A lighting group communication cooperative control method and system based on a swarm intelligence algorithm

Abstract

This invention relates to the field of group control technology, specifically to a method and system for collaborative control of lighting groups based on swarm intelligence algorithms. The method includes the following steps: extracting node attributes to determine expected power and time; comparing the superimposed total power with a threshold and priority delay to obtain the actual execution time; identifying boundary luminance deviations and spacing to determine smooth transition luminous intensity; and performing pulse width conversion mapping upon reaching the execution time. In this invention, by obtaining the expected power changes and initial planned trigger times of lighting nodes, delaying node scheduling based on a priority yielding mechanism, identifying logical control partition boundaries, quantifying the absolute value of spatial luminance deviations between adjacent areas, and combining the three-dimensional linear physical spacing between nodes to dynamically calculate dimming compensation reduction and smooth transition target luminous intensity, the invention achieves gradual brightness changes and smooth transitions across lighting area boundaries, improving the overall spatial light environment comfort and the precision of collaborative control of lighting groups.

Description

Technical Field

[0001] This invention relates to the field of group control technology, and in particular to a lighting group communication and collaborative control method and system based on swarm intelligence algorithms. Background Technology

[0002] Group control technology primarily involves control mechanisms for the centralized and collaborative management of multiple independent devices or individuals. Its main purpose is to facilitate collaboration among multiple nodes through unified command issuance and status monitoring, and it is widely used in industries such as smart homes, industrial automation, and the Internet of Things (IoT). Specifically, lighting group communication and collaborative control refers to the operational process of information interaction and synchronous regulation for a network of multiple lighting devices. It is mainly used to uniformly configure and link the status of multiple lighting terminals within a region. It typically utilizes existing communication technologies such as wireless sensor networks, radio frequency communication modules, or wired control buses, combined with microprocessors to issue group control commands, thereby achieving centralized scheduling and unified control of various lighting node groups.

[0003] In the traditional process of lighting group communication and collaborative control, although unified commands can be issued through existing communication methods such as wireless sensor networks or wired buses to achieve centralized scheduling and status linkage of multiple lighting terminals in the area, there are problems such as the superimposed power exceeding the carrying capacity limit of the power supply branch caused by the instantaneous large-scale action when the group nodes respond to the control commands on a large scale or the brightness requirements of adjacent control zones are large, which can lead to grid impact. At the same time, there are problems such as the light contrast at the boundaries of different logical zones being too strong, resulting in visual discontinuity and abrupt transition. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a lighting group communication cooperative control method based on swarm intelligence algorithms.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a lighting group communication cooperative control method based on swarm intelligence algorithm, comprising the following steps:

[0006] S1: Collect the physical attribute data set of the lighting node, extract the three-dimensional coordinates of the lighting node, the power supply branch number of the lighting node transformer, the logical control partition identifier of the lighting node, and the preset backoff priority sequence number of the lighting node, receive the group status change signaling to obtain the target luminous intensity and timestamp value, and determine the expected power change value and the initial planned trigger time point value of the lighting node.

[0007] S2: Based on the expected power change value and the initial planned trigger time point value, select target interaction data frames from the local communication network, and determine the sum of expected transient power at the target time according to the external expected power change value in the target interaction data frame and the expected power change value.

[0008] S3: Based on the sum of the expected transient power at the target time and the power supply carrying capacity increment threshold, construct the priority sorting sequence of external lighting nodes and determine the last node, delay the value of the initial planned trigger time point, and output the value of the actual action execution time point of the lighting node.

[0009] S4: Obtain the adjacent control zone identifier, compare it with the logical control zone identifier to identify the zone boundary, determine the absolute value of the spatial photometric deviation between the target luminous intensity and the adjacent luminous intensity, compare the absolute value of the spatial photometric deviation with the illumination change judgment threshold, and determine the illumination boundary change judgment result.

[0010] S5: Based on the lighting boundary abrupt change determination result, determine the straight-line physical distance value according to the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates, determine the dimming compensation reduction value and the smooth transition target luminous intensity value with the absolute value of the spatial luminous deviation, determine the smooth transition target luminous intensity value, obtain the device running time, and when the device running time reaches the actual action execution time value of the lighting node, perform digital pulse width conversion mapping, based on the lighting group communication collaborative control parameters.

[0011] As a further aspect of the present invention, the power supply carrying capacity increment threshold is obtained from the power supply infrastructure power safety verification process by retrieving transformer rated capacity data and combining it with the safety margin ratio.

[0012] If the sum of the expected transient power at the target time is greater than the power supply carrying capacity increment threshold, then the external lighting node backoff priority sequence number is extracted from the target interactive data frame, and a backoff priority sorting sequence is constructed based on the external lighting node backoff priority sequence number and the lighting node preset backoff priority sequence number.

[0013] As a further aspect of the present invention, the threshold for determining sudden changes in illumination is obtained by querying a visual comfort standard database;

[0014] If the absolute value of the spatial photometric deviation is greater than the light change determination threshold, it is determined that there is a light breakup state; if the absolute value of the spatial photometric deviation is not greater than the light change determination threshold, it is determined that there is a smooth transition state.

[0015] As a further aspect of the present invention, step S1 specifically comprises:

[0016] S111: Collect the attribute data set of lighting nodes, extract the three-dimensional coordinate values ​​of lighting nodes, the transformer power supply branch number, the logical control partition identifier value, and the preset yield priority sequence number, receive group status change signaling and extract the target action status value, target luminous intensity value, and timestamp value of lighting nodes, and establish multi-dimensional configuration parameters of lighting nodes.

[0017] S112: Based on the multi-dimensional configuration parameters of the lighting node, obtain the current operating status of the lighting node, parse the hardware specification table to obtain the device power mapping coefficient, and calculate the expected power change value by combining the current operating status value with the target operating status value in the multi-dimensional configuration parameters of the lighting node and the device power mapping coefficient.

[0018] S113: Read the communication protocol configuration to obtain the basic delay duration value, perform time axis superposition calculation based on the timestamp value and the basic delay duration value in the multi-dimensional configuration parameters of the lighting node, and synchronize the expected power change value with the basic delay duration value to obtain the initial planned trigger time point value.

[0019] As a further aspect of the present invention, step S2 specifically comprises:

[0020] S211: Construct an initial interaction data frame based on the expected power change value and the initial planned trigger time point value, receive adjacent interaction data frames, extract the external transformer power supply branch number, the external initial planned trigger time point, the external expected power change value and the external yield priority sequence number, and obtain external interaction multidimensional parameters.

[0021] S212: Based on the external interaction multidimensional parameters, the external power supply branch number is matched and compared with the current node power supply branch number, and the external initial plan trigger time point value is matched and compared with the initial plan trigger time point value to filter and extract data nodes, and generate target interaction data frame.

[0022] S213: For the target interactive data frame, extract the external expected power change value, sum the external expected power change value and the expected power change value, calculate the total power synchronization demand of multiple lighting nodes within the target time and evaluate the load status, and obtain the expected transient power superposition sum value.

[0023] As a further aspect of the present invention, step S3 specifically comprises:

[0024] S311: Detect the rated capacity of the transformer and combine it with the safety margin ratio to obtain the power supply carrying capacity increment threshold. Compare the expected transient power sum value with the power supply carrying capacity increment threshold to determine the value. When the expected transient power sum value is greater than the power supply carrying capacity increment threshold, extract the external lighting node backoff priority sequence number from the target interactive data frame and establish a backoff priority determination sequence number.

[0025] S312: Construct a priority sorting sequence based on the priority determination sequence number and filter the last node, extract the initial planned trigger time point of the last node, read the clock beat configuration to obtain the time offset step, superimpose compensation on the value of the initial planned trigger time point and calculate the compensation delay, and generate the first corrected time point value.

[0026] S313: Based on the first corrected time point value, set the trigger reference time, and repeatedly compare the expected transient power sum value with the power supply carrying capacity increment threshold. When the expected transient power sum value is not greater than the power supply carrying capacity increment threshold, terminate the yield delay judgment and lock the current time beat to obtain the actual action execution time point value of the lighting node.

[0027] As a further aspect of the present invention, step S4 specifically comprises:

[0028] S411: Monitor adjacent operating status data frames and extract adjacent control partition identifiers, three-dimensional coordinates and current luminous intensity. Match and compare the values ​​of the logical control partition identifiers to determine the association status between the values ​​of adjacent control partition identifiers and logical control partition identifiers, and obtain partition boundary determination features.

[0029] S412: Based on the partition boundary determination features, extract the target luminous intensity value and the adjacent luminous intensity values, calculate the difference and take the absolute value, obtain the illuminance intensity difference between adjacent nodes in space, convert and map the difference to quantify the illuminance gradient change amplitude feature, and generate the absolute value of spatial luminance deviation.

[0030] S413: Based on the absolute value of the spatial photometric deviation, query the visual comfort standard database to obtain the illumination change judgment threshold. If the absolute value of the spatial photometric deviation is greater than the illumination change judgment threshold, the illumination is determined to be in a state of discontinuity. If it is not greater than the illumination change judgment threshold, the illumination is determined to be in a state of smooth transition. The node judgment comparison information is summarized and classified and labeled to obtain the illumination boundary change judgment result.

[0031] As a further aspect of the present invention, step S5 specifically comprises:

[0032] S511: Extract the lighting breakup features based on the lighting boundary abruptness determination result, load the building information model to obtain the environmental scale space, map the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates to the environmental scale space, perform coordinate difference calculation according to Euclidean distance metric to calculate the straight-line distance between the coordinates of the two nodes, and establish the spatial straight-line spacing value.

[0033] S512: Calculate the illuminance gradient coefficient by dividing the spatial straight-line spacing value by the absolute value of the spatial luminance deviation, extract the buffer weight parameter by matching the surface material reflectivity, multiply the gradient coefficient and the weight parameter to generate the dimming compensation reduction value, and adjust the node output by subtracting the dimming compensation reduction from the target luminous intensity value to obtain the smooth transition target luminous intensity value.

[0034] S513: Based on the light intensity value of the smooth transition target, obtain the execution timing monitoring of the lighting equipment running time. When the running time reaches the actual action execution time value of the lighting node, convert the light intensity value of the smooth transition target into a duty cycle control command and output the lighting group communication and coordination control parameters.

[0035] A lighting group communication and cooperative control system based on swarm intelligence algorithms includes:

[0036] The status change parsing module collects the physical attribute data set of the lighting nodes, extracts the three-dimensional coordinates of the lighting nodes, the power supply branch number of the lighting node transformer, the logical control partition identifier of the lighting node, and the preset backoff priority sequence number of the lighting node, receives group status change signaling to obtain the target luminous intensity and timestamp value, and determines the expected power change value and the initial planned trigger time point value of the lighting node.

[0037] The transient power determination module filters target interaction data frames from the local communication network based on the expected power change value and the initial planned trigger time point value, and determines the sum of expected transient power at the target time according to the external expected power change value in the target interaction data frame and the expected power change value.

[0038] The action time determination module compares the sum of the expected transient power at the target time with the power supply carrying capacity increment threshold, constructs an external lighting node backoff priority sorting sequence and determines the last node, performs a delay processing on the initial planned trigger time point value, and outputs the actual action execution time point value of the lighting node.

[0039] The boundary abrupt change determination module acquires the adjacent control zone identifier, compares it with the logical control zone identifier to identify the zone boundary, determines the absolute value of the spatial photometric deviation between the target luminous intensity and the adjacent luminous intensity, compares the absolute value of the spatial photometric deviation with the illumination abrupt change determination threshold, and determines the illumination boundary abrupt change determination result.

[0040] The control parameter generation module, based on the lighting boundary abrupt change judgment result, determines the straight-line physical distance value according to the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates, determines the dimming compensation reduction value and the smooth transition target luminous intensity value with the absolute value of the spatial luminous deviation, determines the smooth transition target luminous intensity value, obtains the device running time, and performs digital pulse width conversion mapping when the device running time reaches the actual action execution time value of the lighting node, generating lighting group communication collaborative control parameters.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0042] In this invention, by obtaining the expected power change of lighting nodes and the initial planned trigger time, calculating the sum of the expected transient power in the target interaction network and comparing it with the power supply carrying capacity increment threshold, and relying on the backoff priority mechanism to perform delayed scheduling of nodes, the peak-shifting action of lighting equipment in the group is realized, preventing instantaneous overload of power supply transformer branches and ensuring the safe and stable operation of the power grid.

[0043] Meanwhile, by identifying the boundaries of logical control zones, quantifying the absolute value of spatial luminance deviation between adjacent areas, and combining the three-dimensional linear physical spacing between nodes to dynamically calculate dimming compensation reduction and smooth transition target luminous intensity, the brightness gradient and smooth transition across lighting area boundaries are achieved, eliminating the visual harshness caused by sudden changes in lighting at the boundary, and improving the overall spatial light environment comfort and the precision of collaborative control of lighting groups. Attached Figure Description

[0044] Figure 1 This is a flowchart of the main steps of the present invention;

[0045] Figure 2 This is a flowchart of step S1 of the present invention;

[0046] Figure 3 This is a flowchart of step S2 of the present invention;

[0047] Figure 4 This is a flowchart of step S3 of the present invention;

[0048] Figure 5 This is a flowchart of step S4 of the present invention;

[0049] Figure 6 This is a flowchart of step S5 of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] Please see Figure 1 A lighting group communication cooperative control method based on swarm intelligence algorithm includes the following steps:

[0052] S1: Collect the physical attribute data set of the lighting nodes, extract the three-dimensional coordinates of the lighting nodes, the power supply branch number of the lighting node transformer, the logical control partition identifier of the lighting node, and the preset backoff priority sequence number of the lighting node, receive the group status change signaling to obtain the target luminous intensity and timestamp value, and determine the expected power change value and the initial planned trigger time point value of the lighting node.

[0053] S2: Based on the expected power change value and the initial planned trigger time point value, select target interaction data frames from the local communication network, and determine the sum of the expected transient power at the target time according to the external expected power change value and the expected power change value in the target interaction data frame.

[0054] S3: Based on the comparison between the sum of the expected transient power at the target time and the power supply carrying capacity increment threshold, construct the priority sorting sequence of external lighting nodes and determine the last node, perform delay processing on the initial planned trigger time point value, and output the actual action execution time point value of the lighting node.

[0055] Among them, the power supply carrying capacity increment threshold is obtained from the power supply infrastructure power safety verification process by retrieving transformer rated capacity data and combining it with the safety margin ratio.

[0056] If the sum of the expected transient power at the target time is greater than the power supply carrying capacity increment threshold, then the external lighting node backoff priority sequence number is extracted from the target interactive data frame, and a backoff priority sorting sequence is constructed based on the external lighting node backoff priority sequence number and the preset backoff priority sequence number of the lighting node.

[0057] S4: Obtain the adjacent control zone identifier, compare it with the logical control zone identifier to identify the zone boundary, determine the absolute value of the spatial photometric deviation between the target luminous intensity and the adjacent luminous intensity, compare the absolute value of the spatial photometric deviation with the illumination change judgment threshold, and determine the illumination boundary change judgment result.

[0058] The threshold for determining sudden changes in illumination was obtained by querying the visual comfort standard database.

[0059] If the absolute value of the spatial photometric deviation is greater than the threshold for judging sudden changes in illumination, it is determined that there is a state of illumination fragmentation; if the absolute value of the spatial photometric deviation is not greater than the threshold for judging sudden changes in illumination, it is determined that there is a smooth transition state.

[0060] S5: Based on the result of the change in lighting boundary, determine the physical distance value of the straight line according to the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates, determine the dimming compensation reduction value and the luminous intensity value of the smooth transition target according to the absolute value of the spatial luminous deviation, determine the luminous intensity value of the smooth transition target, obtain the equipment running time, and when the equipment running time reaches the actual action execution time value of the lighting node, perform digital pulse width conversion mapping to generate lighting group communication and collaborative control parameters.

[0061] Please see Figure 2 Step S1 is as follows:

[0062] S111: Collect the attribute data set of lighting nodes, extract the three-dimensional coordinate values ​​of lighting nodes, the transformer power supply branch number, the logical control partition identifier value, and the preset yield priority sequence number, receive group status change signaling and extract the target action status value, target luminous intensity value, and timestamp value of lighting nodes, and establish multi-dimensional configuration parameters of lighting nodes.

[0063] By reading the current lighting hardware basic registration table in real time, the three-dimensional coordinate values ​​of specific lighting nodes are extracted, such as 15.5 meters, 20.0 meters, and 4.5 meters. At the same time, the corresponding transformer power supply branch number 10 is extracted from the power supply network topology association library, and the logical control partition identifier value 3 and the preset yield priority sequence number 2 are read. The group status change signaling from the central dispatch is continuously monitored and received through the narrowband IoT communication channel. This signaling serves as the global triggering benchmark for the collaborative control of lighting group communication and initiates the underlying node-based autonomous interactive network. Cyclic redundancy check and unpacking parsing operations are performed on the data frame message of this signaling to accurately extract the target action status value 1 of the lighting node contained in the instruction. Here, the status value 1 represents the start operation. The target luminous intensity value of 6000 candela and the absolute timestamp value of 1700000000 milliseconds attached when the instruction was issued are extracted simultaneously. By aligning and concatenating the extracted 3D coordinates, branch numbers, partition identifiers, priorities, action states, luminous intensity, and timestamps into consecutive memory blocks according to a preset structure order, a multi-dimensional configuration parameter for lighting nodes is established, encompassing spatial coordinates, electrical topology, logical control, and temporal status domains. This concatenation process is shown in Table 1, where assembly is completed by extracting structured fields. The execution node of this data combination operation lies in the data parsing process, which, based on complete verification, uses identifier calculations to derive field offset results, thereby triggering subsequent parameter structure encapsulation operations in the data encapsulation process.

[0064] Table 1. Multi-dimensional Configuration Parameters for Lighting Nodes

[0065] Spatial three-dimensional coordinate parameters Floating-point precision 15.5 meters, 20.0 meters, 4.5 meters Substation branch number short integer type 10 Control partition identifier number short integer type 3 Set yield priority number short integer type 2 Target action status value Boolean logic type 1 Target luminous intensity requirements Floating-point precision 6000 candela Absolute timestamp record value Long integer type 1700000000 milliseconds

[0066] Table 1 details the specific mapping content of each dimension parameter, providing basic data support for subsequent operational status prediction.

[0067] S112: Based on the multi-dimensional configuration parameters of the lighting node, obtain the current operating status of the lighting node, parse the hardware specification table to obtain the device power mapping coefficient, and calculate the expected power change value by multiplying the current operating status value with the target operating status value in the multi-dimensional configuration parameters of the lighting node and the device power mapping coefficient.

[0068] Based on the established multi-dimensional configuration parameters of the lighting nodes, the current operating action state value of 0 is obtained from the real-time state storage medium. This value of 0 represents the current target being in an off state. An addressing request is sent to the local non-volatile storage area to parse the fixed hardware specification information mapping table. The device power mapping coefficient of 200 watts is obtained by querying the electrical characteristic field corresponding to the current model. State value difference calculation is performed, subtracting the target action state value of 1 and the current operating action state value of 0 from the lighting node multi-dimensional configuration parameters to obtain a state transition difference of 1. This state transition difference is directly multiplied by the device power mapping coefficient, and the value of 1 is multiplied by 200 watts to derive the expected power change value of 200 watts. In this process, the algorithm base introduces the individual state evaluation model from the swarm intelligence algorithm, where each individual node defines its own expected power increment as the initial fitness variable in the local optimization space. ,in Represents the individual's initial expected workload. Represents the state difference. This represents the power mapping coefficient, which provides an individual verification benchmark for subsequent group iterations. The execution node of this calculation logic is that the control calculation process detects a trigger condition where the state transition difference is greater than 0, calculates the expected power load increment based on the state transition difference and the mapping coefficient, and then triggers subsequent grid synchronization and interactive data frame construction operations to ensure the continuity and real-time response performance of edge-side data flow and eliminate blind spots in state prediction during the initial stage of load surge.

[0069] S113: Read the communication protocol configuration to obtain the basic delay duration value, perform time axis superposition calculation based on the timestamp value and the basic delay duration value in the multi-dimensional configuration parameters of the lighting node, and synchronize with the expected power change value and the basic delay duration value to obtain the initial planned trigger time point value.

[0070] The underlying communication protocol specification configuration file is read, and the basic latency value of 150 milliseconds corresponding to the network transmission layer is extracted. The absolute timestamp value of 1700000000 milliseconds is retrieved from the previously established multi-dimensional configuration parameters of the lighting nodes. The timestamp value and the basic latency value are summed forward along the time axis to obtain the basic synchronization time node of 1700000150 milliseconds. A step-size-based delay conversion quantization is performed on the expected power change value of 200 watts. According to the preset fixed conversion rule of 5 milliseconds compensation for every 100-watt load increment, a combined division and multiplication operation is performed. 200 watts divided by 100 watts yields a quotient of 2. This quotient of 2 is multiplied by 5 milliseconds to obtain the additional compensation delay of 10 milliseconds caused by the power surge. The additional compensation delay of 10 milliseconds is then directly added to the basic synchronization time node of 1700000150 milliseconds to achieve synchronization and matching in the time dimension, ultimately obtaining the initial planned trigger time point value of 1700000160 milliseconds. This timing calculation process based on power compensation is the core component for achieving microsecond-level time slot synchronization in the collaborative control of lighting group communication. It effectively avoids communication congestion caused by the concurrent start and stop of massive nodes, laying the foundation for subsequent collision-free data frame transmission. The execution node of this timing superposition logic is that the synchronization process obtains an accurate trigger time reference result based on the summation of timestamp, basic delay, and power-added delay, thereby triggering subsequent comparison of adjacent associated time axes and anti-concurrency conflict operations.

[0071] Please see Figure 3 Step S2 is as follows:

[0072] S211: Construct an initial interaction data frame based on the expected power change value and the initial planned trigger time point value, receive adjacent interaction data frames, extract the external transformer power supply branch number, the external initial planned trigger time point, the external expected power change value and the external yield priority sequence number, and obtain external interaction multi-dimensional parameters.

[0073] The expected power change of 200 watts and the initial planned trigger time of 1700000160 milliseconds are extracted and written into the payload area of ​​the local broadcast communication message using specific bitstream encapsulation rules. This constructs a 64-byte initial interaction data frame, which is then broadcast omnidirectionally to the surrounding network. This local broadcast mechanism deeply relies on the lighting group communication collaborative control architecture. By establishing a decentralized mesh communication topology, each physical node in the system can act as an equal information source and relay point. Simultaneously, the promiscuous listening mode of the RF receiving channel is activated to continuously receive neighboring interaction data frames captured in the spatial electromagnetic channel. Cyclic redundancy check decoding is used to separate the frame header and trailer, accurately extracting external interaction multi-dimensional parameters from the payload area. Specifically, the extracted parameters include the external transformer power supply branch number 10, the external initial planned trigger time of 1700000165 milliseconds, the external expected power change of 700 watts, and the external backoff priority sequence number 1. This high-frequency data listening and parameter capture behavior essentially constitutes the pheromone sharing and distribution detection mechanism within the swarm intelligence algorithm framework. Each node, by analyzing the parameter beams emitted by its neighbors, quickly and dynamically reconstructs the topology distribution map of the power load status within its local neighborhood. The execution node of this data listening and extraction process lies in the radio frequency communication processing, which, based on the energy mutation of the wireless channel and message verification calculations, obtains the result of legitimate neighboring data frames. This triggers subsequent processing operations such as data filtering and load concurrency superposition. The local broadcast interaction strategy, employing a simplified data frame structure, improves the throughput of concurrent information interaction compared to long-connection handshake communication protocols. In scenarios of densely deployed energy-saving lighting, it can avoid the blocking and stagnation of the communication bus.

[0074] S212: Based on the external interaction multidimensional parameters, match and compare the external power supply branch number with the current node power supply branch number, match and compare the external initial plan trigger time point value with the initial plan trigger time point value to filter and extract data nodes, and generate the target interaction data frame.

[0075] Based on the acquired external interaction multidimensional parameters, the current node's power supply branch number (10) is retrieved from the local storage medium. The extracted external power supply branch number (10) is compared with the current node's power supply branch number (10) using an XOR logic operation. If the difference between the two values ​​is 0, they are considered completely identical, and the target is determined to belong to the same power transformer branch. At this point, the companion feature recognition logic within the swarm intelligence algorithm is triggered. The initial planned trigger time point value (1700000160 milliseconds) is extracted and compared with the external initial planned trigger time point value (1700000165 milliseconds). The absolute value of the time difference is calculated as 5 milliseconds. This absolute time difference is compared with a preset conflict determination time window threshold of 20 milliseconds. If the absolute time difference value of 5 milliseconds is less than the 20 millisecond threshold, the external target is determined to be within a potential concurrent triggering range. By filtering and extracting data nodes with identical power supply branches and overlapping time windows, irrelevant cross-domain interference data is accurately eliminated from the lighting group communication and collaborative control network. Furthermore, eligible adjacent interactive data frames are tagged with high-risk tags and transferred to a high-priority cache queue, generating target interactive data frames. The execution point of this judgment logic lies in the fact that the data filtering process, based on the comparison of branch consistency and absolute time difference, yields an accurate collision candidate set, thereby triggering subsequent power accumulation and safety margin assessment operations.

[0076] S213: For the target interactive data frame, extract the external expected power change value, sum the external expected power change value and the expected power change value, calculate the total power synchronization demand of multiple lighting nodes within the target time and evaluate the load status, and obtain the expected transient power superposition sum value.

[0077] For the target interactive data frame retained in the cache queue, the expected external power change value of 700 watts is precisely read and extracted by byte offset index, and the expected power change value of 200 watts stored in the storage medium is retrieved. The expected external power change value of 700 watts and the expected power change value of 200 watts are then fed into an arithmetic logic operation process to perform numerical accumulation and summation. The two values ​​are merged using integer addition rules, resulting in a total sum of 900 watts. Here, the swarm intelligence algorithm performs a typical local cluster feature accumulation operation, and the relevant calculation formula is expressed as follows: ,in This indicates the total load demand. Represents a node The extracted instantaneous power, leveraging the parallel computing power of distributed nodes, calculates the total synchronous power demand of multiple lighting nodes under the same power supply branch within a very short timeframe. It then assesses the peak load capacity within the current microsecond-level time slice and outputs a precise expected sum of transient power of 900 watts. The execution node for this summation logic is located at the microprocessor, where the expected power accumulation calculation based on its own power and that of external objects from the same source yields the instantaneous load extreme value. This triggers subsequent transformer total capacity comparison and priority-based mandatory scheduling operations. By employing distributed, localized load accumulation and aggregation calculations, compared to a network architecture relying on centralized cloud-based computation, the efficiency of local computing power collaborative processing is improved, ensuring absolutely real-time tracking of power surge impact predictions for each lighting node.

[0078] Please see Figure 4 Step S3 is as follows:

[0079] S311: Detect the rated capacity of the transformer and combine it with the safety margin ratio to obtain the power supply carrying capacity increment threshold. Compare the expected transient power sum value with the power supply carrying capacity increment threshold to determine the value. When the expected transient power sum value is greater than the power supply carrying capacity increment threshold, extract the external lighting node backoff priority sequence number from the target interactive data frame and establish a backoff priority determination sequence number.

[0080] The external power supply network's current operating nameplate parameters are detected via a hard-wired telemetry channel. The transformer's rated total capacity of 1000 watts is read, and the dynamically issued safety margin ratio of 0.8 is obtained from the power grid safety configuration file. The transformer's rated total capacity of 1000 watts and the safety margin ratio of 0.8 are multiplied to obtain a power supply carrying capacity increment threshold of 800 watts to limit peak loads. The previously generated expected transient power sum of 900 watts is called, and this is compared with the power supply carrying capacity increment threshold of 800 watts. Differential calculations identify that 900 watts is greater than 800 watts, clearly indicating a risk of transient surge overload. A high-frequency interrupt service response is triggered. The external lighting node's backoff priority sequence number 1 is read and extracted from the target interaction data frame. Combined with its existing preset backoff priority sequence number 2, these two sequence numbers are sequentially written into a temporary sorting array buffer pool to establish a backoff priority determination sequence number set for subsequent avoidance scheduling. This resource occupancy determination process fully reflects the competition and backoff mechanisms designed in swarm intelligence algorithms to resolve local conflicts; when the assessment finds that the global resource of power supply capacity is limited, the nodes autonomously utilize the safety margin formula. ,in For the threshold limit, This is the rated total capacity. To ensure safety, a hard-wired level concurrency prevention warning is activated, enabling the network to spontaneously negotiate the optimal safety avoidance and load queuing strategy through the lighting group communication and collaborative control protocol without the intervention of a central server. The execution node of this comparison warning logic is that the warning judgment process is based on the comparison calculation between the accumulated load value and the dynamic safety threshold to obtain a clear over-limit trigger result, which in turn triggers the node avoidance sequence sorting and delay backoff compensation operation.

[0081] S312: Based on the priority of the yielding sequence number, construct a priority sorting sequence and filter the last node, extract the initial planned trigger time point of the last node, read the clock tick configuration to obtain the time offset step, add compensation to the value of the initial planned trigger time point and calculate the compensation delay, and generate the value of the first corrected time point.

[0082] The system reads data from the priority determination sequence number set, executes the bubble sort algorithm, and deeply embeds a multi-agent crowding distribution queuing strategy from a swarm intelligence algorithm. The sequence numbers are arranged in ascending order of value, constructing an ascending priority sorting sequence. Here, a larger sequence number indicates a lower priority and requires more backing. The system then selects the target corresponding to sequence number 2 at the end of the sequence as the last node. The initial planned trigger time point value of 1700000160 milliseconds is extracted from the cache of this last node. The system accesses the real-time time registry and reads the clock tick configuration to obtain the base time offset step value of 100 milliseconds. This base time offset step is directly quantized into a compensation delay constant. This compensation delay of 100 milliseconds is added to the initial planned trigger time point value of 1700000160 milliseconds, and a direct scalar addition calculation is performed to generate the first revised time point value of 1700000260 milliseconds. The system then uses the time shift formula... ,in, This represents the correction time. The initial time, The offset step size represents the time anchor point transfer. This compensation delay strategy ensures that the last node in the lighting group communication and collaborative control link, which is at a priority disadvantage, can proactively and orderly relinquish its current competing time slot, thereby maximizing the group power supply benefits while reducing the risk of local overload. The scheduling execution process is based on the extraction of the last node in the priority sequence and the addition of the step offset to obtain a new time anchor point result that avoids timing conflicts, which then triggers the cyclic re-verification of the subsequent transient power state and the final clock lock operation.

[0083] S313: Based on the value of the first corrected time point, set the trigger reference time, and repeatedly compare the expected sum of transient power with the power supply carrying capacity increment threshold. When the expected sum of transient power is not greater than the power supply carrying capacity increment threshold, terminate the yield delay judgment and lock the current time beat to obtain the actual action execution time point value of the lighting node.

[0084] Based on the generated first corrected time point value of 1700000260 milliseconds, the countdown register reference of the internal hardware timer is reset and set as the new trigger reference time. A microsecond-level polling probe detection task is started, and the load status is re-scanned cyclically within this delayed time window to re-statistically analyze the adjacent interaction characteristics and obtain a new expected transient power sum value. After the yielding period, it is detected that the high-priority object has completed its execution and released the load, and the re-acquired expected transient power sum value drops back to 700 watts. The power supply carrying capacity increment threshold of 800 watts is called again to perform a size comparison operation. The judgment logic confirms that 700 watts is not greater than 800 watts, which meets the grid safety requirements. A termination yielding delay judgment signal is issued to the interrupt control operation to forcibly suspend the probe polling loop, and at the same time, the time tick record stored in the current timer is locked, finally obtaining the exact actual action execution time point value of the lighting node, 1700000260 milliseconds. After multiple rounds of probe interaction and load convergence iteration under the swarm intelligence algorithm mechanism, a single node finds the optimal timing action solution to avoid peaks within its own local listening range. Thus, within the execution closed loop of the lighting group communication and collaborative control, a completely safe and collision-free final output action rhythm is indisputably established. The execution node of this cyclic verification logic is that the monitoring and processing process calculates a safe passage permission result based on the comparison between the updated total load and the safety threshold, thereby triggering the solidification and storage of the action execution time node and the instruction issuance operation, eliminating the probability of secondary impact collisions to the power grid. Compared with a single blind delay start strategy, the overall current harmonic distortion amplitude is attenuated.

[0085] Please see Figure 5 Step S4 is as follows:

[0086] S411: Monitor adjacent operating status data frames and extract adjacent control zone identifiers, three-dimensional coordinates and current luminous intensity. Match and compare the values ​​of logical control zone identifiers to determine the association status between adjacent control zone identifier values ​​and logical control zone identifier values, and obtain zone boundary determination features.

[0087] By activating the parallel bus receiving port of the photoelectric sensing step, the system continuously monitors the surrounding operational status data frames and performs masking parsing on the intercepted binary data packets. From this, the adjacent control partition identifier value 4, the three-dimensional coordinates of adjacent nodes (12.0 meters, 18.5 meters, and 4.5 meters), and the current actual luminous intensity value of 2000 candela are precisely extracted. The system retrieves its own logical control partition identifier value 3, which is stored locally. The adjacent control partition identifier value 4 and the logical control partition identifier value 3 are input into a value comparison operation to perform a source-based comparison. The comparison reveals that value 4 and value 3 are not equal, thus clearly indicating that the association between the adjacent control partition identifier value and the logical control partition identifier value is a cross-regional boundary critical state. Based on this state characteristic, the system outputs and obtains the partition boundary determination feature variable, marking the existence of a physical boundary visual seam in the adjacent area. By relying on logical self-verification and a bottom-up boundary detection approach using adjacent data packets, this study demonstrates the cutting-edge application of swarm intelligence algorithms in the field of environmental spatial feature recognition. Each node only needs to continuously communicate with the illumination group to coordinate and control heartbeat frames, adaptively outlining a virtual optical contour line for determining cross-zone control isolation. The execution node of this boundary recognition logic lies in the fact that the boundary recognition process derives the cross-zone spatial division attribute result based on the consistency comparison of internal and external logical identifiers, thereby triggering subsequent precise calculation of photometric deviation and assessment of visual fragmentation.

[0088] S412: Based on the partition boundary determination features, extract the target luminous intensity value and the adjacent luminous intensity values, calculate the difference and take the absolute value, obtain the illuminance intensity difference between adjacent nodes in space, convert and map the difference to quantify the illuminance gradient change amplitude feature, and generate the absolute value of spatial luminance deviation.

[0089] Based on the obtained partition boundary determination feature variables, the process of entering the cross-regional boundary visual transition assessment is confirmed. The target luminous intensity value of 6000 candela is retrieved from the memory stack, and the adjacent luminous intensity value of 2000 candela is extracted. The two luminous intensity data are loaded into the arithmetic logic operation process to perform unsigned integer difference calculation. Subtracting 2000 candela from 6000 candela yields an initial difference of 4000 candela. The absolute value of this difference is obtained through two's complement arithmetic to obtain the spatial adjacent node illuminance difference of 4000 candela. This difference is then input into a preset logarithmic function mapping step for transformation. Quantization is performed according to the rule that every 1000 candela difference maps to one luminance gradient. Dividing 4000 candela by the basic parameter 1000 candela yields a quantized illuminance gradient change amplitude feature value of 4, generating a spatial luminance deviation absolute value of 4 to characterize the visual tomographic intensity. This nonlinear quantization process from physical light intensity to visual gradient, from the perspective of the swarm intelligence algorithm's computational model, is equivalent to solving the multidimensional state divergence between adjacent luminescent particles. The specific calculation formula is as follows: ,in This represents the absolute value of the deviation. and They represent themselves and their neighbor, Guangqiang, respectively. To map the gradient step size constant, this process provides a highly accurate numerical target for the system to subsequently automatically smooth out abrupt changes in light spots across regions. The execution node of this data quantization logic is that the photometric analysis process calculates the absolute difference in intensity and maps it proportionally to obtain specific visual difference magnitude results, thereby triggering visual comfort threshold comparison and smooth transition demand determination operations.

[0090] S413: Based on the absolute value of spatial photometric deviation, query the visual comfort standard database to obtain the threshold for judging sudden changes in illumination. If the absolute value of spatial photometric deviation is greater than the threshold for judging sudden changes in illumination, it is judged as a state of illumination fragmentation. If it is not greater than the threshold for judging sudden changes in illumination, it is judged as a state of smooth transition. The node judgment comparison information is summarized and classified and labeled to obtain the result of the judgment of sudden changes in illumination boundary.

[0091] Based on the quantified absolute value of spatial luminance deviation 4, the local visual comfort standard database is queried via the internal bus, and the illumination change judgment threshold 2 is retrieved according to the background illuminance of the current time period. The absolute value of spatial luminance deviation 4 and the illumination change judgment threshold 2 are compared through a numerical comparison operation. If the absolute value of spatial luminance deviation 4 is greater than the illumination change judgment threshold 2, the logical path directly determines that there is an abrupt boundary between adjacent lighting spaces, indicating a state of illumination fragmentation, and assigns a fragmentation warning classification label to it. If the comparison finds that the deviation is not greater than the threshold, it is judged as a smooth transition state and assigned a regular label. In the data aggregation stage, all judgment comparison information is classified and archived according to the label type, generating a complete set of lighting boundary change judgment results array. Combined with the data cascading and aggregation mechanism embedded in the lighting group communication collaborative control architecture, each dispersed node in the area accurately pushes and pulls up its own state classification label, which is independently calculated based on the dynamic threshold, to the nearest local multicast cluster head node, thereby quickly constructing a digital twin topology distribution map that reflects the visual comfort status of the entire area within the system control stack. The mutation comparison reference parameters are shown in Table 2. The execution node of this assessment logic is that the state classification process calculates the regional coherence disruption indicator based on the comparison between the deviation value and the comfort threshold, thereby triggering the subsequent building spatial distance measurement operation.

[0092] Table 2. Criteria for Judging Visual Comfort

[0093] absolute value of spatial photometric deviation 4 Variables to be matched Threshold for detecting sudden changes in light intensity 2 Evaluation benchmark limit line Features exceeding the threshold Boolean true state Light-induced fragmentation state Features within tolerance range Boolean false state Smooth transition state

[0094] Table 2 shows the comparison results between the absolute value of the deviation and the comfort judgment threshold, providing decision guidance for the visual transition repair of each lighting node boundary.

[0095] Please see Figure 6 The S5 steps are as follows:

[0096] S511: Extract illumination fragmentation features from the illumination boundary abrupt change judgment results, load the building information model to obtain the environmental scale space, map the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates to the environmental scale space, perform coordinate difference calculation based on Euclidean distance metric to calculate the straight-line distance between the coordinates of the two nodes, and establish the spatial straight-line spacing value.

[0097] For the generated array of lighting boundary abrupt change judgment results, the node array marked as lighting breakage state is extracted. The graphics rendering step loads the background building information model 3D mesh file to obtain the resolved environmental scale spatial absolute coordinate system. The current lighting node's 3D coordinate values ​​(15.5m, 20.0m, 4.5m) and the adjacent node's 3D coordinate values ​​(12.0m, 20.0m, 4.5m) are synchronously input into a transformation function and mapped to this environmental scale spatial coordinate system. Based on the Euclidean distance measurement mathematical principle, the coordinate axes are subtracted to calculate the X-axis difference of 3.5m, the Y-axis difference of 0.0m, and the Z-axis difference of 0.0m. The sum of the squares of each difference is then performed, followed by a square root operation to obtain the pure physical distance value of 3.5m. This establishes a spatial straight-line distance value of 3.5m to characterize the path length of light propagation attenuation. This 3D spatial measurement step precisely utilizes spatial analytical geometry formulas. ,in, Indicates the physical distance value. , , For each coordinate component, a rigorous grid coordinate projection model is established, providing essential key geometric input parameters for the activation of the spatial distance penalty function within the subsequent swarm intelligence algorithm framework. The execution node of this spatial metric logic lies in the fact that the spatial calculation process, based on the spatial mapping of the three-dimensional coordinate system and the difference operation of Euclidean distance, obtains the accurate physical straight line span result, thereby triggering the subsequent accurate calculation of the illumination gradient coefficient and the attenuation amplitude of transition compensation.

[0098] S512: The illuminance gradient coefficient is extracted by dividing the spatial linear spacing value and the absolute value of the spatial luminance deviation. The buffer weight parameter is extracted by matching the surface material reflectivity. The gradient coefficient and the weight parameter are multiplied to generate the dimming compensation reduction value. The node output is adjusted by subtracting the dimming compensation reduction from the target luminous intensity value to obtain the target luminous intensity value for smooth transition.

[0099] The algorithm calls upon the previously determined spatial linear spacing value of 3.5 meters and the original brightness difference of 4000 candela corresponding to the absolute value of the spatial luminance deviation. This is then input into a floating-point arithmetic process for division, dividing 4000 candela by 3.5 meters to obtain the illuminance gradient coefficient of 1142 candela per meter, characterizing the steepness of the spatial brightness abrupt change. Through environmental perception, the current light-projected wall surface material is identified as a frosted diffuse reflection paint, and the corresponding buffer weight parameter of 0.7 is extracted from the material attribute dictionary. The illuminance gradient coefficient of 1142 candela per meter is multiplied by the buffer weight parameter of 0.7 to generate a dimming compensation reduction value of 800 candela to offset the brightness discontinuity. The target luminous intensity value of 6000 candela is extracted, and this dimming compensation reduction value of 800 candela is subtracted to negatively adjust the node output luminance, obtaining a smoother, more gradual transition target luminous intensity value of 5200 candela. At this point, the iterative calculation of the swarm intelligence algorithm converges to the final optimal solution for regional luminance smoothing control, fully utilizing the collaborative reduction calculation formula. ,in Represents the target intensity after smoothing. Indicates the original brightness difference. This represents the material reflection weighting parameter. Adjustments were made to bridge the light intensity difference between multiple adjacent nodes, ensuring a natural and consistent visual extension experience for adjacent lighting devices in the boundary area of ​​the lighting group's communication and collaborative control light domain. The compensation parameter values ​​are shown in Table 3. The execution node of this correction logic is where the dimming core processing, based on distance attenuation division and material absorption weighted multiplication, derives a precise luminous emission reduction compensation result, thereby triggering the duty cycle command conversion output operation.

[0100] Table 3 Mapping Table of Surface Material Reflectivity and Buffer Weight

[0101] Frosted diffuse reflection coating surface Moderate diffuse reflection characteristics 0.7 Smooth marble mirror area Highly specular reflection light 0.9 Dark-colored sound-absorbing fabric wall panels Extremely low reflection and high absorption 0.3 Standard white wall matte finish Uniform halo diffuse reflection 0.5

[0102] Table 3 details the reflection coefficient buffer mapping weights for different environmental materials, effectively ensuring the environmental adaptability of attenuation calculation.

[0103] S513: Based on the target luminous intensity value of smooth transition, obtain the execution timing monitoring of the lighting equipment running time. When the running time reaches the actual action execution time value of the lighting node, convert the target luminous intensity value of smooth transition into duty cycle control command and output the lighting group communication and collaborative control parameters.

[0104] Based on the calculated target luminous intensity value of 5200 candela for smooth transition, the underlying time monitoring cycle is initiated, starting a microsecond-level timing monitoring loop for device runtime execution. The current system clock cycle is compared with the recorded actual action execution time of the lighting node (1700000260 milliseconds). When the absolute value of the runtime accurately accumulates and reaches the actual action execution time, a wake-up command is immediately sent to the pulse width modulation drive process. The device's rated maximum luminous output capacity of 10000 candela is read. The 5200 candela is divided by 10000 candela for proportional conversion, yielding a duty cycle percentage of 52%. This result is then packaged into a 52% duty cycle control command. By merging duty cycle, drive frequency, and address identification code, the node drive module in this output stage fully relies on the forward-looking prediction and dynamic correction results of the global lighting power supply status made by the swarm intelligence algorithm. The results are deeply encoded into the pulse command frame, and the lighting group communication and collaborative control parameters are output to the end-point light-emitting drive component to execute the lighting action. This completely closes the loop and establishes a seamless feedback link between regional distributed autonomous decision-making and the physical execution of the underlying power devices. The execution node of this output execution logic lies in the fact that the command issuance process, based on high-precision microsecond-level time matching and linear duty cycle ratio conversion calculation, obtains the final electrical drive pulse width result, thereby triggering the physical smooth start-up lighting operation of the solid-state light-emitting components. Compared with the independent response mode where each component operates independently, the consistency of global synchronous gradual brightening is improved, effectively eliminating visual light spots and flickering phenomena.

[0105] A lighting group communication and cooperative control system based on swarm intelligence algorithms includes:

[0106] The state change parsing module is used to execute S1: collect the physical attribute data set of the lighting node, extract the three-dimensional coordinates of the lighting node, the power supply branch number of the lighting node transformer, the logical control partition identifier of the lighting node, and the preset backoff priority sequence number of the lighting node, receive the group state change signaling to obtain the target luminous intensity and timestamp value, and determine the expected power change value and the initial planned trigger time point value of the lighting node.

[0107] The transient power determination module is used to execute S2: based on the expected power change value and the initial planned trigger time point value, filter target interaction data frames from the local communication network, and determine the sum of the expected transient power at the target time according to the external expected power change value and the expected power change value in the target interaction data frame;

[0108] The action time determination module is used to execute S3: Based on the sum of the expected transient power at the target time and the power supply carrying capacity increment threshold, it constructs the priority sorting sequence of external lighting nodes and determines the last node, delays the initial planned trigger time point value, and outputs the actual action execution time point value of the lighting node.

[0109] The boundary mutation determination module is used to execute S4: obtain the adjacent control zone identifier, compare it with the logical control zone identifier to identify the zone boundary, determine the absolute value of the spatial photometric deviation between the target luminous intensity and the adjacent luminous intensity, compare the absolute value of the spatial photometric deviation with the illumination mutation determination threshold, and determine the illumination boundary mutation determination result.

[0110] The control parameter generation module is used to execute S5: based on the lighting boundary abrupt change judgment result, determine the straight physical distance value according to the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates, determine the dimming compensation reduction value and the smooth transition target luminous intensity value with the absolute value of the spatial luminous deviation, determine the smooth transition target luminous intensity value, obtain the equipment running time, and when the equipment running time reaches the actual action execution time value of the lighting node, perform digital pulse width conversion mapping, and coordinate control parameters based on lighting group communication.

[0111] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for collaborative control of lighting group communication based on swarm intelligence algorithm, characterized in that, Includes the following steps: S1: Collect the physical attribute data set of the lighting node, extract the three-dimensional coordinates of the lighting node, the power supply branch number of the lighting node transformer, the logical control partition identifier of the lighting node, and the preset backoff priority sequence number of the lighting node, receive the group status change signaling to obtain the target luminous intensity and timestamp value, and determine the expected power change value and the initial planned trigger time point value of the lighting node. S2: Based on the expected power change value and the initial planned trigger time point value, select target interaction data frames from the local communication network, and determine the sum of expected transient power at the target time according to the external expected power change value in the target interaction data frame and the expected power change value. S3: Based on the sum of the expected transient power at the target time and the power supply carrying capacity increment threshold, construct the priority sorting sequence of external lighting nodes and determine the last node, delay the value of the initial planned trigger time point, and output the value of the actual action execution time point of the lighting node. S4: Obtain the adjacent control zone identifier, compare it with the logical control zone identifier to identify the zone boundary, determine the absolute value of the spatial photometric deviation between the target luminous intensity and the adjacent luminous intensity, compare the absolute value of the spatial photometric deviation with the illumination change judgment threshold, and determine the illumination boundary change judgment result. S5: Based on the lighting boundary abrupt change determination result, determine the straight-line physical distance value according to the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates, determine the dimming compensation reduction value and the smooth transition target luminous intensity value with the absolute value of the spatial luminous deviation, determine the smooth transition target luminous intensity value, obtain the device running time, and when the device running time reaches the actual action execution time value of the lighting node, perform digital pulse width conversion mapping to generate lighting group communication collaborative control parameters. 2.The method according to claim 1, wherein: The power supply carrying capacity increment threshold is obtained from the power supply infrastructure power safety verification process by retrieving transformer rated capacity data and combining it with the safety margin ratio. If the sum of the expected transient power at the target time is greater than the power supply carrying capacity increment threshold, then the external lighting node backoff priority sequence number is extracted from the target interactive data frame, and a backoff priority sorting sequence is constructed based on the external lighting node backoff priority sequence number and the lighting node preset backoff priority sequence number. 3.The method of claim 1, wherein, The threshold for determining sudden changes in illumination is obtained by querying a visual comfort standard database. If the absolute value of the spatial photometric deviation is greater than the light change determination threshold, it is determined that there is a light breakup state; if the absolute value of the spatial photometric deviation is not greater than the light change determination threshold, it is determined that there is a smooth transition state. 4.The method according to claim 1, wherein, The specific steps of S1 are as follows: S111: Collect the attribute data set of lighting nodes, extract the three-dimensional coordinate values ​​of lighting nodes, the transformer power supply branch number, the logical control partition identifier value, and the preset yield priority sequence number, receive group status change signaling and extract the target action status value, target luminous intensity value, and timestamp value of lighting nodes, and establish multi-dimensional configuration parameters of lighting nodes. S112: Based on the multi-dimensional configuration parameters of the lighting node, obtain the current operating status of the lighting node, parse the hardware specification table to obtain the device power mapping coefficient, and calculate the expected power change value by combining the current operating status value with the target operating status value in the multi-dimensional configuration parameters of the lighting node and the device power mapping coefficient. S113: Read the communication protocol configuration to obtain the basic delay duration value, perform time axis superposition calculation based on the timestamp value and the basic delay duration value in the multi-dimensional configuration parameters of the lighting node, and synchronize the expected power change value with the basic delay duration value to obtain the initial planned trigger time point value. 5.The method of claim 1, wherein, The specific steps of S2 are as follows: S211: Construct an initial interaction data frame based on the expected power change value and the initial planned trigger time point value, receive adjacent interaction data frames, extract the external transformer power supply branch number, the external initial planned trigger time point, the external expected power change value and the external yield priority sequence number, and obtain external interaction multidimensional parameters. S212: Based on the external interaction multidimensional parameters, the external power supply branch number is matched and compared with the current node power supply branch number, and the external initial plan trigger time point value is matched and compared with the initial plan trigger time point value to filter and extract data nodes, and generate target interaction data frame. S213: For the target interactive data frame, extract the external expected power change value, sum the external expected power change value and the expected power change value, calculate the total power synchronization demand of multiple lighting nodes within the target time and evaluate the load status, and obtain the expected transient power superposition sum value. 6.The method of claim 1, wherein, The specific steps of S3 are as follows: S311: Detect the rated capacity of the transformer and combine it with the safety margin ratio to obtain the power supply carrying capacity increment threshold. Compare the expected transient power sum value with the power supply carrying capacity increment threshold to determine the value. When the expected transient power sum value is greater than the power supply carrying capacity increment threshold, extract the external lighting node backoff priority sequence number from the target interactive data frame and establish a backoff priority determination sequence number. S312: Construct a priority sorting sequence based on the priority determination sequence number and filter the last node, extract the initial planned trigger time point of the last node, read the clock beat configuration to obtain the time offset step, superimpose compensation on the value of the initial planned trigger time point and calculate the compensation delay, and generate the first corrected time point value. S313: Based on the first corrected time point value, set the trigger reference time, and repeatedly compare the expected transient power sum value with the power supply carrying capacity increment threshold. When the expected transient power sum value is not greater than the power supply carrying capacity increment threshold, terminate the yield delay judgment and lock the current time beat to obtain the actual action execution time point value of the lighting node. 7.The method of claim 1, wherein, The specific steps of S4 are as follows: S411: Monitor adjacent operating status data frames and extract adjacent control partition identifiers, three-dimensional coordinates and current luminous intensity. Match and compare the values ​​of the logical control partition identifiers to determine the association status between the values ​​of adjacent control partition identifiers and logical control partition identifiers, and obtain partition boundary determination features. S412: Based on the partition boundary determination features, extract the target luminous intensity value and the adjacent luminous intensity values, calculate the difference and take the absolute value, obtain the illuminance intensity difference between adjacent nodes in space, convert and map the difference to quantify the illuminance gradient change amplitude feature, and generate the absolute value of spatial luminance deviation. S413: Based on the absolute value of the spatial photometric deviation, query the visual comfort standard database to obtain the illumination change judgment threshold. If the absolute value of the spatial photometric deviation is greater than the illumination change judgment threshold, the illumination is determined to be in a state of discontinuity. If it is not greater than the illumination change judgment threshold, the illumination is determined to be in a state of smooth transition. The node judgment comparison information is summarized and classified and labeled to obtain the illumination boundary change judgment result. 8.The method of claim 1, wherein, The specific steps of S5 are as follows: S511: Extract the lighting breakup features based on the lighting boundary abruptness determination result, load the building information model to obtain the environmental scale space, map the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates to the environmental scale space, perform coordinate difference calculation according to Euclidean distance metric to calculate the straight-line distance between the coordinates of the two nodes, and establish the spatial straight-line spacing value. S512: Calculate the illuminance gradient coefficient by dividing the spatial straight-line spacing value by the absolute value of the spatial luminance deviation, extract the buffer weight parameter by matching the surface material reflectivity, multiply the gradient coefficient and the weight parameter to generate the dimming compensation reduction value, and adjust the node output by subtracting the dimming compensation reduction from the target luminous intensity value to obtain the smooth transition target luminous intensity value. S513: Based on the light intensity value of the smooth transition target, obtain the execution timing monitoring of the lighting equipment running time. When the running time reaches the actual action execution time value of the lighting node, convert the light intensity value of the smooth transition target into a duty cycle control command and output the lighting group communication and coordination control parameters.

9. A lighting group communication cooperative control system based on a swarm intelligence algorithm, characterized in that, The system is used to implement the method according to any one of claims 1-8, comprising: The status change parsing module collects the physical attribute data set of the lighting nodes, extracts the three-dimensional coordinates of the lighting nodes, the power supply branch number of the lighting node transformer, the logical control partition identifier of the lighting node, and the preset backoff priority sequence number of the lighting node, receives group status change signaling to obtain the target luminous intensity and timestamp value, and determines the expected power change value and the initial planned trigger time point value of the lighting node. The transient power determination module filters target interaction data frames from the local communication network based on the expected power change value and the initial planned trigger time point value, and determines the sum of expected transient power at the target time according to the external expected power change value in the target interaction data frame and the expected power change value. The action time determination module compares the sum of the expected transient power at the target time with the power supply carrying capacity increment threshold, constructs an external lighting node backoff priority sorting sequence and determines the last node, performs a delay processing on the initial planned trigger time point value, and outputs the actual action execution time point value of the lighting node. The boundary abrupt change determination module acquires the adjacent control zone identifier, compares it with the logical control zone identifier to identify the zone boundary, determines the absolute value of the spatial photometric deviation between the target luminous intensity and the adjacent luminous intensity, compares the absolute value of the spatial photometric deviation with the illumination abrupt change determination threshold, and determines the illumination boundary abrupt change determination result. The control parameter generation module, based on the lighting boundary abrupt change judgment result, determines the straight-line physical distance value according to the three-dimensional coordinates of the lighting node and the adjacent three-dimensional coordinates, determines the dimming compensation reduction value and the smooth transition target luminous intensity value with the absolute value of the spatial luminous deviation, determines the smooth transition target luminous intensity value, obtains the device running time, and performs digital pulse width conversion mapping when the device running time reaches the actual action execution time value of the lighting node, generating lighting group communication collaborative control parameters.

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