LED illumination control system based on wireless networking

Abstract

The invention discloses an LED lighting control system based on wireless networking, and relates to the technical field of lighting control, node position parameters Pos are obtained through calculation, so that a spatial relation table Srt is generated and updated, the spatial relation of a lamp can be automatically updated after nodes are newly added, nodes are replaced or the environment is changed, and manual coordinate reconfiguration is not needed; the uniformity evaluation module accurately identifies a space region with insufficient illumination or excessive illumination by calculating an illumination uniformity coefficient Luc, an over-bright ratio Ovl and a dark ratio Drk, and generates a region set Seg, so that a problem is positioned to a specific region coordinate; and finally, the illumination uniformity is issued to corresponding nodes in the node set Set through wireless networking to execute dimming, so that automatic optimization of the illumination uniformity is completed under the condition of not depending on manual intervention. The system dynamically adapts to changes of a space structure and use conditions, and long-term stable and energy-saving illumination uniformity control is achieved.

Description

Technical Field

[0001] This invention relates to the field of lighting control technology, specifically to an LED lighting control system based on wireless networking. Background Technology

[0002] The information and communication technology field has given rise to the Internet of Things (IoT) and smart construction systems. Wireless self-organizing networks within the IoT are gradually becoming the communication foundation for building electromechanical systems. In the specific area of ​​building lighting, the high efficiency and controllability of semiconductor light sources have driven lighting from fixed power supply to programmable control. Further convergence along this link involves constructing communication networks between lighting terminals, regional control nodes, and management platforms through wireless self-organizing or mesh topologies, forming wireless networks. Combined with real-world building needs, typical application scenarios include flexible zoned lighting in smart office floors, dynamic lighting in logistics warehouses accompanying shelving reconfiguration, localized accent lighting in exhibition spaces as exhibits change, safety lighting in underground parking garages adapting to traffic flow and obstacles, and circadian rhythmic lighting in hospital corridors and wards. All of these scenarios require large-scale, low-cost, and sustainably optimized lighting management through wireless networking without altering existing electrical wiring.

[0003] Existing practices for the above scenarios typically involve completing the lighting fixture layout and illuminance simulation during the design and construction phases, and maintaining lighting uniformity during operation by relying on fixed zones, static parameters, and manual verification. However, long-term environmental changes are difficult to dynamically absorb: relocation of office partitions, changes in warehouse shelf height and reflectivity, cyclical changes in exhibition hall setups, the addition of new lighting fixtures, or the aging of old fixtures can all disrupt the original illuminance distribution. Although wireless networking provides inter-node communication capabilities and network signal indicators, mainstream systems do not reconstruct spatial relationships from these network indicators, nor do they adaptively correct illuminance uniformity based on operational data, resulting in lighting uniformity relying on initial design assumptions in the long term. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an LED lighting control system based on wireless networking, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an LED lighting control system based on wireless networking, comprising the following modules: The wireless data acquisition module collects signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux from a set of nodes consisting of LED lamps with wireless communication capabilities. The self-localization construction module estimates the distance parameter Dis between nodes using the signal strength parameter Rss and the link delay parameter Del; it calculates the node location parameter Pos; and it generates and updates the spatial relationship table Srt. The spatial relationship and mapping module generates an illuminance mapping layer Lay based on the spatial relationship table Srt and the node position parameter Pos, combined with static optical parameters; and calibrates the illuminance mapping layer Lay using the measured illuminance parameter Lux. The uniformity assessment module calculates the illuminance uniformity coefficient Luc based on the illuminance mapping layer Lay, and obtains the overbrightness ratio Ovl and the underbrightness ratio Drk; the results are combined to generate the region set Seg; The optimized execution module receives the region set Seg, generates a control command set Ctr, and sends it to the node set Set via wireless networking.

[0006] Preferably, the wireless data acquisition module includes a data acquisition unit and a data processing unit; The data acquisition unit interacts with the node set Set based on the aggregation node Hub, establishes a polling list of the node set Set according to the network discovery results of the wireless network, and periodically accesses each node in the node set Set using the node identifier parameter Nid as the primary key. During each access process, the data acquisition unit receives and triggers a report to obtain the signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux, which are bound one-to-one with the node identification parameter Nid, and merges them with the corresponding node identification parameter Nid to form a sampling record Rec. As polling continues, the sampled records Rec accumulate to form the raw data set Raw. After reaching the preset sampling period and triggering conditions, the raw data set Raw is output to the data processing unit to form the time series set Seq. The aggregation node Hub is used to provide a unified time base and is responsible for initiating probes and receiving reports from network-side devices or gateway devices to the node set Set. The node set Set consists of multiple LED light fixture nodes with wireless communication capabilities, and can perform bidirectional data interaction with the aggregation node Hub. The node identifier parameter Nid is used to uniquely identify a single node in the node set Set, and is the primary key for data binding during the entire polling process of the data acquisition unit; the node identifier parameter Nid is written to each sampling record Rec; The signal strength parameter Rss is measured and stored as the signal strength parameter Rss when the aggregation node Hub receives the periodic broadcast frame and response frame of the node identification parameter Nid; and it is bound to the corresponding node identification parameter Nid. The link delay parameter Del is obtained by the aggregation node Hub initiating a probe request to the target node identification parameter Nid, recording the round-trip time between initiation and receiving the response, subtracting the processing delay, and then binding it with the node identification parameter Nid. The measured illuminance parameter Lux is generated by the built-in sensor of the target node identifier parameter Nid. The node detects the ambient illuminance at its location in real time and reports it to the aggregation node Hub to form the measured illuminance parameter Lux, which is then bound to the corresponding node identifier parameter Nid. If the node identifier parameter Nid has no sensor, this field is empty and the marker is missing.

[0007] Preferably, the data processing unit performs cleaning and consistency processing on the multiple sample records Rec included in the acquired raw data set Raw; The cleaning process involves using median filtering to check the integrity of each sample record Rec, node identifier parameter Nid, signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux, and removing outliers and duplicates. The consistency processing uses a linear interpolation method to align the original dataset Raw to a unified time base provided by the aggregation node Hub, synchronizing parameter data from different times and nodes to the same time window; during the alignment process, missing or abnormal data is interpolated. The raw data set Raw, which completes the data processing unit, is organized into a time-series set Seq.

[0008] Preferably, the self-localization construction module includes a distance estimation unit and a position calculation unit; The distance estimation unit extracts the corresponding signal strength parameter Rss and link delay parameter Del for each node identification parameter Nid based on the time series set Seq, and obtains two initial observation distances from the node identification parameter Nid to the sink node Hub through path loss mapping and propagation delay mapping, respectively. Then, using the observation fusion model constructed based on the weighted fusion method, the two initial observation distances are input into the observation fusion model for fusion to obtain the comprehensive observation distance parameter Dhb from the node identifier parameter Nid to the sink node Hub; Once the observation distance parameters Dhb of all nodes in the time series set Seq are set, the weighted least squares method is used for global calculation to obtain the node distance parameter Dis between any pair of node identifier parameters Nid. The specific form of the arbitrary node identifier parameter Nid is: combining the i-th node with the j-th node to obtain a node pair: (node ​​identifier parameter Nid_i, node identifier parameter Nid_j); Organize the distance parameters Dis between all node pairs into a distance result set Dset according to the node pair index.

[0009] Preferably, the location calculation unit, based on the acquired distance result set Dset, uses the node distance parameter Dis corresponding to each node pair as input constraint, and employs a spatial calculation strategy based on multi-dimensional scale analysis to derive the node location parameter Pos corresponding to each node identifier parameter Nid in the relative coordinate system. During the calculation process, iterative optimization is used to reduce the residual between the geometric space reconstructed by the distance parameter Dis between nodes and the actual observation. Incremental updates are performed when new nodes are added and old nodes are removed from the node set Set to ensure the continuity and stability of the global topology. All node position parameters Pos are compiled into a spatial relationship table Srt.

[0010] Preferably, the spatial relationship and mapping module includes an illuminance modeling unit and an illuminance calibration unit; The illuminance modeling unit receives the spatial relationship table Srt and all node position parameters Pos, and combines them with the static optical parameter Opt corresponding to the node identifier parameter Nid, to calculate the theoretical illuminance value at the coordinate point in the spatial region according to the law of light propagation in a unified coordinate system. Among them, the node position parameter Pos is used to determine the spatial position of the light source, while the static optical parameter Opt is used to describe the beam angle and luminous flux of the light source; based on the spatial position, beam angle and luminous flux, the illuminance distribution of each node identification parameter Nid at each region coordinate point is derived using photometric calculation methods. The illuminance distribution of all nodes is overlaid point by point to generate an initial illuminance mapping layer (Lay) covering the target area, reflecting the spatial distribution of the overall lighting environment.

[0011] Preferably, the initial illuminance mapping layer Lay generated by the illuminance calibration unit and the measured illuminance parameter Lux collected by the illuminance sensor are compared point by point on the regional coordinate points. For each spatial region, the residual between the theoretical illuminance value in the illuminance mapping layer Lay and the observed illuminance value in the measured illuminance parameter Lux is calculated, and the residual is optimized based on the minimum residual correction method. During the optimization process, the weight coefficients of the illuminance distribution at each node are adjusted to minimize the sum of squared residuals in the global range of the calibrated illuminance mapping layer Lay, and the calibrated illuminance mapping layer Lay is output.

[0012] Preferably, the uniformity assessment module includes a uniformity calculation unit and a region division unit; The uniformity calculation unit is based on the illuminance mapping layer Lay, and statistically analyzes the illuminance distribution point by point within the spatial area it covers; by normalizing and analyzing the illuminance values ​​of each region's coordinate points, it calculates the overall illuminance uniformity coefficient Luc, which is used to quantify the uniformity of the illuminance distribution. When the illuminance uniformity coefficient Luc is greater than the preset uniformity threshold, the overall LED lighting illuminance distribution is determined to be uniform, and the illuminance uniformity coefficient Luc is directly output as the evaluation result; otherwise, the overall illuminance distribution is determined to be uneven, and the point-by-point illuminance value is compared with the preset illuminance area threshold. The illuminance zone threshold includes a zone upper limit threshold and an interval lower limit threshold; The specific comparison method is as follows: When the illuminance value of a spatial coordinate point is greater than the upper limit threshold of the region, the spatial coordinate point is marked as an overbright region point and included in the overbrightness set; When the illuminance value of a spatial coordinate point is less than the lower limit threshold of the interval, the spatial coordinate point is marked as a darker area point and included in the darker point set; When the lower limit threshold of the interval is less than the illuminance value of the spatial coordinate point and the upper limit threshold of the region, the spatial coordinate point is not marked. The overbrightness ratio Ovl is generated by statistically analyzing the proportion of the accumulated bright area of ​​overbright areas to the total area; the underbrightness ratio Drk is generated by statistically analyzing the proportion of the accumulated dark area of ​​underbright areas to the total area.

[0013] Preferably, the region division unit is based on the set of overly bright points and the set of slightly dark points. The connected component segmentation method is used to divide the set of overly bright points into several overly bright regions, divide the set of slightly dark points into several slightly dark regions, and uniformly classify the remaining unclassified coordinate points into the balanced region. Then, spatial boundaries and category labels are generated for each overly bright, underly dark, and balanced region, and all regions are organized into a region set Seg.

[0014] Preferably, the optimization execution module includes a control execution unit; The control execution unit, based on the region set Seg, identifies the category and boundary information of each region and formulates a targeted optimization scheme Opt. The optimization scheme Opt includes reducing over-illumination in overly bright areas by reducing the output power of some nodes by 5% and adjusting the beam distribution by ±1°. In darker areas, insufficient illumination is compensated by increasing the output power of some nodes by 5% and adjusting the beam direction by ±1°. In the equilibrium region, maintain the current illuminance level without adjustment; The optimization scheme Opt is then translated into an executable set of control commands Ctr, and then distributed to the lamps corresponding to the node identifier parameter Nid in the node set Set via wireless networking.

[0015] This invention provides an LED lighting control system based on wireless networking, which has the following advantages: (1) By calculating the node position parameter Pos, a spatial relationship table Srt is generated and updated, so that the spatial relationship of the luminaires can be automatically updated after adding nodes, replacing nodes or changing the environment, without relying on manual reconfiguration of coordinates; Secondly, the spatial relationship and mapping module generates an illuminance mapping layer Lay based on the spatial relationship table Srt and the node position parameter Pos, and uses the measured illuminance parameter Lux for calibration, so that the illumination distribution can be dynamically corrected according to the actual observation, avoiding local over-brightness and under-brightness caused by construction deviations or environmental adjustments; Furthermore, the uniformity assessment module accurately identifies the spatial areas with insufficient or excessive illuminance by calculating the illuminance uniformity coefficient Luc, the over-brightness ratio Ovl and the under-brightness ratio Drk, and generates a region set Seg, thereby locating the problem to the specific region coordinates; Finally, the optimization execution module can automatically generate a control command set Ctr for the region set Seg, and send it to the corresponding node in the node set Set through wireless networking to perform dimming, thereby completing the automatic optimization of illuminance uniformity without relying on manual intervention. Dynamically adapting to changes in spatial structure and usage conditions, it achieves long-term stable and energy-saving lighting uniformity control.

[0016] (2) The observation distance parameter Dhb from the node identifier parameter Nid to the convergence node Hub is calculated by the observation fusion model. Then, the distance parameter Dis between nodes is calculated globally by the weighted least squares method Lsq. Finally, the position parameters Pos of all nodes are derived by the position calculation unit and summarized into the spatial relationship table Srt. In this way, the actual layout of the luminaires can be automatically reconstructed. When nodes are added or replaced, they can be immediately incorporated into the overall topology without redrawing drawings or manually configuring them one by one. The spatial relationship table Srt can be automatically updated so that newly added luminaires can immediately participate in the illuminance mapping and optimization process, avoiding the situation of local areas being too bright or too dark. It can be seen that this not only avoids the high cost and high error rate caused by manual maintenance, but also ensures the continuity and accuracy of illuminance optimization and control strategies in dynamic environments.

[0017] (3) By generating a structured region set Seg, a direct basis is provided for subsequent optimization. Specifically, the uniformity calculation unit first calculates the illuminance uniformity coefficient Luc based on the illuminance mapping layer Lay. When it is lower than a preset threshold, a point-by-point illuminance value comparison is triggered to obtain the overbrightness ratio Ovl and the underbrightness ratio Drk, forming an overbrightness point set and an underbrightness point set. Subsequently, the region division unit uses the connected component segmentation method to divide these point sets into continuous overbrightness and underbrightness regions, and organizes them together with the balanced region into a region set Seg. This quantification method that combines global and local approaches can not only identify whether the overall uniformity is good, but also locate the specific areas of non-uniformity. While ensuring overall uniformity, it can also accurately quantify and locate local abnormal lighting, significantly improving lighting comfort and the level of intelligent control. Attached Figure Description

[0018] Figure 1 This is a schematic block diagram of an LED lighting control system based on wireless networking according to the present invention; Figure 2 This is a schematic diagram of the data stream transmission process. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example 1

[0020] This invention provides an LED lighting control system based on wireless networking. Please refer to [link / reference]. Figure 1 and Figure 2 It includes the following modules: The wireless data acquisition module collects signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux from a set of nodes consisting of LED lamps with wireless communication capabilities. The self-localization construction module estimates the distance parameter Dis between nodes using the signal strength parameter Rss and the link delay parameter Del; it calculates the node location parameter Pos; and it generates and updates the spatial relationship table Srt. The spatial relationship and mapping module generates an illuminance mapping layer Lay based on the spatial relationship table Srt and the node position parameter Pos, combined with static optical parameters; and calibrates the illuminance mapping layer Lay using the measured illuminance parameter Lux. The uniformity assessment module calculates the illuminance uniformity coefficient Luc based on the illuminance mapping layer Lay, and obtains the overbrightness ratio Ovl and the underbrightness ratio Drk; the results are combined to generate the region set Seg; The optimized execution module receives the region set Seg, generates a control command set Ctr, and sends it to the node set Set via wireless networking.

[0021] In this embodiment, the wireless data acquisition module continuously collects signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux, ensuring the real-time nature of luminaire status and spatial illumination data. Secondly, the self-localization construction module dynamically estimates the distance parameter Dis between nodes based on the signal strength parameter Rss and the link delay parameter Del, and calculates the node position parameter Pos, thereby generating and updating the spatial relationship table Srt. This allows the spatial relationship of the luminaires to be automatically updated after the addition or replacement of nodes or environmental changes, without requiring manual reconfiguration of coordinates. Thirdly, the spatial relationship and mapping module generates an illuminance mapping layer La based on the spatial relationship table Srt and the node position parameter Pos. The system uses measured illuminance parameter Lux for calibration, enabling dynamic correction of the light distribution based on actual observations. This avoids localized over-brightness or under-brightness caused by construction deviations or environmental adjustments. Furthermore, the uniformity assessment module calculates the illuminance uniformity coefficient Luc, the over-brightness ratio Ovl, and the under-brightness ratio Drk to accurately identify spatial areas with insufficient or excessive illuminance, generating a region set Seg to pinpoint the problem to specific area coordinates. Finally, the optimization execution module automatically generates a control command set Ctr for the region set Seg, which is then wirelessly distributed to the corresponding nodes in the node set Set to perform dimming, thus achieving automatic optimization of illuminance uniformity without manual intervention. Taking a real office space as an example, when adjustments to partitions cause shadows or insufficient illumination in some areas, this system can detect a decrease in the illuminance uniformity coefficient Luc in real time, triggering re-optimization of that area to reduce the dark ratio Drk and restore a reasonable illuminance distribution. Similarly, when glare occurs in a shopping mall due to excessively strong light sources in some display cases, this system will identify an increase in the overbrightness ratio Ovl and automatically adjust the power to restore balanced lighting in the space. Therefore, this system can dynamically adapt to changes in spatial structure and usage conditions, achieving long-term stable and energy-efficient lighting uniformity control. Example 2

[0022] Specifically: the wireless data acquisition module includes a data acquisition unit and a data processing unit; The data acquisition unit interacts with the node set Set based on the aggregation node Hub, establishes a polling list of the node set Set according to the network discovery results of the wireless network, and periodically accesses each node in the node set Set using the node identifier parameter Nid as the primary key. During each access process, the data acquisition unit receives and triggers a report to obtain the signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux, which are bound one-to-one with the node identification parameter Nid, and merges them with the corresponding node identification parameter Nid to form a sampling record Rec. As polling continues, the sampled records Rec accumulate to form the raw data set Raw. After reaching the preset sampling period and triggering conditions, the raw data set Raw is output to the data processing unit to form the time series set Seq, which is then used by the self-localization construction module, the spatial relationship and mapping module, and the uniformity evaluation module. The aggregation node Hub is used to provide a unified time base and is responsible for initiating probes and receiving reports from network-side devices or gateway devices to the node set Set. The node set Set consists of multiple LED light fixture nodes with wireless communication capabilities, and can perform bidirectional data interaction with the aggregation node Hub. The node identifier parameter Nid is used to uniquely identify a single node in the node set Set, and is the primary key for data binding during the entire polling process of the data acquisition unit. The node identifier parameter Nid is written to each sampling record Rec and maintains consistency within the original data set Raw and the time series set Seq. The signal strength parameter Rss is measured and stored as signal strength parameter Rss when the aggregation node Hub receives the periodic broadcast frame and response frame of the node identification parameter Nid; and it is bound to the corresponding node identification parameter Nid; the signal strength parameter Rss is used to quantify the wireless link quality and approximate spatial distance relationship of LED lighting nodes in wireless networking. The link delay parameter Del is obtained by the aggregation node Hub initiating a probe request to the target node identification parameter Nid, recording the round-trip time between initiation and receiving the response, and subtracting the processing delay. This link delay parameter Del is then bound to the node identification parameter Nid. The link delay parameter Del is used to quantify the transmission delay between the LED lighting node and the aggregation node Hub, reflecting the spatial propagation distance and network path stability. The measured illuminance parameter Lux is generated by the built-in sensor of the target node identifier parameter Nid. The node detects the ambient illuminance at its location in real time and reports it to the aggregation node Hub, forming the measured illuminance parameter Lux, which is then bound to the corresponding node identifier parameter Nid. If the node identifier parameter Nid has no sensor, this field is empty and marked as missing. The measured illuminance parameter Lux is used to represent the actual lighting brightness level at the spatial location of the LED luminaire node. The built-in sensor includes an embedded photodiode array or a photodiode chip. When the built-in sensor detects the ambient light intensity, it synchronously converts it into an electrical signal, which is then output by the edge processor of the node identification parameter Nid and marked as the measured illuminance parameter Lux.

[0023] The data processing unit cleans and performs consistency processing on the multiple sample records Rec included in the acquired raw data set Raw. The cleaning process involves using median filtering to check the integrity of each sample record Rec, node identifier parameter Nid, signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux, and removing outliers and duplicates. The consistency processing uses a linear interpolation method to align the original dataset Raw to a unified time base provided by the aggregation node Hub, synchronizing parameter data from different times and nodes to the same time window; during the alignment process, missing or abnormal data is interpolated. The raw data set Raw, which completes the data processing unit, is organized into a time-series set Seq.

[0024] In this embodiment, the wireless data acquisition module ensures consistency and reliability in the acquisition and organization of all data, even with a large number of nodes and dynamic network changes. The data acquisition unit periodically polls each node in the node set Set using the node identifier parameter Nid as the primary key. During the interaction, it binds the signal strength parameter Rss, link delay parameter Del, and measured illumination parameter Lux to form a sampling record Rec. This ensures that the subsequently formed raw data set Raw maintains a one-to-one traceability relationship, avoiding data chaos caused by missing device identifiers or parameter misalignment in traditional systems. Furthermore, the data processing unit uses median filtering to remove anomalies and duplicates, and combines linear interpolation with a unified time reference provided by the aggregation node Hub to complete time alignment. This synchronizes all sampling records Rec to the same time window, ultimately forming a time-series set Seq, ensuring horizontal comparability between different nodes and parameters. Taking a large warehouse space as an example, when the wireless signal of multiple LED lights becomes unstable due to the movement of shelves, without the above processing, the collected results often show drastic fluctuations in the signal strength parameter Rss of some lights, or a serious discrepancy between the measured illuminance parameter Lux and the actual illumination. Traditional systems struggle to form a reliable analytical basis globally. This system, however, maintains the continuity and consistency of the time series set Seq by removing abnormal records and imputing missing data. This allows the subsequent self-localization construction module and spatial relationship and mapping module to obtain accurate and traceable data input even in complex environments, thereby improving the robustness of the entire system in dynamic environments. Example 3

[0025] Specifically: the self-localization construction module includes a distance estimation unit and a position calculation unit; The distance estimation unit extracts the corresponding signal strength parameter Rss and link delay parameter Del for each node identification parameter Nid based on the time series set Seq, and obtains two initial observation distances from the node identification parameter Nid to the sink node Hub through path loss mapping and propagation delay mapping, respectively. Then, using the observation fusion model constructed based on the weighted fusion method, the two initial observation distances are input into the observation fusion model for fusion to obtain the comprehensive observation distance parameter Dhb from the node identifier parameter Nid to the sink node Hub; Once the observation distance parameters Dhb of all nodes in the time series set Seq are formed into a set, the weighted least squares method is used for global calculation to obtain the node distance parameter Dis between any pair of node identifier parameters Nid (e.g., node identifier parameter Nid_i, node identifier parameter Nid_j). The specific form of the arbitrary node identifier parameter Nid is: combining the i-th node and the j-th node to obtain a node pair: (node ​​identifier parameter Nid_i, node identifier parameter Nid_j). The weighted fusion method is a model construction method based on weighted average, which assigns weights according to the confidence of the two types of observations to reduce the impact of errors from a single observation. In this scheme, it is used to construct an observation fusion model. Organize the distance parameters Dis between all node pairs into a distance result set Dset according to the node pair index; It should be noted that the observation distance parameter Dhb is a comprehensive distance result calculated by the observation fusion model from the observation data of a single node identifier parameter Nid relative to the sink node Hub. It reflects the relative spatial distance between the node and the sink node Hub and is the basic input for global calculation. The observation fusion model is constructed by a weighted fusion method, which takes the path loss mapping result based on the signal strength parameter Rss and the propagation delay mapping result based on the link delay parameter Del as two types of input observations, and generates a single observation distance parameter Dhb through weighted fusion. The node distance parameter Dis represents the distance between any two nodes (node ​​identifier parameter Nid_i, node identifier parameter Nid_j) in the relative space. It is calculated globally by weighted least squares method Lsq, with all observed distance parameters Dhb as constraints. The distance result set Dset is a set of nodes with node pairs (node ​​identifier parameter Nid_i, node identifier parameter Nid_j) as unique indices and bound to the corresponding distance parameters Dis between nodes. It covers all node pairs in the system and serves as the direct input to the location calculation unit.

[0026] The location calculation unit, based on the acquired distance result set Dset, takes the inter-node distance parameter Dis corresponding to each node pair (node ​​identifier parameter Nid_i, node identifier parameter Nid_j) as input constraints, and uses a spatial calculation strategy based on multi-dimensional scale analysis to derive the node position parameter Pos corresponding to each node identifier parameter Nid in the relative coordinate system. It should be noted that: the node position parameter Pos is a spatial coordinate result derived by the position calculation unit based on the node distance parameter Dis. It represents the position of a single node identifier parameter Nid in the coordinate system established within the system in the form of three-dimensional relative coordinates. The node position parameter Pos is not an absolute coordinate in the real physical space, but a point distribution reconstructed in the relative coordinate system through optimization algorithms. Its geometric feature is to ensure that the Euclidean distance between any two node identifier parameters Nid is as close as possible to the corresponding node distance parameter Dis. The calculation result of this relative coordinate can reflect the spatial relationship and topological structure between nodes, which is sufficient to support the construction of the spatial relationship table Srt and the generation of the illumination mapping layer Lay. In existing LED lighting control systems, the spatial position of luminaires is usually determined by construction drawings during the design phase and fixedly configured using unique identifiers after installation. However, this approach has significant shortcomings: First, installation deviations are inevitable during actual construction, and the actual position of the luminaires often differs from the design coordinates. Second, in office, commercial, or public spaces, ceiling adjustments, partition modifications, or lighting expansions are frequent occurrences, and luminaire replacements are also common. The previously statically set coordinates become immediately invalid in such cases. Furthermore, when new luminaires are added to the network or existing luminaires are replaced, manually reconfiguring their spatial coordinates not only increases maintenance costs but also easily leads to inconsistencies between the control strategy and the actual lighting environment. The observation distance parameter Dhb is calculated based on the signal strength parameter Rss and the link delay parameter Del. Furthermore, the node position parameter Pos and the spatial relationship table Srt are calculated globally using the weighted least squares method Lsq. This self-positioning method can automatically correct deviations caused by construction errors or environmental changes, ensuring that the lighting fixture spatial relationship table is consistent with the actual layout. Compared with existing technologies that rely on manual configuration or static drawings, this solution has dynamic adaptability and can automatically update the topology relationship when nodes are added, removed, or their positions change. In this way, the system not only improves the accuracy of spatial mapping and illuminance distribution uniformity optimization, but also significantly reduces the manual cost of maintenance and reconfiguration, and achieves long-term sustainable operation. It can be seen that self-positioning calculation is not a complicated treatment of the problem, but a necessary way to solve the stability and intelligence of lighting control in dynamic and changing scenarios in practical applications. During the calculation process, iterative optimization is used to reduce the residual between the geometric space reconstructed by the distance parameter Dis between nodes and the actual observation. Incremental updates are performed when new nodes are added and old nodes are removed from the node set Set to ensure the continuity and stability of the global topology. All node position parameters Pos are compiled into a spatial relationship table Srt; It should be noted that the spatial relationship table Srt is a structured data table consisting of all node location parameters Pos. Each node identifier parameter Nid is bound to its corresponding node location parameter Pos using the node identifier parameter Nid as the unique index key. The spatial relationship table Srt does not record absolute geographical locations, but rather maintains the spatial topology of the system in a relative coordinate system. In this table, the row and column fields contain both the "relative position corresponding to a single node index" and the distance parameter Dis between nodes can be reconstructed from the difference between any two node location parameters Pos. Thus, both "individual node position" and "relative relationship between node pairs" information are stored in the same data structure. The spatial relationship table Srt, as the output, is directly provided to the spatial relationship and mapping module for generating the illuminance mapping layer Lay and performing illuminance calibration.

[0027] In this embodiment, the self-positioning construction module can maintain the relative topological relationships of the luminaires in space in real time without manual intervention. Traditional LED lighting control systems typically rely on static coordinates from construction drawings or manual configuration. Once there are changes such as ceiling modifications, partition adjustments, or the addition of new luminaires, the original spatial coordinates immediately become invalid, causing the control strategy to fail to correspond to the actual lighting environment. In this solution, the distance estimation unit establishes path loss mapping and propagation delay mapping for the signal strength parameter Rss and link delay parameter Del, respectively. Then, the observation fusion model calculates the observation distance parameter Dhb from the node identifier parameter Nid to the convergence node Hub. Subsequently, the distance parameter Dis between nodes is calculated globally using the weighted least squares method Lsq. Finally, the position calculation unit derives the position parameters Pos of all nodes and summarizes them into a spatial relationship table Srt. In this way, the actual layout of the luminaires can be automatically reconstructed, and when nodes are added or replaced, they can be immediately incorporated into the overall topology without the need to redraw drawings or manually configure them one by one. Taking shopping malls as an example, when operators temporarily replace some exhibition area lights or add hanging lights during holidays, traditional systems often require engineers to re-measure coordinates, increasing time and labor costs. This system, however, can automatically update the spatial relationship table (Srt), allowing newly added lights to immediately participate in the illuminance mapping and optimization process, preventing areas from being too bright or too dark. Therefore, this system not only avoids the high costs and error rates of manual maintenance but also ensures the continuity and accuracy of illuminance optimization and control strategies in dynamic environments. Example 4

[0028] Specifically: the spatial relationship and mapping module includes an illuminance modeling unit and an illuminance calibration unit; The illuminance modeling unit receives the spatial relationship table Srt and all node position parameters Pos, and combines them with the static optical parameter Opt corresponding to the node identifier parameter Nid, to calculate the theoretical illuminance value at the coordinate point in the spatial region according to the law of light propagation in a unified coordinate system. Among them, the node position parameter Pos is used to determine the spatial position of the light source, while the static optical parameter Opt is used to describe the beam angle and luminous flux of the light source; based on the spatial position, beam angle and luminous flux, the illuminance distribution of each node identification parameter Nid at each region coordinate point is derived using photometric calculation methods. The illuminance distribution of all nodes is superimposed point by point to generate an initial illuminance mapping layer Lay covering the target area, so as to reflect the spatial distribution of the overall lighting environment. It should be noted that the static optical parameter Opt is used to bind a fixed optical characteristic parameter to a single node identifier parameter Nid, representing the designed light properties of the LED luminaire, such as beam angle and rated luminous flux; the static optical parameter Opt remains unchanged during system operation and is one of the basic inputs for constructing the illuminance mapping layer Lay; The illuminance mapping layer (Lay) is a spatial two-dimensional or three-dimensional illuminance distribution layer generated by the illuminance modeling unit. It records light intensity values ​​using regional coordinates as an index, representing the spatial light distribution after the superposition of all node light sources.

[0029] The initial illuminance mapping layer Lay generated by the illuminance calibration unit and the measured illuminance parameter Lux collected by the illuminance sensor are compared point by point on the regional coordinate points. For each spatial region, the residual between the theoretical illuminance value in the illuminance mapping layer Lay and the observed illuminance value in the measured illuminance parameter Lux is calculated, and the residual is optimized based on the minimum residual correction method. During the optimization process, by adjusting the weight coefficients of the illuminance distribution at each node, the calibrated illuminance mapping layer Lay minimizes the sum of squared residuals globally. The output calibrated illuminance mapping layer Lay can reflect the spatial distribution law of the theoretical model and closely approximate the actual illuminance level, providing reliable input for the analysis of the uniformity assessment module. It should be noted that the minimum residual correction method constructs a residual function to transform the point-by-point difference between the illuminance mapping layer Lay and the measured illuminance parameter Lux into an error sum of squares objective function, and iteratively optimizes the weight coefficients of the illuminance distribution at each node, finally obtaining the calibrated illuminance mapping layer Lay.

[0030] In this embodiment, the spatial relationship and mapping module organically combines theoretical modeling and actual observation of illuminance distribution, overcoming the disconnect in existing technologies where "the construction design stage relies solely on optical calculations, while the operation stage relies solely on local measurements." Specifically, the illuminance modeling unit uses the spatial relationship table Srt and the position parameter Pos of each node, combined with the corresponding static optical parameter Opt, to generate an initial illuminance mapping layer Lay covering the entire area according to photometric calculation methods, making the overall illumination distribution predictable. The illuminance calibration unit then introduces the measured illuminance parameter Lux, compares point by point, and iteratively optimizes the residuals using the minimum residual correction method. This ensures that the calibrated illuminance mapping layer Lay retains the regularity of the theoretical distribution while also conforming to the actual environmental illuminance. Taking a large library as an example, although the beam angle and luminous flux of the lighting fixtures are determined during the design phase, some areas may experience shadows or insufficient illuminance as the bookshelves increase in height or the layout changes. If only the theoretical distribution on the construction drawings is relied upon, the system cannot reflect this change; if only the observation values ​​of local sensors are relied upon, the overall lighting uniformity is easily overlooked. This system combines illuminance modeling and illuminance calibration units, enabling the calibrated illuminance mapping layer (Lay) to reflect the true illuminance state globally. This provides reliable input for the subsequent uniformity evaluation module, thereby maintaining global consistency and accuracy of illuminance distribution in dynamic scenes. Example 5

[0031] Specifically: the uniformity assessment module includes a uniformity calculation unit and a region division unit; The uniformity calculation unit is based on the illuminance mapping layer Lay, and statistically analyzes the illuminance distribution point by point within the spatial area it covers; by normalizing and analyzing the illuminance values ​​of each region's coordinate points, it calculates the overall illuminance uniformity coefficient Luc, which is used to quantify the uniformity of the illuminance distribution. When the illuminance uniformity coefficient Luc is greater than the preset uniformity threshold, the overall LED lighting illuminance distribution is determined to be uniform, and the illuminance uniformity coefficient Luc is directly output as the evaluation result; otherwise, the overall illuminance distribution is determined to be uneven, and the point-by-point illuminance value is compared with the preset illuminance area threshold. The illuminance zone threshold includes a zone upper limit threshold and an interval lower limit threshold; The specific comparison method is as follows: When the illuminance value of a spatial coordinate point is greater than the upper limit threshold of the region, the spatial coordinate point is marked as an overbright region point and included in the overbrightness set; When the illuminance value of a spatial coordinate point is less than the lower limit threshold of the interval, the spatial coordinate point is marked as a darker area point and included in the darker point set; When the lower limit threshold of the interval is less than the illuminance value of the spatial coordinate point and the upper limit threshold of the region, the spatial coordinate point is not marked. The overbrightness ratio Ovl is generated by statistically analyzing the proportion of the accumulated bright area of ​​overbright areas to the total area; the underbrightness ratio Drk is generated by statistically analyzing the proportion of the accumulated dark area of ​​underbright areas to the total area. It should be noted that the overbrightness ratio Ovl is calculated as the ratio of the area of ​​the area marked as overbright to the total area of ​​the entire area, and is used to indicate the severity of over-illumination in the overall environment. The darkness ratio Drk is calculated as the proportion of the area of ​​the region marked as a dark area to the total area of ​​the entire region, and is used to characterize the severity of insufficient illumination in the overall environment.

[0032] The region division unit is based on the set of overly bright points and the set of slightly dark points. It uses the connected component segmentation method to divide the set of overly bright points into several overly bright regions, divide the set of slightly dark points into several slightly dark regions, and uniformly classify the remaining unclassified coordinate points into the balanced region. Then, spatial boundaries and category labels are generated for each overly bright, underly dark, and balanced region, and all regions are organized into a region set Seg. It should be noted that: in the coordinate system of the illumination mapping layer Lay, there is a set consisting of overly bright areas, underly dark areas, and balanced areas. Each area is defined by spatial boundaries and category labels to ensure the mutual exclusion and complete coverage of the three types of areas. This is the direct input for optimizing the execution control strategy of the execution module. The connected component segmentation method is an adjacency-based aggregation method that merges adjacent coordinate points of the same type into connected regions. In this scheme, it is used to extract spatially continuous overly bright and underly dark regions from the set of overly bright points and the set of underly dark points.

[0033] The optimized execution module includes a control execution unit; The control execution unit, based on the region set Seg, identifies the category and boundary information of each region and formulates a targeted optimization scheme Opt. The optimization scheme Opt includes reducing over-illumination in overly bright areas by reducing the output power of some nodes by 5% and adjusting the beam distribution by ±1°. In darker areas, insufficient illumination is compensated by increasing the output power of some nodes by 5% and adjusting the beam direction by ±1°. In the equilibrium region, maintain the current illuminance level without adjustment; The optimization scheme Opt is then translated into an executable set of control commands Ctr, and then distributed to the lamps corresponding to the node identifier parameter Nid in the node set Set via wireless networking for execution. After execution, the system re-enters the wireless data acquisition module to collect updated signal strength parameters Rss, link delay parameters Del, and measured illumination parameters Lux, forming a new time series set Seq, thereby achieving closed-loop operation of optimization and evaluation.

[0034] In this embodiment, the uniformity assessment module not only provides quantitative indicators of overall illuminance uniformity but also automatically identifies overly bright and underly dark areas within a local spatial range, generating a structured region set Seg, providing a direct basis for subsequent optimization. Specifically, the uniformity calculation unit first calculates the illuminance uniformity coefficient Luc based on the illuminance mapping layer Lay. When it falls below a preset threshold, it triggers a point-by-point illuminance value comparison, obtaining the overly bright ratio Ovl and the underly dark ratio Drk, forming sets of overly bright and underly dark points. Subsequently, the region division unit uses a connected component segmentation method to divide these point sets into continuous overly bright and underly dark regions, organizing them together with the balanced regions into a region set Seg. This quantification method, combining global and local approaches, enables the system to not only identify "whether the overall system is uniform" but also pinpoint "specific locations where uniformity is not achieved." Taking an open-plan office area as an example, when a certain area experiences excessive lighting due to its proximity to floor-to-ceiling windows, traditional systems typically only display that the overall lighting is roughly normal, easily overlooking glare issues at employee workstations. This system, however, precisely locates the lighting anomalies near the floor-to-ceiling windows by analyzing the increase in the overbrightness ratio (Ovl) and the overbright areas generated in the area set (Seg). This provides direct input for the optimization execution module to issue targeted control command sets (Ctr), thereby quickly reducing the illuminance level in the glare area. Therefore, this system, while ensuring overall uniformity, can also accurately quantify and locate localized abnormal lighting, significantly improving lighting comfort and the level of intelligent control.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A wireless networking-based LED lighting control system, characterized in that: Includes the following modules: The wireless data acquisition module collects signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux from a set of nodes consisting of LED lamps with wireless communication capabilities. The self-localization construction module estimates the distance parameter Dis between nodes using the signal strength parameter Rss and the link delay parameter Del; it calculates the node location parameter Pos; and it generates and updates the spatial relationship table Srt. The spatial relationship and mapping module generates an illuminance mapping layer Lay based on the spatial relationship table Srt and the node position parameter Pos, combined with static optical parameters. The measured illuminance parameter Lux was used to calibrate the illuminance mapping layer Lay. The uniformity assessment module calculates the illuminance uniformity coefficient Luc based on the illuminance mapping layer Lay, and obtains the overbrightness ratio Ovl and the underbrightness ratio Drk; the results are combined to generate the region set Seg; The optimized execution module receives the region set Seg, generates a control command set Ctr, and sends it to the node set Set via wireless networking.

2. The LED lighting control system based on wireless networking according to claim 1, characterized in that: The wireless data acquisition module includes a data acquisition unit and a data processing unit; The data acquisition unit interacts with the node set Set based on the aggregation node Hub, establishes a polling list of the node set Set according to the network discovery results of the wireless network, and periodically accesses each node in the node set Set using the node identifier parameter Nid as the primary key. During each access process, the data acquisition unit receives and triggers a report to obtain the signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux, which are bound one-to-one with the node identification parameter Nid, and merges them with the corresponding node identification parameter Nid to form a sampling record Rec. As polling continues, the sampled records Rec accumulate to form the raw data set Raw. After reaching the preset sampling period and triggering conditions, the raw data set Raw is output to the data processing unit to form the time series set Seq. The aggregation node Hub is used to provide a unified time base and is responsible for initiating probes and receiving reports from network-side devices or gateway devices to the node set Set. The node set Set consists of multiple LED light fixture nodes with wireless communication capabilities, and can perform bidirectional data interaction with the aggregation node Hub. The node identifier parameter Nid is used to uniquely identify a single node in the node set Set; The signal strength parameter Rss is measured and stored as signal strength parameter Rss when the aggregation node Hub receives the periodic broadcast frame and acknowledgment frame of the node identification parameter Nid. The link delay parameter Del is obtained by the aggregation node Hub sending a probe request to the target node identifier parameter Nid, recording the round-trip time between the request and the received response, and subtracting the processing delay. The measured illuminance parameter Lux is generated by the built-in sensor of the target node identifier parameter Nid. The node detects the ambient illuminance at its location in real time and reports it to the aggregation node Hub.

3. The LED lighting control system based on wireless networking according to claim 2, characterized in that: The data processing unit cleans and performs consistency processing on the multiple sample records Rec included in the acquired raw data set Raw. The cleaning process involves using median filtering to check the integrity of each sample record Rec, node identifier parameter Nid, signal strength parameter Rss, link delay parameter Del, and measured illuminance parameter Lux, and removing outliers and duplicates. The consistency processing uses a linear interpolation method to time-align the raw dataset Raw with a unified time reference provided by the aggregation node Hub, synchronizing parameter data from different times and different nodes into the same time window; During the alignment process, missing or abnormal data is imputed; The raw data set Raw, which completes the data processing unit, is organized into a time-series set Seq.

4. The LED lighting control system based on wireless networking according to claim 3, characterized in that: The self-localization construction module includes a distance estimation unit and a position calculation unit; The distance estimation unit extracts the corresponding signal strength parameter Rss and link delay parameter Del for each node identification parameter Nid based on the time series set Seq, and obtains two initial observation distances from the node identification parameter Nid to the sink node Hub through path loss mapping and propagation delay mapping, respectively. Then, using the observation fusion model constructed based on the weighted fusion method, the two initial observation distances are input into the observation fusion model for fusion to obtain the comprehensive observation distance parameter Dhb from the node identifier parameter Nid to the sink node Hub; Once the observation distance parameters Dhb of all nodes in the time series set Seq are set, the weighted least squares method is used for global calculation to obtain the node distance parameter Dis between any pair of node identifier parameters Nid. The specific form of the arbitrary node identifier parameter Nid is: to obtain a node pair by combining the i-th node and the j-th node; Organize the distance parameters Dis between all node pairs into a distance result set Dset according to the node pair index.

5. The LED lighting control system based on wireless networking according to claim 4, characterized in that: The location calculation unit, based on the acquired distance result set Dset, takes the node distance parameter Dis corresponding to each node as the input constraint, and uses a spatial calculation strategy based on multi-dimensional scale analysis to derive the node location parameter Pos corresponding to each node identifier parameter Nid in the relative coordinate system. During the calculation process, iterative optimization is used to reduce the residual between the geometric space reconstructed by the distance parameter Dis between nodes and the actual observation. Incremental updates are performed when new nodes are added and old nodes are removed from the node set Set to ensure the continuity and stability of the global topology. All node position parameters Pos are compiled into a spatial relationship table Srt.

6. The LED lighting control system based on wireless networking according to claim 5, characterized in that: The spatial relationship and mapping module includes an illuminance modeling unit and an illuminance calibration unit; The illuminance modeling unit receives the spatial relationship table Srt and all node position parameters Pos, and combines them with the static optical parameter Opt corresponding to the node identifier parameter Nid, to calculate the theoretical illuminance value at the coordinate point in the spatial region according to the law of light propagation in a unified coordinate system. The illuminance distribution of all nodes is overlaid point by point to generate an initial illuminance mapping layer (Lay) covering the target area, reflecting the spatial distribution of the overall lighting environment.

7. The LED lighting control system based on wireless networking according to claim 6, characterized in that: The initial illuminance mapping layer Lay generated by the illuminance calibration unit and the measured illuminance parameter Lux collected by the illuminance sensor are compared point by point on the regional coordinate points. For each spatial region, the residual between the theoretical illuminance value in the illuminance mapping layer Lay and the observed illuminance value in the measured illuminance parameter Lux is calculated, and the residual is optimized based on the minimum residual correction method. During the optimization process, the weight coefficients of the illuminance distribution at each node are adjusted to minimize the sum of squared residuals in the global range of the calibrated illuminance mapping layer Lay, and the calibrated illuminance mapping layer Lay is output.

8. The LED lighting control system based on wireless networking according to claim 7, characterized in that: The uniformity assessment module includes a uniformity calculation unit and a region division unit; The uniformity calculation unit is based on the illuminance mapping layer Lay, and statistically analyzes the illuminance distribution point by point within the spatial area it covers; by normalizing and analyzing the illuminance values ​​of each region's coordinate points, it calculates the overall illuminance uniformity coefficient Luc, which is used to quantify the uniformity of the illuminance distribution. When the illuminance uniformity coefficient Luc is greater than the preset uniformity threshold, the overall LED lighting illuminance distribution is determined to be uniform, and the illuminance uniformity coefficient Luc is directly output as the evaluation result; otherwise, the overall illuminance distribution is determined to be uneven, and the point-by-point illuminance value is compared with the preset illuminance area threshold. The illuminance zone threshold includes a zone upper limit threshold and an interval lower limit threshold; The specific comparison method is as follows: When the illuminance value of a spatial coordinate point is greater than the upper limit threshold of the region, the spatial coordinate point is marked as an overbright region point and included in the overbrightness set; When the illuminance value of a spatial coordinate point is less than the lower limit threshold of the interval, the spatial coordinate point is marked as a darker area point and included in the darker point set; When the lower limit threshold of the interval is less than the illuminance value of the spatial coordinate point and the upper limit threshold of the region, the spatial coordinate point is not marked. The overbrightness ratio Ovl is generated by statistically analyzing the proportion of the accumulated bright area of ​​overbright areas to the total area; the underbrightness ratio Drk is generated by statistically analyzing the proportion of the accumulated dark area of ​​underbright areas to the total area.

9. The LED lighting control system based on wireless networking according to claim 8, characterized in that: The region division unit is based on the set of overly bright points and the set of slightly dark points. It uses the connected component segmentation method to divide the set of overly bright points into several overly bright regions, divide the set of slightly dark points into several slightly dark regions, and uniformly classify the remaining unclassified coordinate points into the balanced region. Then, spatial boundaries and category labels are generated for each overly bright, underly dark, and balanced region, and all regions are organized into a region set Seg.

10. The LED lighting control system based on wireless networking according to claim 9, characterized in that: The optimized execution module includes a control execution unit; The control execution unit, based on the region set Seg, identifies the category and boundary information of each region and formulates a targeted optimization scheme Opt. The optimization scheme Opt includes reducing over-illumination in overly bright areas by reducing the output power of some nodes by 5% and adjusting the beam distribution by ±1°. In darker areas, insufficient illumination is compensated by increasing the output power of some nodes by 5% and adjusting the beam direction by ±1°. In the equilibrium region, maintain the current illuminance level without adjustment; The optimization scheme Opt is then translated into an executable set of control commands Ctr, and then distributed to the lamps corresponding to the node identifier parameter Nid in the node set Set via wireless networking.

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