Intelligent lighting data acquisition control system and method based on wireless networking

Abstract

The application discloses a kind of intelligent lighting data acquisition control system and method based on wireless networking, comprising the following steps: deploying luminaire cell and environment perception node, generating environment time series dataset and behavior time series dataset;Extract illumination change feature, environmental change feature and behavior activity feature, generate luminaire cell state sequence;Establish the adjacent communication relationship between luminaire cell, form luminaire cell self-organizing communication topology structure;Construct local cell community, generate local cell community state set;Calculate lighting area illumination gradient field, generate luminaire cell illumination adjustment parameter;Construct luminaire cell energy function, update luminaire cell illumination adjustment parameter;Update luminaire cell light source driving parameter, generate adaptive lighting control result of lighting area.The application adopts wireless networking and luminaire cell self-organizing collaborative method, realizes lighting data acquisition and adaptive control, with the advantages of high illumination uniformity and energy consumption optimization.

Description

Technical Field

[0001] This invention relates to the field of intelligent lighting data acquisition and control, and in particular to an intelligent lighting data acquisition and control system and method based on wireless networking. Background Technology

[0002] With the development of IoT and wireless communication technologies, smart lighting is gradually being applied to office buildings, commercial spaces, and public places. Existing smart lighting technologies typically collect information such as ambient light and human activity through sensors, and use wireless communication networks to centrally or rule-driven control of the lamps to achieve automatic dimming and energy-saving control. Most of these technologies adopt fixed control strategies or adjustment methods based on single lamp nodes, adjusting the brightness and color temperature of the lamps through preset thresholds or simple logic rules, thereby improving the automation level of the lighting system to a certain extent.

[0003] Existing technologies still have certain shortcomings in practical applications. Traditional intelligent lighting control methods usually rely on a central control node or fixed control rules, and there is a lack of effective coordination mechanisms between lighting nodes. It is difficult to optimize the overall system based on the light distribution relationship between multiple lighting fixtures in an area. At the same time, the response to environmental changes and human activities is mostly limited to single-point data processing, and there is a lack of comprehensive analysis of the spatial relationship between lighting nodes. This leads to problems such as uneven light distribution, low energy consumption regulation efficiency, and insufficient system adaptability in complex lighting scenarios. Summary of the Invention

[0004] One objective of this invention is to propose an intelligent lighting data acquisition and control system and method based on wireless networking. This invention adopts a wireless networking and lamp cell self-organization collaborative method to realize lighting data acquisition and adaptive control, which has the advantages of high illumination uniformity and optimized energy consumption.

[0005] A smart lighting data acquisition and control method based on wireless networking according to an embodiment of the present invention includes the following steps: Multiple luminaire cells with wireless communication modules and environmental sensing nodes are deployed within the target lighting area, and environmental time-series datasets and behavioral time-series datasets are generated through preprocessing. The environmental time series dataset and the behavioral time series dataset are serialized according to a preset time window. Lighting change features, environmental change features and behavioral activity features are extracted, mapped into lamp cell state vectors, and lamp cell state sequences are generated. Wireless networking is used to establish adjacency communication relationships between lamp cells. The neighborhood radius of the lamp cell is determined based on the communication signal strength and spatial distance. Communication edges are established within the neighborhood radius to form a self-organized communication topology structure for lamp cells. In the self-organized communication topology of lamp cells, a local cell community is constructed with each lamp cell as the center. The state vector of the target lamp cell is combined with the state vector of the neighboring lamp cells to generate a set of local cell community states. The illumination gradient field of the illumination area is calculated using the local cell community state set, and the illumination adjustment parameters of the lamp cells are generated according to the illumination difference between adjacent lamp cells and the preset illumination diffusion coefficient. A lamp cell energy function is constructed based on the lamp cell illumination regulation parameters, and the lamp cell illumination regulation parameters are updated by minimizing the lamp cell energy function; The light source driving parameters of the luminaire cells are updated according to the updated luminaire cell illumination adjustment parameters to generate adaptive lighting control results for the lighting area.

[0006] Optionally, the environmental sensing node collects data on light intensity, temperature, humidity, airflow, and human activity. The preprocessing specifically includes time synchronization, outlier identification and removal, data missing completion, multi-source data alignment, data noise suppression, data normalization, and time-series resampling.

[0007] Optionally, the generation of the lamp cell state sequence specifically includes: The environmental time series dataset and the behavioral time series dataset are sorted according to a unified time index, and a sliding window is performed according to a preset time window length to generate time window data subsequences. Light intensity data is extracted from a time window data subsequence, and light change characteristics are generated by calculating the difference between light intensity at adjacent time points. Temperature data, humidity data, and airflow data are identified in the data subsequence of the time window. The temperature difference, humidity difference, and airflow change difference are calculated respectively. The temperature change features, humidity change features, and airflow change features are combined to generate a set of environmental change features. Statistical analysis is performed on the human activity data in the subsequence of the time window data, and behavioral activity characteristics are generated by statistically analyzing the frequency and duration of human activity signals within the time window; The characteristics of light change, environmental change, and behavioral activity are fused to generate a comprehensive feature set, which is then converted into a lamp cell state vector through feature mapping. The lamp cell state vectors generated in each time window are arranged in chronological order to generate a lamp cell state sequence.

[0008] Optionally, the generation of the lamp cell self-organizing communication topology specifically includes: Node identification and spatial location recording are performed on multiple lamp cells deployed within the target lighting area. A unique node identifier is assigned to each lamp cell, and a set of spatial locations of lamp cells is generated based on the lamp installation location. The spatial adjacency relationship between each lamp cell is calculated based on the spatial location set of lamp cells. By comparing the spatial distance between lamp cells with the preset neighborhood radius, a set of lamp cell neighborhood relationships is generated. In the set of neighborhood relationships of lamp cells, identify pairs of lamp cell nodes with neighborhood relationships, and establish wireless communication connections between the corresponding nodes to form a set of lamp cell communication connections; A lamp cell adjacency matrix is ​​constructed based on the lamp cell communication connection set. The communication connection status between lamp cells is recorded through the adjacency matrix to generate the lamp cell communication adjacency structure. The topology of the lamp cell nodes is processed by using the lamp cell communication adjacency structure, and the lamp cell nodes are combined with their corresponding communication connections to form a lamp cell communication network structure. Based on the lamp cell communication network structure, the communication connections between each lamp cell node are reorganized to generate a lamp cell self-organized communication topology.

[0009] Optionally, the generation of the local cell community state set specifically includes: Read the connection relationships of lamp cell nodes in the self-organized communication topology of lamp cells, identify lamp cell nodes that have communication connections with the target lamp cell node, and generate a set of lamp cell neighborhood nodes; Locate the lamp cell state vector corresponding to the target lamp cell node in the lamp cell state sequence, and extract the lamp cell state vector corresponding to each lamp cell node in the set of neighboring lamp cell nodes to generate a set of neighboring lamp cell state vectors. The target luminaire cell state vector and the set of neighboring luminaire cell state vectors are combined according to the node association relationship to generate a local cell community node set; Based on the local cell community node set, establish the community connection relationship between the target lamp cell node and the neighboring lamp cell nodes to generate the local cell community structure; Based on the local cell community structure, the lamp cell state vectors corresponding to each lamp cell node in the community are collected, and the lamp cell state vectors in the community are integrated to generate a local cell community state set.

[0010] Optionally, the generation of the lamp cell illumination adjustment parameters specifically includes: Read the lamp cell state vector corresponding to each lamp cell node in the local cell community state set, extract the light intensity value from the lamp cell state vector, and arrange them according to the spatial coordinates of the lamp cell node to generate a lamp cell light intensity distribution set. Calculate the light intensity difference between adjacent lamp cell nodes based on the set of lamp cell light intensity distribution, and generate a set of lamp cell light intensity difference values. The rate of change of light intensity between lamp cell nodes is calculated based on the set of light difference values ​​of lamp cells and the set of spatial positions of lamp cells, and a set of light gradients of lamp cells is generated. The illumination adjustment parameters of each lamp cell node are calculated based on the lamp cell illumination gradient set and the preset illumination diffusion coefficient, and a lamp cell illumination adjustment parameter set is generated. The illumination regulation parameters corresponding to each lamp cell node are recorded based on the lamp cell illumination regulation parameter set and arranged in the order of the lamp cell nodes to generate the lamp cell illumination regulation parameter sequence.

[0011] Optionally, updating the illumination adjustment parameters specifically includes: Based on the sequence of lighting regulation parameters of lamp cells and the set of spatial locations of lamp cells, the light intensity values ​​corresponding to each lamp cell node are extracted and arranged in the order of lamp cell nodes to generate a set of lighting states of lamp cells. By combining the spatial location set of lamp cells, the adjacency relationship between adjacent lamp cell nodes is identified. The difference between the light intensity values ​​in the light state set of lamp cells is calculated, the light intensity difference between adjacent lamp cell nodes is recorded, and a set of light difference between lamp cells is generated. A lamp cell energy function is constructed using the set of lamp cell illumination differences. The illumination differences corresponding to each lamp cell node are mapped to energy values. The energy values ​​of each lamp cell node are then summarized to generate a set of lamp cell node energy values. The energy set of lamp cell nodes is updated by combining the lamp cell illumination regulation parameter sequence. By updating the energy value corresponding to each lamp cell node, an updated energy set of lamp cell nodes is generated. The updated luminaire cell node energy set is used to recalculate the luminaire cell illumination regulation parameters, and the updated illumination regulation parameters are recorded to generate an updated luminaire cell illumination regulation parameter sequence.

[0012] Optionally, the generation of the adaptive lighting control results specifically includes: Obtain the updated sequence of lighting regulation parameters for lamp cells and the set of spatial locations of lamp cells, extract the lighting regulation parameter values ​​corresponding to each lamp cell node, and organize them according to the order of lamp cell nodes to generate a set of lighting regulation states for lamp cells. By combining the spatial location set of lamp cells, the adjacency relationship between lamp cell nodes is identified. The difference of the illumination regulation parameters in the illumination regulation state set of lamp cells is calculated, and the regulation difference between adjacent lamp cell nodes is recorded to generate a lamp cell regulation difference set. The illumination regulation parameters of each lamp cell node are driven by the set of lamp cell regulation differences, and the driving values ​​of each lamp cell node are recorded to generate a set of lamp cell driving parameters. Write the set of lighting cell driving parameters into the light source driving interface corresponding to each lighting cell node, update the light source driving parameters of each lighting cell node, and generate the set of lighting cell light source driving parameters. The light source output of each lamp cell node is controlled according to the light source driving parameter set of the lamp cell, and the light source output state of each lamp cell node is recorded to generate a light source output state set of the lamp cell. The set of light source output states of the luminaire cells is summarized and organized according to the spatial position of the luminaire cell nodes to generate adaptive lighting control results for the lighting area.

[0013] According to an embodiment of the present invention, a smart lighting data acquisition and control system based on wireless networking includes: The data acquisition module is used to collect environmental perception data and human activity data of the target lighting area, and generate environmental time series datasets and behavioral time series datasets; The state sequence generation module is used to generate lamp cell state vectors and lamp cell state sequences based on environmental time-series datasets and behavioral time-series datasets. A self-organizing communication building module is used to generate a self-organizing communication topology for lamp cells through wireless networking. The cell community generation module is used to generate a set of local cell community states based on the self-organized communication topology of the lamp cells. The illumination gradient calculation module is used to calculate the illumination gradient field of the illuminated area based on the local cell community state set, and generate the illumination adjustment parameters of the lamp cells. The energy optimization module is used to construct the lamp cell energy function based on the lamp cell illumination regulation parameters and update the lamp cell illumination regulation parameters. The lighting control execution module is used to update the light source driving parameters of the luminaire cells according to the updated luminaire cell illumination adjustment parameters, and generate adaptive lighting control results.

[0014] The beneficial effects of this invention are: This invention proposes an intelligent lighting data acquisition and control method based on wireless networking. By deploying wirelessly-enabled lamp cells and environmental sensing nodes within the lighting area, it constructs environmental time-series datasets and behavioral time-series datasets, and generates lamp cell state sequences based on these datasets. This enables the lighting control process to be uniformly modeled and dynamically analyzed based on multi-source environmental information and human activity information. Compared with the traditional method of lighting adjustment relying solely on single-point sensors or preset rules, this invention comprehensively extracts and fuses characteristics of light change, environmental change, and behavioral activity, allowing lamp nodes to reflect real environmental changes and spatial activity states. This provides a more accurate data foundation for subsequent lighting control, improving the environmental adaptability and data acquisition integrity of the lighting adjustment process.

[0015] This invention constructs a self-organizing communication topology for lighting fixtures through wireless networking, and forms a local cell community state set based on this structure. This enables stable adjacency communication relationships between lighting fixture nodes within the lighting area, achieving information collaboration and state sharing among the lighting fixtures. By analyzing the local cell community state set and calculating the illumination gradient field of the lighting area, the differences in illumination distribution between different spatial locations can be effectively identified, thereby generating illumination adjustment parameters for each lighting fixture node. This method breaks through the traditional mode of independent adjustment of a single lighting fixture in intelligent lighting, transforming lighting control from single-point control to a spatially distributed adjustment mode based on community collaboration, thereby improving the uniformity of illumination distribution in the lighting area as a whole.

[0016] This invention constructs a lamp cell energy function and updates the lamp cell illumination adjustment parameters by minimizing the energy function. This allows the lamp node adjustment process to achieve overall energy optimization while meeting illumination requirements. During this process, the illumination output of each lamp node can be dynamically adjusted according to the illumination status of neighboring lamps, thereby avoiding the problem of local areas being too bright or too dark. By generating corresponding light source driving parameters from the updated illumination adjustment parameters and executing control, this invention can achieve adaptive lighting control of the lighting area. This enables the lighting system to maintain a stable, balanced, and energy-saving lighting effect under different environmental conditions and personnel activity states, improving the overall operating efficiency and intelligence level of the lighting system. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of an intelligent lighting data acquisition and control system and method based on wireless networking proposed in this invention; Figure 2This is a schematic diagram of the local cell community structure formation process of an intelligent lighting data acquisition and control system and method based on wireless networking proposed in this invention. Figure 3 This is a schematic diagram illustrating the generation of an illumination gradient field in an intelligent lighting data acquisition and control system and method based on wireless networking proposed in this invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0019] refer to Figures 1-3 A method for intelligent lighting data acquisition and control based on wireless networking includes the following steps: Multiple luminaire cells with wireless communication modules and environmental sensing nodes are deployed within the target lighting area, and environmental time-series datasets and behavioral time-series datasets are generated through preprocessing. The environmental time series dataset and the behavioral time series dataset are serialized according to a preset time window. Lighting change features, environmental change features and behavioral activity features are extracted, mapped into lamp cell state vectors, and lamp cell state sequences are generated. Wireless networking is used to establish adjacency communication relationships between lamp cells. The neighborhood radius of the lamp cell is determined based on the communication signal strength and spatial distance. Communication edges are established within the neighborhood radius to form a self-organized communication topology structure for lamp cells. In the self-organized communication topology of lamp cells, a local cell community is constructed with each lamp cell as the center. The state vector of the target lamp cell is combined with the state vector of the neighboring lamp cells to generate a set of local cell community states. The illumination gradient field of the illumination area is calculated using the local cell community state set, and the illumination adjustment parameters of the lamp cells are generated according to the illumination difference between adjacent lamp cells and the preset illumination diffusion coefficient. A lamp cell energy function is constructed based on the lamp cell illumination regulation parameters, and the lamp cell illumination regulation parameters are updated by minimizing the lamp cell energy function; The light source driving parameters of the luminaire cells are updated according to the updated luminaire cell illumination adjustment parameters to generate adaptive lighting control results for the lighting area.

[0020] In this embodiment, the environmental sensing node collects data on light intensity, temperature, humidity, airflow, and human activity. The preprocessing specifically includes time synchronization, outlier identification and removal, data missing completion, multi-source data alignment, data noise suppression, data normalization, and time-series resampling.

[0021] In this embodiment, the generation of the lamp cell state sequence specifically includes: The environmental time series dataset and the behavioral time series dataset are sorted according to a unified time index, and a sliding window is performed according to a preset time window length to generate time window data subsequences. Light intensity data is extracted from a time window data subsequence, and light change characteristics are generated by calculating the difference between light intensity at adjacent time points. Temperature data, humidity data, and airflow data are identified in the data subsequence of the time window. The temperature difference, humidity difference, and airflow change difference are calculated respectively. The temperature change features, humidity change features, and airflow change features are combined to generate a set of environmental change features. Statistical analysis is performed on the human activity data in the subsequence of the time window data, and behavioral activity characteristics are generated by statistically analyzing the frequency and duration of human activity signals within the time window; The characteristics of light change, environmental change, and behavioral activity are fused to generate a comprehensive feature set, which is then converted into a lamp cell state vector through feature mapping. The lamp cell state vectors generated in each time window are arranged in chronological order to generate a lamp cell state sequence.

[0022] In this embodiment, the generation of the lamp cell self-organized communication topology specifically includes: Node identification and spatial location recording are performed on multiple lamp cells deployed within the target lighting area. A unique node identifier is assigned to each lamp cell, and a set of spatial locations of lamp cells is generated based on the lamp installation location. The spatial adjacency relationship between each lamp cell is calculated based on the spatial location set of lamp cells. By comparing the spatial distance between lamp cells with the preset neighborhood radius, a set of lamp cell neighborhood relationships is generated. In the set of neighborhood relationships of lamp cells, identify pairs of lamp cell nodes with neighborhood relationships, and establish wireless communication connections between the corresponding nodes to form a set of lamp cell communication connections; A lamp cell adjacency matrix is ​​constructed based on the lamp cell communication connection set. The communication connection status between lamp cells is recorded through the adjacency matrix to generate the lamp cell communication adjacency structure. The generation of the lamp cell communication adjacency structure specifically includes: Read the lamp cell node identifiers from the lamp cell communication connection set, and establish a lamp cell node index sequence according to the node identifier order. Based on the lamp cell node index sequence, construct the matrix row index and matrix column index correspondence to generate a lamp cell node index matrix structure. Traverse the lamp cell node pairs in the lamp cell communication connection set, mapping each pair with a communication connection relationship to the corresponding row and column positions in the lamp cell node index matrix structure, and write the communication connection identifier value to the corresponding matrix element position to generate a lamp cell communication connection matrix. Organize the matrix elements in the lamp cell communication connection matrix and read... The matrix elements that indicate communication connections are identified, and the corresponding lamp cell node index pairs are recorded to generate a lamp cell node connection record set. Based on this set, node association mapping is performed on the communication connections between lamp cell nodes, associating each lamp cell node with its corresponding communication connection node to generate a lamp cell node communication association set. The communication connections between each lamp cell node are then structurally aggregated according to this set, and the communication adjacency node sets corresponding to each node are arranged in the order of their indexes to generate the lamp cell communication adjacency structure. The topology of the lamp cell nodes is processed by using the lamp cell communication adjacency structure, and the lamp cell nodes are combined with their corresponding communication connections to form a lamp cell communication network structure. The generation of the lamp cell communication network structure specifically includes: The process involves: obtaining the set of adjacent nodes corresponding to each lamp cell node in the lamp cell communication adjacency structure; organizing the adjacency relationships of each node according to the lamp cell node index order to generate a lamp cell node adjacency relationship set; identifying the corresponding communication connection relationships between each lamp cell node based on the lamp cell node adjacency relationship set; combining lamp cell nodes and their corresponding adjacent nodes according to node association relationships to generate a lamp cell node connection relationship set; structurally organizing each lamp cell node and its communication connection relationship in the lamp cell node connection relationship set, treating lamp cell nodes as network nodes and communication connections between lamp cell nodes as network edges to generate a lamp cell node communication edge set; integrating the network structure of communication connections between lamp cell nodes based on the lamp cell node communication edge set, associating and combining lamp cell nodes with their corresponding communication edges to generate a lamp cell communication network node set; and topologically organizing the communication connection relationships between lamp cell nodes based on the lamp cell communication network node set, recording the communication connection relationships corresponding to each lamp cell node according to the lamp cell node index order to generate a lamp cell communication network structure. Based on the lamp cell communication network structure, the communication connections between each lamp cell node are reorganized to generate a lamp cell self-organized communication topology. The generation of the self-organized communication topology of lamp cells specifically includes: This process involves extracting the set of lighting cell nodes and the communication connections between them from the lighting cell communication network structure. Node association identification is performed on the communication connections between lighting cell nodes to generate a lighting cell node communication association set. Based on this set, the neighboring nodes of each lighting cell node are aggregated, and the neighboring node set corresponding to each node is recorded according to the lighting cell node index order, generating a lighting cell neighborhood node set. The communication propagation paths between lighting cell nodes are identified based on the neighborhood node set, and lighting cell nodes are associated and combined with their corresponding neighboring nodes according to the communication connection relationships, generating a lighting cell node propagation connection set. The node connection relationships in the lighting cell node propagation connection set are structurally organized, and the communication connections between lighting cell nodes are topologically organized according to the node neighborhood relationships, generating a lighting cell node topology association set. Based on this topology association set, the communication connection relationships between lighting cell nodes are structurally aggregated, and the topological adjacency relationships corresponding to each node are recorded according to the lighting cell node index order, generating a lighting cell self-organized communication topology structure.

[0023] In this embodiment, the generation of the local cell community state set specifically includes: Read the connection relationships of lamp cell nodes in the self-organized communication topology of lamp cells, identify lamp cell nodes that have communication connections with the target lamp cell node, and generate a set of lamp cell neighborhood nodes; Locate the lamp cell state vector corresponding to the target lamp cell node in the lamp cell state sequence, and extract the lamp cell state vector corresponding to each lamp cell node in the set of neighboring lamp cell nodes to generate a set of neighboring lamp cell state vectors. The target luminaire cell state vector and the set of neighboring luminaire cell state vectors are combined according to the node association relationship to generate a local cell community node set; Based on the local cell community node set, establish the community connection relationship between the target lamp cell node and the neighboring lamp cell nodes to generate the local cell community structure; The formation of local cell community structure specifically includes: The process involves identifying target and neighboring luminaire cell nodes within a local cellular community node set, arranging these nodes according to their node identifiers to generate a community node arrangement set. Based on this arrangement set, the communication connections between the luminaire cell nodes are identified, and the target luminaire cell node is connected to its corresponding neighboring luminaire cell node according to node association relationships, generating a community node connection relationship set. The connection relationships within this set are then structurally organized, with the communication connections between luminaire cell nodes arranged according to their neighboring relationships, generating a community node connection structure set. This set is then used to structurally integrate the connections between luminaire cell nodes within the community, combining the connections between the target and neighboring luminaire cell nodes to generate a local cellular community connection structure. Finally, the connection relationships between luminaire cell nodes within the local cellular community connection structure are recorded according to the community node arrangement set, and the connection relationships within the community are structurally organized to generate the local cellular community structure. Based on the local cell community structure, the lamp cell state vectors corresponding to each lamp cell node in the community are collected, and the lamp cell state vectors in the community are integrated to generate a local cell community state set. The generation of local cell community state sets specifically includes: The process involves identifying the set of lamp cell nodes in a local cell community structure, arranging these nodes according to their node identifiers to generate a community node sequence set. Then, it involves locating the lamp cell state vectors corresponding to each lamp cell node in the community node sequence set within the lamp cell state sequence, extracting these vectors according to their node identifiers to generate a community state vector set. Next, it involves performing node association mapping on the lamp cell state vectors in the community state vector set based on the community node sequence set, recording each lamp cell node and its corresponding lamp cell state vector to generate a community node state mapping set. Finally, it involves structurally integrating the lamp cell state vectors in the community node state mapping set, combining the lamp cell state vectors corresponding to each lamp cell node according to their node association relationships to generate a community state combination set. Finally, it involves structurally aggregating the lamp cell state vectors corresponding to each lamp cell node in the community based on the community state combination set, recording the combination results according to their node identifiers to generate a local cell community state set.

[0024] In this embodiment, the generation of the lamp cell illumination adjustment parameters specifically includes: Read the lamp cell state vector corresponding to each lamp cell node in the local cell community state set, extract the light intensity value from the lamp cell state vector, and arrange them according to the spatial coordinates of the lamp cell node to generate a lamp cell light intensity distribution set. Calculate the light intensity difference between adjacent lamp cell nodes based on the set of lamp cell light intensity distribution, and generate a set of lamp cell light intensity difference values. The rate of change of light intensity between lamp cell nodes is calculated based on the set of light difference values ​​of lamp cells and the set of spatial positions of lamp cells, and a set of light gradients of lamp cells is generated. The illumination adjustment parameters of each lamp cell node are calculated based on the lamp cell illumination gradient set and the preset illumination diffusion coefficient, and a lamp cell illumination adjustment parameter set is generated. The generation of the lamp cell illumination regulation parameter set specifically includes: The process involves identifying the illumination gradient values ​​corresponding to each lamp cell node in the lamp cell illumination gradient set, arranging the lamp cell nodes according to their identifier order, and generating a lamp cell illumination gradient sequence set. Then, combining the lamp cell spatial location set, node association matching is performed on the illumination gradient values ​​in the lamp cell illumination gradient sequence set, recording each lamp cell node and its corresponding illumination gradient value to generate a lamp cell gradient mapping set. Based on the lamp cell gradient mapping set, the spatial adjacency relationships between lamp cell nodes are identified, and diffusion mapping calculations are performed on the illumination gradient changes between lamp cell nodes using a preset illumination diffusion coefficient, generating a lamp cell illumination diffusion adjustment set. Finally, the diffusion adjustment values ​​of each lamp cell node in the lamp cell illumination diffusion adjustment set are structurally organized, and the diffusion adjustment results corresponding to each lamp cell node are recorded according to node order to generate a lamp cell illumination adjustment parameter set. The illumination regulation parameters corresponding to each lamp cell node are recorded based on the lamp cell illumination regulation parameter set and arranged in the order of the lamp cell nodes to generate the lamp cell illumination regulation parameter sequence.

[0025] In this embodiment, updating the illumination adjustment parameters specifically includes: Based on the sequence of lighting regulation parameters of lamp cells and the set of spatial locations of lamp cells, the light intensity values ​​corresponding to each lamp cell node are extracted and arranged in the order of lamp cell nodes to generate a set of lighting states of lamp cells. By combining the spatial location set of lamp cells, the adjacency relationship between adjacent lamp cell nodes is identified. The difference between the light intensity values ​​in the light state set of lamp cells is calculated, the light intensity difference between adjacent lamp cell nodes is recorded, and a set of light difference between lamp cells is generated. A lamp cell energy function is constructed using the set of lamp cell illumination differences. The illumination differences corresponding to each lamp cell node are mapped to energy values. The energy values ​​of each lamp cell node are then summarized to generate a set of lamp cell node energy values. The generation of the energy set of the lamp cell node specifically includes: The process involves extracting the illumination difference values ​​corresponding to each lamp cell node from the lamp cell illumination difference set, arranging the lamp cell nodes according to their identifiers, and generating a lamp cell illumination difference sequence set. Then, combining the lamp cell spatial location set, a node association mapping is performed on the illumination difference values ​​in the lamp cell illumination difference sequence set, recording each lamp cell node and its corresponding illumination difference value, generating a lamp cell difference mapping set. Based on the lamp cell difference mapping set, a lamp cell energy function is constructed, mapping the illumination difference values ​​corresponding to each lamp cell node to their corresponding node energy values, generating a lamp cell node energy value set. The energy values ​​of each lamp cell node are then structurally organized according to the lamp cell node energy value set, and the node energy values ​​are recorded according to their identifiers, generating a lamp cell node energy sequence set. Finally, the energy values ​​corresponding to each lamp cell node are aggregated based on the lamp cell node energy sequence set, generating a lamp cell node energy set. The energy set of lamp cell nodes is updated by combining the lamp cell illumination regulation parameter sequence. By updating the energy value corresponding to each lamp cell node, an updated energy set of lamp cell nodes is generated. The generation of the updated lamp cell node energy set specifically includes: Extract the node energy values ​​corresponding to each lamp cell node from the lamp cell node energy set, and arrange the lamp cell nodes according to their identifier order to generate a lamp cell node energy sequence set; locate the illumination adjustment parameters corresponding to each lamp cell node in the lamp cell illumination adjustment parameter sequence, establish node association relationships between each lamp cell node and its corresponding illumination adjustment parameters, and generate a lamp cell parameter mapping set; perform parameter update calculations on the node energy values ​​in the lamp cell node energy sequence set based on the lamp cell parameter mapping set, introduce the lamp cell illumination adjustment parameters into the node energy update process, and generate a lamp cell node updated energy value set; organize the structure of each lamp cell node energy value in the lamp cell node updated energy value set, and record the updated energy values ​​of each node according to their identifier order to generate a lamp cell node updated energy sequence set; collect the updated energy values ​​corresponding to each lamp cell node based on the lamp cell node updated energy sequence set to generate an updated lamp cell node energy set; The updated luminaire cell node energy set is used to recalculate the luminaire cell illumination regulation parameters, and the updated illumination regulation parameters are recorded to generate the updated luminaire cell illumination regulation parameter sequence. The generation of the updated lamp cell illumination regulation parameter sequence specifically includes: The process involves: acquiring the node energy values ​​of each lamp cell node in the updated lamp cell node energy set; arranging the lamp cell nodes according to their identifiers to generate a lamp cell node energy sequence set; performing node association mapping on the node energy values ​​in the lamp cell node energy sequence set based on the lamp cell spatial location set, recording each lamp cell node and its corresponding node energy value to generate a lamp cell energy mapping set; calculating illumination adjustment parameters for each lamp cell node based on the lamp cell energy mapping set, mapping the node energy values ​​to the corresponding illumination adjustment parameter values ​​to generate a lamp cell illumination adjustment parameter set; structuring the illumination adjustment parameters of each lamp cell node in the lamp cell illumination adjustment parameter set, recording the illumination adjustment parameter values ​​of each node according to their identifiers to generate a lamp cell illumination adjustment parameter sequence set; and finally, aggregating the illumination adjustment parameters corresponding to each lamp cell node based on the lamp cell illumination adjustment parameter sequence set to generate an updated lamp cell illumination adjustment parameter sequence.

[0026] In this embodiment, the generation of adaptive lighting control results specifically includes: Obtain the updated sequence of lighting regulation parameters for lamp cells and the set of spatial locations of lamp cells, extract the lighting regulation parameter values ​​corresponding to each lamp cell node, and organize them according to the order of lamp cell nodes to generate a set of lighting regulation states for lamp cells. By combining the spatial location set of lamp cells, the adjacency relationship between lamp cell nodes is identified. The difference of the illumination regulation parameters in the illumination regulation state set of lamp cells is calculated, and the regulation difference between adjacent lamp cell nodes is recorded to generate a lamp cell regulation difference set. The illumination regulation parameters of each lamp cell node are driven by the set of lamp cell regulation differences, and the driving values ​​of each lamp cell node are recorded to generate a set of lamp cell driving parameters. Write the set of lighting cell driving parameters into the light source driving interface corresponding to each lighting cell node, update the light source driving parameters of each lighting cell node, and generate the set of lighting cell light source driving parameters. The light source output of each lamp cell node is controlled according to the light source driving parameter set of the lamp cell, and the light source output state of each lamp cell node is recorded to generate a light source output state set of the lamp cell. The set of light source output states of the luminaire cells is summarized and organized according to the spatial position of the luminaire cell nodes to generate adaptive lighting control results for the lighting area.

[0027] A smart lighting data acquisition and control system based on wireless networking includes: The data acquisition module is used to collect environmental perception data and human activity data of the target lighting area, and generate environmental time series datasets and behavioral time series datasets; The state sequence generation module is used to generate lamp cell state vectors and lamp cell state sequences based on environmental time-series datasets and behavioral time-series datasets. A self-organizing communication building module is used to generate a self-organizing communication topology for lamp cells through wireless networking. The cell community generation module is used to generate a set of local cell community states based on the self-organized communication topology of the lamp cells. The illumination gradient calculation module is used to calculate the illumination gradient field of the illuminated area based on the local cell community state set, and generate the illumination adjustment parameters of the lamp cells. The energy optimization module is used to construct the lamp cell energy function based on the lamp cell illumination regulation parameters and update the lamp cell illumination regulation parameters. The lighting control execution module is used to update the light source driving parameters of the luminaire cells according to the updated luminaire cell illumination adjustment parameters, and generate adaptive lighting control results.

[0028] Example 1: To verify the feasibility of this invention in practice, it was applied to the public office lighting area of ​​a large office building located in an urban business district. The building features open-plan office spaces with numerous workstations, meeting areas, and public passageways. The lighting equipment is densely distributed and used for extended periods. In traditional lighting methods, lamps are typically centrally controlled according to fixed zones or uniformly adjusted according to preset time strategies. When the number of office workers changes or natural light conditions change, the lighting brightness often fails to match the environmental requirements in a timely manner. This can lead to insufficient light intensity in some areas or excessive light in others. Furthermore, continuous lighting persists in areas with few or no personnel, resulting in increased overall energy consumption and affecting the visual comfort of office workers. To address these issues, lamp cells with wireless communication modules and environmental sensing nodes were deployed within the lighting area of ​​the office building. Communication between the lamps was established through wireless networking, enabling the lighting equipment to form a self-organizing lighting network with collaborative capabilities. This facilitates lighting data acquisition and adaptive control.

[0029] In practical applications, multiple lamp cells are first evenly installed on the ceiling of the office area. Each lamp cell integrates a wireless communication module and records the installation position of the corresponding lamp in the space. At the same time, environmental sensing nodes are deployed in the office area to continuously collect information on light intensity, temperature, humidity, airflow, and personnel activity. During operation, the environmental sensing nodes transmit the collected data to the lamp cell nodes via wireless communication. The lamp cells perform unified time synchronization processing on the received environmental data and personnel activity data. In the data processing stage, outlier identification, data completion, noise suppression, and multi-source data alignment are completed to form a stable environmental time-series dataset and behavioral time-series dataset. Subsequently, the lamp cells serialize the above data according to a preset time window. By analyzing changes in light intensity, environmental changes, and personnel activity, a lamp cell state vector that reflects the working status of the lamps and changes in the surrounding environment is generated, and a lamp cell state sequence is formed according to the time sequence. In this way, the lighting system can continuously monitor changes in the internal environment and personnel activity in the lighting area, providing a real-time data foundation for subsequent lighting adjustments.

[0030] After data processing, each lamp cell establishes communication relationships via wireless networking. The lamp cells automatically identify neighboring nodes based on communication signal strength and spatial distance, establishing communication connections within their neighborhood. This forms a self-organizing communication topology among the lamp cells. In this structure, each lamp cell can acquire not only its own state information but also the state information of surrounding lamp nodes. Through this adjacency communication method, the lighting system forms a distributed information network based on lamp nodes. Subsequently, within this communication topology, a local cell community structure is established centered on each lamp cell. The state vector of the target lamp cell is combined with the state vectors of neighboring lamp cells to form a local cell community state set. In this way, the lighting system can comprehensively analyze the state relationships between multiple lamp nodes within a local area, thereby obtaining more comprehensive spatial lighting state information.

[0031] After obtaining the local cell community state set, the system organizes the light intensity information of each lamp node within the community and calculates the light gradient field within the lighting area based on the installation position of the lamps in space. By analyzing the light differences between different lamp nodes, the system can identify the trend of light distribution changes within the lighting area, thereby determining which areas have high light intensity and which areas have low light intensity. Based on this, the system generates lamp cell light adjustment parameters according to the light differences between adjacent lamp nodes and the preset light diffusion coefficient, enabling the lamps to automatically adjust according to the lighting status of surrounding lamps. Compared with the traditional independent lamp adjustment method, this method makes the lighting adjustment process more in line with the spatial light distribution law, thereby improving the overall uniformity of regional lighting.

[0032] To further improve the operating efficiency of the lighting system, this invention constructs a lamp cell energy function after generating the lamp cell illumination adjustment parameters, and updates the illumination adjustment parameters by minimizing the energy function. During the energy function calculation process, the illumination difference between lamp nodes is mapped to an energy change relationship. By continuously updating the energy values ​​corresponding to the lamp nodes, the lighting system can gradually tend towards a stable state. When the energy function gradually decreases, it indicates that the illumination difference between lamps within the lighting area is decreasing, and the lighting distribution is gradually becoming more balanced. Subsequently, the corresponding light source driving parameters are generated based on the updated lamp cell illumination adjustment parameters, and the driving parameters are written into the light source driving interface of the lamp, thereby realizing the dynamic adjustment of the lamp brightness output.

[0033] During actual operation in the office area, when the number of people in some areas gradually increases, the environmental sensing nodes detect changes in the activity signals of the people. At the same time, the state vectors of the lamp cells in the corresponding areas change. The system quickly identifies the changes in the lighting demand of the area based on the lamp cell state sequence. Subsequently, the local cell community state set is recalculated, and the system identifies that the area needs to increase the light intensity through light gradient field analysis. Neighboring lamps gradually adjust their light output while maintaining overall lighting balance, so that the areas where people are active receive more sufficient lighting, while avoiding over-lighting in other areas. When the number of people in the office area decreases or natural light increases, the system again automatically reduces the light output of some lamps through the above process, thereby maintaining overall lighting comfort.

[0034] Long-term operation and observation have revealed that after adopting the method of this invention in the office building's lighting area, the lighting system can continuously collect environmental and behavioral data and dynamically adjust based on the collaborative relationship between the lamps. The system can maintain a stable lighting effect in different time periods, different office areas, and different personnel activities. Compared with traditional lighting methods, the application of this method in urban office building environments effectively improves the problem of uneven light distribution, enabling the lamps to form a collaborative adjustment relationship. At the same time, the optimization mechanism based on energy functions makes the lighting adjustment process smoother, reducing visual discomfort caused by frequent changes. This invention can operate stably in office lighting scenarios and improve lighting adjustment efficiency while maintaining lighting comfort, thereby realizing intelligent lighting data acquisition and adaptive control based on wireless networking.

[0035] Table 1. Comparison of the overall performance of the present invention and traditional lighting control methods.

[0036] As shown in Table 1, after adopting the wireless networking self-organizing lighting control method proposed in this invention in the office lighting area, the lighting system showed significant improvement in several key indicators. Firstly, regarding the average illuminance stability deviation, the traditional timed control method, which relies solely on preset time strategies for lamp control, struggles to adjust the light output in a timely manner when personnel activities or natural light conditions change, resulting in an illuminance deviation of 78 lx. The traditional single-point sensing dimming method adjusts the illuminance deviation to 52 lx through local sensor data, but due to the lack of a collaborative mechanism between lamps, the overall illumination of the area still fluctuates. This invention, by constructing a self-organizing communication topology for lamp cells, enables adjacent lamps to share status information and perform collaborative adjustments, thus further reducing the average illuminance stability deviation to 29 lx, indicating a significant improvement in the lighting stability of the lighting area.

[0037] Regarding the uniformity index of illumination space, the uniformity coefficient of the traditional timing control method is 0.63, mainly because this method is difficult to dynamically adjust according to the lighting needs of different spatial areas. The single-point sensing dimming method adjusts local areas through sensor feedback, improving the uniformity coefficient to 0.70. This invention calculates the illumination gradient field within local cell communities and generates illumination adjustment parameters based on the illumination differences between lamps, enabling the lamps to coordinately adjust according to their spatial distribution. Therefore, the uniformity coefficient is improved to 0.79, indicating a more balanced illumination distribution in the lighting area. This improvement mainly comes from the illumination gradient field calculation mechanism, which enables the system to identify the illumination change trends between different lamp nodes, thereby achieving more reasonable illumination adjustment.

[0038] In terms of energy consumption, this invention also has significant advantages. The regional energy consumption intensity of the traditional timed control method is 126.8 kWh / day, while the single-point sensing dimming method reduces energy consumption by adjusting local sensing, with an energy consumption intensity of 111.7 kWh / day. This invention performs collaborative analysis of lamp nodes through local cell community state sets and optimizes them in conjunction with the lamp cell energy function, enabling the lamp adjustment process to achieve energy balance while meeting lighting needs. As a result, the regional energy consumption intensity is reduced to 96.3 kWh / day. At the same time, the average power utilization rate of the lamps is reduced from 74.2% and 69.1% of the traditional method to 62.7%, indicating that the lamp output power is closer to the actual lighting needs and reduces the energy consumption caused by ineffective lighting.

[0039] In terms of system response capability, this invention also demonstrates significant advantages. The response delay for personnel activity in the traditional timed control method is 6.5 seconds, while the single-point sensing dimming method reduces the delay to 4.1 seconds through a sensor triggering mechanism. This invention establishes communication relationships between lamp cells through wireless networking, enabling lamps to quickly share environmental change information within a neighborhood. Therefore, the response delay for personnel activity is shortened to 2.4 seconds. At the same time, in terms of the light adjustment stabilization time index, this invention reduces the time from 11.2 seconds and 8.6 seconds in the traditional method to 4.9 seconds, indicating that the system can reach a stable lighting state more quickly.

[0040] Furthermore, the advantages of this invention are even more pronounced in terms of the proportion of coordinated adjustment among lamps. In traditional timed control methods, there is virtually no coordinated adjustment among lamps, with a proportion of 9.3%. In single-point sensing dimming methods, some lamps can make local adjustments based on sensor information, with a proportion of 22.4%. This invention, by constructing a self-organized communication topology for lamp cells and sharing lamp state information within local cell communities, enables a large number of lamps to participate in coordinated adjustment, thus increasing the coordinated adjustment proportion to 63.5%. Based on the above data, it can be seen that this invention, by introducing a self-organized communication mechanism for lamp cells, local cell community state modeling, and illumination gradient field analysis methods, transforms lighting control from single-point adjustment to community coordinated adjustment, thereby achieving significant improvements in lighting uniformity, system response speed, and energy consumption control.

[0041] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent lighting data acquisition and control based on wireless networking, characterized in that, Includes the following steps: Multiple luminaire cells with wireless communication modules and environmental sensing nodes are deployed within the target lighting area, and environmental time-series datasets and behavioral time-series datasets are generated through preprocessing. The environmental time series dataset and the behavioral time series dataset are serialized according to a preset time window. Lighting change features, environmental change features and behavioral activity features are extracted, mapped into lamp cell state vectors, and lamp cell state sequences are generated. Wireless networking is used to establish adjacency communication relationships between lamp cells. The neighborhood radius of the lamp cell is determined based on the communication signal strength and spatial distance. Communication edges are established within the neighborhood radius to form a self-organized communication topology structure for lamp cells. In the self-organized communication topology of lamp cells, a local cell community is constructed with each lamp cell as the center. The state vector of the target lamp cell is combined with the state vector of the neighboring lamp cells to generate a set of local cell community states. The illumination gradient field of the illumination area is calculated using the local cell community state set, and the illumination adjustment parameters of the lamp cells are generated according to the illumination difference between adjacent lamp cells and the preset illumination diffusion coefficient. A lamp cell energy function is constructed based on the lamp cell illumination regulation parameters, and the lamp cell illumination regulation parameters are updated by minimizing the lamp cell energy function; The light source driving parameters of the luminaire cells are updated according to the updated luminaire cell illumination adjustment parameters to generate adaptive lighting control results for the lighting area.

2. The intelligent lighting data acquisition and control method based on wireless networking according to claim 1, characterized in that, The environmental sensing node collects data on light intensity, temperature, humidity, airflow, and human activity. The preprocessing specifically includes time synchronization, outlier identification and removal, data missing completion, multi-source data alignment, data noise suppression, data normalization, and time-series resampling.

3. The intelligent lighting data acquisition and control method based on wireless networking according to claim 1, characterized in that, The generation of the lamp cell state sequence specifically includes: The environmental time series dataset and the behavioral time series dataset are sorted according to a unified time index, and a sliding window is performed according to a preset time window length to generate time window data subsequences. Light intensity data is extracted from a time window data subsequence, and light change characteristics are generated by calculating the difference between light intensity at adjacent time points. Temperature data, humidity data, and airflow data are identified in the data subsequence of the time window. The temperature difference, humidity difference, and airflow change difference are calculated respectively. The temperature change features, humidity change features, and airflow change features are combined to generate a set of environmental change features. Statistical analysis is performed on the human activity data in the subsequence of the time window data, and behavioral activity characteristics are generated by statistically analyzing the frequency and duration of human activity signals within the time window; The characteristics of light change, environmental change, and behavioral activity are fused to generate a comprehensive feature set, which is then converted into a lamp cell state vector through feature mapping. The lamp cell state vectors generated in each time window are arranged in chronological order to generate a lamp cell state sequence.

4. The intelligent lighting data acquisition and control method based on wireless networking according to claim 1, characterized in that, The generation of the lamp cell self-organized communication topology specifically includes: Node identification and spatial location recording are performed on multiple lamp cells deployed within the target lighting area. A unique node identifier is assigned to each lamp cell, and a set of spatial locations of lamp cells is generated based on the lamp installation location. The spatial adjacency relationship between each lamp cell is calculated based on the spatial location set of lamp cells. By comparing the spatial distance between lamp cells with the preset neighborhood radius, a set of lamp cell neighborhood relationships is generated. In the set of neighborhood relationships of lamp cells, identify pairs of lamp cell nodes with neighborhood relationships, and establish wireless communication connections between the corresponding nodes to form a set of lamp cell communication connections; A lamp cell adjacency matrix is ​​constructed based on the lamp cell communication connection set. The communication connection status between lamp cells is recorded through the adjacency matrix to generate the lamp cell communication adjacency structure. The topology of the lamp cell nodes is processed by using the lamp cell communication adjacency structure, and the lamp cell nodes are combined with their corresponding communication connections to form a lamp cell communication network structure. Based on the lamp cell communication network structure, the communication connections between each lamp cell node are reorganized to generate a lamp cell self-organized communication topology.

5. The intelligent lighting data acquisition and control method based on wireless networking according to claim 1, characterized in that, The generation of the local cell community state set specifically includes: Read the connection relationships of lamp cell nodes in the self-organized communication topology of lamp cells, identify lamp cell nodes that have communication connections with the target lamp cell node, and generate a set of lamp cell neighborhood nodes; Locate the lamp cell state vector corresponding to the target lamp cell node in the lamp cell state sequence, and extract the lamp cell state vector corresponding to each lamp cell node in the set of neighboring lamp cell nodes to generate a set of neighboring lamp cell state vectors. The target luminaire cell state vector and the set of neighboring luminaire cell state vectors are combined according to the node association relationship to generate a local cell community node set; Based on the local cell community node set, establish the community connection relationship between the target lamp cell node and the neighboring lamp cell nodes to generate the local cell community structure; Based on the local cell community structure, the lamp cell state vectors corresponding to each lamp cell node in the community are collected, and the lamp cell state vectors in the community are integrated to generate a local cell community state set.

6. The intelligent lighting data acquisition and control method based on wireless networking according to claim 1, characterized in that, The generation of the lamp cell illumination adjustment parameters specifically includes: Read the lamp cell state vector corresponding to each lamp cell node in the local cell community state set, extract the light intensity value from the lamp cell state vector, and arrange them according to the spatial coordinates of the lamp cell node to generate a lamp cell light intensity distribution set. Calculate the light intensity difference between adjacent lamp cell nodes based on the set of lamp cell light intensity distribution, and generate a set of lamp cell light intensity difference values. The rate of change of light intensity between lamp cell nodes is calculated based on the set of light difference values ​​of lamp cells and the set of spatial positions of lamp cells, and a set of light gradients of lamp cells is generated. The illumination adjustment parameters of each lamp cell node are calculated based on the lamp cell illumination gradient set and the preset illumination diffusion coefficient, and a lamp cell illumination adjustment parameter set is generated. The illumination regulation parameters corresponding to each lamp cell node are recorded based on the lamp cell illumination regulation parameter set and arranged in the order of the lamp cell nodes to generate the lamp cell illumination regulation parameter sequence.

7. The intelligent lighting data acquisition and control method based on wireless networking according to claim 1, characterized in that, The updating of the illumination adjustment parameters specifically includes: Based on the sequence of lighting regulation parameters of lamp cells and the set of spatial locations of lamp cells, the light intensity values ​​corresponding to each lamp cell node are extracted and arranged in the order of lamp cell nodes to generate a set of lighting states of lamp cells. By combining the spatial location set of lamp cells, the adjacency relationship between adjacent lamp cell nodes is identified. The difference between the light intensity values ​​in the light state set of lamp cells is calculated, the light intensity difference between adjacent lamp cell nodes is recorded, and a set of light difference between lamp cells is generated. A lamp cell energy function is constructed using the set of lamp cell illumination differences. The illumination differences corresponding to each lamp cell node are mapped to energy values. The energy values ​​of each lamp cell node are then summarized to generate a set of lamp cell node energy values. The energy set of lamp cell nodes is updated by combining the lamp cell illumination regulation parameter sequence. By updating the energy value corresponding to each lamp cell node, an updated energy set of lamp cell nodes is generated. The updated luminaire cell node energy set is used to recalculate the luminaire cell illumination regulation parameters, and the updated illumination regulation parameters are recorded to generate an updated luminaire cell illumination regulation parameter sequence.

8. The intelligent lighting data acquisition and control method based on wireless networking according to claim 1, characterized in that, The generation of the adaptive lighting control results specifically includes: Obtain the updated sequence of lighting regulation parameters for lamp cells and the set of spatial locations of lamp cells, extract the lighting regulation parameter values ​​corresponding to each lamp cell node, and organize them according to the order of lamp cell nodes to generate a set of lighting regulation states for lamp cells. By combining the spatial location set of lamp cells, the adjacency relationship between lamp cell nodes is identified. The difference of the illumination regulation parameters in the illumination regulation state set of lamp cells is calculated, and the regulation difference between adjacent lamp cell nodes is recorded to generate a lamp cell regulation difference set. The illumination regulation parameters of each lamp cell node are driven by the set of lamp cell regulation differences, and the driving values ​​of each lamp cell node are recorded to generate a set of lamp cell driving parameters. Write the set of lighting cell driving parameters into the light source driving interface corresponding to each lighting cell node, update the light source driving parameters of each lighting cell node, and generate the set of lighting cell light source driving parameters. The light source output of each lamp cell node is controlled according to the light source driving parameter set of the lamp cell, and the light source output state of each lamp cell node is recorded to generate a light source output state set of the lamp cell. The set of light source output states of the luminaire cells is summarized and organized according to the spatial position of the luminaire cell nodes to generate adaptive lighting control results for the lighting area.

9. A wireless networking-based intelligent lighting data acquisition and control system, comprising the wireless networking-based intelligent lighting data acquisition and control method according to any one of claims 1 to 8, characterized in that, include: The data acquisition module is used to collect environmental perception data and human activity data of the target lighting area, and generate environmental time series datasets and behavioral time series datasets; The state sequence generation module is used to generate lamp cell state vectors and lamp cell state sequences based on environmental time-series datasets and behavioral time-series datasets. A self-organizing communication building module is used to generate a self-organizing communication topology for lighting cells via wireless networking. The cell community generation module is used to generate a set of local cell community states based on the self-organized communication topology of the lamp cells. The illumination gradient calculation module is used to calculate the illumination gradient field of the illumination area based on the local cell community state set, and generate the illumination adjustment parameters of the lamp cells. The energy optimization module is used to construct the lamp cell energy function based on the lamp cell illumination regulation parameters and update the lamp cell illumination regulation parameters. The lighting control execution module is used to update the light source driving parameters of the luminaire cells according to the updated luminaire cell illumination adjustment parameters, and generate adaptive lighting control results.

Images