, and the state identifier code parameter for fracture nodes with curvature abrupt changes is set to 1. Substituting the results obtained from the previous operation, the position numbers of nodes 2 and 3 do not exist in the curvature difference index set, so their state identifier code parameters are assigned to 0. The position number of node 4 is recorded in the curvature difference index set, so the state identifier code parameter of node 4 in the original curvature feature sequence is directly appended to the attribute field of that location, setting it to 1, thus marking a fracture. After completing the above state identifier code parameter assignment operation for all discrete points, a fracture marker distribution state sequence is formed, consisting of alternating 0s and 1s. Subsequently, a sequence dimensionality reduction extraction operation is performed. This involves traversing the sequence of break marker distribution states and using logical judgment statements to filter out all node data with a state marker code parameter of 0, retaining only the location coordinates and sequence number of nodes with a state marker code parameter of 1. These filtered break marker positions are then rearranged in a single-dimensional linear fashion according to the physical spatial order of the path extension, eliminating irrelevant attribute data to generate a simplified break marker sequence. This sequence visually reflects the distribution of segmented turning nodes in the spatial form of the entire garden path.

[0029] S203: The curvature feature sequence is divided into intervals according to the fracture marker sequence, the boundaries of continuous segments are divided according to the marker positions, the discrete points in multiple segments are aggregated into multiple independent fragment sets in the original order, and the fragment sets are organized to generate a path segment sequence. The generated fracture marker sequence is read, and the fracture node position numbers are extracted as physical cutting boundaries. Spatial interval segmentation is then performed on the original complete curvature feature sequence. Specifically, the continuous discrete points from the original path start point to the first fracture node are divided into the first continuous segment; the continuous discrete points from the first fracture node to the second fracture node are divided into the second continuous segment, and so on. Substituting the data results from the previous step, interval segmentation calculations are performed. Node 4 has been identified as the first fracture node with a status identifier parameter of 1. Therefore, node 4 is used as the first segmentation boundary, and nodes 1, 2, and 3, which are located before node 4 and have status identifier parameters of 0, are truncated and extracted. The spatial coordinates and curvature features of these three nodes are extracted, and they are packaged and aggregated according to the original walking path's connection order to construct the first independent segment set. This segment set represents a smooth walking path interval without sharp turns. Next, starting from node 4, the process continues reading until the next breakpoint, assuming it's node 89, is encountered. All discrete points between nodes 4 and 88 are packaged and aggregated in the same way to form the second independent segment set. After performing the above boundary segmentation and interval aggregation operations on the entire garden path, multiple independent segment sets are obtained, but their beginnings and ends are spatially adjacent. Finally, a global index directory is established to encode and organize these independent segment sets sequentially, generating a structured sequence of path segments. The advantage of this operational logic is that the hard boundary segmentation strategy based on morphological abrupt change points decouples the originally long and complex non-stationary path sequence into multiple independent micro-segments with relatively stable curvature characteristics.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the path segment sequence, determine the consistency between the curvature symbol identifier of the current discrete point and the curvature symbol identifier of the adjacent discrete points within the independent subsequence, encode the discrete points corresponding to the symbol difference positions, and aggregate all symbol switching positions to obtain the curvature symbol switching index set. The generated path segment sequence is retrieved, and the second independent segment set is extracted as the processing object. The direction sign of the curvature feature parameters attached to each continuous discrete point within this subsequence is traversed. The curvature feature value of the known preceding node 4 is retrieved as -0.85 radians, and its direction sign is negative. Simultaneously, the curvature feature parameter of the adjacent node 5 is retrieved as -0.82 radians with a negative direction sign, and the curvature feature parameter of node 6 is retrieved as +0.41 radians with a positive direction sign. A sign consistency judgment operation is performed by comparing the direction sign of the preceding discrete point with that of the following discrete point using Boolean equality comparison. If both are positive or both are negative, the signs are considered consistent; otherwise, they are considered different. Substituting the data, the negative direction signs of nodes 4 and 5 are completely consistent, indicating no curve reversal. The negative direction sign of node 5 conflicts with the positive direction sign of node 6, indicating a sign difference. For the discrete point corresponding to the location where a sign difference exists, i.e., node 6, an identification encoding operation is performed. Node 6 is assigned a switching position encoding parameter, which is set as a preset reversal critical identifier code. This preset reversal critical identifier code serves as a feature value representing the reversal critical point. After completing the encoding operation for all sign switching positions in the sequence, the global index numbers of all discrete points assigned the preset reversal critical identifier code are extracted. The global index numbers are then aggregated and encapsulated into a one-dimensional array according to physical order to generate a curvature sign switching index set. The advantage of this operational logic is that it extracts the inflection point position features of continuous curves by combining Boolean equality comparison with specific encoding parameters.

[0031] S302: Switch the index set according to the curvature sign, perform state mapping on the corresponding discrete points in the independent subsequence, map the index position to the turning point marker, assign a decreasing illuminance sequence to the discrete points in the independent subsequence, and divide the path segment illuminance sequence according to the spatial coordinates of the turning point marker. Retrieve the generated curvature symbol switching index set, extract the internal global index number and map it back to the second independent segment set, and change the state attribute of the preceding node 6 to the inflection point identifier. Assign an initial decreasing illuminance sequence to all discrete points in the second independent segment set. Obtain the initial reference illuminance value of the main lighting fixture, and perform attenuation calculation based on the cumulative physical step length of the discrete point's distance from the fixture. Multiply the cumulative physical step length by the ambient light attenuation coefficient to obtain the illuminance loss value, and derive the current illuminance value by subtracting the illuminance loss value from the initial reference illuminance value. Set the initial reference illuminance value to 300 lux and the ambient light attenuation coefficient to 2.5 lux per meter. Node 4 has a cumulative physical step length of 12 meters and a calculated illuminance loss of 30 lux, resulting in a current illuminance value of 270 lux. Node 5 has a cumulative physical step length of 13 meters and a calculated illuminance loss of 32.5 lux, resulting in a current illuminance value of 267.5 lux. Node 6 has a cumulative physical step length of 14 meters and a calculated current illuminance value of 265 lux. This process continues to form a decreasing illuminance data set. Then, the spatial coordinates of node 6 are retrieved as a turning point marker. Using node 6 as a boundary, the decreasing illuminance sequence is divided. The data from node 4 to node 5 is divided into the first illuminance interval, and the data from node 6 and subsequent nodes are divided into the second illuminance interval, generating a path-segmented illuminance sequence containing multiple intervals. The advantage of this operational logic is that it obtains basic distribution data by introducing a step-length attenuation-based optical quantization and a hard interval segmentation mechanism at turning points.

[0032] S303: Based on the path segmented illuminance sequence, the illuminance values ​​in the path interval after the turning point are reversed in order, the illuminance values ​​in the interval are rearranged in reverse according to the index, and the reversed results of multiple intervals are concatenated to obtain the reversed illuminance sequence. The generated path segment illuminance sequence is retrieved, and the illuminance intervals located after the turning point markers are extracted and processed. The previously defined sub-segment illuminance interval, consisting of node 6 and its subsequent consecutive nodes, is used as the operation object. This sub-segment illuminance interval contains four discrete points, from node 6 to node 9. The current illuminance value of node 6 has been calculated to be 265 lux. Following the aforementioned attenuation logic, the current illuminance values ​​of node 7, 7, 8, and 9 are calculated to be 262.5 lux, 8, and 9, respectively, and 257.5 lux. The illuminance value sequence within this sub-segment illuminance interval is then reversed. Specifically, the initial index sequence of each illuminance value within the interval is extracted, and the initial index sequence is mirrored by swapping the first and last elements. This causes the minimum illuminance value, originally at the end, to move directly to the beginning of the interval, and the maximum illuminance value, originally at the beginning, to move to the end of the interval. Substituting the aforementioned parameters, the original illuminance values ​​arranged in the order of nodes 6 to 9 were 265 lux, 262.5 lux, 260 lux, and 257.5 lux, respectively. After the reverse rearrangement operation, node 6 is reassigned the inverted illuminance value of 257.5 lux, node 7 is assigned 260 lux, node 8 is assigned 262.5 lux, and node 9 is assigned 265 lux. After completing the inversion of this interval, the intervals that were not inverted are concatenated with the inverted intervals. The first illuminance interval containing nodes 4 and 5, whose preceding values ​​remain unchanged, is extracted and stitched together with the inverted second illuminance interval according to the original spatial physical extension order of discrete nodes 4 to 9, ultimately outputting a globally continuous inverted illuminance sequence.

[0033] Please see Figure 5 The specific steps of S4 are as follows: S401: Calculate the spatial distance between the continuous break point and the adjacent related discrete points based on the three-dimensional coordinates of discrete points. Normalize the ratio of the multiple spatial distance values ​​to the maximum value of the distance set, construct a dimensionless distance sequence, and add a preset non-zero bias term to perform reciprocal transformation operation on each term to obtain the inverse distance weight set. The system retrieves the global index and its 3D physical coordinates in the environment space corresponding to the identified continuity break points. The preceding break point marked as having abrupt curvature change, node 4, is extracted. Its 3D coordinates are extracted using the sensor positioning network as ±10.0m x, ±15.0m x, and ±2.0m x. Following the index order, a specific retrieval step is extended forward and backward to extract the 3D coordinates of adjacent discrete points, namely the preceding node 3 and the following node 5. The coordinates of node 3 are obtained as ±8.0m x, ±14.0m x, and ±2.0m x, and the coordinates of node 5 are obtained as ±11.0m x, ±16.0m x, and ±2.0m x. Spatial distance quantization is performed on the extracted coordinate groups. The square differences between nodes 4 and 3, and between nodes 4 and 5, are calculated on the three coordinate axes. The three square results in the same group are summed, and the square root value is extracted to derive the absolute spatial physical straight-line distance. Substituting the coordinate data above, the spatial distance between node 4 and node 3 is calculated to be 2.23 meters, and the spatial distance between node 4 and node 5 is calculated to be 1.41 meters. Following this calculation rule, the step size is expanded to extract nodes 2 and 6, and the corresponding distances are calculated to be 4.47 meters and 3.16 meters, respectively. All the derived distance values ​​are then recombined in one dimension to construct a distance set sequence. The maximum distance feature value is retrieved from this distance set sequence and used as the normalization denominator parameter; the maximum distance feature value in the current set is 4.47 meters. Each spatial distance value in the distance set sequence is iterated over, and each value is divided by the maximum distance feature value of 4.47 meters, performing a ratio normalization operation. Substituting the aforementioned data, the division operation on 2.23 meters for node 3 yields a normalized dimensionless distance parameter of 0.50, and the division operation on 1.41 meters for node 5 yields 0.32. These parameters are then aggregated to construct a dimensionless distance sequence. The parameters within the dimensionless distance sequence are read, and the numerical reciprocal transformation algorithm is invoked. By dividing the natural number 1 by each normalized dimensionless distance parameter, the corresponding inverse proportional weight coefficient is derived. Substituting the data into the calculation, dividing the natural number 1 by 0.50 for node 3 yields an inverse proportional weight coefficient of 2.00, dividing the natural number 1 by 0.32 for node 5 yields an inverse proportional weight coefficient of 3.13, and so on. All calculated weight coefficients are aggregated to generate an inverse proportional distance weight set. If the value in this inverse proportional distance weight set is within the range of 1.50 to 4.00, it indicates that the neighboring node has a high-intensity influence on the optical compensation of the break point.

[0034] S402: Based on the inverse distance weight set, combined with the index of the corresponding discrete point of the inverse illuminance sequence, the corresponding illuminance is extracted in the index order and an illuminance data column is constructed. Based on the inverse distance weight set, the illuminance data column is weighted and summed item by item to obtain the transition illuminance set. Read the generated inverse distance weight set and the inverse weight coefficients corresponding to each of its neighboring discrete points. Extract the inverse weight coefficient of node 3 (2.00) and node 5 (3.13) calculated earlier. Combine the previously generated inverse illuminance sequence and use the spatial index identifier of the associated discrete points as the search keyword to perform precise position matching in the global data table of the inverse illuminance sequence. Based on the index numbers of nodes 3 and 5, extract the current illuminance feature parameters corresponding to them in the sequence. Substitute the data already defined in the previous operation to extract the original derived illuminance value of node 3 in the first illuminance interval that has not been changed by the inversion operation (272.5 lux), and the illuminance value of node 5 (267.5 lux). Reorganize these extracted illuminance values ​​distributed in different intervals according to the original physical spatial index order to construct a structured illuminance data column. Call the values ​​in the illuminance data column and the weight coefficients of the corresponding nodes in the inverse distance weight set, and perform the product operation operation on the corresponding items one by one. The inverse proportional weight coefficient of node 3 (2.00) is extracted and multiplied by its illuminance value of 272.5 lux, yielding the first multiplicative distribution parameter of 545.0 lux. The inverse proportional weight coefficient of node 5 (3.13) is extracted and multiplied by its illuminance value of 267.5 lux, yielding the second multiplicative distribution parameter of 837.28 lux. All the generated multiplicative distribution parameters are placed into an accumulation register for continuous summation and aggregation. The multiplicative distribution parameters of nodes 3 and 5 are extracted and summed; 545.0 lux plus 837.28 lux yields an initial aggregation value of 1382.28 lux. This initial aggregation value is then divided by the sum of all the inverse proportional weight coefficients involved in the calculation. Adding the coefficient of node 3 (2.00) and node 5 (3.13) to the total weight yields a weight sum of 5.13. Dividing the initial aggregated value of 1382.28 lux by this weight sum of 5.13, the environmental compensation transition illuminance value for break node 4 is derived to be 269.45 lux. All environmental compensation transition illuminance values ​​calculated individually for different break points along the entire physical path are arrayed and encapsulated according to the order in which the break points are generated, resulting in a transition illuminance set. The obtained environmental compensation transition illuminance values ​​are compared with a preset baseline difference range. If the difference between the value and the original break point illuminance is less than 2.0 lux, it indicates that the light environment fluctuation in that area is within a very high smoothness range.

[0035] S403: Spatial location indexing is performed on the original illuminance at the continuity break point, the elements in the transition illuminance set are mapped to the coordinates of the associated break point, and the original illuminance values ​​at the continuity break point are overwritten and replaced item by item. The illuminance data is reorganized according to the path starting index order to establish a global illuminance sequence. The global spatial location indexes of each discrete point known as a continuity breakpoint are retrieved, and the underlying data record list is used for location search. The index of node 4, which was previously in a breakpoint state, is extracted, and the original illuminance value of this continuity breakpoint is located in its corresponding initial light environment attribute record field. The initial current illuminance value of node 4, derived from distance attenuation calculations, is 270.0 lux, and this value is retrieved as the baseline data to be processed and marked in the cache column. The previously generated transitional illuminance set is read, and the environmental compensation transitional illuminance value calculated independently for this breakpoint location is extracted. The transitional illuminance value of node 4, 269.45 lux, calculated based on associated adjacent points, is extracted. Using the same spatial index coordinates as a mapping bridge, the extracted transitional illuminance value is precisely pointed to the physical storage address of node 4 in the original sequence. A forced overwrite replacement action is triggered for the original illuminance value cached at the continuity breakpoint. The original 270.0 lux data block is erased using an assignment instruction, and the previously transmitted 269.45 lux value is simultaneously written. Following this process, all breakpoint markers along the entire path are traversed, and data-level replacement of all distorted illuminance features is performed. All discrete point attribute records containing overwritten and updated data, as well as attribute records of regular smooth transition nodes in the path that have not undergone overwriting operations, are read. Using the starting coordinate reference point of the physical path spatial dimension as index zero, and following an increasing index order based on the extension step from the origin, the discrete illuminance data are rearranged and combined. The original illuminance of nodes 1 to 3 that have not been changed is extracted, concatenated with the illuminance of node 4 (which has been overwritten and replaced with 269.45 lux), and then concatenated with the original illuminance of node 5 and the reversed illuminance of nodes 6 to 9 (which underwent inversion operations). The illuminance parameters of all discrete points are then concatenated end-to-end, constructing a global illuminance sequence describing the entire park path in the data pool. If the difference in illuminance between two adjacent points in the recombined sequence, excluding the splicing points of the inverted interval, is always within the range of -1.5 lux to +1.5 lux, it means that the data coherence test of the entire path meets the application standard requirements.

[0036] Please see Figure 6 The specific steps of S5 are as follows: S501: Extract discrete point direction vectors from the direction vector sequence within the curvature feature sequence, index and arrange the multiple discrete point direction vectors to obtain an initial vector data column, and perform normalized division on the multiple vector components to generate a discrete direction vector set. Retrieve the direction vector sequence within the curvature feature sequence. Extract the direction vector of each discrete point in the spatial coordinate system from this sequence. Assign corresponding physical location indexes to the extracted multi-discrete-point direction vectors for labeling. Arrange the labeled direction vectors in ascending order of their physical indices to construct an initial vector data column. Obtain the initial direction vector of node 4 in the initial vector data column, which contains a horizontal axis original component value of +3.0 meters, a vertical axis original component value of +4.0 meters, and a vertical axis original component value of +0.0 meters. Extract the initial direction vector of the adjacent node 5, obtaining its horizontal axis original component value of +4.0 meters, vertical axis original component value of +3.0 meters, and vertical axis original component value of +0.0 meters. Normalized division is performed on multiple vector component parameters, and each vector component parameter is independently squared. The squared values ​​within the same direction vector are summed and aggregated. The square root of this aggregated sum is extracted to derive the absolute modulus parameter. Each original vector component parameter is then divided by this absolute modulus parameter. Substituting the component value of node 4, the squares of +3.0, +4.0, and +0.0 are performed and summed to obtain an aggregated square sum of 25.0. The square root of this sum yields an absolute modulus parameter of 5.0 for node 4. Dividing the horizontal axis component +3.0 by the modulus 5.0 yields a normalized horizontal axis component of +0.6. Dividing the vertical axis component +4.0 by the modulus 5.0 yields a normalized vertical axis component of +0.8 and a normalized vertical axis component of +0.0. The normalized horizontal axis component of node 5 is then derived as +0.8 and a normalized vertical axis component of +0.6. All parameters are then encapsulated and aggregated to generate a discrete direction vector set. If the absolute modulus of each group of normalized components in the generated set is always 1.0, it means that the dimensionless elimination operation conforms to the specification.

[0037] S502: Call the elements in the discrete direction vector set and perform a dot product summation operation with the preset standard line of sight vector, extract the dot product value and perform an inverse cosine transformation to obtain the line of sight angle, record the associated position index when the line of sight angle value does not reach the preset angle threshold, and obtain the line of sight angle filtering index set. Retrieve the generated discrete direction vector set and extract the corresponding normalized component elements. Call the preset standard line-of-sight vector, which is derived from the monitoring matrix and represents the pedestrian's forward reference observation orientation. Set the horizontal axis standard component to +1.0, the vertical axis standard component to +0.0, and the center axis standard component to +0.0. Extract the horizontal axis normalized component (+0.6), vertical axis normalized component (+0.8), and center axis normalized component (+0.0) of the preceding node 4, and perform a dot product summation operation on each item. Multiply the corresponding spatial axis components separately, and sum the three derived product results to obtain the dot product value. Substitute the node 4 value into the calculation to obtain a horizontal axis product of 0.6, a vertical axis product of 0.0, and a center axis product of 0.0. Sum the terms to obtain a dot product value of 0.6 for node 4. Extract the normalized components (+0.8), +0.6, and +0.0 of node 5, substitute them into the calculation, and sum them to obtain a dot product value of 0.8 for node 5. An inverse cosine transformation is performed on each dot product value to calculate the line-of-sight angle parameter, representing the deviation angle of the field of view. For node 4, the dot product value of 0.6 is calculated using inverse cosine, yielding a line-of-sight angle of 53.1 degrees. For node 5, the dot product value of 0.8 is calculated using inverse cosine, yielding a line-of-sight angle of 36.9 degrees. A preset line-of-sight angle threshold parameter of 45.0 degrees is retrieved from the visual feature library. Each calculated line-of-sight angle value is compared with the preset threshold parameter. The indexes of discrete points whose line-of-sight angle values ​​do not reach the preset threshold are recorded. Based on the comparison results, node 4 (53.1 degrees) is removed because it exceeds 45.0 degrees, and node 5 (36.9 degrees) is extracted and recorded separately in an array because it does not reach the set threshold of 45.0 degrees. The indexes that meet the boundary judgment conditions are aggregated to generate a line-of-sight angle filtering index set.

[0038] S503: Based on the line of sight angle, filter the index set to perform position matching and positioning in the global illuminance sequence, extract the illuminance values ​​associated with the positioning position to construct the extracted illuminance data column, multiply and scale it with the scaling factor to obtain the illuminance distribution sequence, and convert it into a pulse width modulation electrical signal to generate dimming control instructions. The system reads the location identifiers recorded in the generated line-of-sight angle filtering index set. It then uses this index set in the retrieval mechanism to match and locate the position within the derived global illuminance sequence containing light intensity data. Based on the associated index of node 5, which is determined to be retained, it extracts the illuminance value associated with the location from the global illuminance sequence data pool. The system extracts the current illuminance value of node 5, 267.5 lux, which was retained after previous processing. It stacks the extracted illuminance parameters that meet the field-of-sight conditions in ascending order of spatial physical extension, constructing an extracted illuminance data column in the buffer. It calls the scaling control factor derived from the upper limit matrix of lighting equipment power consumption, setting the parameter value of this control factor to 1.20. It performs a product expansion operation on the illuminance values ​​in the extracted illuminance data column with this control factor item by item. Multiplying the illuminance value of node 5, 267.5 lux, by the control factor 1.20, the calculated and derived compensated optical output intensity value is 321.0 lux. Finally, it aggregates the corrected multi-node data sequentially to obtain the illuminance distribution sequence. The microprocessor's analog-to-digital converter port is invoked to convert the parameters within the illuminance distribution sequence into a pulse-width modulated electrical signal. During the conversion operation, the rated maximum output limit illuminance value of the streetlight fixture, 500.0 lux, is used as the mapping reference denominator. The corrected optical output intensity value is divided by the reference denominator to derive the signal waveform duty cycle value. Dividing 321.0 lux at node 5 by 500.0 lux yields a corresponding output waveform duty cycle of 0.642. Based on the duty cycle, the trigger duration ratio of the square wave high level in the drive circuit is adjusted to construct a waveform instruction set that can be called by hardware, which is then packaged to generate a dimming control instruction to be delivered to the target device.

[0039] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A garden lighting design method, characterized in that, Includes the following steps: S1: Collect the three-dimensional coordinates of discrete points on the walking path of the garden entity, calculate the direction vector sequence for the three-dimensional coordinates of adjacent discrete points, extract the positive and negative directions of adjacent direction vectors in the direction vector sequence, and mark the curvature and sign of the corresponding discrete points to construct a curvature feature sequence. S2: Perform subtraction on the curvature of adjacent discrete points within the curvature feature sequence, mark the discrete points whose difference exceeds a preset curvature difference threshold as continuity breakpoints, and segment the curvature feature sequence to construct a path segment sequence; S3: Based on the path segment sequence, determine the positive and negative sign switching state of the curvature sign identifiers of the current and adjacent discrete points in the independent subsequence, mark the corresponding discrete points as turning point markers, and allocate decreasing illuminance in combination with the independent subsequence. Reverse the order of decreasing illuminance in the path interval after the turning point marker to generate a reverse illuminance sequence. S4: Calculate the spatial distance between the continuity break point and the adjacent associated discrete points based on the three-dimensional coordinates of the discrete points. Extract the illuminance of the associated discrete points in the reverse illuminance sequence and calculate the transition illuminance based on the spatial distance. Replace the original illuminance at the continuity break point with the transition illuminance to construct a global illuminance sequence.

2. The garden lighting design method according to claim 1, characterized in that, The curvature feature sequence includes discrete point curvature, direction sign identifier, and discrete point index; the path segment sequence includes segment boundary identifier, segment length parameter, and segment continuity identifier; the inverted illuminance sequence includes base illuminance, decreasing gradient parameter, and inverted interval identifier; and the global illuminance sequence includes transitional illuminance, original illuminance mapping, and spatial distance weight.

3. The garden lighting design method according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the three-dimensional coordinates of discrete points on the walking path of the garden entity through a three-dimensional laser scanning device, perform vector difference calculation on the three-dimensional coordinates of adjacent discrete points, subtract the previous coordinate from the next coordinate to obtain the direction vector component set, and serialize and arrange all direction vectors according to the path order to obtain the direction vector sequence. S102: Based on the adjacent direction vectors in the direction vector sequence, perform cross product calculation, extract the sign information in the cross product result, mark positive values ​​as positive directions, mark negative values ​​as negative directions, and mark all adjacent vector pairs with signs to obtain a direction sign sequence. S103: Based on the discrete point positions corresponding to the direction symbol sequence, call the three-dimensional coordinates of the discrete points to perform curvature numerical calculation, use the change in the angle between adjacent vectors as the basis for curvature numerical calculation, and synchronously assign the direction symbols to the corresponding curvature numerical positions to complete point-by-point labeling and integration, and generate a curvature feature sequence.

4. The garden lighting design method according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Perform a difference operation on the curvature of adjacent discrete points in the curvature feature sequence, extract the curvature values ​​of each pair of adjacent points and perform item-by-item subtraction to obtain a difference vector, and compare the multiple elements in the difference vector with the preset curvature difference threshold, record the index of the out-of-limit position, and obtain the curvature difference index set. S202: Based on the curvature difference index set, mark the corresponding discrete points, map the index set to the corresponding position in the original curvature feature sequence and add fracture markers to obtain the fracture marker distribution state sequence, and arrange the marker positions in the sequence to obtain the fracture marker sequence; S203: The curvature feature sequence is segmented into intervals according to the fracture identifier sequence, the boundaries of continuous segments are divided according to the identifier position, the discrete points in multiple segments are aggregated into multiple independent fragment sets in the original order, and the fragment sets are organized to generate a path sub-segment sequence.

5. A garden lighting design method according to claim 4, characterized in that, The curvature difference threshold is determined by extracting the curvature data of discrete points on the smooth path, calculating the mean and standard deviation of the curvature difference between adjacent points, constructing a statistical benchmark value, calling the sampling resolution and spatial positioning error tolerance parameters of the current three-dimensional laser scanning equipment, and performing linear weighted compensation correction on the statistical benchmark value.

6. A garden lighting design method according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the path segment sequence, determine the consistency between the curvature symbol identifier of the current discrete point and the curvature symbol identifier of the adjacent discrete points within the independent subsequence, encode the discrete points corresponding to the symbol difference positions, and aggregate all symbol switching positions to obtain a curvature symbol switching index set. S302: According to the curvature symbol switching index set, perform state mapping on the corresponding discrete points in the independent subsequence, map the index position to the turning point marker, assign a decreasing illuminance sequence to the discrete points in the independent subsequence, and divide the path segment illuminance sequence according to the spatial coordinates of the turning point marker. S303: Based on the path segmented illuminance sequence, the illuminance values ​​in the path interval after the turning point are reversed in order, the illuminance values ​​in the interval are rearranged in reverse according to the index, and the reversed results of multiple intervals are spliced ​​together to obtain the reversed illuminance sequence.

7. A garden lighting design method according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Calculate the spatial distance between the continuous break point and the adjacent related discrete points based on the three-dimensional coordinates of discrete points. Normalize the ratio of the multiple spatial distance values ​​to the maximum value of the distance set, construct a dimensionless distance sequence, and perform reciprocal transformation operation on each item to obtain the inverse distance weight set. S402: Based on the inverse distance weight set, combined with the index corresponding to the discrete point of the associated location retrieved by the inverse illuminance sequence, the corresponding illuminance is extracted in index order and an illuminance data column is constructed. Based on the inverse distance weight set, a weighted summation operation is performed on the illuminance data column item by item to obtain the transitional illuminance set. S403: Spatial location indexing is performed on the original illuminance of the continuity break point, the elements in the transition illuminance set are mapped to the coordinate position of the associated break point, and the original illuminance values ​​at the continuity break point are overwritten and replaced item by item. The illuminance data is reorganized according to the path starting index order to establish a global illuminance sequence.

8. A garden lighting design method according to claim 1, characterized in that, The method further includes: S5: Extract discrete point direction vectors from the curvature feature sequence, perform dot product and inverse cosine calculation of the line angle by combining the preset standard line angle vector, and associate the discrete points that do not reach the preset angle threshold with the illuminance in the global illuminance sequence to calculate the illuminance distribution sequence and convert it into a pulse width modulation electrical signal to generate a dimming control command. The dimming control command includes pulse width parameters, signal duty cycle, and output channel identifier.

9. A garden lighting design method according to claim 8, characterized in that, The specific steps of S5 are as follows: S501: Extract discrete point direction vectors from the direction vector sequence within the curvature feature sequence, index and arrange the multiple discrete point direction vectors to obtain an initial vector data column, and perform normalized division on the multiple vector components to generate a discrete direction vector set. S502: Call the elements in the discrete direction vector set and perform a dot product summation operation with the preset standard line of sight vector, extract the dot product value and perform an inverse cosine transformation to obtain the line of sight angle, record the associated position index when the line of sight angle value does not reach the preset angle threshold, and obtain the line of sight angle filtering index set. S503: Based on the line-of-sight angle filtering index set, perform position matching and positioning in the global illuminance sequence, extract the illuminance values ​​associated with the positioning position to construct an extracted illuminance data column, multiply and scale it with a scaling factor to obtain an illuminance distribution sequence, and convert it into a pulse width modulation electrical signal to generate a dimming control command.

10. A garden lighting design method according to claim 9, characterized in that, The included angle threshold is determined by extracting the gaze trajectory data of people walking in the garden, filtering the angle between the gaze point direction and the standard forward direction to construct a sample set, calculating the statistical mean and standard deviation of the included angle values ​​of the sample set, calling the parameter of the difference between the average eye height of people and the height of ground lighting facilities, calculating the compensation angle based on the eye height and the height difference, and summing the compensation angle with the statistical mean.

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