A Dimension Adaptive Lighting Fixture Arrangement Method Based on Particle Swarm Optimization Algorithm

Abstract

The present invention discloses a dimension-adaptive lamp layout method based on a particle swarm algorithm, which includes selecting the type of lamp layout; determining the illuminance requirements of each area in the space and simultaneously dividing the space into a planar grid; initializing the lamp layout parameters and optimizing the lamp layout form through a standard particle swarm algorithm; checking whether all area illuminance requirements are met. If they are met, a low-carbon energy-saving type of adaptive correction is performed. If not, a compliance type of adaptive correction is performed; optimizing the layout form of the lamps after adaptive correction based on the standard particle swarm algorithm; for the compliance type of adaptive correction, until all area illuminance requirements are met, the last iteration parameters and optimization results that meet all area illuminance requirements are the final results; for the low-carbon energy-saving type of adaptive correction, until all area illuminance requirements cannot be met, the last iteration parameters and optimization results that meet all area illuminance requirements are the final results.

Description

Technical Field

[0001] The present invention relates to the technical field of lighting optimization design, and in particular to a dimension adaptive lamp layout method based on a particle swarm algorithm. Background Art

[0002] With the rapid economic development and social progress of our country, the contradiction between energy supply and demand has become increasingly prominent. The proportion of lighting electricity consumption in power consumption has been increasing year by year, yet the problems of power and energy shortage have always existed. Existing research shows that the scheme design stage has the greatest impact on building performance and can generate a performance improvement potential of more than 40%. As an effective tool for weighing multiple complex factors, intelligent optimization algorithms can provide scientific and efficient design assistance decisions for designers. However, current optimization design algorithms need to consider and solve optimization problems by setting fixed-dimensional input and output values. For the early stage of lighting design, designers are still unable to accurately control the current scheme. Taking the number of lamps as an example, although the number of lamps is an important factor affecting the lighting effect and energy consumption of the design scheme, designers cannot accurately obtain the number of lamps that meet the lighting design specifications and achieve the optimal low-carbon energy-saving effect at the beginning of the design, so they cannot determine the dimension of the input variables of the optimization algorithm. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a dimension adaptive lamp layout method based on a particle swarm algorithm, which can determine an adaptive adjustment strategy for the search dimension according to the global optimal value in the current search space, and while optimizing the layout position of the lamps, minimize energy consumption as much as possible.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A dimension adaptive lamp layout method based on a particle swarm algorithm includes the following steps:

[0006] S1. Select the lamp layout type, specifically including the regular layout method and the free layout method;

[0007] S2. Determine the illuminance requirements of each area in the space according to the lighting standard and the space function partition situation, and at the same time perform a plane grid division on the space to calculate the grid position matrix (X, Y) and the corresponding illuminance requirement matrix I of the grid D ;

[0008] S3. Initialize the lamp layout parameters, and the lamp layout parameters include room shape parameters, the number of lamp parameters, and the lamp height H L , the working surface height H S ; the room shape parameters include the bay H and the depth W;

[0009] S4. Optimize the layout form under the lighting fixture layout parameters through the standard particle swarm optimization algorithm, and record the globally optimal individual g best of the layout form;

[0010] S5. Check whether the globally optimal layout form meets the illumination requirements of all areas. If it meets, perform low-carbon energy-saving adaptive correction; if not, perform compliance-based adaptive correction;

[0011] S6. Optimize the layout form of the lighting fixtures after adaptive correction based on the standard particle swarm optimization algorithm;

[0012] S7. For compliance-based adaptive correction, repeat steps S5 and S6 until the illumination requirements of all areas are met, and the dimension-adaptive optimization process is completed. The number of lighting fixtures and their corresponding illuminance in the last iteration that meets the illumination requirements of all areas are the final results;

[0013] For low-carbon energy-saving adaptive correction, repeat steps S5 and S6 until the illumination requirements of all areas cannot be met, and the dimension-adaptive optimization process is completed. The number of lighting fixtures and their corresponding illuminance in the last iteration that meets the illumination requirements of all areas are the final results.

[0014] Furthermore, in step S1, the regular layout method can optimize the number of rows and columns of lighting fixtures in the space and the positions of lighting fixtures in each row and column, minimizing the lighting energy consumption while meeting the illumination requirements of different areas.

[0015] Furthermore, in step S1, the free layout method can optimize the number of lighting fixtures in the space and the positions of each lighting fixture, minimizing the lighting energy consumption while meeting the illumination requirements of different areas.

[0016] Furthermore, in step S3, for the lighting fixture quantity parameter, in the regular layout method, the input form of the lighting fixture quantity parameter is the number of rows N x , the number of columns N y , the positions of lighting fixtures in each row L rx , the positions of lighting fixtures in each column L cy ; in the free layout method, the input form of the lighting fixture quantity parameter is the number of units N, and the positions of each lighting fixture (L jx , L jy ).

[0017] Furthermore, step S4 specifically includes:

[0018] (401) Initialize the parameters of the particle swarm optimization algorithm, specifically including the population size N p , the number of iterations g, the inertia factor ω, the learning factors c 1 and c 2 ;

[0019] (402) Initialize the initial population, and the initial velocity of each particle The initial position X i is a random number between the bay width H and the depth W;

[0020] (403) Calculate the luminaire position information based on the position information of each particle, and calculate the spatial illuminance after turning on the lights through the daylighting simulation software Radiance;

[0021] (404) Calculate the fitness function E(N) of each particle. Among them, the fitness function E(N) is defined as follows:

[0022]

[0023] Among them, n is the grid label, m is the number of grids, and I n is the illuminance value at the center point of the nth grid after turning on the lights, and I D,n is the illuminance requirement value at the center point of the nth grid;

[0024] (405) Detect whether the illuminance values at the center points of each grid in the space after turning on the lights all meet the illuminance requirement values. If not, calculate the penalty function U(N), and the penalty function U(N) is defined as:

[0025] U(N) = E(N) + C

[0026] Among them, C is the penalty term, and its value is a positive integer;

[0027] (406) Determine and record the individual optimal p best of each particle in the current population and the global optimal individual g best ,

[0028] (407) Update the velocity v i and position x i of each particle in the population, and their definitions are:

[0029]

[0030] Among them, d is the current search dimension, d = 1, 2, ……, D; i is the ith particle in the population; k is the kth generation population; r 1 and r 2 are random numbers;

[0031] (408) Judge whether it converges. If it does not converge, repeat steps (402)-(407) until the population converges;

[0032] (409) Output the global optimal individual g best .

[0033] Further, in step S5, the compliance-based adaptive correction steps are as follows:

[0034] (5011) Initialize the parameters related to the adaptive mechanism. The parameters related to the adaptive mechanism include the adaptive level CL = 1, the adjustment range DR = 2 CL-1 , the number of adaptive adjustments C = 0, and the threshold T for the change of adjustment level;

[0035] (5012) C = C + i, where i is the number of times of performing the adaptive lamp quantity correction for the i-th time;

[0036] (5013) Judge the size relationship between the current number of adaptive adjustments C and the threshold T for the change of adjustment level. When C is greater than or equal to T and the optimization result of the current state meets the illumination requirements of all areas, adjust the current adaptive level CL to CL = CL + 1, and update the adjustment range DR, DR = 2 CL-1 ;

[0037] (5014) Judge the size relationship between the current number of adaptive adjustments C and the threshold T for the change of adjustment level. When C is less than T, the current adaptive level CL remains unchanged, and update the adjustment range DR, DR = 2 CL-1 ;

[0038] (5015) Adjust the existing lamp quantity according to the adjustment range and the lamp layout type;

[0039] When the lamp layout type is a regular layout method, the input form of the lamp quantity parameter is the number of rows N x , the number of columns N y , compare the size relationship between H / W and N x / N y When H / W > N x / N y , adaptively correct the number of lamp rows N x , N x = N x + DR. When H / W < N x / N y , adaptively correct the number of lamp columns N y , N y = N y + DR,

[0040] When the lamp layout type is a free layout method, adaptively correct the number of lamps N, N = N + DR.

[0041] Further, in step S5, the low-carbon energy-saving adaptive correction steps are as follows:

[0042] (5021) Initialize the parameters related to the adaptive mechanism. The parameters related to the adaptive mechanism include the adaptive level CL = 1, the adjustment range DR = 2 CL-1, the adaptive adjustment count C = 0, the adjustment level change threshold T;

[0043] (5022) C = C + i, where i is the i-th execution of the adaptive luminaire quantity correction;

[0044] (5023) Determine the magnitude relationship between the current adaptive adjustment count C and the adjustment level change threshold T. When C is greater than or equal to T and the optimization result of the current state meets the illuminance requirements of all areas, adjust the current adaptive level CL to CL = CL + 1, and update the adjustment amplitude DR, DR = 2 CL-1 ;

[0045] (5024) Determine the magnitude relationship between the current adaptive adjustment count C and the adjustment level change threshold T. When C is less than T, the current adaptive level CL remains unchanged, and update the adjustment amplitude DR, DR = 2 CL-1 ;

[0046] (5025) Adjust the existing luminaire quantity according to the adjustment amplitude and the luminaire layout type,

[0047] When the luminaire layout type is a regular layout method, compare the magnitude relationship between H / W and N x / N y When H / W > N x / N y Then, adaptively correct the number of luminaire columns N y , N y = N y - DR. When H / W < N x / N y Then, adaptively correct the number of luminaire rows N x , N x = N x - DR;

[0048] When the luminaire layout type is a free layout method, adaptively correct the number of luminaires N, N = N - DR.

[0049] Compared with the prior art, the beneficial effects brought by the technical solution of the present invention are:

[0050] 1. The dimension adaptive correction technology is used to optimize the lighting fixture layout problem. On the premise of meeting the indoor light environment illuminance requirements, redundant lighting is minimized to achieve energy conservation and emission reduction effects. Compared with traditional optimization algorithms, adaptive dimension correction can dynamically change the optimization dimension (i.e., the number of lighting fixtures) of the optimization algorithm. By analyzing the current optimization result and the expected goal (or specification standard), this method can adjust the adaptive level and the amplitude of adaptive correction, breaking through the deficiencies of traditional methods where the optimization dimension cannot be changed and manual debugging is time-consuming and laborious. It can not only quickly optimize the layout of indoor lighting fixtures but also avoid light waste.

[0051] 2. Based on the above contradictions, the present invention develops an adaptive particle swarm algorithm by expanding the optimization algorithm process on the basis of the standard particle swarm algorithm. This algorithm can realize the self-elevation and self-lowering of dimensions according to the optimization result during the optimization process, solve the problem that designers cannot determine the specific scheme at the initial stage of design through the adaptive dimension reduction process, maximize the design efficiency, and ensure the optimization effect.

[0052] 3. The present invention can adapt to the dimension changes in the lighting fixture layout optimization, minimize the lighting energy consumption while meeting all illuminance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flowchart of the adaptive lighting fixture layout method according to the embodiment of the present application;

[0054] Figure 2 It is a schematic diagram of the target office grid division and lighting fixture layout in the method according to the embodiment of the present application;

[0055] Figure 3 It is a flowchart of the standard particle swarm optimization algorithm for step S4 in the method according to the embodiment of the present application;

[0056] Figure 4 It is a flowchart of the compliance type adaptive correction in the regular layout mode for step S6 in the method according to the embodiment of the present application;

[0057] Figure 5 It is a flowchart of the compliance type adaptive correction in the free layout mode for step S6 in the method according to the embodiment of the present application;

[0058] Figure 6 It is a flowchart of the low-carbon energy-saving type adaptive correction in the regular layout mode for step S6 in the method according to the embodiment of the present application;

[0059] Figure 7 It is a flowchart of the low-carbon energy-saving type adaptive correction in the free layout mode for step S6 in the method according to the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not used to limit the present invention. The present invention covers any alternatives, modifications, equivalent methods, and solutions made within the spirit and scope of the present invention.

[0061] The present invention will be described more specifically by way of example in the following paragraphs with reference to the accompanying drawings. It should be noted that the drawings are all in a relatively simplified form and use non-precise scales, only for conveniently and clearly assisting in explaining the purpose of the embodiments of the present invention.

[0062] In this embodiment, a rectangular office space in a certain area is used as the target space. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0063] Embodiment 1.

[0064] Combined with Figures 1-4 and Figure 6 shown, this embodiment is a method for arranging office lighting fixtures based on the dimension adaptation of the particle swarm algorithm, including the following steps:

[0065] S1: Select the type of lighting fixture arrangement;

[0066] In this embodiment, the regular arrangement method is adopted, and the lighting fixture arrangement form is in a determinant form (i.e., the lighting fixtures are arranged in a horizontal and vertical grid), and the number of lighting fixtures is determined by the number of rows N x , the number of columns N y , the positions of the lighting fixtures in each row are represented as L rx , and the positions of the lighting fixtures in each column are represented as L cy ;

[0067] S2: Space grid division and determination of partition illuminance;

[0068] As Figure 2 shown, the working surface of this space is divided into plane grids. In this embodiment, the grid density is 2m * 2m.

[0069] According to the lighting standard and the space function partition situation, the illuminance of each area in the space is divided. In this embodiment, there are a total of 3 levels of illuminance requirements, which are 200 lux, 300 lux, and 400 lux respectively,

[0070] Calculate the grid node coordinate information matrix (X, Y) and the corresponding illuminance requirement matrix I of the grid D ;

[0071] S3: Initialization of lighting fixture arrangement parameters,

[0072] The lamp layout parameters include the lamp layout type (regular), the room shape parameters of the bay width H and the depth W (in this embodiment, H = 28, W = 20), the lamp height H L (In this embodiment, H L = 3.3), the working surface height H S (In this embodiment, H S = 0.9), the regular lamp quantity parameter N x , N y (In this embodiment, N x = 3, N y = 4);

[0073] S4: As Figure 3 shown, optimize the position layout of the existing lamps based on the standard particle swarm algorithm,

[0074] (1) Initialize the particle swarm algorithm parameters, specifically including the population size N p , the number of iterations g, the inertia factor ω, the learning factors c 1 and c 2 ,

[0075] (2) Initialize the initial population, and the initial velocity of each particle The initial position X i is a random number between the bay width H and the depth W,

[0076] (3) Calculate the lamp position information based on the position information of each particle, and calculate the spatial illuminance situation after turning on the lights through Radiance (specifically using the standard PSO algorithm);

[0077] (4) Calculate the fitness function E(N) of each particle. Among them, the fitness function E(N) is defined as follows:

[0078]

[0079] Among them, n is the grid label, m is the number of spatial grids, I n is the illuminance value at the center point of the nth grid after turning on the lights, I D,n is the illuminance requirement value at the center point of the nth grid,

[0080] (5) Detect whether the illuminance values at the center points of each grid in the space after turning on the lights all meet the illuminance requirement values. If not, calculate the penalty function U(N). Among them, the penalty function U(N) is defined as:

[0081] U(N) = E(N) + C

[0082] Among them, C is the penalty term, and its value is a large positive integer,

[0083] (6) Determine and record the individual optimal p of each particle in this generation population best and the population optimal individual g best ,

[0084] (7) Update the velocity v i and position x i of each particle in the population, which are defined as:

[0085]

[0086] where d is the current search dimension, d = 1, 2, ……, D; i is the i-th particle in the population; k is the k-th generation population; r 1 and r 2 are random numbers;

[0087] (8) Determine whether convergence occurs. If not, repeat steps (2)-(7) until the population converges;

[0088] (9) Output the population optimal individual g best ;

[0089] S5: Check whether the current layout form meets the illuminance requirements of all areas. If it does, perform low-carbon energy-saving adaptive correction; if not, perform compliance-based adaptive correction.

[0090] S6: Adaptive correction of the number of luminaires. It includes:

[0091] Compliance-based adaptive correction, see Figure 4 :

[0092] (6011) Initialize the parameters related to the adaptive mechanism, specifically including the adaptive level CL = 1, the adjustment amplitude DR = 2 CL-1 and the number of adaptive adjustments C = 0, the threshold T for the change of the adjustment level;

[0093] (6012) C = C + i, where i is the i-th execution of the adaptive correction of the number of luminaires;

[0094] (6013) Determine the magnitude relationship between the current number of adaptive adjustments C and the threshold T for the change of the adjustment level. When C is greater than or equal to T and the optimization result of the current state meets the illuminance requirements of all areas, adjust the current adaptive level CL to CL = CL + 1, and update the adjustment amplitude DR, DR = 2 CL-1 ;

[0095] (6014) Determine the magnitude relationship between the current number of adaptive adjustments C and the threshold T for the change of the adjustment level. When C is less than T, do not adjust the current adaptive level CL, and update the adjustment amplitude DR, DR = 2 CL-1 ;

[0096] (6015) Adjust the existing number of lamps according to the adjustment range and lamp layout type. The specific method is as follows: Compare the ratio of H / W with N x / N y When H / W > N x / N y Adaptively correct the number of lamp rows N x N x = N x + DR. When H / W < N x / N y Adaptively correct the number of lamp columns N y N y = N y + DR;

[0097] Low-carbon energy-saving type adaptive correction, see Figure 6 :

[0098] (6021) Initialize the parameters related to the adaptive mechanism, specifically including the adaptive level CL = 1, the adjustment range DR = 2 CL-1 , the number of adaptive adjustments C = 0, and the threshold T for the change of adjustment level;

[0099] (6022) C = C + i, where i is the i-th execution of the adaptive correction of the number of lamps;

[0100] (6023) Judge the size relationship between the current number of adaptive adjustments C and the threshold T for the change of adjustment level. When C is greater than or equal to T and the optimization result of the current state meets the illuminance requirements of all areas, adjust the current adaptive level CL to CL = CL + 1 and update the adjustment range DR, DR = 2 CL-1 ;

[0101] (6024) Judge the size relationship between the current number of adaptive adjustments C and the threshold T for the change of adjustment level. When C is less than T, do not adjust the current adaptive level CL and update the adjustment range DR, DR = 2 CL-1 ;

[0102] (6025) Adjust the existing number of lamps according to the adjustment range and lamp layout type. The specific method is as follows: Compare the ratio of H / W with N x / N y When H / W > N x / N y Adaptively correct the number of lamp columns N y N y = N y - DR. When H / W < N x / N y Adaptively correct the number of lamp rows N x N x = N x-DR.

[0103] S7: Optimize the layout form of the luminaires after adaptive correction based on the standard particle swarm optimization algorithm.

[0104] S8: For compliance-type adaptive correction, repeat steps S5 - S7 until the illuminance requirements of all areas are met. The optimization process of dimension adaptation is completed, and the parameters and optimization results of the last iteration that meet the illuminance requirements of all areas are the final results.

[0105] For low-carbon energy-saving type adaptive correction, repeat steps S5 - S7 until the illuminance requirements of all areas cannot be met. The optimization process of dimension adaptation is completed, and the parameters and optimization results of the last iteration that meet the illuminance requirements of all areas are the final results.

[0106] Embodiment 2.

[0107] Combined with Figures 1-3 and Figure 5 and Figure 7 As shown, the method for dimension-adaptive office luminaire layout based on the particle swarm algorithm in this embodiment includes the following steps:

[0108] S1: Select the luminaire layout type;

[0109] In this embodiment, the free layout method is adopted, and the luminaires can be freely arranged at any position. The number of luminaires is represented as N, and the positions of each luminaire are represented as (L jx , L jy );

[0110] S2: Space grid division and determination of partition illuminance;

[0111] As Figure 2 shown, the working surface of this space is divided into plane grids. In this embodiment, the grid density is 2m * 2m.

[0112] According to the lighting standard and the space function partition situation, the illuminance of each area in the space is divided. In this embodiment, there are 3 levels of illuminance requirements, which are 200 lux, 300 lux, and 400 lux respectively.

[0113] Calculate the grid node coordinate information matrix (X, Y) and the corresponding illuminance requirement matrix I of the grid D ;

[0114] S3: Initialize the luminaire layout parameters,

[0115] The luminaire layout parameters include the luminaire layout type (free style), the room shape parameters of the bay H and the depth W (in this embodiment, L = 28, W = 20), and the luminaire height H L (in this embodiment, H L= 3.3), the working surface height H S (In this embodiment, H S = 0.9), the lamp parameters N (in this embodiment, N = 12);

[0116] S4: As Figure 3 shown, optimize the position layout of existing lamps based on the standard particle swarm algorithm,

[0117] (1) Initialize the parameters of the particle swarm algorithm, specifically including the population size N p , the number of iterations g, the inertia factor ω, the learning factors c 1 and c 2 ,

[0118] (2) Initialize the initial population, and the initial velocity of each particle The initial position X i is a random number between the bay H and the depth W,

[0119] (3) Calculate the lamp position information based on the position information of each particle, and calculate the spatial illuminance after turning on the lights through Radiance (specifically using the standard PSO algorithm);

[0120] (4) Calculate the fitness function E(N) of each particle. Among them, the fitness function E(N) is defined as follows:

[0121]

[0122] Among them, n is the grid label, m is the number of spatial grids, and I n is the illuminance value at the center point of the nth grid after turning on the light, and I D,n is the illuminance requirement value at the center point of the nth grid,

[0123] (5) Detect whether the illuminance values at the center points of each grid in the space after turning on the lights all meet the illuminance requirement values. If not, calculate the penalty function U(N). Among them, the penalty function U(N) is defined as:

[0124] U(N) = E(N) + C

[0125] Among them, C is the penalty term, and its value is a relatively large positive integer,

[0126] (6) Determine and record the individual optimal p of each particle in this generation of population best and the population optimal individual g best ,

[0127] (7) Update the velocity v of each particle in the population i and the position x i , and their definitions are:

[0128]

[0129] where d is the current search dimension, d = 1, 2, ……, D; i is the i-th particle in the population; k is the k-th generation population; r 1 and r 2 are random numbers;

[0130] (8) Determine whether convergence has occurred. If not, repeat steps (2)-(7) until the population converges;

[0131] (9) Output the optimal individual g of the population best ;

[0132] S5: Check whether the current layout form meets the illumination requirements of all areas. If it does, perform low-carbon energy-saving adaptive correction; if not, perform compliance-based adaptive correction.

[0133] S6: Adaptive correction of the number of luminaires. It includes:

[0134] Compliance-based adaptive correction, see Figure 5 :

[0135] (6011) Initialize the parameters related to the adaptive mechanism, specifically including the adaptive level CL = 1, the adjustment amplitude DR = 2 CL-1 and the number of adaptive adjustments C = 0, the threshold T for the change of the adjustment level;

[0136] (6012) C = C + i, where i is the i-th execution of the adaptive correction of the number of luminaires;

[0137] (6013) Judge the size relationship between the current number of adaptive adjustments C and the threshold T for the change of the adjustment level. When C is greater than or equal to T and the optimization result of the current state meets the illumination requirements of all areas, adjust the current adaptive level CL to CL = CL + 1, and update the adjustment amplitude DR, DR = 2 CL-1 ;

[0138] (6014) Judge the size relationship between the current number of adaptive adjustments C and the threshold T for the change of the adjustment level. When C is less than T, do not adjust the current adaptive level CL, and update the adjustment amplitude DR, DR = 2 CL-1 ;

[0139] (6015) Adjust the existing number of luminaires according to the adjustment amplitude and the luminaire layout type. The specific method is: N = N + DR.

[0140] Low-carbon energy-saving adaptive correction, see Figure 7 :

[0141] (6021) Initialize the parameters related to the adaptive mechanism, specifically including the adaptive level CL = 1, the adjustment amplitude DR = 2 CL-1, Adaptive adjustment times C = 0, adjustment level change threshold T;

[0142] (6022) C = C + i, where i is the i-th execution of adaptive luminaire quantity correction;

[0143] (6023) Judge the size relationship between the current adaptive adjustment times C and the adjustment level change threshold T. When C is greater than or equal to T and the optimization result of the current state meets the illuminance requirements of all areas, adjust the current adaptive level CL to CL = CL + 1, and update the adjustment amplitude DR, DR = 2 CL-1 ;

[0144] (6024) Judge the size relationship between the current adaptive adjustment times C and the adjustment level change threshold T. When C is less than T, do not adjust the current adaptive level CL, and update the adjustment amplitude DR, DR = 2 CL-1 ;

[0145] (6025) According to the adjustment amplitude and luminaire layout type, adjust the existing luminaire quantity. The specific method is: N = N - DR.

[0146] S7: Optimize the layout form of the luminaires after adaptive correction based on the standard particle swarm algorithm.

[0147] S8: For compliance type adaptive correction, repeat steps S5 - S7 until the illuminance requirements of all areas are met, and the optimization process of dimension adaptation is completed. The last iteration parameters and optimization results that meet the illuminance requirements of all areas are the final results.

[0148] For low - carbon energy - saving type adaptive correction, repeat steps S5 - S7 until the illuminance requirements of all areas cannot be met, and the optimization process of dimension adaptation is completed. The last iteration parameters and optimization results that meet the illuminance requirements of all areas are the final results.

[0149] Finally, it should be pointed out that the above examples are only used to illustrate the calculation process of the present invention, rather than limiting it. Although the present invention has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the calculation process recorded in the foregoing examples, or make equivalent replacements for some of the parameters. These modifications or replacements do not cause the essence of the corresponding calculation method to deviate from the spirit and scope of the calculation method of the present invention.

[0150] The present invention is not limited to the embodiments described above. The above description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention. The above specific embodiments are merely illustrative and not restrictive. Without departing from the spirit of the present invention and the scope protected by the claims, those of ordinary skill in the art can make many specific transformations in various forms under the inspiration of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A dimension - adaptive lamp layout method based on the particle swarm algorithm, characterized in that, it includes the following steps: S1. Select the lamp layout type, specifically including the regular layout method and the free layout method; S2. Determine the illuminance requirements for each area in the space according to the lighting standard and the spatial function zoning. At the same time, divide the space into a planar grid, and calculate the grid position matrix (X, Y) and the corresponding illuminance requirement matrix I for the grid D ; S3. Initialize the lighting layout parameters, where the lighting layout parameters include room shape parameters, the number of lamps parameter, and the lamp height H L , the working surface height H S ; the room shape parameters include the bay width H and the depth W; S4. Optimize the layout form under the lighting fixture layout parameters through the standard particle swarm optimization algorithm, and record the layout form of the globally optimal individual g best of the layout form; S5. Check whether the group - optimal layout form meets the illuminance requirements of all areas. If it meets, perform low - carbon energy - saving type adaptive correction. If it does not meet, perform compliance type adaptive correction; The steps of compliance type adaptive correction are as follows: (5011) Initialize the parameters related to the adaptive mechanism. The parameters related to the adaptive mechanism include the adaptive level CL = 1, the adjustment amplitude DR = 2 CL-1 , the number of adaptive adjustments C = 0, and the threshold T for the change in the adjustment level; (5012) C = C + i, where i is the i - th execution of adaptive lamp quantity correction; (5013) Determine the magnitude relationship between the current adaptive adjustment count C and the adjustment level change threshold T. When C is greater than or equal to T and the optimization result of the current state meets the illuminance requirements of all areas, adjust the current adaptive level CL to CL = CL + 1, and update the adjustment range DR, where DR = 2 CL-1 ; (5014) Determine the magnitude relationship between the current adaptive adjustment count C and the adjustment level change threshold T. When C is less than T, the current adaptive level CL is not adjusted, and the adjustment range DR is updated. DR = 2 CL-1 ; (5015) Adjust the existing lamp quantity according to the adjustment range and the lamp layout type; When the lamp layout type is a regular layout method, the input form of the lamp quantity parameter is the number of rows N x , the number of columns N y , compare the size relationship between H / W and N x / N y . When H / W > N x / N y , adaptively correct the number of lamp rows N x , N x = N x + DR. When H / W < N x / N y , adaptively correct the number of lamp columns N y , N y = N y + DR, When the lamp layout type is the free layout method, adaptively correct the number of lamps N, N = N + DR; The steps of low - carbon energy - saving type adaptive correction are as follows: (5021) Initialize the parameters related to the adaptive mechanism. The parameters related to the adaptive mechanism include the adaptive level CL = 1, the adjustment amplitude DR = 2 CL-1 , the number of adaptive adjustments C = 0, and the threshold T for the change in the adjustment level; (5022) C = C + i, where i is the i - th execution of adaptive lamp quantity correction; (5023) Determine the magnitude relationship between the current adaptive adjustment count C and the adjustment level change threshold T. When C is greater than or equal to T and the optimization result of the current state meets the illuminance requirements of all areas, adjust the current adaptive level CL to CL = CL + 1, and update the adjustment range DR, where DR = 2 CL-1 ; (5024) Determine the magnitude relationship between the current adaptive adjustment count C and the adjustment level change threshold T. When C is less than T, the current adaptive level CL remains unchanged, and update the adjustment range DR, where DR = 2 CL-1 ; (5025) Adjust the existing lamp quantity according to the adjustment range and the lamp layout type, When the lighting fixture layout type is a regular layout method, compare H / W with N x / N y For the magnitude relationship, when H / W > N x / N y , adaptively correct the number of lighting fixture columns N y , N y = N y - DR, when H / W < N x / N y , adaptively correct the number of lighting fixture rows N x , N x = N x - DR; When the lamp layout type is the free layout method, adaptively correct the number of lamps N, N = N - DR; S6. Optimize the layout form of the lamps after adaptive correction based on the standard particle swarm algorithm; S7. For compliance type adaptive correction, repeat steps S5 and S6 until the illuminance requirements of all areas are met, and the dimension - adaptive optimization process is completed. The lamp quantity and its corresponding illuminance in the last iteration that meets the illuminance requirements of all areas are the final results; For low - carbon energy - saving type adaptive correction, repeat steps S5 and S6 until the illuminance requirements of all areas cannot be met, and the dimension - adaptive optimization process is completed. The lamp quantity and its corresponding illuminance in the last iteration that meets the illuminance requirements of all areas are the final results.

2. The dimension - adaptive lamp layout method based on the particle swarm algorithm according to claim 1, characterized in that, in step S1, the regular layout method can optimize the number of lamp rows, the number of lamp columns, and the positions of lamps in each row and column in the space, and minimize the lighting energy consumption while meeting the illuminance requirements of different areas.

3. The dimension - adaptive lamp layout method based on the particle swarm algorithm according to claim 1, characterized in that, in step S1, the free layout method can optimize the number of lamps and the positions of each lamp in the space, and minimize the lighting energy consumption while meeting the illuminance requirements of different areas.

4. The dimension - adaptive lamp layout method based on the particle swarm algorithm according to claim 1, characterized in that, In step S3, for the luminaire quantity parameter, in the regular layout mode, the input form of the luminaire quantity parameter is the number of rows N x , the number of columns N y , the positions of the luminaires in each row L rx , the positions of the luminaires in each column L cy ; in the free layout mode, the input form of the luminaire quantity parameter is the number N, and the positions of each luminaire are (L jx , L jy ).

5. The dimension - adaptive lamp layout method based on the particle swarm algorithm according to claim 1, characterized in that, step S4 specifically includes: (401) Initialize the parameters of the particle swarm optimization algorithm, specifically including the population size N p , the number of iterations g, the inertia factor ω, the learning factors c 1 and c 2 ; (402) Initialize the initial population, and the initial velocity of each particle Initial position X i is a random number between the bay width H and the depth W; (403) Calculate the lamp position information based on the position information of each particle, and calculate the illuminance situation in the space after turning on the lights through the daylighting simulation software Radiance; (404) Calculate the fitness function E(N) of each particle, where the fitness function E(N) is defined as follows: Among them, n is the grid label, m is the number of grids, and I n is the illuminance value at the center point of the nth grid after the light in the grid is turned on, and I D,n is the illuminance requirement value at the center point of the nth grid; (405) Detect whether the illuminance values at the center points of each grid in the space after turning on the lights all meet the illuminance requirement values. If they do not meet, calculate the penalty function U(N), and the penalty function U(N) is defined as: U(N) = E(N) + C where C is a penalty term and its value is a positive integer; (406) Determine and record the individual optimal p of each particle within the contemporary population best and the group optimal individual g best ; (407) Update the velocity v of each particle in the population i and the position x i , which is defined as: where d is the current search dimension, d = 1, 2, ……, D; i is the i-th particle in the population; k is the k-th generation population; r 1 and r 2 are random numbers; (408) Determine whether convergence is achieved. If not, repeat steps (402)-(407) until the population converges; (409) Output the optimal individual g of the population best .

Images