Lamp intelligent energy-saving control method and system based on wireless Internet of Things

Abstract

The invention relates to the technical field of illumination control, and discloses a lamp intelligent energy-saving control method and system based on a wireless internet of things. According to the method, a lamp control area is set, the set lamp control area is divided, wireless Internet of Things equipment and a control gateway are installed in each divided area based on the obtained divided lamp control area, and a brightness data acquisition period of the wireless Internet of Things equipment is set; meanwhile, brightness data, collected in real time, of a lamp control area are analyzed and summarized, the summarized real-time brightness data are processed in a data optimization mode, lamp brightness parameters are determined, finally, the determined lamp brightness parameters are input into the lamp energy-saving adjustment module, and the lamp energy-saving adjustment module conducts adjustment. And meanwhile, real-time verification is carried out, so that the intelligence of energy-saving control of the lamp is improved.

Description

Technical Field

[0001] The present invention relates to the field of lighting control technology, and in particular to a method and system for intelligent energy-saving control of lamps based on wireless Internet of Things. Background Art

[0002] With the development and application of smart lamps, the power consumption of smart lamps has also increased; the energy saving of smart lamps has gradually attracted attention, but the energy saving of existing smart lamps is often achieved by reducing the brightness and reducing the power output, but reducing the brightness can easily cause troubles to people's daily lives.

[0003] The existing open patent application CN118317492A, the invention sets the main light in the constant illumination mode in the target area, collects the position information of the main light and the auxiliary light respectively through the wireless communication control module set on the main light; when the brightness of the main light and the auxiliary light is detected to change, the neighboring lamps in the predetermined neighborhood are determined based on the position information of the main light and the auxiliary light respectively; the corresponding brightness adjustment instructions are sent to the neighboring lamps through the wireless communication control module, and the brightness of the neighboring lamps is adjusted to maintain the constant illumination mode. However, the long-term constant lighting does not take into account the energy saving of the lamps and the outdoor brightness that changes over time, which causes a power burden and has certain limitations. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a method and system for intelligent energy-saving control of lamps based on wireless Internet of Things, which has the advantages of precision, real-time, high efficiency and energy saving, and solves the problem of increasing power consumption of smart lamps.

[0005] (II) Technical solution In order to solve the technical problem of the increasing power consumption of the above-mentioned smart lamps, the present invention provides the following technical solutions: The present invention discloses a method for intelligent energy-saving control of lamps based on wireless Internet of Things, which specifically comprises the following steps: S1. Setting a lamp control area, and dividing the set lamp control area to obtain divided lamp control areas; S2. Based on the obtained divided lighting control areas, install wireless IoT devices and control gateways in each divided area, and set the brightness data collection cycle of the wireless IoT devices; S3, collecting brightness data of the lighting control area in real time based on a set period, and analyzing the real-time collected brightness data to obtain analyzed real-time brightness data; S4, summarizing the analyzed real-time brightness data, and dynamically determining the brightness parameters of the lamps through data optimization; S5. Setting a lamp energy-saving adjustment module based on dynamically determined lamp brightness parameters, and performing intelligent energy-saving control of the lamp through the set lamp energy-saving adjustment module.

[0006] The present invention sets a lamp control area and divides the set lamp control area. Based on the obtained divided lamp control area, a wireless Internet of Things device and a control gateway are installed in each divided area, and a brightness data collection cycle of the wireless Internet of Things device is set. At the same time, the brightness data of the lamp control area is collected in real time and analyzed and summarized, and the summarized real-time brightness data is processed in a data optimization manner to determine the lamp brightness parameters. Finally, the determined lamp brightness parameters are input into a lamp energy-saving adjustment module, which adjusts the lamp energy-saving adjustment module and performs real-time verification, thereby improving the intelligence of the lamp energy-saving control.

[0007] Preferably, the step of setting the lamp control area and dividing the set lamp control area to obtain the divided lamp control area comprises the following steps: Measure the area of ​​the lighting fixture control area, divide the measured lighting fixture control area into equal proportions, and record the number of each area after division.

[0008] Preferably, the steps of installing a wireless Internet of Things device and a control gateway in each divided area based on the obtained divided lighting control area, and setting a brightness data collection cycle of the wireless Internet of Things device include the following steps: S21. Set each wireless IoT device as a wireless IoT device node, and abstractly define the set wireless IoT device node and control gateway; S22, establishing a network node topology map based on the connection status of the wireless Internet of Things device nodes according to the wireless Internet of Things device node numbers and the numbers of each divided area; All wireless IoT device nodes in the lighting control area are connected to the control gateway. The control gateway numbers all connected wireless IoT device nodes based on the IP addresses that establish connections with the wireless IoT device nodes, and saves a list of wireless IoT device node numbers. A network node topology diagram is established based on the wireless IoT device node numbers and the numbers of each divided area.

[0009] Preferably, setting each wireless Internet of Things device as a wireless Internet of Things device node and abstractly defining the set wireless Internet of Things device node and the control gateway includes the following steps: Set the installed control gateway as the root node , the number of wireless IoT device nodes is N; For each wireless IoT device node, a unique IP address is set as the attribute of the wireless IoT device node; The set of all data node IP addresses is represented as: ; in, Represents the set of IP addresses of all data nodes. Represents the first wireless IoT device node, Indicates Wireless IoT device nodes.

[0010] The present invention sets each wireless Internet of Things device as a wireless Internet of Things device node, abstractly defines the set wireless Internet of Things device node and the control gateway, establishes a network node topology map based on the connection status of the wireless Internet of Things device node according to the wireless Internet of Things device node number and the number of each divided area, and determines the position of each wireless Internet of Things device node and the corresponding area through the established network node topology map, thereby improving the accuracy of indoor brightness data collection.

[0011] Preferably, the step of collecting brightness data of the lighting control area in real time based on a set period, analyzing the real-time collected brightness data, and obtaining the analyzed real-time brightness data comprises the following steps: S31, collecting image data in the lighting control area in real time based on the installed wireless IoT device; Dividing the collected image data into: a black area, a shadow area, a normal brightness area, a highlight area, and a white area based on pixel values ​​in the collected image data; Traverse and summarize the number of pixels in each area; Construct an image brightness curve based on the number of pixels in each region.

[0012] S32, constructing an indoor brightness curve based on the collected image data of the lamp control area; S33, analyzing the constructed indoor brightness curve, and outputting the analyzed real-time brightness data.

[0013] Preferably, the step of constructing an indoor brightness curve based on collecting image data of the lamp control area comprises the following steps: Based on the obtained image data collected in real time by each wireless IoT device, a fitting analysis is performed on the image data collected in real time by Gaussian fitting; Set the horizontal axis of the curve to represent the pixel value, with a value range of 0-255, and the vertical axis to represent the number of pixels in the image data that the pixel value has; The Gaussian fitting expression is as follows: ; in, Indicates the number of wireless IoT devices, represents the ordinate of the i-th brightness curve, Indicates the coordinates of the center position of the i-th brightness curve on the horizontal axis, represents the abscissa of the ith brightness curve, represents the brightness curve after fitting, represents the pixel value, is a natural constant.

[0014] Preferably, the step of analyzing the constructed indoor brightness curve and outputting the analyzed real-time brightness data comprises the following steps: Calculate real-time brightness data based on the fitted indoor brightness curve; The formula for weighted calculation of real-time brightness data is as follows: ; in, Represents real-time brightness data, represents the weight of the black area, Represents the black area in the indoor brightness curve after fitting. represents the weight of the shadow area, Represents the shadow area in the indoor brightness curve after fitting, represents the weight of the normal brightness area, It represents the normal brightness area in the indoor brightness curve after fitting. Represents the highlight area weight, Represents the highlight area in the indoor brightness curve after fitting. represents the weight of the white area, Represents the white area in the indoor brightness curve after fitting.

[0015] The present invention collects image data in the lamp control area in real time based on the installed wireless Internet of Things device, constructs an indoor brightness curve by analyzing the collected image data, and finally summarizes the constructed indoor brightness curve to output the analyzed real-time brightness data, thereby improving the reliability of real-time brightness data analysis.

[0016] Preferably, the real-time brightness data obtained after analysis is summarized, and the brightness parameters of the lamps are dynamically determined by data optimization, including the following steps: S41, initializing the analyzed real-time brightness data, performing chromosome encoding on the analyzed real-time brightness data and brightness data in adjacent periods based on a hybrid genetic algorithm, and constructing a population set; Set each set of chromosome encoding to represent a set of brightness data, set the population size, and the maximum number of iterations ; S42, setting a fitness function of brightness data based on the constructed population set size; Set the fitness function: ; ; in, represents the fitness function of the brightness data, Indicates the change in brightness data, Represents real-time brightness data, Indicates that the current time is cycles, Indicates Brightness data of each cycle; S43, genetic operator selection operation; S44, chromosome crossover operation; A crossover point is randomly selected from the chromosome codes in the population set, and the chromosome code segments between the crossover points are exchanged to form a new chromosome code, thereby forming a new population; The duplicate chromosome codes are removed in order, and the length of the chromosome codes is ensured to be consistent to form daughter chromosomes.

[0017] S45, chromosome mutation operation; The mutation process is to replace the gene fragment of the parent chromosome to produce a chromosome code different from the parent; based on the set mutation probability Mutate the parent chromosome code, thereby producing a new generation of daughter chromosomes; The fitness of all offspring chromosomes is calculated. If the offspring is better than the parent, the parent chromosome code is replaced. Otherwise, if the parent is better, it means that the mutation has failed, and the parent is selected to continue the iteration operation.

[0018] S46, output the optimal solution to the problem; Set the iteration threshold Y, set the iteration If the chromosome code output after the last iteration remains unchanged or the hybrid genetic algorithm reaches the set maximum number of iterations, the hybrid genetic algorithm is terminated and the chromosome code output for the last time is used as the brightness parameter of the lamp.

[0019] Preferably, the genetic operator selection operation comprises the following steps: Assume the probability of chromosome encoding being selected to be ; ; in, is the population size, The fitness encoded for the kth chromosome.

[0020] The present invention uses a hybrid genetic algorithm and, based on the obtained real-time brightness data, analyzes the obtained real-time brightness data by constructing a brightness data fitness function and iterative calculation. After the iteration is completed, the output determines the brightness parameters of the lamp, thereby improving the real-time performance of determining the brightness parameters of the lamp.

[0021] Preferably, the step of setting a lamp energy-saving adjustment module based on dynamically determined lamp brightness parameters, and performing intelligent energy-saving control of the lamp through the set lamp energy-saving adjustment module comprises the following steps: S51, setting indoor brightness data standard based on normal exposure parameters of image data; The indoor brightness data standard is: the ratio of the normal brightness area to the total area conforms to the normal distribution; S52, inputting the determined lamp brightness parameter into the lamp energy-saving adjustment module, and the lamp energy-saving adjustment module adjusts the lamp brightness based on the received lamp brightness parameter; S53, after the lamp brightness adjustment is completed, the adjusted indoor brightness data is collected through the wireless Internet of Things device, and it is determined whether the adjusted indoor brightness data meets the set indoor brightness data standard; When the adjusted indoor brightness data does not meet the set indoor brightness data standard, the adjusted indoor brightness data is used as the real-time brightness data to re-determine the brightness parameters of the lamp; When the adjusted indoor brightness data meets the set indoor brightness data standard, set regular monitoring and dynamically calculate the brightness parameters of the lamps.

[0022] The present invention sets an indoor brightness data standard, and inputs the determined lamp brightness parameters into a lamp energy-saving adjustment module for adjustment, and judges whether the adjusted indoor brightness data meets the set indoor brightness data standard; further determines the adjusted indoor brightness data by a secondary judgment method, thereby improving the energy saving of lamp control.

[0023] The present invention also discloses a lamp intelligent energy-saving control system based on wireless Internet of Things, which is used to implement a lamp intelligent energy-saving control method based on wireless Internet of Things. The system includes: a wireless Internet of Things device, a control gateway, a brightness data analysis module, a brightness parameter determination module, and a lamp energy-saving adjustment module; The wireless Internet of Things device is used to collect brightness data of the lighting control area in real time; The control gateway is used to set the collection cycle of the wireless Internet of Things device; The brightness data analysis module is used to analyze the brightness data of the lighting control area collected in real time to obtain the analyzed brightness data; The brightness parameter determination module is used to iteratively calculate the analyzed brightness data to determine the brightness parameters of the lamp; The lamp energy-saving adjustment module is used to adjust the brightness of the lamp according to the determined lamp brightness parameters.

[0024] (III) Beneficial effects Compared with the prior art, the present invention provides a method and system for intelligent energy-saving control of lamps based on wireless Internet of Things, which has the following beneficial effects: 1. The invention sets a lamp control area and divides the set lamp control area. Based on the divided lamp control area, a wireless Internet of Things device and a control gateway are installed in each divided area, and a brightness data collection cycle of the wireless Internet of Things device is set. At the same time, the brightness data of the lamp control area is collected in real time and analyzed and summarized, and the summarized real-time brightness data is processed by data optimization to determine the lamp brightness parameters. Finally, the determined lamp brightness parameters are input into the lamp energy-saving adjustment module, which adjusts the lamp energy-saving adjustment module and performs real-time verification, thereby improving the intelligence of the lamp energy-saving control.

[0025] 2. The invention sets each wireless Internet of Things device as a wireless Internet of Things device node, and abstractly defines the set wireless Internet of Things device node and the control gateway. At the same time, based on the connection status of the wireless Internet of Things device node, a network node topology map is established according to the wireless Internet of Things device node number and the number of each divided area. The location of each wireless Internet of Things device node and the corresponding area are determined through the established network node topology map, thereby improving the accuracy of indoor brightness data collection.

[0026] 3. The invention collects image data in the lighting control area in real time based on the installed wireless Internet of Things devices, and constructs an indoor brightness curve by analyzing the collected image data. Finally, the constructed indoor brightness curve is summarized and the analyzed real-time brightness data is output, thereby improving the reliability of real-time brightness data analysis.

[0027] 4. The present invention uses a hybrid genetic algorithm and, based on the obtained real-time brightness data, analyzes the obtained real-time brightness data by constructing a brightness data fitness function and iterative calculation. After the iteration is completed, the output determines the brightness parameters of the lamp, thereby improving the real-time performance of determining the brightness parameters of the lamp.

[0028] 5. The invention sets the indoor brightness data standard, and inputs the determined lamp brightness parameters into the lamp energy-saving adjustment module for adjustment, and judges whether the adjusted indoor brightness data meets the set indoor brightness data standard; further determines the adjusted indoor brightness data through secondary judgment, thereby improving the energy saving of lamp control. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of the intelligent energy-saving control method for lamps of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example 1 See also Figure 1 This embodiment discloses a lighting intelligent energy-saving control method based on wireless Internet of Things, which specifically includes the following steps: S1. Setting a lamp control area, and dividing the set lamp control area to obtain divided lamp control areas; S2. Based on the obtained divided lighting control areas, install wireless IoT devices and control gateways in each divided area, and set the brightness data collection cycle of the wireless IoT devices; S3, collecting brightness data of the lighting control area in real time based on a set period, and analyzing the real-time collected brightness data to obtain analyzed real-time brightness data; S4, summarizing the analyzed real-time brightness data, and dynamically determining the brightness parameters of the lamps through data optimization; S5. Setting a lamp energy-saving adjustment module based on dynamically determined lamp brightness parameters, and performing intelligent energy-saving control of the lamp through the set lamp energy-saving adjustment module.

[0032] For further information, see Figure 1 , setting a lighting control area, and dividing the set lighting control area, and obtaining the divided lighting control area includes the following steps: Measure the area of ​​the lighting fixture control area, divide the measured lighting fixture control area into equal proportions, and record the number of each area after division.

[0033] For further information, see Figure 1 Based on the obtained divided lighting control areas, wireless IoT devices and control gateways are installed in each divided area, and the wireless IoT device is set to collect brightness data cycle, including the following steps: S21. Set each wireless IoT device as a wireless IoT device node, and abstractly define the set wireless IoT device node and control gateway; Set the installed control gateway as the root node , the number of wireless IoT device nodes is N; For each wireless IoT device node, a unique IP address is set as the attribute of the wireless IoT device node; The set of all data node IP addresses is represented as: ; in, Represents the set of IP addresses of all data nodes. Represents the first wireless IoT device node, Indicates Wireless IoT device nodes.

[0034] S22, establishing a network node topology map based on the connection status of the wireless Internet of Things device nodes according to the wireless Internet of Things device node numbers and the numbers of each divided area; All wireless IoT device nodes in the lighting control area are connected to the control gateway. The control gateway numbers all connected wireless IoT device nodes based on the IP addresses that establish connections with the wireless IoT device nodes, and saves a list of wireless IoT device node numbers. A network node topology diagram is established based on the wireless IoT device node numbers and the numbers of each divided area.

[0035] For further information, see Figure 1 , based on the set period, real-time collection of brightness data of the lighting control area, and analysis of the real-time collected brightness data, to obtain the analyzed real-time brightness data includes the following steps: S31, collecting image data in the lighting control area in real time based on the installed wireless IoT device; Dividing the collected image data into: a black area, a shadow area, a normal brightness area, a highlight area, and a white area based on pixel values ​​in the collected image data; Further, the number of pixels in each area is traversed and summarized; Construct an image brightness curve based on the number of pixels in each region.

[0036] S32, constructing an indoor brightness curve based on the collected image data of the lamp control area; Based on the obtained image data collected in real time by each wireless IoT device, a fitting analysis is performed on the image data collected in real time by Gaussian fitting; Set the horizontal axis of the curve to represent the pixel value, with a value range of 0-255, and the vertical axis to represent the number of pixels in the image data that the pixel value has; The Gaussian fitting expression is as follows: ; in, Indicates the number of wireless IoT devices, represents the ordinate of the i-th brightness curve, Indicates the coordinates of the center position of the i-th brightness curve on the horizontal axis, represents the abscissa of the ith brightness curve, represents the brightness curve after fitting, represents the pixel value, is a natural constant.

[0037] S33, analyzing the constructed indoor brightness curve, and outputting the analyzed real-time brightness data; Calculate real-time brightness data based on the fitted indoor brightness curve; The formula for weighted calculation of real-time brightness data is as follows: ; in, Represents real-time brightness data, represents the weight of the black area, Represents the black area in the indoor brightness curve after fitting. represents the weight of the shadow area, Represents the shadow area in the indoor brightness curve after fitting, represents the weight of the normal brightness area, It represents the normal brightness area in the indoor brightness curve after fitting. Represents the highlight area weight, Represents the highlight area in the indoor brightness curve after fitting. represents the weight of the white area, Represents the white area in the indoor brightness curve after fitting.

[0038] For further information, see Figure 1 , summarizing the analyzed real-time brightness data, and dynamically determining the brightness parameters of the lamps through data optimization, including the following steps: S41, initializing the analyzed real-time brightness data, performing chromosome encoding on the analyzed real-time brightness data and brightness data in adjacent periods based on a hybrid genetic algorithm, and constructing a population set; Set each set of chromosome encoding to represent a set of brightness data, set the population size, and the maximum number of iterations .

[0039] S42, setting a fitness function of brightness data based on the constructed population set size; Set the fitness function: ; ; in, represents the fitness function of the brightness data, Indicates the change in brightness data, Represents real-time brightness data, Indicates that the current time is cycles, Indicates Brightness data for each cycle.

[0040] S43, genetic operator selection operation; Assume the probability of chromosome encoding being selected to be ; ; in, is the population size, The fitness encoded for the kth chromosome.

[0041] S44, chromosome crossover operation; A crossover point is randomly selected from the chromosome codes in the population set, and the chromosome code segments between the crossover points are exchanged to form a new chromosome code, thereby forming a new population; Remove duplicate chromosome codes in order and ensure that the length of chromosome codes is consistent to form daughter chromosomes; S45, chromosome mutation operation; The mutation process is to replace the gene fragment of the parent chromosome to produce a chromosome code different from the parent; based on the set mutation probability Mutate the parent chromosome code, thereby producing a new generation of daughter chromosomes; The fitness of all offspring chromosomes is calculated. If the offspring is better than the parent, the parent chromosome code is replaced. Otherwise, if the parent is better, it means that the mutation has failed, and the parent is selected to continue the iteration operation.

[0042] S46, output the optimal solution to the problem; Set the iteration threshold Y, set the iteration If the chromosome code output after the last iteration remains unchanged or the hybrid genetic algorithm reaches the set maximum number of iterations, the hybrid genetic algorithm is terminated, and the chromosome code output for the last time is used as the brightness parameter of the lamp; For further information, see Figure 1 , a lamp energy-saving adjustment module is set based on the dynamically determined lamp brightness parameters, and intelligent energy-saving control of the lamp is performed through the set lamp energy-saving adjustment module.

[0043] S51, setting indoor brightness data standard based on normal exposure parameters of image data; The indoor brightness data standard is: the ratio of the normal brightness area to the total area conforms to the normal distribution; S52, inputting the determined lamp brightness parameter into the lamp energy-saving adjustment module, and the lamp energy-saving adjustment module adjusts the lamp brightness based on the received lamp brightness parameter; S53, after the lamp brightness adjustment is completed, the adjusted indoor brightness data is collected through the wireless Internet of Things device, and it is determined whether the adjusted indoor brightness data meets the set indoor brightness data standard; When the adjusted indoor brightness data does not meet the set indoor brightness data standard, the adjusted indoor brightness data is used as the real-time brightness data to re-determine the brightness parameters of the lamp; When the adjusted indoor brightness data meets the set indoor brightness data standard, set regular monitoring and dynamically calculate the brightness parameters of the lamps.

[0044] Example 2 This embodiment also discloses a lamp intelligent energy-saving control system based on wireless Internet of Things, which is used to implement a lamp intelligent energy-saving control method based on wireless Internet of Things. The system includes: a wireless Internet of Things device, a control gateway, a brightness data analysis module, a brightness parameter determination module, and a lamp energy-saving adjustment module; The wireless Internet of Things device is used to collect brightness data of the lighting control area in real time; The control gateway is used to set the collection cycle of the wireless Internet of Things device; The brightness data analysis module is used to analyze the brightness data of the lighting control area collected in real time to obtain the analyzed brightness data; The brightness parameter determination module is used to iteratively calculate the analyzed brightness data to determine the brightness parameters of the lamp; The lamp energy-saving adjustment module is used to adjust the brightness of the lamp according to the determined lamp brightness parameters.

[0045] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent energy-saving control of lamps based on wireless Internet of Things, characterized in that: The following steps are involved: S1. Setting a lamp control area, and dividing the set lamp control area to obtain divided lamp control areas; S2. Based on the obtained divided lighting control areas, a wireless Internet of Things device and a control gateway are installed in each divided area, and a brightness data collection cycle of the wireless Internet of Things device is set; S3, collecting brightness data of the lighting control area in real time based on a set period, and analyzing the real-time collected brightness data to obtain analyzed real-time brightness data; S4, summarizing the analyzed real-time brightness data, and dynamically determining the brightness parameters of the lamps through data optimization; S5. Setting a lamp energy-saving adjustment module based on dynamically determined lamp brightness parameters, and performing intelligent energy-saving control of the lamp through the set lamp energy-saving adjustment module.

2. According to claim 1, a method for intelligent energy-saving control of lamps based on wireless Internet of Things is characterized in that: The method of installing a wireless Internet of Things device and a control gateway in each divided area based on the obtained divided lighting control area, and setting a brightness data collection cycle of the wireless Internet of Things device includes the following steps: S21. Set each wireless IoT device as a wireless IoT device node, and abstractly define the set wireless IoT device node and control gateway; S22, establishing a network node topology map based on the connection status of the wireless Internet of Things device nodes according to the wireless Internet of Things device node numbers and the numbers of each divided area; All wireless IoT device nodes in the lighting control area are connected to the control gateway. The control gateway numbers all connected wireless IoT device nodes based on the IP addresses that establish connections with the wireless IoT device nodes, and saves a list of wireless IoT device node numbers. A network node topology diagram is established based on the wireless IoT device node numbers and the numbers of each divided area.

3. According to claim 2, a method for intelligent energy-saving control of lamps based on wireless Internet of Things is characterized in that: The step of setting each wireless IoT device as a wireless IoT device node and abstractly defining the set wireless IoT device node and control gateway includes the following steps: Set the installed control gateway as the root node , the number of wireless IoT device nodes is N; For each wireless IoT device node, a unique IP address is set as the attribute of the wireless IoT device node; The set of all data node IP addresses is represented as: ； in, Represents the set of IP addresses of all data nodes. Represents the first wireless IoT device node, Indicates Wireless IoT device nodes.

4. According to the wireless Internet of Things based intelligent energy-saving control method for lamps as claimed in claim 1, it is characterized in that: The real-time acquisition of brightness data of the lighting control area based on the set period, and analysis of the real-time acquired brightness data to obtain the analyzed real-time brightness data includes the following steps: S31, collecting image data in the lighting control area in real time based on the installed wireless IoT device; Dividing the collected image data into: a black area, a shadow area, a normal brightness area, a highlight area, and a white area based on pixel values ​​in the collected image data; Traverse and summarize the number of pixels in each area; Construct an image brightness curve based on the number of pixels in each area; S32, constructing an indoor brightness curve based on the collected image data of the lamp control area; S33, analyzing the constructed indoor brightness curve, and outputting the analyzed real-time brightness data.

5. According to the wireless Internet of Things-based intelligent energy-saving control method for lamps in claim 4, it is characterized in that: The method of constructing an indoor brightness curve based on collecting image data of the lamp control area includes the following steps: Based on the obtained image data collected in real time by each wireless IoT device, a fitting analysis is performed on the image data collected in real time by Gaussian fitting; Set the horizontal axis of the curve to represent the pixel value, with a value range of 0-255, and the vertical axis to represent the number of pixels in the image data that the pixel value has; The Gaussian fitting expression is as follows: ； in, Indicates the number of wireless IoT devices, represents the ordinate of the i-th brightness curve, Indicates the coordinates of the center position of the i-th brightness curve on the horizontal axis, represents the abscissa of the ith brightness curve, represents the brightness curve after fitting, represents the pixel value, is a natural constant.

6. According to the wireless Internet of Things-based intelligent energy-saving control method for lamps in claim 4, it is characterized in that: The step of analyzing the constructed indoor brightness curve and outputting the analyzed real-time brightness data comprises the following steps: Calculate real-time brightness data based on the fitted indoor brightness curve; The formula for weighted calculation of real-time brightness data is as follows: ； in, Represents real-time brightness data, represents the weight of the black area, Represents the black area in the indoor brightness curve after fitting. represents the weight of the shadow area, Represents the shadow area in the indoor brightness curve after fitting, represents the weight of the normal brightness area, It represents the normal brightness area in the indoor brightness curve after fitting. Represents the highlight area weight, It represents the highlight area in the indoor brightness curve after fitting. represents the weight of the white area, Represents the white area in the indoor brightness curve after fitting.

7. According to the wireless Internet of Things based intelligent energy-saving control method for lamps as claimed in claim 1, it is characterized in that: The real-time brightness data obtained by the analysis is summarized, and the brightness parameters of the lamps are dynamically determined by data optimization, including the following steps: S41, initializing the analyzed real-time brightness data, performing chromosome encoding on the analyzed real-time brightness data and brightness data in adjacent periods based on a hybrid genetic algorithm, and constructing a population set; Set each set of chromosome encoding to represent a set of brightness data, set the population size, and the maximum number of iterations ; S42, setting a fitness function of brightness data based on the constructed population set size; Set the fitness function: ； ； in, represents the fitness function of brightness data, Indicates the change in brightness data, Represents real-time brightness data, Indicates that the current time is cycles, Indicates Brightness data of each cycle; S43, genetic operator selection operation; S44, chromosome crossover operation; A crossover point is randomly selected from the chromosome codes in the population set, and the chromosome code segments between the crossover points are exchanged to form a new chromosome code, thereby forming a new population; Remove duplicate chromosome codes in order and ensure that the length of chromosome codes is consistent to form daughter chromosomes; S45, chromosome mutation operation; The mutation process is to replace the gene fragment of the parent chromosome to produce a chromosome code different from the parent; based on the set mutation probability Mutate the parent chromosome code, thereby producing a new generation of daughter chromosomes; The fitness of all offspring chromosomes is calculated. If the offspring is better than the parent, the parent chromosome code is replaced. Otherwise, if the parent is better, it means that the mutation has failed, and the parent is selected to continue the iteration operation. S46, output the optimal solution to the problem; Set the iteration threshold Y, set the iteration If the chromosome code output after the last iteration remains unchanged or the hybrid genetic algorithm reaches the set maximum number of iterations, the hybrid genetic algorithm is terminated and the chromosome code output for the last time is used as the brightness parameter of the lamp.

8. The intelligent energy-saving control method for lamps based on wireless Internet of Things according to claim 7 is characterized in that: The genetic operator selection operation comprises the following steps: Assume the probability of chromosome encoding being selected ; ； in, is the population size, The fitness encoded for the kth chromosome.

9. The intelligent energy-saving control method for lamps based on wireless Internet of Things according to claim 1 is characterized in that: The method of setting a lamp energy-saving adjustment module based on dynamically determined lamp brightness parameters and performing intelligent energy-saving control of the lamp through the set lamp energy-saving adjustment module comprises the following steps: S51, setting indoor brightness data standard based on normal exposure parameters of image data; The indoor brightness data standard is: the ratio of the normal brightness area to the total area conforms to the normal distribution; S52, inputting the determined lamp brightness parameter into the lamp energy-saving adjustment module, and the lamp energy-saving adjustment module adjusts the lamp brightness based on the received lamp brightness parameter; S53, after the lamp brightness adjustment is completed, the adjusted indoor brightness data is collected through the wireless Internet of Things device, and it is determined whether the adjusted indoor brightness data meets the set indoor brightness data standard; When the adjusted indoor brightness data does not meet the set indoor brightness data standard, the adjusted indoor brightness data is used as the real-time brightness data to re-determine the brightness parameters of the lamp; When the adjusted indoor brightness data meets the set indoor brightness data standard, set regular monitoring and dynamically calculate the brightness parameters of the lamps.

10. A system for implementing the intelligent energy-saving control method for lamps based on wireless Internet of Things as described in any one of claims 1 to 9, characterized in that: include: Wireless IoT devices, control gateways, brightness data analysis modules, brightness parameter determination modules, and lamp energy-saving adjustment modules; The wireless Internet of Things device is used to collect brightness data of the lighting control area in real time; The control gateway is used to set the collection cycle of the wireless Internet of Things device; The brightness data analysis module is used to analyze the brightness data of the lighting control area collected in real time to obtain the analyzed brightness data; The brightness parameter determination module is used to iteratively calculate the analyzed brightness data to determine the brightness parameters of the lamp; The lamp energy-saving adjustment module is used to adjust the brightness of the lamp according to the determined lamp brightness parameters.

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