Z-wave-based LED pixel lamp multi-node addressing and dimming method and system

Abstract

The application relates to a Z-Wave-based LED pixel lamp multi-node addressing and dimming method and system. Pixel position sequences and target color value time sequence matrices are fused into a space-time light effect evolution matrix, sparse feature nodes are extracted in the space and time directions, and a light effect spline control point parameter set is generated; a virtual pixel index table is established through hash mapping, and the control point parameter set is encapsulated as a dimension-reduced custom data frame for downward transmission. A terminal analyzes the data frame and reconstructs a light effect continuous function model, a local timer is used to drive time steps, virtual pixel indexes are traversed, and pixel driving signals are calculated in real time, model parameters are used to replace frame-by-frame color value transmission, and virtual addressing is used to replace original node addressing, so that the application can reduce fragmentation and channel occupation, avoid address resource constraints caused by separately assigning Z-Wave network node addresses or endpoint addresses to each pixel lamp, reduce transmission jitter influence, and realize continuous and synchronous output of complex dynamic light effects.

Description

Technical Field

[0001] This invention relates to the fields of electric light source control and wireless communication technology, and in particular to a multi-node addressing dimming method and system for LED pixel lights based on Z-Wave. Background Technology

[0002] As smart home lighting evolves towards pixelation, dynamism, and personalization, courtyard light strips, corridor lights, and decorative lighting fixtures require independent addressing of hundreds or even thousands of LED pixel lights, continuously presenting complex lighting effects such as flowing water, chasing, breathing, and gradations. Z-Wave features low power consumption, strong wall penetration, and high mesh networking security, making it suitable for wireless control of smart home devices. However, there is a significant conflict between its communication resources and the high-frequency refresh requirements of pixel lights. Therefore, it is necessary to construct a multi-node addressing dimming method and system for LED pixel lights based on Z-Wave.

[0003] Current dimming methods typically involve the gateway calculating the color value of each pixel frame-by-frame and then sending it to the luminaire terminal via unicast, multi-channel endpoints, or fragmented messages. Some solutions pre-store lighting effect files and only send trigger commands. However, Z-Wave link bandwidth is usually only 9.6–100 kbps, and the effective payload of a single frame after security encryption is approximately 50 bytes, with a limited number of native node addresses. Taking a 300 RGBW pixel luminaire as an example, one frame requires approximately 1200 bytes. Continuous refreshes will generate a large amount of fragmentation, addressing overhead, and retransmission messages, easily causing channel congestion, transmission jitter, and asynchronous updates of pixels, resulting in a visible popcorn effect. The fundamental reason is that the existing control architecture of the gateway calculating frame-by-frame and the terminal passively executing makes dimming performance directly constrained by wireless throughput and endpoint resources, while the pre-stored method cannot support arbitrary lighting effects generated by users in real time. Furthermore, when using parameterized data to reduce transmission volume, soft errors during terminal buffer residency may also corrupt control point parameters, causing color spikes or brightness jumps in the reconstructed light effect. Additional checks or retransmission requests will further consume the limited payload and increase dimming latency.

[0004] Therefore, an optimized Z-Wave-based multi-node addressing dimming scheme for LED pixel lights is desired. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a Z-Wave-based multi-node addressing dimming method and system for LED pixel lights.

[0006] According to one aspect of this application, a multi-node addressing dimming method for LED pixel lights based on Z-Wave is provided, comprising: S1, perform full-link spatiotemporal light effect data fusion to construct a pixel-level spatiotemporal light effect evolution matrix by fusing the pixel position distribution sequence with the target color value time series matrix; S2, perform curvature analysis and sparse feature node extraction on the pixel-level spatiotemporal light effect evolution matrix along the spatial evolution direction and the temporal evolution direction to obtain the light effect spline control point parameter set; S3, based on the pixel position distribution sequence, a virtual pixel index table is generated by hash mapping, and the light effect spline control point parameter set and the virtual pixel index table are byte-aligned and serialized to generate a dimension-reduced custom encapsulated data frame. S4, through the Z-Wave terminal node, performs radio frequency reception and deserialization parsing and reconstruction of the dimension-reduced custom encapsulated data frame to obtain the light effect continuous function model and virtual pixel index table; S5 uses the local timer to drive the time step, traverses the logical node indexes in the virtual pixel index table, substitutes them into the light effect continuous function model to perform real-time sampling calculations, and outputs pixel-level duty cycle driving timing signals.

[0007] According to another aspect of this application, a Z-Wave-based multi-node addressing dimming system for LED pixel lights is provided, comprising: The spatiotemporal light effect construction module is used to perform full-link spatiotemporal light effect data fusion on the pixel position distribution sequence and the target color value time series matrix to obtain a pixel-level spatiotemporal light effect evolution matrix. The control point extraction module is used to perform curvature analysis and sparse feature node extraction on the pixel-level spatiotemporal light effect evolution matrix along the spatial evolution direction and the temporal evolution direction to obtain the control point parameter set of the light effect spline. The virtual addressing encapsulation module is used to generate a virtual pixel index table based on the pixel position distribution sequence through hash mapping, and to encapsulate the light effect spline control point parameter set with the virtual pixel index table through byte alignment serialization to generate a dimension-reduced custom encapsulation data frame. The terminal parsing and reconstruction module is used to perform radio frequency reception and deserialization parsing and reconstruction of the dimension-reduced custom encapsulated data frame through the Z-Wave terminal node to obtain the light effect continuous function model and virtual pixel index table. The sampling drive output module is used to traverse the logical node indexes in the virtual pixel index table by using the time step driven by the local timer, substitute them into the light effect continuous function model to perform real-time sampling calculations, and output pixel-level duty cycle drive timing signals.

[0008] Compared with existing technologies, this invention proposes a multi-node addressing dimming method for LED pixel lights based on Z-Wave. First, it fuses the pixel position distribution sequence with the target color value temporal matrix to form a pixel-level spatiotemporal luminous effect evolution matrix. Then, it extracts sparse feature nodes along the spatial and temporal evolution directions, converting pixel-by-pixel, time-by-time luminous effect data into a luminous effect spline control point parameter set. Simultaneously, it establishes a virtual pixel index table through hash mapping and encapsulates it and the luminous effect spline control point parameter set into a dimensionality-reduced custom data frame, which is then sent to the terminal. After parsing the data frame, the terminal reconstructs the continuous luminous effect function model, uses a local timer to drive time stepping, traverses the virtual pixel index, and calculates the driving signal for each pixel in real time. This replaces high-frequency frame-by-frame color value transmission with the transmission of a small number of model parameters, and replaces native node-by-node addressing with virtual addressing, reducing the number of slices and channel occupancy, avoiding dimming asynchrony caused by node address limitations and transmission jitter, and achieving continuous, real-time output of complex dynamic luminous effects across multiple pixel nodes. Attached Figure Description

[0009] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 This is a flowchart of a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to an embodiment of this application. Figure 2 This is a schematic diagram of the data flow of a multi-node addressing dimming method for LED pixel lights based on Z-Wave according to an embodiment of this application; Figure 3 This is a flowchart illustrating a multi-node addressing dimming method for LED pixel lights based on Z-Wave according to an embodiment of this application. The flowchart describes the process of performing curvature analysis and sparse feature node extraction along the spatial and temporal evolution directions of the pixel-level spatiotemporal light effect evolution matrix to obtain the light effect spline control point parameter set. Figure 4 This is a flowchart illustrating a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to an embodiment of this application. The method generates a virtual pixel index table based on the pixel position distribution sequence through hash mapping, and then performs byte-aligned serialization and encapsulation of the light effect spline control point parameter set and the virtual pixel index table to generate a dimension-reduced custom encapsulated data frame. Figure 5This is a flowchart illustrating a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to an embodiment of this application. The method involves using a Z-Wave terminal node to perform radio frequency reception, deserialization, parsing, and reconstruction of a dimension-reduced custom encapsulated data frame to obtain a continuous luminous efficacy function model and a virtual pixel index table. Figure 6 This is a flowchart illustrating the extraction of second-order difference features of control points, location of abnormal control points, and repair of continuity constraints in a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to a second embodiment of this application. Figure 7 This is a block diagram of a Z-Wave-based multi-node addressing dimming system for LED pixel lights according to an embodiment of this application. Detailed Implementation

[0011] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0012] Existing Z-Wave-based lighting control methods typically address each native node or endpoint individually, with the gateway sending color value data for each LED pixel frame by frame. However, Z-Wave suffers from issues such as low communication bandwidth, small payload per frame, and a limited number of native node addresses. When faced with complex dynamic dimming requirements involving hundreds or even thousands of pixel nodes, pixel-by-pixel, frame-by-frame transmission easily generates a large number of fragmented messages and addressing overhead, leading to channel congestion, transmission delays, and asynchronous pixel rendering, making it difficult to balance the complexity of lighting effects with real-time refresh. Therefore, the technical solution of this application proposes a multi-node addressing dimming method for LED pixel lights based on Z-Wave. This method first fuses the pixel position distribution sequence and the target color value time series matrix to obtain a pixel-level spatiotemporal light effect evolution matrix. Then, curvature analysis and sparse feature node extraction are performed along the spatial evolution direction and the temporal evolution direction to generate a light effect spline control point parameter set. Subsequently, a virtual pixel index table is established based on the pixel position distribution sequence, and it is encapsulated with the light effect spline control point parameter set into a dimension-reduced custom encapsulated data frame. The Z-Wave terminal node receives and parses the data frame, reconstructs the light effect continuous function model, generates a time step using a local timer, traverses the virtual pixel index, and calculates the duty cycle driving timing signal of each pixel in real time. This reduces wireless transmission volume and native address resource occupation, reduces the impact of network jitter, and realizes continuous synchronous adjustment of complex dynamic light effects of multiple pixel nodes.

[0013] Figure 1 This is a flowchart of a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to an embodiment of this application. Figure 2This is a schematic diagram of the data flow of a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to an embodiment of this application. Figure 1 and Figure 2 As shown, an embodiment of this application provides a multi-node addressing dimming method for LED pixel lights based on Z-Wave, comprising the following steps: S1, fusing the pixel position distribution sequence and the target color value time sequence matrix with full-link spatiotemporal luminous efficacy data to construct a pixel-level spatiotemporal luminous efficacy evolution matrix; S2, performing curvature analysis and sparse feature node extraction along the spatial and temporal evolution directions of the pixel-level spatiotemporal luminous efficacy evolution matrix to obtain a luminous efficacy spline control point parameter set; S3, generating a virtual pixel index table based on the pixel position distribution sequence through hash mapping, and encapsulating the luminous efficacy spline control point parameter set and the virtual pixel index table with byte alignment serialization to generate a dimension-reduced custom encapsulated data frame; S4, performing radio frequency reception and deserialization parsing and reconstruction on the dimension-reduced custom encapsulated data frame through a Z-Wave terminal node to obtain a luminous efficacy continuous function model and a virtual pixel index table; S5, using a time step driven by a local timer, traversing the logical node indexes in the virtual pixel index table and substituting them into the luminous efficacy continuous function model for real-time sampling calculation and outputting a pixel-level duty cycle driving timing signal.

[0014] Specifically, in step S1, the pixel location distribution sequence and the target color value time series matrix are fused across the entire spatiotemporal light effect data to construct a pixel-level spatiotemporal light effect evolution matrix. It should be noted that since the pixel location distribution sequence describes pixel nodes using spatial coordinates and hardware connection order, while the target color value time series matrix describes dynamic light effects using time slices and absolute color values, their data dimensions and indexing methods differ, making them unsuitable for direct use in subsequent spatiotemporal curvature analysis. Therefore, the technical solution of this application first fuses the pixel location distribution sequence and the target color value time series matrix across the entire spatiotemporal light effect data to form a pixel-level spatiotemporal light effect evolution matrix where spatial location, time slices, and color channels correspond. Through the above processing, the spatiotemporal index and numerical dimensions of the light effect data can be unified, providing a structured data foundation for sparse feature node extraction.

[0015] More specifically, in a specific example of this application, step S1 includes: performing data structure decomposition and feature extraction on the pixel position distribution sequence and the target color value temporal matrix to obtain discrete spatial distribution feature vectors, discrete time feature vectors, and the original color value feature tensor to be mapped; performing full combination spatiotemporal coordinate cross-pairing on the discrete spatial distribution feature vectors and discrete time feature vectors to obtain a two-dimensional spatiotemporal reference grid; performing multi-channel targeted addressing and dimensionality-upgrading filling on the original color value feature tensor to be mapped based on the joint coordinate index of each intersection point in the two-dimensional spatiotemporal reference grid to obtain an initial three-dimensional light effect state tensor; and performing dimensional transformation and structured encapsulation on the initial three-dimensional light effect state tensor to obtain a pixel-level spatiotemporal light effect evolution matrix.

[0016] In other words, for a courtyard light strip containing 300 RGBW pixel lights, the pixel position distribution sequence records the physical coordinates and daisy chain connection order of each pixel light, and the target color value temporal matrix records the start and end timestamps of the lighting effect, as well as the absolute color values ​​of the red, green, blue, and white channels of the 300 pixel lights in each time slice. The overall implementation process sequentially completes data decomposition, spatiotemporal grid construction, color value upscaling mapping, and matrix encapsulation, so that each physical pixel has a definite multi-channel light effect state in each time slice.

[0017] First, the pixel location distribution sequence is decomposed according to pixel identifier, physical coordinates, and hardware connection number, and the physical coordinates are arranged according to the hardware connection order to form a discrete spatial distribution feature vector. The start timestamp, end timestamp, and intermediate time slices of the target color value time sequence matrix are extracted and sorted in ascending order to form a discrete time feature vector. Then, the 4-channel absolute color values ​​of each pixel lamp under each time slice are organized into the original color value feature tensor to be mapped according to the spatial index, time index, and channel index, thereby maintaining the original correspondence between physical nodes, time status, and color value data.

[0018] Subsequently, a full combination cross-pairing is performed on each spatial location in the discrete spatial distribution feature vector and each time slice in the discrete time feature vector. The resulting joint coordinates are arranged into a two-dimensional spatiotemporal reference grid according to the spatial and temporal dimensions. The intersection point calculation relationship is as follows:

[0019] in, Indicates the first The position of the pixel is related to the _ ... Intersection of grids formed by time slices Indicates the first The physical coordinates of each pixel light Indicates the first A time slice Indicates the number of pixel lights. This indicates the number of time slices. Therefore, each grid intersection corresponds to a specific pixel node and a specific lighting effect moment.

[0020] Subsequently, using the spatial and temporal indices of each intersection point in the two-dimensional spatiotemporal reference grid as joint addressing keys, the corresponding 4-channel absolute color values ​​are located in the original color value feature tensor to be mapped, and the location results are filled into the channel dimension of the corresponding grid intersection point to form the initial three-dimensional light effect state tensor. The mapping relationship is as follows:

[0021] in, The first element in the initial three-dimensional light effect state tensor represents the first element. The pixel light in the first The first time slice Channel color values, This represents the absolute color value located by the same joint index in the feature tensor of the original color value to be mapped. These correspond to the red, green, blue, and white channels, respectively. After filling all intersections according to the hardware connection sequence, the spatial location, temporal evolution, and multi-channel color values ​​are organized into a unified tensor structure.

[0022] Finally, the absolute color values ​​in the initial three-dimensional light effect state tensor, ranging from 0 to 255, are subjected to duty cycle dimension transformation. The transformation relationship is as follows:

[0023] in, This represents the normalized channel value after dimension conversion, with a value range from 0 to 1. This represents the absolute color value before dimensional transformation. Further, the storage order is determined according to the spatial, temporal, and channel dimensions. Information describing the number of pixels, time slices, channels, and hardware connection order is written, and boundary alignment and structured encapsulation are completed to obtain a pixel-level spatiotemporal light effect evolution matrix, which is then used for subsequent curvature analysis along the spatial and temporal evolution directions.

[0024] Specifically, in step S2, curvature analysis and sparse feature node extraction are performed on the pixel-level spatiotemporal light effect evolution matrix along both the spatial and temporal evolution directions to obtain a set of light effect spline control point parameters. It should be noted that, given that the pixel-level spatiotemporal light effect evolution matrix corresponding to 300 RGBW pixel lights contains pixel-by-pixel color values ​​under each time slice, direct transmission would exceed the carrying capacity of Z-Wave's low bandwidth and small payload, and there is a lot of redundant data in the smooth light effect region. Based on this, the technical solution of this application further performs curvature analysis and sparse feature node extraction on the pixel-level spatiotemporal light effect evolution matrix along both the spatial and temporal evolution directions to retain color abrupt change points and local extreme points and generate a set of light effect spline control point parameters. Through the above processing, complex dynamic light effects can be characterized with a small number of control point parameters, reducing Z-Wave message fragmentation and channel occupancy, and providing a parameter basis for the terminal to reconstruct continuous light effects.

[0025] Figure 3 This is a flowchart illustrating a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to an embodiment of this application. The flowchart describes the process of performing curvature analysis and sparse feature node extraction along both the spatial and temporal evolution directions of the pixel-level spatiotemporal luminous effect evolution matrix to obtain a set of luminous effect spline control points. For example... Figure 3 As shown, step S2 includes: S21, quantizing the color values ​​of each channel in the pixel-level spatiotemporal light effect evolution matrix along the spatial and temporal directions using biaxial curvature change rate quantization to obtain the spatiotemporal curvature gradient tensor; S22, based on the preset maximum visual tolerance threshold and local extremum discrimination condition, filtering abrupt change points and removing redundant smooth regions from the spatiotemporal curvature gradient tensor to obtain a sparse feature node coordinate set; S23, solving for control points and encapsulating parameters for the sampling points corresponding to the sparse feature node coordinate set in the pixel-level spatiotemporal light effect evolution matrix to obtain a light effect spline control point parameter set.

[0026] In step S21, the spatiotemporal curvature gradient tensor is obtained by quantizing the color values ​​of each channel in the pixel-level spatiotemporal light effect evolution matrix along both spatial and temporal directions using biaxial curvature change rate quantization. It should be noted that since the pixel-level spatiotemporal light effect evolution matrix simultaneously contains the color value changes of 300 RGBW pixel lights in both spatial location and temporal slices, relying solely on color value differences in a single direction is insufficient to fully locate the spatial gradient boundaries and temporal change nodes in the flowing light effect. Therefore, the technical solution of this application further quantizes the spatiotemporal curvature gradient tensor by quantizing the color values ​​of each channel in the pixel-level spatiotemporal light effect evolution matrix along both spatial and temporal directions. Through the above processing, the degree of light effect bending and abrupt changes at each pixel position at different times can be characterized, providing a quantitative basis for subsequent screening of sparse feature nodes.

[0027] More specifically, in a specific example of this application, the pixel-level spatiotemporal light effect evolution matrix is ​​first read according to the spatial index, temporal index, and color channel index. The spatial index is arranged according to the hardware connection order of the 300 pixel lights, the temporal index is arranged in ascending order of time slices, and the color channel index corresponds to the red, green, blue, and white channels in sequence. Then, for each channel color value, the values ​​of the current pixel and its adjacent pixels in the same time slice are read along the spatial direction. The spatial curvature change is calculated using the central second-order difference to reflect the degree of color gradient curvature between adjacent pixels in the light strip. The values ​​of the same pixel in the current time slice and its adjacent time slices are read along the temporal direction. The temporal curvature change is calculated using the central second-order difference to reflect the brightness or color transition degree of the pixel over time. Subsequently, the spatial curvature change and temporal curvature change are scale-normalized and weighted to obtain the curvature gradient values ​​corresponding to each spatiotemporal coordinate and each color channel. The calculation formula is as follows:

[0028] in, Indicates the first The pixel light in the first The first time slice The curvature gradient values ​​of each color channel. This represents the normalized channel color value at the corresponding position in the pixel-level spatiotemporal light effect evolution matrix. Indicates the interval between adjacent spatial sampling positions. Indicates the interval between adjacent time slices. This represents the weighting coefficients used to balance the differences between spatial and temporal scales. For the beginning and end positions in space and the beginning and end slices in time, the same second-order difference calculation is performed after padding the boundaries with adjacent effective values. Finally, all curvature gradient values ​​are arranged according to the original spatial, temporal, and channel dimensions and encapsulated in a structured manner to obtain the spatiotemporal curvature gradient tensor corresponding to the pixel-level spatiotemporal light effect evolution matrix index. Larger gradient values ​​correspond to spatial color boundaries or temporal light effect transition positions, while smaller gradient values ​​correspond to continuous and smooth light effect regions.

[0029] In step S22, based on the preset maximum visual tolerance threshold and local extremum discrimination condition, the spatiotemporal curvature gradient tensor is subjected to abrupt change point screening and redundant smoothing region removal to obtain a sparse feature node coordinate set. It should be noted that since the spatiotemporal curvature gradient tensor still retains the curvature gradient values ​​of 300 RGBW pixel lights across all time slices and four color channels, the continuous gradient regions contain many repetitive coordinates that contribute little to the light effect's shape. Therefore, the technical solution of this application further performs abrupt change point screening and redundant smoothing region removal on the spatiotemporal curvature gradient tensor based on the preset maximum visual tolerance threshold and local extremum discrimination condition to obtain a sparse feature node coordinate set. Through the above processing, the color boundaries and temporal transition positions in the flowing light effect can be preserved, reducing the amount of data required for subsequent spline fitting and data transmission.

[0030] More specifically, in a concrete example of this application, a maximum visual tolerance threshold is first set based on the normalized color value range and the human eye-discernible light effect transition deviation, and the spatiotemporal curvature gradient tensor is read sequentially according to the spatial index, temporal index, and color channel index. For the red, green, blue, and white channel curvature gradient values ​​of the i-th pixel light in the j-th time slice, the maximum value is selected as the comprehensive curvature representation value of that spatiotemporal coordinate; when the comprehensive curvature representation value exceeds the maximum visual tolerance threshold, the corresponding coordinate is marked as a mutation candidate point to retain the beginning and end of the chasing light effect, color boundaries, and locations of rapid brightness changes. Subsequently, a local neighborhood composed of adjacent spatial positions and adjacent time slices is established with the current spatiotemporal coordinate as the center, and the current curvature gradient value is compared with the curvature gradient values ​​of the same channel in the neighborhood; when the current value satisfies the local maximum condition, it is marked as a feature peak point; when the current value satisfies the local minimum condition and the difference between the minimum value and the curvature value of the adjacent neighborhood reaches the maximum visual tolerance threshold, it is retained as the boundary point of the light effect change interval. The filtering relationship for feature nodes is as follows:

[0031] Where S represents the sparse feature node coordinate set, This represents the spatial coordinates of the i-th pixel light. This represents the j-th time slice. This represents the curvature gradient value of the k-th color channel in the corresponding spatiotemporal coordinates. This indicates the preset maximum visual tolerance threshold. The value represents the local extremum discrimination result, set to 1 if the preset local extremum condition is met, and 0 otherwise. Next, candidate coordinates generated from all channels are merged and deduplicated, retaining coordinates with larger comprehensive curvature representation values ​​when adjacent candidate points form continuous clusters. Coordinates whose curvature gradient values ​​for each channel do not exceed the maximum visual tolerance threshold and do not meet the local extremum condition are determined to be located in redundant smooth regions and are discarded. Finally, the retained coordinates are arranged according to the time slice order and pixel hardware connection order to form a sparse feature node coordinate set, which is used to extract corresponding color values ​​from the pixel-level spatiotemporal light effect evolution matrix and solve for spline control points.

[0032] In step S23, control points are solved and parameters are encapsulated for the sampling points corresponding to the sparse feature node coordinate set in the pixel-level spatiotemporal light effect evolution matrix to obtain the light effect spline control point parameter set. It should be noted that since the sparse feature node coordinate set only records the location of light effect changes, it cannot directly characterize the color values ​​of 300 RGBW pixel lights that continuously evolve over time. Therefore, the technical solution of this application further solves for control points and encapsulates parameters for the sampling points corresponding to the sparse feature node coordinate set in the pixel-level spatiotemporal light effect evolution matrix to obtain the light effect spline control point parameter set. Through the above processing, continuous flowing light effects can be described with a finite number of control points, reducing the amount of data required for subsequent encapsulation and transmission.

[0033] More specifically, in a concrete example of this application, firstly, the pixel-level spatiotemporal light effect evolution matrix is ​​jointly indexed according to the spatial and temporal coordinates of the sparse feature node coordinate set. The normalized color values ​​of the red, green, blue, and white channels corresponding to each coordinate are extracted sequentially, and the spatial coordinates, temporal coordinates, and 4-channel color values ​​are combined into spline fitting sampling points. Then, the sampling points are arranged in ascending order of spatial and temporal coordinates, and node vectors in the spatial and temporal directions are constructed respectively. The spline order and the number of control points are determined, and the spline basis function value corresponding to each sampling point is calculated. Subsequently, with the goal of minimizing the sum of squared residuals between the actual channel color values ​​of each sampling point and the estimated color values ​​of the spline surface, the control point coordinates and weighting factors of each color channel are jointly solved, and the calculation relationship is as follows:

[0034] in, This represents the sum of squared residuals from the fit of all sampling points. Represents the set of coordinates of sparse feature nodes. and These represent spatial index and time index, respectively. Indicates the color channel index. This represents the sampled color value in the pixel-level spatiotemporal light effect evolution matrix. and These represent the spatial coordinates and time coordinates of the sampling point, respectively. and Let represent the d-order spline basis functions in the spatial and temporal directions, respectively. This represents the weight factor of the control point in row u and column w. This represents the parameter to be determined for the corresponding control point in the k-th color channel. and These represent the maximum indices of the control points in the two directions, respectively. The combined term in the fraction represents the surface-estimated color value formed by the combined effects of the basis functions and weights in the spatial and temporal directions of the control point color values. The result is obtained by solving using least squares. The coordinates of the control points with the minimum values ​​and the weighting factors are selected, and the color value deviation between the sampling points and the fitted surface is verified according to the maximum visual tolerance threshold. Finally, the data is arranged in a structured manner according to the fixed field order of spline order, spatial node vector, temporal node vector, control point coordinates, weighting factors, and color channels, and the corresponding data length and quantity information is written to form the light effect spline control point parameter set, which is then serialized and encapsulated with the virtual pixel index table.

[0035] Specifically, in step S3, a virtual pixel index table is generated based on the pixel position distribution sequence through hash mapping. The light effect spline control point parameter set and the virtual pixel index table are then byte-aligned and serialized to generate a dimensionality-reduced custom-encapsulated data frame. It should be noted that if 300 RGBW pixel lights each occupy Z-Wave native node or endpoint addresses, the number of nodes will be limited. Furthermore, the data structures of the light effect spline control point parameters and pixel addressing data are different, making it impossible to directly adapt to the approximately 50-byte single-frame payload after secure encryption. Therefore, the technical solution of this application further generates a virtual pixel index table based on the pixel position distribution sequence through hash mapping, and then byte-aligns and serializes the light effect spline control point parameter set and the virtual pixel index table to generate a dimensionality-reduced custom-encapsulated data frame. Through the above processing, a correspondence between logical node indices and pixel physical locations can be established, reducing native address resource usage and adapting spline parameters and addressing information to the Z-Wave payload format, thereby effectively reducing the number of packets and channel occupancy.

[0036] Figure 4 This document describes a flowchart illustrating a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to an embodiment of this application. It details how a virtual pixel index table is generated through hash mapping based on the pixel position distribution sequence, and how the luminous effect spline control point parameter set and the virtual pixel index table are byte-aligned and serialized to generate a dimensionality-reduced custom encapsulated data frame. Figure 4As shown, step S3 includes: S31, performing virtual address space transformation on the coordinates of each physical node in the pixel position distribution sequence to obtain a virtual pixel index table; S32, concatenating and serializing the light effect spline control point parameter set and the virtual pixel index table through network big-endian byte order boundary alignment and offset metadata descriptor injection to obtain an aligned and merged core data sequence; S33, based on the maximum effective payload size constraint of a single frame under the security encryption protocol, performing dynamic sliding window segmentation and custom instruction class encapsulation on the aligned and merged core data sequence to obtain a dimension-reduced custom encapsulated data frame.

[0037] In step S31, a virtual address space transformation is performed on the coordinates of each physical node in the pixel position distribution sequence to obtain a virtual pixel index table. It should be noted that configuring each of the 300 RGBW pixel lights with a Z-Wave native node address would be limited by the number of native addresses, and the physical coordinates cannot be directly used as logical indices for terminal traversal and driver output. Therefore, the technical solution of this application further performs a virtual address space transformation on the coordinates of each physical node in the pixel position distribution sequence to obtain a virtual pixel index table. Through the above processing, a definite correspondence between the physical position, connection order, and virtual logical address of each pixel light can be established without increasing the number of Z-Wave native nodes.

[0038] More specifically, in a concrete example of this application, the physical coordinates of 300 RGBW pixel LEDs are first read in daisy-chain order. Fixed-point quantization is performed on different coordinate dimensions, and the quantized coordinate values ​​are concatenated with the hardware connection sequence number to form the physical node feature data of each pixel LED. Subsequently, a 16-bit hash operation is performed on each physical node feature data, and the hash result is mapped to the virtual address space using a preset salt value. The mapping relationship is as follows:

[0039] in, Indicates the first The virtual logical address of each pixel LED. This represents a 16-bit hash function. Indicates by the first The physical node feature data consists of the quantized physical coordinates of each pixel and its hardware connection number. This indicates the preset salt value. This indicates a bitwise XOR operation. This represents the virtual address space capacity. After a single address mapping is completed, the obtained virtual logical address is compared with the allocated addresses one by one. When an address conflict occurs, an unused address is searched sequentially in ascending order, and the found address is determined as the virtual logical address of the current pixel. Finally, the virtual logical address, physical coordinates, and connection sequence number of each pixel are recorded according to the hardware connection order, forming a virtual pixel index table. This allows subsequent terminals to locate the corresponding pixel in a defined order and generate driver data when traversing the virtual logical addresses.

[0040] In step S32, the control point parameter set of the light effect spline and the virtual pixel index table are concatenated and serialized through network big-endian byte order boundary alignment and offset metadata descriptor injection to obtain an aligned and merged core data sequence. It should be noted that since the control point parameter set of the light effect spline contains floating-point control points, weight factors, and node vectors, while the virtual pixel index table contains fixed-point virtual addresses and hardware connection order, their data types, field lengths, and storage structures differ. Direct concatenation can easily lead to byte order differences and parsing boundary misalignments. Therefore, the technical solution of this application further uses network big-endian byte order boundary alignment and offset metadata descriptor injection to concatenate and serialize the control point parameter set of the light effect spline and the virtual pixel index table to obtain an aligned and merged core data sequence. Through the above processing, the transmission format of heterogeneous data can be unified, and a data foundation can be provided for the terminal to accurately locate and recover spline parameters and pixel indices.

[0041] More specifically, in a concrete example of this application, the alignment and merging core data sequence is first determined to consist of metadata descriptors, spline parameter data segments, and virtual pixel index data segments in sequence, with 4 bytes set as the boundary alignment unit. For the light effect spline control point parameter set corresponding to 300 RGBW pixel lights, the spline order, spatial node vector, temporal node vector, control point coordinates, and weight factors are read sequentially. Integer parameters are converted to network big-endian byte order, and control point coordinates and weight factors are converted to consecutive bytes according to 32-bit single-precision floating-point format. Then, the virtual pixel index table is read according to the hardware connection order, and each virtual logical address, connection sequence number, and corresponding node information is converted to network big-endian byte order. Subsequently, the actual byte lengths of the spline parameter data segment and the virtual pixel index data segment are calculated respectively, and 0-value bytes are padded to the end of each data segment so that the starting address of the next data segment falls on the 4-byte boundary. Next, based on the aligned field positions, the starting offsets of the spline parameter data segment and the virtual pixel index data segment relative to the sequence's starting address are determined, and the data version, alignment unit, number of spline parameters, number of pixel indices, length of each data segment, and starting offset are written to the metadata descriptor. Finally, continuous byte concatenation is performed in the order of the metadata descriptor, spline parameter data segment, and virtual pixel index data segment to form an aligned and merged core data sequence, which is then used for subsequent fragmentation and encapsulation based on Z-Wave single-frame payload constraints.

[0042] In step S33, based on the maximum effective payload size constraint of a single frame under the security encryption protocol, the aligned and merged core data sequence is dynamically sliced ​​using a sliding window and encapsulated with a custom instruction class to obtain a dimensionality-reduced custom encapsulated data frame. It should be noted that because the second-generation security encryption protocol occupies part of the message fields, the application data that a single Z-Wave frame can carry is approximately 50 bytes, and the aligned and merged core data sequence cannot be directly sent completely in a single frame. Therefore, the technical solution of this application further utilizes the maximum effective payload size constraint of a single frame under the security encryption protocol to dynamically slice the aligned and merged core data sequence using a sliding window and encapsulate with a custom instruction class to obtain a dimensionality-reduced custom encapsulated data frame. Through the above processing, the spline parameters and virtual addressing data can conform to the Z-Wave frame length limit, providing a frame structure basis for the terminal to receive data in sequence and reassemble it completely.

[0043] More specifically, in a concrete example of this application, the allowed single-frame length when the current Z-Wave link uses the second-generation security encryption protocol is first read. The number of bytes occupied by the security header, custom instruction class identifier, fragment metadata, and authentication fields is deducted to determine the maximum payload size of a single frame. Then, the total length of the aligned and merged core data sequence containing spline parameters and virtual index data related to 300 RGBW pixel lights is read. Subsequently, using the sequence start address as the starting position of the sliding window, the current fragment is sliced ​​according to the maximum payload size of a single frame, and the length of the last fragment is dynamically adjusted based on the remaining number of bytes. The payload length of the q-th fragment is determined according to the following formula:

[0044] in, This represents the load length of the q-th segment. This indicates the maximum payload size of a single frame under the constraints of the security encryption protocol. This indicates the total length of the aligned and merged core data sequences. This represents the cumulative number of bytes carried by the first q-1 fragments. The minimum value operation ensures that the current fragment does not exceed the single-frame payload limit and does not exceed the remaining data boundary. After each fragmentation, the window's starting position is moved forward according to the number of bytes already fragmented until all data is fragmented. Then, the custom instruction class identifier, data frame identifier, fragment sequence number, total number of fragments, starting offset, and current payload length are written into the header of each fragment, and the corresponding payload is written into the data field of the custom instruction class. Finally, each custom instruction class message is encrypted, authenticated, and encapsulated according to the second-generation security encryption protocol to form a dimensionality-reduced custom encapsulated data frame with a defined transmission order and reassembly relationship, which is used by Z-Wave terminal nodes for radio frequency reception, authentication, and reassembly.

[0045] Specifically, in step S4, the Z-Wave terminal node performs RF reception, deserialization, and reconstruction of the dimensionality-reduced custom encapsulated data frame to obtain the luminous efficacy continuous function model and the virtual pixel index table. It should be noted that, given that the dimensionality-reduced custom encapsulated data frame carries luminous efficacy spline control point parameters and virtual addressing data in encrypted fragments and continuous byte streams, it cannot be directly used for real-time driving of 300 RGBW pixel lamps. Therefore, the technical solution of this application further utilizes the Z-Wave terminal node to perform RF reception, authentication and reassembly, deserialization, and continuous function reconstruction of the dimensionality-reduced custom encapsulated data frame to obtain the luminous efficacy continuous function model and the virtual pixel index table. Through the above processing, the correspondence between spline parameters and the logical indices of each pixel can be restored, providing a data foundation for the terminal to calculate pixel color values ​​in real time according to local time steps, thereby reducing the dependence on frame-by-frame wireless transmission.

[0046] Figure 5This is a flowchart illustrating a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to an embodiment of this application. The method involves using a Z-Wave terminal node to perform radio frequency reception, deserialization, parsing, and reconstruction of a dimensionality-reduced custom-encapsulated data frame to obtain a continuous luminous efficacy function model and a virtual pixel index table. Figure 5 As shown, step S4 includes: S41, performing radio frequency reception and authentication reassembly restoration on the dimensionality-reduced custom encapsulated data frame to obtain the decrypted and reassembled binary payload stream; S42, performing byte order conversion and deserialization splitting on the decrypted and reassembled binary payload stream based on the length offset in the metadata descriptor of the payload header to obtain the terminal parsing spline parameter set and virtual pixel index table; S43, performing continuous surface function reconstruction on the control point vectors and weight factors in the terminal parsing spline parameter set to obtain the light effect continuous function model.

[0047] In step S41, the dimensionality-reduced custom-encapsulated data frame is subjected to radio frequency reception and authentication reassembly to obtain a decrypted and reassembled binary payload stream. It should be noted that because the dimensionality-reduced custom-encapsulated data frame is fragmented for transmission due to the single-frame payload size limitation under the second-generation security encryption protocol, and contains encrypted payload, fragment sequence number, and authentication information, it cannot be directly parsed as a continuous byte stream. Based on this, the technical solution of this application further performs radio frequency reception and authentication reassembly on the dimensionality-reduced custom-encapsulated data frame to obtain a decrypted and reassembled binary payload stream. Through the above processing, the original arrangement relationship of the fragmented data and the plaintext payload content can be restored, providing complete data input for subsequent deserialization and fragmentation.

[0048] More specifically, in a concrete example of this application, firstly, a custom-encapsulated data frame carrying spline parameters and virtual index data related to 300 RGBW pixel lamps is received sequentially via the Z-Wave RF transceiver interface. The source node identifier, data frame identifier, fragment sequence number, total number of fragments, starting offset, and encrypted payload of each data frame are extracted. Fragments belonging to the same aligned and merged core data sequence are assigned to the same receiving buffer based on the data frame identifier. Next, based on the second-generation security encryption protocol, a pre-configured network security key is invoked to perform advanced encryption standard counter mode authentication and decryption on each fragment. The recalculated message authentication code is compared with the message authentication code carried in the data frame. If the authentication matches, the corresponding plaintext payload is retained; if the authentication does not match, writing of the fragment to the reassembly buffer is stopped. Subsequently, the authenticated plaintext payload is written to a pre-allocated contiguous buffer space according to the fragment sequence number and starting offset, and the arrival status of each fragment is recorded using a fragment receiving bitmap. For fragments arriving repeatedly, deduplication is performed based on the data frame identifier and fragment sequence number; for fragments arriving out of order, they are written to the corresponding buffer location based on the starting offset. Finally, after the fragmented receiving bitmap indicates that all fragments have arrived, the boundary continuity of adjacent payloads is checked according to the fragment sequence number, and each plaintext payload is spliced ​​together in sequence. The custom instruction class header, fragmented metadata and security authentication fields are removed, and the decrypted and reconstructed binary payload stream consisting of metadata descriptors, spline parameter data segments and virtual pixel index data segments is restored.

[0049] In step S42, based on the length offset in the payload header metadata descriptor, the decrypted and reconstructed binary payload stream is byte-order converted and deserialized to obtain the terminal parsing spline parameter set and virtual pixel index table. It should be noted that, given that the decrypted and reconstructed binary payload stream stores spline parameter data and virtual index data for 300 RGBW pixels continuously in big-endian byte order, and that the terminal processor's local byte order and data structure differ from the transmission format, it cannot be directly used for function reconstruction and pixel traversal. Therefore, the technical solution of this application further performs byte-order conversion and deserialization on the decrypted and reconstructed binary payload stream based on the length offset in the payload header metadata descriptor to obtain the terminal parsing spline parameter set and virtual pixel index table. Through the above processing, data such as control point vectors, weight factors, node vectors, and virtual logical addresses can be accurately recovered, while maintaining the correspondence between each logical index and the physical connection order of pixels, providing a structured data foundation for subsequent reconstruction of the light effect continuous function model and output pixel driving signals.

[0050] More specifically, in a concrete example of this application, the metadata descriptor of the decrypted and reconstructed binary payload stream header is first read. From this, the data version, alignment unit, number of spline parameters, number of pixel indices, start offset and length of the spline parameter data segment, and start offset and length of the virtual pixel index data segment are extracted. The start and end boundaries of each data segment are verified based on the total length of the payload stream. Subsequently, using the start offset and length of the spline parameter data segment as the slice boundary, the parameter substream is stripped from the continuous byte stream, and the index substream is stripped using the start offset and length of the virtual pixel index data segment as the slice boundary. For the parameter substream, the zero-padding bytes written during boundary alignment are removed according to the field definitions. The spline order, spatial node vector, temporal node vector, control point vector, and weight factor are converted from network big-endian byte order to the local byte order used by the terminal processor. Then, each field is restored according to the 32-bit single-precision floating-point format and integer format, and encapsulated into a terminal-parsed spline parameter set according to the control point arrangement order. For the index substream, the virtual logical address, physical coordinates, and hardware connection sequence number are read one by one according to the field length of a single index record. After byte order conversion, the index records are arranged in a daisy chain connection order of 300 RGBW pixel LEDs to form a virtual pixel index table, so that each logical node index can correspond to a specific physical pixel position.

[0051] In a Z-Wave-based multi-node addressing dimming system for LED pixel lights, the gateway aligns, serializes, and dynamically fragments the luminous effect spline control point parameter set, then sends it to the terminal node via a Z-Wave mesh network RF link in the form of multi-frame encrypted messages. Upon receiving the fragmented messages, the terminal node performs a deserialization operation in sub-step S42. This involves reading the metadata descriptor of the decrypted and reconstructed binary payload stream header to obtain the offset boundaries. The continuous byte stream is then sliced ​​into parameter sub-streams and index sub-streams. Zero bytes are removed from the parameter sub-streams and they are restored to floating-point control point coordinate structures. The index sub-streams are byte-order converted and restored to a virtual pixel index table.

[0052] However, the above deserialization process has a critical weakness: this step does not consider the inherent parametric smoothness continuity property of the spline control point parameter set. Specifically, for a d-order B-spline curve, adjacent control points must mathematically satisfy C... (d-1) The first embodiment imposes a continuity constraint, meaning that the difference vectors between adjacent control points cannot exhibit arbitrary drastic changes. However, the first embodiment directly passes the deserialized control point values ​​to the subsequent NURBS function instantiation engine, completely lacking verification and utilization of this inherent mathematical constraint.

[0053] In practical LED pixel lamp deployments, Z-Wave terminal nodes typically employ battery-powered low-power microcontrollers (such as Cortex-M4 level). Their SRAM capacity is limited, and there is memory bus access contention between hardware interrupt service routines (for receiving subsequent RF slices) and the main program (for caching decrypted data). When multiple slices arrive asynchronously at different times, the data from the earlier slices, decrypted and written to SRAM first, must wait in a buffer for all subsequent slices to be in place before complete assembly. During this waiting window, the terminal MCU's SRAM is exposed to risks such as cosmic ray single-event upsets, transient noise caused by power supply ripple, and bus conflicts between DMA and the CPU, resulting in a non-zero probability of local bit flips in the buffer. The AES-128 CCM message authentication code in the Z-Wave S2 encryption framework only guarantees the integrity of a single frame transmission at the RF link level. Once the frame data is correctly decrypted and written to the terminal SRAM, the protection boundary of this security mechanism terminates. The S2 framework is powerless against soft errors that occur while data resides in terminal memory.

[0054] When this bit flip happens to occur on the floating-point coordinate byte of a control point, the deserialization process faithfully restores a corrupted value to a seemingly valid but actually extremely far-off control point parameter. If this outlier control point is directly substituted into the NURBS continuous function instantiation engine in sub-step 4.3, it will cause severe Runge oscillations in the neighborhood of that point on the reconstructed 3D continuous surface. Mapped to the physical light strip, this manifests as sudden color flickering spikes or brightness jumps in the corresponding pixel segment, severely disrupting the smooth, flowing lighting effect experience expected by users in scenes such as courtyards and corridors. Traditional solutions, such as adding extra CRC check bytes or triggering NAK retransmission requests, are both undesirable. The former further consumes the already extremely scarce single-frame payload space of Z-Wave (only about 50 bytes after S2 encryption), while the latter causes additional RF interaction delays and exacerbates congestion in low-bandwidth channels.

[0055] Therefore, in the second embodiment of this application, the inherent continuity constraint of spline control points is further utilized to perform local curvature feature extraction, abnormal mutation identification and continuity repair on the control points obtained by deserialization, so as to improve the reliability of terminal-side light effect reconstruction.

[0056] Figure 6 This is a flowchart illustrating the extraction of second-order difference features of control points, location of abnormal control points, and repair of continuity constraints in a multi-node addressing dimming method for LED pixel lights based on Z-Wave, according to a second embodiment of this application. Figure 6As shown, in the second embodiment, after obtaining the terminal parsing spline parameter set in step S42, the process further includes a control point continuity repair sub-process: S421, extracting local curvature change features from the ordered control point coordinate sequence in the terminal parsing spline parameter set to obtain a second-order difference feature sequence of control points; S422, using a robust nonparametric statistical deviation index constructed based on the global median and the absolute deviation of the median, marking the difference values ​​of each node in the second-order difference feature sequence of control points with out-of-threshold abnormal mutations to obtain an abnormal control point index marking table; S423, based on the indexes of each abnormal point located in the abnormal control point index marking table and the effective neighboring anchors before and after which they are not marked as abnormal, performing continuity constraint repair and parameter set reconstruction on the corresponding abnormal control points in the terminal parsing spline parameter set to obtain the repaired terminal spline parameter set.

[0057] In step S421, local curvature change features are extracted from the ordered sequence of control point coordinates in the terminal parsing spline parameter set to obtain a second-order difference feature sequence of control points. It should be noted that, given the risk of local bit flipping during the residence of the terminal parsing spline parameter set in static random access memory, damaged floating-point control points can still be deserialized to form valid values, but this will disrupt the smooth continuity between adjacent control points. Therefore, the technical solution of this application further extracts local curvature change features from the ordered sequence of control point coordinates in the terminal parsing spline parameter set to obtain a second-order difference feature sequence of control points. Through the above processing, the continuity between control points can be transformed into quantifiable curvature change features, providing data for subsequent location of abnormal control points.

[0058] More specifically, in a concrete example of this application, for the deserialized and restored terminal parsing spline parameter set, the ordered sequence of control point coordinates carries the shape information of the spatiotemporal luminous efficacy curve of the LED light strip. First, based on the spatial and temporal control point indices recorded in the spline parameters, the coordinates of each control point are arranged in an ordered manner, and the control point coordinate values ​​are extracted according to the red, green, blue, and white channels respectively. Subsequently, for each non-boundary control point in the sequence, the difference between the control point and the previous control point, and the difference between the subsequent control point and the control point are calculated to obtain the first-order difference vector between adjacent control points. The first-order difference characterizes the rate of color change between adjacent control points, while the second-order difference further characterizes the change rate itself, i.e., the acceleration of the color gradient. Then, the adjacent first-order difference vectors are subtracted again to obtain the second-order difference value of each control point in the corresponding color channel, calculated as follows:

[0059] in, Indicates the first The control point at the ... The second-order difference value under each color channel represents the acceleration of the light effect gradient in the corresponding pixel segment at that control point. Indicates the first term in the terminal parsing spline parameter set. The control point at the ... Coordinate values ​​under each color channel This represents the coordinate value of the next adjacent control point in the same color channel. This represents the coordinates of the preceding adjacent control point in the same color channel. Indicates the control point sequence index. This represents the color channel index. For the control points at the beginning and end of the sequence, boundary differences are calculated using adjacent control points on one side to avoid missing features. Finally, the absolute values ​​of the second-order differences for each color channel are taken and combined according to the order of the control points to obtain the second-order difference feature sequence of the control points. For legitimate breathing, chasing, and gradient lighting effects, this sequence maintains continuous variation between adjacent nodes; if a control point deviates from the normal range due to bit flipping, its difference relationship with its preceding and following neighboring control points will form a spike deviating from the global statistical level, thus providing a basis for subsequent anomaly localization.

[0060] For example, when a garden light strip composed of 300 RGBW pixel lights plays an aurora gradient light effect, the red channel coordinates of adjacent control points are 0.45, 0.47, and 0.50 respectively, corresponding to a second-order difference value of 0.01, indicating that the color change rate of this pixel segment remains continuous. When the middle control point is restored to 1.86 due to a memory bit flip, the corresponding second-order difference value becomes -2.77, and its absolute value forms a local spike with neighboring nodes. After this spike is written into the second-order difference feature sequence of the control points, abnormal control points can be located in subsequent processing based on the global median and the absolute deviation of the median, without increasing the Z-Wave transmission payload or initiating a retransmission request.

[0061] In step S422, a robust nonparametric statistical deviation index, constructed based on the global median and median absolute deviation, is used to locate and mark the abnormal abrupt changes of the difference values ​​at each node in the second-order difference feature sequence of the control points, thereby obtaining an abnormal control point index label table. It should be noted that, given that the transition segments at the beginning and end of the chasing light effect and the uniform speed segment in the middle have different degrees of curvature change, a fixed absolute threshold can easily misjudge normal light effect changes as abnormal, while outliers generated by a small number of bit flips can skew the mean and standard deviation. Therefore, the technical solution of this application further uses a robust nonparametric statistical deviation index, constructed based on the global median and median absolute deviation, to locate and mark the abnormal abrupt changes of the difference values ​​at each node in the second-order difference feature sequence of the control points, thereby obtaining an abnormal control point index label table. Through the above processing, it is possible to adapt to the overall change characteristics of the current light effect and locate abnormal nodes that deviate from the continuous relationship of adjacent control points.

[0062] More specifically, in a concrete example of this application, the second-order difference feature sequence of control points is first read according to the control point sequence index, and the second-order difference values ​​corresponding to the red, green, blue, and white channels are collected. In the actual luminous efficacy of LED pixel lights, the gradual change in different sections of the light strip has reasonable differences. The acceleration of the transition segment at the beginning and end of the chasing light effect is higher than that of the uniform speed segment in the middle, so a fixed absolute threshold cannot be used to judge anomalies. Then, all the second-order difference values ​​are arranged from smallest to largest, and the value located in the middle position of the sequence is taken as the global median. The absolute deviation between each second-order difference value and the global median is calculated, and the median of all absolute deviations is taken as the median absolute deviation. The calculation formula is:

[0063]

[0064] in, This represents the global median of the second-order difference feature sequence of the control points. Indicates the absolute deviation of the median. Indicates the first The control point at the ... Second-order difference values ​​for each color channel Indicates the control point sequence index. This represents the color channel index. The median and median absolute deviation are chosen as statistical benchmarks because they are robust to a small number of extreme outliers and can still reflect the statistical center and dispersion of most normal control points even when bit-flip outliers exist. Subsequently, the degree to which the second-order difference value of each channel at each node deviates from the global median is calculated, and a preset multiple of the median absolute deviation is used as the anomaly detection threshold. When the deviation of any color channel exceeds this threshold, the corresponding control point is marked as an anomaly; otherwise, it is marked as normal. The judgment formula is:

[0065] in, Indicates the first in the abnormal control point index label table A Boolean flag value for each control point, where a value of 1 indicates that the control point is considered abnormal, and a value of 0 indicates that the control point is considered normal. Indicates the corresponding node at the . Second-order difference values ​​for each color channel This represents the global median. Indicates the absolute deviation of the median. This represents the preset anomaly detection threshold, an integer between 3 and 5. The maximum value operation performs a joint judgment on the four color channels of the same control point; if any channel exceeds the threshold, the control point is marked as a suspected bit-flip damage point. Finally, all Boolean flag values ​​are written in the original order of the control points to form an anomaly control point index flag table. This identifies pixel segments that may cause unnatural color abrupt changes in the spatiotemporal mapping coordinates of the light strip and limits the scope of subsequent local repair.

[0066] For example, when 300 RGBW pixel LEDs are playing a gradient lighting effect, the second-order difference values ​​of a certain group of control points are concentrated between 0.01 and 0.03, the global median is 0.02, and the median absolute deviation is 0.01. When the anomaly detection magnification threshold is 5, the detection threshold is 0.05. If a control point's second-order difference value in the red channel becomes -2.77 due to a storage bit flip, its deviation from the global median is 2.79, exceeding the detection threshold, and the corresponding flag value of that control point is set to 1. The remaining control points in the transition section at the beginning and end of the chasing lighting effect but whose deviation does not exceed the threshold retain a flag value of 0, thus distinguishing between normal changes in the curvature of the lighting effect and abnormal abrupt changes caused by bit flips.

[0067] In step S423, based on the abnormal point indices located in the abnormal control point index marking table and their preceding and following valid neighboring anchors that are not marked as abnormal, continuity constraint repair and parameter set reconstruction are performed on the corresponding abnormal control points in the terminal parsed spline parameter set to obtain the repaired terminal spline parameter set. It should be noted that if abnormal control points are directly used for continuous function reconstruction, color spikes or brightness jumps will form in the corresponding pixel segments, and re-acquiring parameters through a negative response request will introduce radio frequency round-trip delay and occupy the Z-Wave channel. Therefore, the technical solution of this application further performs continuity constraint repair and parameter set reconstruction on the corresponding abnormal control points in the terminal parsed spline parameter set based on the abnormal point indices located in the abnormal control point index marking table and their preceding and following valid neighboring anchors that are not marked as abnormal, to obtain the repaired terminal spline parameter set. Through the above processing, the continuous change relationship of control points can be restored locally on the terminal, avoiding additional network interactions.

[0068] More specifically, in a concrete example of this application, the abnormal control point index label table is first traversed according to the control point arrangement order. The index of the abnormal point with a label value of 1 is read, and the corresponding control point is located in the terminal parsing spline parameter set. For each abnormal control point, two valid neighboring control points not marked as abnormal are searched forward and backward as interpolation anchor points. During the search, control points with a label value of 1 are skipped until two valid neighboring anchor points are obtained before and after the abnormal point. Subsequently, the coordinate values ​​of the four anchor points are read according to the red, green, blue, and white channels respectively. The repair replacement value of the abnormal control point in each color channel is calculated using a local cubic uniform B-spline reconstruction kernel, and this repair replacement value is used to overwrite the original abnormal value. The calculation formula is:

[0069] in, Indicates the first The abnormal control point is at the _ Repair and replacement coordinate values ​​under each color channel and These represent the second and first valid neighboring anchor points before the anomaly, respectively. Coordinate values ​​under each color channel and These represent the first and second valid neighboring anchor points after the anomaly, respectively. Coordinate values ​​under each color channel Indicates the sequential index of the exception control point. This represents the color channel index. The weighted structure of -1, +4, +4, -1 divided by 6 constitutes the local reconstruction kernel of the cubic uniform B-spline at the intermediate node, ensuring that the repaired control points meet the second-order continuity requirement with the surrounding effective control points. This continuity connects the color gradient speed and gradient acceleration of the corresponding pixel segment with the adjacent region and avoids introducing new abrupt changes in light effect. After repairing all abnormal control points, the original control points, node vectors, spline order, and weight factors that were not marked as abnormal are retained. The repaired replacement values ​​are written into the corresponding control point field, and then encapsulated according to the original parameter structure and arrangement order to obtain the repaired terminal spline parameter set, which is used for subsequent reconstruction of the light effect continuous function model. The lowest order of cubic splines that can satisfy the second-order continuity is selected to complete the repair, adapting to the floating-point operation capability of the terminal's low-power microcontroller, and completing parameter recovery within a single refresh cycle.

[0070] For example, when 300 RGBW pixel lights play an aurora gradient light effect, the first... The red channel value of each control point is restored to 1.86 due to a storage bit flip. The two preceding valid neighboring anchor values ​​are 0.43 and 0.45, respectively, and the two following valid neighboring anchor values ​​are 0.50 and 0.55, respectively. Substituting the four anchor values ​​into the above formula yields a red channel repair replacement value of 0.47, which overwrites the abnormal value of 1.86. The remaining three color channels are repaired in the same way. This process forms a closed loop entirely on the terminal, without the need to initiate a negative acknowledgment retransmission request, thus avoiding perceptible stuttering of the 60Hz dynamic lighting effect caused by tens to hundreds of milliseconds of radio frequency round-trip latency.

[0071] In step S43, the control point vectors and weight factors in the terminal's analytical spline parameter set are reconstructed using a continuous surface function to obtain a continuous luminous effect function model. It should be noted that since the terminal's analytical spline parameter set only contains discrete control point vectors, weight factors, node vectors, and spline order, it cannot directly generate the driving color values ​​of 300 red, green, blue, and white four-channel pixels within a continuous spatial and temporal range. Therefore, the technical solution of this application further reconstructs the control point vectors and weight factors in the terminal's analytical spline parameter set using a continuous surface function to obtain a continuous luminous effect function model. Through the above processing, discrete luminous effect parameters can be converted into functions that can be continuously sampled according to spatial position and temporal steps, providing a computational basis for the terminal to locally generate pixel color values.

[0072] More specifically, in a specific example of this application, the spline order, spatial node vector, temporal node vector, control point vector, and weight factor of the terminal parsing spline parameter set are first read, and the control point arrangement is restored according to the spatial control point index, temporal control point index, and color channel index. In the case of executing the second embodiment, the repaired terminal spline parameter set replaces the original terminal parsing spline parameter set for reconstruction. Then, the effective domain of the continuous surface is determined based on the spatial node vector and temporal node vector, the spatial positions of the 300 pixel lights are mapped to the spatial parameter interval, and the light effect start timestamp to end timestamp is mapped to the temporal parameter interval. The basis function values ​​corresponding to each spatial control point and temporal control point are then calculated based on the spline order. Subsequently, the spatial basis function value, temporal basis function value, weight factor, and channel coordinate value of the same control point are multiplied, and the product results of all control points are accumulated. Simultaneously, the accumulated result of the basis function value and weight factor is normalized to obtain the continuous surface function corresponding to each color channel.

[0073] in, Indicates spatial location Time and location Passing the exam The continuous luminous efficacy values ​​corresponding to each color channel. This represents the normalized position of the pixel light in the spatial direction of the light strip. This indicates the normalized position of the current moment within the duration of the lighting effect. Indicates the red, green, blue, or white channel index. Indicates the first The corresponding spatial control points Sub-spline basis function values Indicates the first Each time control point corresponds to Sub-spline basis function values Indicates the first The first spatial control point and the first The weighting factor that corresponds to each time control point Indicates the corresponding control point at the th Coordinate values ​​under each color channel The maximum index of the control point in spatial direction. The maximum index of the control point in the time direction is represented. The numerator of the formula represents the weighted color value formed by all control points under the combined effect of the spatial basis function, the temporal basis function, and the weights. The denominator of the formula is used to eliminate the influence of the weight scale on the output color value. Finally, the spatial node vector, temporal node vector, spline order, control point vector, weight factor, and the above function evaluation relationship are associated and stored to form a continuous light effect function model. This allows subsequent processing to substitute the time step generated by the local timer and the spatial position corresponding to the virtual pixel index into this model to obtain the four-channel color value of each pixel lamp in real time.

[0074] Specifically, in step S5, the time step driven by a local timer is used to traverse the logical node indices in the virtual pixel index table and substitute them into the continuous light effect function model for real-time sampling calculation, outputting a pixel-level duty cycle driving timing signal. It should be noted that since the continuous light effect function model only characterizes the mathematical relationship between pixel color values ​​and spatial location and time, it cannot directly drive 300 RGBW pixel lights, and receiving wireless dimming data frame by frame is easily affected by Z-Wave transmission delay and network jitter. Based on this, the technical solution of this application further utilizes the time step driven by a local timer to traverse the logical node indices in the virtual pixel index table and substitute them into the continuous light effect function model for real-time sampling calculation, outputting a pixel-level duty cycle driving timing signal. Through the above processing, driving data for each pixel light can be continuously generated locally on the terminal, reducing the dependence of high-frequency light effect refresh on the wireless link.

[0075] More specifically, in a specific example of this application, step S5 includes: using a monotonically increasing time step generated by a local high-precision hardware timer, cyclically traversing each logical node index in the virtual pixel index table to combine spatiotemporal coordinates to obtain an instantaneous spatial-temporal sampling coordinate pair; substituting the instantaneous spatial-temporal sampling coordinate pair into the light effect continuous function model to perform floating-point dynamic sampling solution and amplitude-limited linear quantization to obtain a single-node real-time absolute color value vector; and using return-to-zero code nanosecond-level pulse width modulation encoding, performing electrical waveform conversion and shifting output on the single-node real-time absolute color value vector according to the daisy-chain physical topology order to generate a pixel-level duty cycle driving timing signal.

[0076] In other words, a local high-precision hardware timer is configured to generate periodic trigger events at a refresh rate of 60 Hz, with each refresh cycle lasting 16.67 milliseconds. A monotonically increasing time step is generated based on the light effect's start timestamp and the timer's accumulated count. The overall implementation process sequentially completes the generation of spatiotemporal sampling coordinates, the solution of continuous color values, and the output of physical drive waveforms. This converts the mathematical color values ​​in the continuous light effect function model into electrical signals that can be directly transmitted to the light strip's data input terminal, following a daisy-chain connection of 300 pixel lights. First, the current count value is read each time the timer triggers, and it is converted into the current time position within the time domain of the continuous light effect function model. The time step relationship is as follows:

[0077] in, Indicates the first The current time position corresponding to each refresh cycle. Indicates the start time and position of the lighting effect. Indicates the number of times the timer has been triggered. Indicates the time interval between adjacent refresh cycles. This indicates the refresh rate; in this example... Take 60 Hz and The time step is 16.67 milliseconds. When the time step reaches the end time of the lighting effect, it is reset to the start position or kept at the end position according to the preset playback method. Then, the logical node indices in the virtual pixel index table are read cyclically according to the hardware connection order, and the normalized spatial position corresponding to each logical node is obtained. The current time position and each normalized spatial position are combined into an instantaneous spatial-temporal sampling coordinate pair. The combination relationship is as follows:

[0078] in, Indicates the first The first refresh cycle Instantaneous space-time sampled coordinate pairs of each logical node Indicates the first The normalized spatial location corresponding to each logical node This indicates the current time position. Therefore, within the same refresh cycle, 300 pixels share the same time step and form defined sampling coordinates according to their respective spatial positions.

[0079] Secondly, substituting each instantaneous spacetime sampling coordinate pair into the continuous light effect function model, the continuous floating-point color values ​​of the red, green, blue, and white channels are solved respectively. The sampling relationship is as follows:

[0080] in, Indicates the first The first refresh cycle The logical node at the _ Continuous floating-point color values ​​in each color channel This represents a continuous function model of luminous efficacy. Indicates the spatial location of the current logical node. Indicates the current time and location. This represents the color channel index. Subsequently, each consecutive floating-point color value is restricted to a valid range of 0 to 1 and linearly mapped to an 8-bit color value range of 0 to 255. The quantization relationship is as follows:

[0081] in, This represents the single-channel absolute color value obtained after limiting and linear quantization. This indicates rounding to the nearest integer. and Together, we can achieve effective range limitation. This represents the continuous floating-point color values ​​before quantization. The absolute color values ​​of the four channels of the same logical node are arranged according to the channel order specified by the target pixel lamp to obtain the real-time absolute color value vector of a single node. The real-time absolute color value vectors of all nodes are then written into the transmit buffer according to the daisy chain connection order recorded in the virtual pixel index table.

[0082] Finally, the binary bits of the real-time absolute color value vector of each single node in the transmit buffer are extracted sequentially, and the logic bits are converted into corresponding high and low level pulses according to the return-to-zero code communication timing. The duration of the high level within a single bit period is determined by the following formula:

[0083] in, This indicates the duration of the high-level signal corresponding to the current binary bit. This represents the binary logical value to be encoded. This indicates the duration of the high level corresponding to logic 1. This indicates the duration of the high level corresponding to logic 0. In this example, a single bit period is set to 1.25 microseconds. Set to 0.8 microseconds and Set to 0.4 microseconds, the signal remains low after the corresponding high level ends until the current bit cycle is complete. Each pixel is encoded in 32-bit order according to the red, green, blue, and white channels. Then, following the daisy-chain physical topology of the 300 pixels, continuous shift output is performed through timer comparison output and direct memory access. After all data is sent, a low-level reset signal is maintained for a specified duration, thereby generating a pixel-level duty cycle driving timing signal. This ensures that each pixel exhibits a flowing gradient effect determined by the continuous light effect function model within the same refresh cycle.

[0084] Furthermore, a multi-node addressing dimming system for LED pixel lights based on Z-Wave is also provided.

[0085] Figure 7 This is a block diagram of a Z-Wave-based multi-node addressing dimming system for LED pixel lights according to an embodiment of this application. Figure 7 As shown, a multi-node addressing dimming system 100 for LED pixel lights based on Z-Wave, according to an embodiment of this application, includes: a spatiotemporal light effect construction module 110, used to perform full-link spatiotemporal light effect data fusion on the pixel position distribution sequence and the target color value time series matrix to construct a pixel-level spatiotemporal light effect evolution matrix; a control point extraction module 120, used to perform curvature analysis and sparse feature node extraction on the pixel-level spatiotemporal light effect evolution matrix along the spatial evolution direction and the temporal evolution direction to obtain a light effect spline control point parameter set; and a virtual addressing encapsulation module 130, used to perform hash mapping based on the pixel position distribution sequence. A virtual pixel index table is generated, and the light effect spline control point parameter set and the virtual pixel index table are byte-aligned and serialized to generate a dimension-reduced custom encapsulated data frame; the terminal parsing and reconstruction module 140 is used to perform radio frequency reception and deserialization parsing and reconstruction on the dimension-reduced custom encapsulated data frame through the Z-Wave terminal node to obtain the light effect continuous function model and the virtual pixel index table; the sampling drive output module 150 is used to use the time step driven by the local timer to traverse the index of each logical node in the virtual pixel index table and substitute it into the light effect continuous function model to perform real-time sampling calculation and output the pixel-level duty cycle drive timing signal.

[0086] As described above, a Z-Wave-based LED pixel lamp multi-node addressing dimming system 100 according to an embodiment of this application can be implemented in various types of computing devices or control units. For example, it can be deployed in a smart lighting gateway or integrated into an LED pixel lamp terminal controller. In one possible implementation, the Z-Wave-based LED pixel lamp multi-node addressing dimming system 100 according to an embodiment of this application can be integrated into the computing device as a software module and / or a hardware module. For example, the system 100 can be a software program for performing spatiotemporal luminous effect data fusion, spline control point extraction, virtual pixel addressing, Z-Wave data frame encapsulation, luminous effect continuous function reconstruction, and pixel driving signal generation. Of course, the system 100 can also be a hardware module in a computing device or control unit, or it can be embedded in a field-programmable gate array circuit or an application-specific integrated circuit to perform luminous effect sampling calculation and pixel-level driving timing signal output.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-node addressing dimming method for LED pixel lights based on Z-Wave, characterized in that, include: S1, perform full-link spatiotemporal light effect data fusion to construct a pixel-level spatiotemporal light effect evolution matrix by fusing the pixel position distribution sequence with the target color value time series matrix; S2, perform curvature analysis and sparse feature node extraction on the pixel-level spatiotemporal light effect evolution matrix along the spatial evolution direction and the temporal evolution direction to obtain the light effect spline control point parameter set; S3, based on the pixel position distribution sequence, a virtual pixel index table is generated by hash mapping, and the light effect spline control point parameter set and the virtual pixel index table are byte-aligned and serialized to generate a dimension-reduced custom encapsulated data frame. S4, through the Z-Wave terminal node, performs radio frequency reception and deserialization parsing and reconstruction of the dimension-reduced custom encapsulated data frame to obtain the light effect continuous function model and virtual pixel index table; S5 uses the local timer to drive the time step, traverses the logical node indexes in the virtual pixel index table, substitutes them into the light effect continuous function model to perform real-time sampling calculations, and outputs pixel-level duty cycle driving timing signals.

2. The multi-node addressing dimming method for LED pixel lights based on Z-Wave according to claim 1, characterized in that, The pixel location distribution sequence includes the physical coordinates of each pixel and the hardware connection order; the target color value time sequence matrix includes the start and end timestamps and the absolute color value of each pixel in each time slice.

3. The multi-node addressing dimming method for LED pixel lights based on Z-Wave according to claim 1, characterized in that, Step S1 includes: Step 1.1: Decompose the data structure and extract features from the pixel position distribution sequence and the target color value time series matrix to obtain the discrete spatial distribution feature vector, the discrete time feature vector, and the original color value feature tensor to be mapped. Step 1.2: Perform a full combination of spatiotemporal coordinate cross-pairing on the discrete spatial distribution feature vector and the discrete time feature vector to obtain a two-dimensional spatiotemporal reference grid; Step 1.3: Based on the joint coordinate index of each intersection point in the two-dimensional spatiotemporal reference grid, perform multi-channel targeted addressing and dimension-upfilling on the original color value feature tensor to be mapped to obtain the initial three-dimensional light effect state tensor. Step 1.4: Perform dimensional transformation and structured encapsulation on the initial three-dimensional light effect state tensor to obtain the pixel-level spatiotemporal light effect evolution matrix.

4. The multi-node addressing dimming method for LED pixel lights based on Z-Wave according to claim 1, characterized in that, Step S2 includes: Step 2.1: The spatiotemporal curvature gradient tensor is obtained by performing biaxial curvature change rate quantization on the color values ​​of each channel in the pixel-level spatiotemporal light effect evolution matrix along the spatial and temporal directions. Step 2.2: Based on the preset maximum visual tolerance threshold and local extremum discrimination conditions, the spatiotemporal curvature gradient tensor is subjected to mutation point screening and redundant smoothing region removal to obtain a sparse feature node coordinate set. Step 2.3: Solve for control points and encapsulate parameters for the sampling points corresponding to the coordinate set of sparse feature nodes in the pixel-level spatiotemporal light effect evolution matrix to obtain the control point parameter set of the light effect spline.

5. The LED pixel lamp multi-node addressing dimming method based on Z-Wave according to claim 1, characterized in that, Step S3 includes: Step 3.1: Perform virtual address space transformation on the coordinates of each physical node in the pixel location distribution sequence to obtain a virtual pixel index table; Step 3.2: By aligning the network big-endian byte order boundary and injecting offset metadata descriptors, the light effect spline control point parameter set and the virtual pixel index table are spliced ​​and serialized to obtain the aligned and merged core data sequence. Step 3.3: Based on the maximum effective payload size constraint of a single frame under the security encryption protocol, the aligned and merged core data sequence is dynamically sliced ​​by sliding window and encapsulated with custom instruction classes to obtain a dimension-reduced custom encapsulated data frame.

6. The multi-node addressing dimming method for LED pixel lights based on Z-Wave according to claim 1, characterized in that, Step S4 includes: Step 4.1: Perform radio frequency reception and authentication reassembly and reconstruction on the dimensionality-reduced custom encapsulated data frame to obtain the decrypted and reassembled binary payload stream; Step 4.2: Based on the length offset in the metadata descriptor of the payload header, perform byte order conversion and deserialization splitting on the decrypted and reconstructed binary payload stream to obtain the terminal parsing spline parameter set and virtual pixel index table; Step 4.3: Reconstruct the control point vectors and weight factors in the terminal analytical spline parameter set using continuous surface functions to obtain the light effect continuous function model.

7. The LED pixel lamp multi-node addressing dimming method based on Z-Wave according to claim 1, characterized in that, Step S5 includes: Step 5.1: Using the monotonically increasing time step generated by the local high-precision hardware timer, traverse the logical node indexes in the virtual pixel index table to combine spatiotemporal coordinates to obtain instantaneous spatial-temporal sampling coordinate pairs. Step 5.2: Substitute the instantaneous spatial-temporal sampling coordinates into the continuous light effect function model to perform floating-point dynamic sampling solution and amplitude-limited linear quantization to obtain the real-time absolute color value vector of a single node; Step 5.3: Through return-to-zero code nanosecond-level pulse width modulation encoding, the real-time absolute color value vector of a single node is electrically converted and shifted according to the physical topology order of the daisy chain to generate a pixel-level duty cycle driving timing signal.

8. The multi-node addressing dimming method for LED pixel lights based on Z-Wave according to claim 1, characterized in that, Step 4.2 includes: Local curvature variation features are extracted from the coordinate sequence of control points arranged in an ordered manner in the terminal analytical spline parameter set to obtain the second-order difference feature sequence of control points; Based on the robust nonparametric statistical deviation index constructed from the global median and the absolute median deviation, the difference values ​​of each node in the second-order difference feature sequence of control points are marked with out-of-threshold abnormal mutations to obtain an abnormal control point index marking table. Based on the index of each abnormal point located by the abnormal control point index marking table and the valid neighboring anchor points before and after it that are not marked as abnormal, the continuity constraint repair and parameter set reconstruction are performed on the corresponding abnormal control points in the terminal parsed spline parameter set to obtain the repaired terminal spline parameter set.

9. A multi-node addressing dimming system for LED pixel lights based on Z-Wave, characterized in that, include: The spatiotemporal light effect construction module is used to perform full-link spatiotemporal light effect data fusion on the pixel position distribution sequence and the target color value time series matrix to obtain a pixel-level spatiotemporal light effect evolution matrix. The control point extraction module is used to perform curvature analysis and sparse feature node extraction on the pixel-level spatiotemporal light effect evolution matrix along the spatial evolution direction and the temporal evolution direction to obtain the control point parameter set of the light effect spline. The virtual addressing encapsulation module is used to generate a virtual pixel index table based on the pixel position distribution sequence through hash mapping, and to encapsulate the light effect spline control point parameter set with the virtual pixel index table through byte alignment serialization to generate a dimension-reduced custom encapsulation data frame. The terminal parsing and reconstruction module is used to perform radio frequency reception and deserialization parsing and reconstruction of the dimension-reduced custom encapsulated data frame through the Z-Wave terminal node to obtain the light effect continuous function model and virtual pixel index table. The sampling drive output module is used to traverse the logical node indexes in the virtual pixel index table by using the time step driven by the local timer, substitute them into the light effect continuous function model to perform real-time sampling calculations, and output pixel-level duty cycle drive timing signals.

10. The Z-Wave-based multi-node addressing dimming system for LED pixel lamps according to claim 9, characterized in that, The terminal parsing and reconstruction module includes: The radio frequency receiving and reassembly unit is used to perform radio frequency reception and authentication reassembly and reconstruction on the dimensionality-reduced custom encapsulated data frame to obtain the decrypted and reassembled binary payload stream. The deserialization parsing unit is used to perform byte order conversion and deserialization splitting on the decrypted and reconstructed binary payload stream based on the length offset in the payload header metadata descriptor to obtain the terminal parsing spline parameter set and virtual pixel index table. The function model reconstruction unit is used to reconstruct the control point vectors and weight factors in the terminal analytical spline parameter set using continuous surface functions to obtain the light effect continuous function model.

Images