Cabin internal illumination calculation and optimization method

Abstract

The invention discloses a cabin internal illumination calculation and optimization method, which comprises the following steps of S1, presetting a lamp and an illumination sensor, modeling a model according to the overall arrangement in a cabin, and adopting an actual-scale cabin and a parameterized cabin three-dimensional model for numerical simulation; s2, calculating direct illumination; s3, calculating indirect illumination; s4, taking the calculated direct illumination and indirect illumination parameters as input of a fuzzy neural network, performing fuzzification processing on the input direct illumination and indirect illumination parameters, and converting the parameters into fuzzy linguistic variables; s5, establishing a fuzzy rule base according to expert knowledge or experience data; and S6, performing fuzzy reasoning according to the input fuzzy language variable and the fuzzy rule base, and converting fuzzy output into a specific brightness adjustment value. The method has the advantages that dynamic optimization control over cabin illumination is achieved, the optimization result is closer to reality, the calculation speed is greatly increased, and the overall ship design efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship lighting, in particular to a cabin interior illuminance calculation and optimization method. BACKGROUND

[0002] In the process of ship lighting system design, the cabin is the focus of lighting design, and the industry has clear illuminance standards for each cabin of the ship, but there is no unified illuminance calculation standard. At present, modeling and simulation calculation are mainly assisted by external software, or directly calculated by lamp manufacturers, and the shipyard relies on the outside to a high degree. On the other hand, when using these software to calculate, there is no corresponding lamp loss and light decay based on special environment, and there is no corresponding illuminance coefficient based on different purpose cabins, so it is difficult for the shipyard to grasp the accuracy of the calculation results.

[0003] After the actual lamp setting in the cabin is completed, the actual illuminance situation will be affected by the internal structure and equipment placement. After a long time of sailing and use, the illuminance of the lamp will also decay. The existing lamp has an illuminance adjustment range. Within the decay range of the lamp and the adjustable illuminance range, how to keep the cabin interior in the optimal illuminance range is a problem to be solved. SUMMARY

[0004] The purpose of the present application is to provide an illuminance optimization method based on the special environment of the cabin. Through the optimization calculation method, the special environment of the ship and the different shapes of the cabin are combined to improve the accuracy of the cabin lighting design, prolong the service life of the lamp, reduce the replacement frequency of the lamp, optimize the illuminance calculation process, standardize the illuminance calculation process, reduce the time of the early cabin design through the later illuminance optimization, and thus improve the efficiency of the overall ship design.

[0005] The above technical purpose of the present application is realized by the following technical scheme:

[0006] A cabin interior illuminance calculation and optimization method, characterized in that it comprises the following steps:

[0007] S1, presetting a lamp and an illuminance sensor, and modeling a model according to the overall arrangement in the cabin. The numerical simulation adopts an actual scale cabin, and a parameterized cabin three-dimensional model;

[0008] S2, calculating the direct illuminance generated by the horizontal plane P point under the irradiation of the preset N group of lamp light sources through a cubic cosine correction model;

[0009] S3, calculating the indirect illuminance generated by the horizontal plane P point under the irradiation of the preset N group of lamp light sources through an equivalent reflective surface light source method;

[0010] S4. The calculated direct and indirect illuminance parameters are used as inputs to the fuzzy neural network, and the input direct and indirect illuminance parameters are fuzzified and converted into fuzzy linguistic variables.

[0011] S5, establish a fuzzy rule base based on expert knowledge or experience data;

[0012] S6 performs fuzzy inference based on the input fuzzy linguistic variables and fuzzy rule base to obtain fuzzy output, and converts the fuzzy output into a specific brightness adjustment value.

[0013] Preferably, the origin is at the geometric center of the cabin, the X-axis is the bow and stern direction, the Y-axis is the port and starboard direction, and the Z-axis is the vertical direction, discretizing the cabin into 20cm×20cm grid points.

[0014] Preferably, the formula for calculating the direct illuminance is:

[0015]

[0016] Where E d (p) represents the direct illuminance at target point p, Φ i Let K be the total luminous flux of the i-th luminaire. i The luminaire utilization factor ranges from 0.6 to 0.9, C. i (θ i ) represents the light distribution curve function, θ i Let α be the angle of departure. i Let d be the angle of incidence of the light ray. i Let be the distance from the luminaire to point p, and w be the air transmittance attenuation coefficient, ranging from 0.01 to 0.03 m. -1 .

[0017] Preferably, the Φ i The calculation formula is

[0018] Φ i =η·P i ·n i (2)

[0019] Where η is the luminous efficacy coefficient, P i n represents the power of a single lamp. i This refers to the number of fluorescent tubes.

[0020] Preferably, the formula for calculating the indirect illuminance is:

[0021]

[0022] Where E r (p) represents the indirect illuminance at target point p, ρ k Let k be the reflectivity of the k-th reflecting surface. β is the total light flux incident to the reflection surface k k D is the angle between the normal of the reflection surface and the line connecting the reflection surface center and point p k G is the distance between the reflection surface center and point p k G is the distance between the reflection surface center and point p

[0023] Preferably, the fuzzy language variable conversion formula is:

[0024]

[0025] Wherein μ A (E d (p), E r (p)) is a fuzzy membership function, c is the center of the fuzzy set, σ 2 is the total variance of the illumination parameters of each illumination sensor.

[0026] Preferably, the calculation formula of σ 2 is as follows:

[0027]

[0028] Wherein X' is the illumination parameter of the cleaned illumination sensor, X is the illumination parameter of the original illumination sensor, and T is the expected illumination value.

[0029] Preferably, the calculation formula of X' is as follows:

[0030]

[0031] Wherein m is the number of illumination sensors, Q i is the weight coefficient of the illumination parameter data of the i th illumination sensor, and X i is the illumination parameter of the i th illumination sensor.

[0032] Preferably, the specific calculation formula of the fuzzy output value is as follows:

[0033]

[0034] y is the output value after deblurring, μ i (E d (p), E r (p) is the membership degree of the i th fuzzy set, and y i is the output value corresponding to the i th fuzzy set.

[0035] In summary, the present application has the following beneficial effects:

[0036] 1. The present application fuzzes the layer processing multi-source heterogeneous data, and optimizes the membership function parameters and rule weights online through the neural network, realizes the dynamic optimization control of cabin illumination, and compared with the traditional VBA method, the optimization result is more close to the actual, and the calculation speed is greatly improved.

[0037] 2. The present application combines the special environment of the ship and the different shapes of the cabin, improves the accuracy of the ship cabin lighting design, prolongs the service life of the lamp, reduces the replacement frequency of the lamp, optimizes the illumination calculation process, standardizes the illumination calculation process, reduces the time of the early cabin design through the later illumination optimization, thereby improving the efficiency of the overall ship design. DETAILED DESCRIPTION

[0038] The specific embodiments of the present application are further described below, and the embodiments do not constitute a limitation on the present application.

[0039] A cabin interior illumination calculation and optimization method, comprising the following steps:

[0040] S1, presetting the lamps and the illumination sensor, and modeling the model according to the overall arrangement in the cabin, the numerical simulation adopts the actual scale cabin, and the parameterized cabin three-dimensional model.

[0041] The origin is at the geometric center of the cabin, the X axis is the bow stern direction, the Y axis is the port starboard direction, and the Z axis is the vertical direction, and the cabin is discretized into 20cm*20cm grid points.

[0042] S2, the direct illumination generated by the preset N groups of lamp light sources under the horizontal plane P point is calculated by the cubic cosine correction model, and the calculation formula of the direct illumination is:

[0043]

[0044] Where E d (p) is the direct illumination of the target point p, Φ i is the total luminous flux of the i th lamp, K i is the utilization coefficient of the lamp, the range is 0.6-0.9, C i (θ i ) is the distribution curve function, θ i is the exit angle, α i is the light incidence angle, d i is the distance from the lamp to the point p, w is the air transmission attenuation coefficient, the range is 0.01-0.03m -1 , the calculation formula of Φ i is:

[0045] Φ i =η·P i ·n i (2)

[0046] where η is the light efficiency coefficient, P i is the single lamp power, n i is the number of lamp tubes.

[0047] S3, the indirect illuminance generated by the preset N groups of lamps and lanterns light source irradiation under the horizontal plane P point is calculated by the equivalent reflection surface light source method, and the calculation formula of the indirect illuminance is:

[0048]

[0049] where E r (p) is the indirect illuminance of the target point p, ρ k is the reflectivity of the kth reflection surface, is the total luminous flux incident to the reflection surface k, β k is the included angle between the reflection surface normal and the p point line, D k is the distance from the reflection surface center to the p point, G k is the geometric attenuation factor.

[0050] S4, the calculated direct and indirect illuminance parameters are taken as the input of the fuzzy neural network, and the input direct and indirect illuminance parameters are subjected to fuzzy processing and converted into fuzzy language variables, and the fuzzy language variable conversion formula is:

[0051]

[0052] where μ A (E d (p), E r (p)) is the fuzzy membership function, c is the center of the fuzzy set, σ 2 is the total variance of the illuminance parameters of each illuminance sensor.

[0053] where the calculation formula of σ 2 is:

[0054]

[0055] where X' is the illuminance parameter of the cleaned illuminance sensor, X is the original illuminance parameter of the illuminance sensor, and T is the expected illuminance value.

[0056] where the calculation formula of X' is:

[0057]

[0058] where m is the number of illuminance sensors, Q i is the weight coefficient of the illuminance parameter data of the ith illuminance sensor, X i is the illuminance parameter of the ith illuminance sensor.

[0059] S5, according to expert knowledge or experience data to establish fuzzy rule base, if E d (p) and E r (p) is too low, output "increase brightness", if E d (p) and E r (p) is too high, output "reduce brightness".

[0060] S6, according to the input fuzzy language variable and fuzzy rule base fuzzy reasoning, get fuzzy output, and convert fuzzy output into specific brightness adjustment value, realize the automatic adjustment of lighting brightness.

[0061] The specific calculation formula of fuzzy output value is:

[0062]

[0063] y is the output value after deblurring, μ i (E d (p), E r (p) is the membership degree of the i th fuzzy set, y i is the output value corresponding to the i th fuzzy set.

[0064] The present application realizes the dynamic optimization control of cabin illumination by processing multi-source heterogeneous data in the fuzzy layer and optimizing membership function parameters and rule weights online through neural network, and the optimization result is closer to the actual situation and the calculation speed is greatly improved compared with the traditional VBA method.

[0065] The present application improves the accuracy of ship cabin lighting design, prolongs the service life of lamps and lanterns, reduces the replacement frequency of lamps and lanterns, optimizes the illumination calculation process, standardizes the illumination calculation process, reduces the time of early cabin design through later illumination optimization, and thus improves the overall ship design efficiency.

[0066] The above is only the preferred embodiment of the present application and is not used to limit the present application, and those skilled in the art can make various modifications or equivalent replacements to the present application within the spirit and protection scope of the present application, and such modifications or equivalent replacements should also be considered as falling within the protection scope of the technical scheme of the present application.

Claims

1. A method for calculating and optimizing illuminance inside a ship's cabin, characterized in that, Includes the following steps: S1, preset the lighting fixtures and illuminance sensors, and model the model according to the overall layout of the cabin. The numerical simulation adopts the actual scale of the cabin and the parameterized three-dimensional model of the cabin. S2, calculates the direct illuminance generated at point P on the horizontal plane under the illumination of N sets of lamp light sources using a cubic cosine correction model; S3, calculate the indirect illuminance generated at point P on the horizontal plane under the illumination of N sets of lamps using the equivalent reflective surface light source method; S4. The calculated direct and indirect illuminance parameters are used as inputs to the fuzzy neural network, and the input direct and indirect illuminance parameters are fuzzified and converted into fuzzy linguistic variables. S5, establish a fuzzy rule base based on expert knowledge or experience data; S6 performs fuzzy inference based on the input fuzzy linguistic variables and fuzzy rule base to obtain fuzzy output, and converts the fuzzy output into a specific brightness adjustment value.

2. The method for calculating and optimizing illuminance inside a ship's cabin according to claim 1, characterized in that: The origin is at the geometric center of the cabin, the X-axis is the bow and stern direction, the Y-axis is the port and starboard direction, and the Z-axis is the vertical direction. The cabin is discretized into 20cm×20cm grid points.

3. The method for calculating and optimizing illuminance inside a ship's cabin according to claim 1, characterized in that: The formula for calculating the direct illuminance is: Where E d (p) represents the direct illuminance at target point p, Φ i Let K be the total luminous flux of the i-th luminaire. i The luminaire utilization factor ranges from 0.6 to 0.9, C. i (θ i ) represents the light distribution curve function, θ i Let α be the angle of departure. i Let d be the angle of incidence of the light ray. i Let be the distance from the luminaire to point p, and w be the air transmittance attenuation coefficient, ranging from 0.01 to 0.03 m. -1 .

4. The method for calculating and optimizing illuminance inside a ship's cabin according to claim 3, characterized in that: The Φ i The calculation formula is F i =η·P i ·n i (2) Where η is the luminous efficacy coefficient, P i n represents the power of a single lamp. i This refers to the number of fluorescent tubes.

5. The method for calculating and optimizing illuminance inside a ship's cabin according to claim 1, characterized in that: The formula for calculating the indirect illuminance is: Where E r (p) represents the indirect illuminance at target point p, ρ k Let k be the reflectivity of the k-th reflecting surface. Let β be the total luminous flux incident on the reflecting surface k. k D is the angle between the normal to the reflecting surface and the line connecting point p. k G is the distance from the center of the reflecting surface to point p. k This is the geometric attenuation factor.

6. The method for calculating and optimizing illuminance inside a ship's cabin according to claim 1, characterized in that: The formula for transforming fuzzy linguistic variables is: Where μ A (E d (p), E r (p)) is the fuzzy membership function, c is the center of the fuzzy set, and σ 2 This represents the total variance of the illuminance parameters for each illuminance sensor.

7. The method for calculating and optimizing illuminance inside a ship's cabin according to claim 6, characterized in that: The σ 2 The calculation formula is: Where X′ is the illuminance parameter of the illuminance sensor after cleaning, X is the illuminance parameter of the original illuminance sensor, and T is the desired illuminance value.

8. The method for calculating and optimizing illuminance inside a ship's cabin according to claim 7, characterized in that: The formula for calculating X′ is: Where m is the number of illuminance sensors, Q i Let X be the weighting coefficient for the illuminance parameter data of the i-th illuminance sensor. i Let be the illuminance parameter of the i-th illuminance sensor.

9. The method for calculating and optimizing illuminance inside a ship's cabin according to claim 1, characterized in that: The specific formula for calculating the fuzzy output value is as follows: y is the deblurred output value, μ i (E d (p),E r (p) represents the membership degree of the i-th fuzzy set, y i is the output value corresponding to the i-th fuzzy set.

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