Illuminating lamp distribution parameter determination method and related equipment

Abstract

The invention provides an illumination lamp distribution parameter determination method and related equipment, and the method comprises the steps: randomly generating an initial population of lamp distribution in a digital twin space, the initial population comprising a first number of initial solutions, and each initial solution corresponding to a first distribution parameter; the first number of initial solutions and the second number of first variation solutions are subjected to crossover processing, a third number of first crossover solutions are obtained, and the first variation solutions are obtained after variation processing according to the initial solutions; and determining a third distribution parameter corresponding to a first target solution in the third number of first crossover solutions as an illumination lamp distribution parameter of the digital twin space under the condition that the change of the fitness value between the third number of first crossover solutions is smaller than or equal to a preset threshold value. In this way, the optimal lighting lamp distribution parameters can be found in the complex search space, and the lighting effect and the energy utilization efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mining engineering, and in particular to a method for determining distribution parameters of lighting fixtures and related equipment. Background Art

[0002] In the field of mining engineering, especially in underground mines and tunnel environments, due to the limitations of geological conditions, insufficient light will have a significant impact on work safety and efficiency. Lighting fixtures are usually installed in underground mines and tunnels to improve the work efficiency and safety of workers and reduce the risk of accidents.

[0003] At present, lighting fixtures are installed in mines and tunnels. The distribution of lighting fixtures is mainly determined by experience and trial and error. For example, a lamp is installed at a certain distance, and the number of lamps is increased or the power of lamps is increased at corners to achieve lighting in mines and tunnels. However, after the lamps are deployed, in the actual operation process, it is easy for some locations to have poor lighting effects due to the long distance between lamps or the small number of lamps, or some locations have high lighting costs due to redundant lamp settings. At this time, adjusting the distribution of lamps is time-consuming and labor-intensive, and may even induce safety accidents.

[0004] It can be seen that the existing lighting fixtures have the problem of poor lighting effect. Summary of the invention

[0005] The main purpose of the present invention is to provide a method for determining distribution parameters of lighting fixtures and related equipment, aiming to solve the problem that the lighting effects of existing lighting fixtures are poor.

[0006] To achieve the above object, the present invention provides a method for determining distribution parameters of lighting fixtures, the method comprising:

[0007] Randomly generate an initial population of lamp distribution in a digital twin space, wherein the initial population includes a first number of initial solutions, each initial solution corresponds to a first distribution parameter, and the digital twin space is a digital space constructed based on a target place;

[0008] Perform cross processing on the initial solution of the first quantity and the first variation solution of the second quantity to obtain the first cross solution of the third quantity, wherein the first variation solution is obtained after variation processing is performed on the initial solution, the second quantity is greater than or equal to the first quantity, the third quantity is greater than or equal to the sum of the first quantity and the second quantity, each first variation solution corresponds to a second distribution parameter, and each first cross solution corresponds to a third distribution parameter, the first distribution parameter, the second distribution parameter and the third distribution parameter are all used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter and the third distribution parameter;

[0009] When the change in fitness values ​​between the third number of first cross solutions is less than or equal to a preset threshold, the third distribution parameter corresponding to the first target solution in the third number of first cross solutions is determined as the lighting fixture distribution parameter of the digital twin space, and the fitness value of the first target solution is greater than the fitness values ​​of other cross solutions in the third number of first cross solutions.

[0010] Optionally, after cross-processing the first number of initial solutions and the second number of first variant solutions to obtain a third number of first cross-solutions, the method further includes:

[0011] When the change in fitness value between the third number of first crossover solutions is greater than a preset threshold, the third number of first crossover solutions and the fourth number of second variation solutions are cross-processed to obtain a fifth number of second crossover solutions, wherein the second variation solution is obtained after variation processing according to the first crossover solution, the fourth number is greater than or equal to the third number, the fifth number is greater than or equal to the sum of the third number and the fourth number, each second variation solution corresponds to a fourth distribution parameter, each second crossover solution corresponds to a fifth distribution parameter, the fourth distribution parameter and the fifth distribution parameter are both used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter;

[0012] When the change in fitness values ​​between the fifth number of second cross solutions is less than or equal to the preset threshold, the fifth distribution parameter corresponding to the second target solution in the fifth number of second cross solutions is determined as the lighting fixture distribution parameter of the digital twin space, and the fitness value of the second target solution is greater than the fitness values ​​of other cross solutions in the fifth number of second cross solutions.

[0013] Optionally, the variation processing is solved according to the following formula:

[0014] V i =X i +F(X j -X k );

[0015] Among them, V i is any of the first variant solutions, X i , X j and X k are any three solutions among the initial solutions, and F is the variation factor.

[0016] Optionally, the cross processing is solved according to the following formula:

[0017] X i =(x i1 , x i2 , …, x id );

[0018] V i =(v i1 , v i2 , …, v id );

[0019] U i =(u i1 ,u i2 ,…,u id );

[0020]

[0021] Among them, rand(0,1) is a random number generator function uniformly distributed in the interval [0,1], CR is the crossover probability, and the value range of CR is [0,1]. rand is a random integer in [1,d], X i is any one of the initial solutions, V i is any one of the first variant solutions, i, d and j are all positive integers, and j is less than or equal to d.

[0022] Optionally, the fitness value is solved according to the following formula:

[0023] f(X)=w1E avg (X)+w2U(X)+P(X);

[0024] Among them, f(X) is the fitness function, which is used to evaluate the fitness value of any first crossover solution X, E avg (X) is the average illumination, U(X) is the illumination uniformity, P(X) is the penalty function, and w1 and w2 are weight coefficients.

[0025] In addition, to achieve the above-mentioned purpose, the present invention also provides a lighting fixture distribution parameter determination device, the device comprising:

[0026] A generation module, used to randomly generate an initial population of lamp distribution in a digital twin space, wherein the initial population includes a first number of initial solutions, each initial solution corresponds to a first distribution parameter, and the digital twin space is a digital space constructed based on a target place;

[0027] A first processing module is used to cross-process the initial solution of the first quantity and the first variation solution of the second quantity to obtain the first cross-solution of the third quantity, wherein the first variation solution is obtained after variation processing is performed on the initial solution, the second quantity is greater than or equal to the first quantity, the third quantity is greater than or equal to the sum of the first quantity and the second quantity, each first variation solution corresponds to a second distribution parameter, and each first cross-solution corresponds to a third distribution parameter, the first distribution parameter, the second distribution parameter and the third distribution parameter are all used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter and the third distribution parameter;

[0028] The first determination module is used to determine the third distribution parameter corresponding to the first target solution in the third number of first cross solutions as the lighting fixture distribution parameter of the digital twin space when the change in fitness values ​​between the third number of first cross solutions is less than or equal to a preset threshold, and the fitness value of the first target solution is greater than the fitness values ​​of other cross solutions in the third number of first cross solutions.

[0029] Optionally, the device further comprises:

[0030] A second processing module is used for, when the change of the fitness value between the first crossover solutions of the third number is greater than a preset threshold, performing crossover processing on the first crossover solutions of the third number and the second variation solutions of the fourth number to obtain the second crossover solutions of the fifth number, wherein the second variation solution is obtained after the variation processing is performed according to the first crossover solution, the fourth number is greater than or equal to the third number, the fifth number is greater than or equal to the sum of the third number and the fourth number, each second variation solution corresponds to a fourth distribution parameter, each second crossover solution corresponds to a fifth distribution parameter, the fourth distribution parameter and the fifth distribution parameter are both used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter;

[0031] A second determination module is used to determine the fifth distribution parameter corresponding to the second target solution in the fifth number of second cross solutions as the lighting fixture distribution parameter of the digital twin space when the change in fitness values ​​between the fifth number of second cross solutions is less than or equal to the preset threshold, and the fitness value of the second target solution is greater than the fitness values ​​of other cross solutions in the fifth number of second cross solutions.

[0032] In order to solve the above technical problems, the embodiment of the present application further provides an electronic device, which adopts the following technical solution:

[0033] The electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods for determining the distribution parameters of lighting fixtures proposed in the embodiments of the present application are implemented.

[0034] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0035] The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of any one of the methods for determining the distribution parameters of lighting fixtures proposed in the embodiments of the present application are implemented.

[0036] In order to solve the above technical problems, the embodiment of the present application also provides a computer program product, which adopts the following technical solution:

[0037] The computer program product includes computer instructions, which, when executed by a processor, implement the steps of any one of the methods for determining the distribution parameters of lighting fixtures proposed in the embodiments of the present application.

[0038] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0039] The present invention provides a method for determining distribution parameters of lighting fixtures and related equipment, the method comprising: randomly generating an initial population of lighting fixtures in a digital twin space, the initial population comprising a first number of initial solutions, each initial solution corresponding to a first distribution parameter, the digital twin space being a digitized space constructed based on a target location; cross-processing the first number of initial solutions with a second number of first variant solutions to obtain a third number of first cross-solutions, the first variant solutions being obtained after variant processing based on the initial solutions, the second number being greater than or equal to the first number, the third number being greater than or equal to the sum of the first number and the second number, each first variant solution corresponding to a second distribution parameter. Parameters, each first crossover solution corresponds to a third distribution parameter, the first distribution parameter, the second distribution parameter and the third distribution parameter are all used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter and the third distribution parameter; when the change in the fitness value between the third number of first crossover solutions is less than or equal to the preset threshold, the third distribution parameter corresponding to the first target solution in the third number of first crossover solutions is determined as the lighting fixture distribution parameter in the digital twin space, and the fitness value of the first target solution is greater than the fitness value of other crossover solutions in the third number of first crossover solutions. In the embodiment of the present invention, the digital twin space can be used to simulate and evaluate different lighting fixture distribution schemes without actually installing the lamps, which greatly improves the optimization efficiency; and the differential evolution algorithm can find the optimal lighting fixture distribution parameters in a complex search space through the steps of randomly generating an initial population, mutation and crossover operations, and fitness value evaluation, so as to improve the lighting effect and energy efficiency. In addition, it is required that there is at least one different deployment information between different distribution parameters, which ensures that the algorithm can fully explore the solution space and avoid falling into a local optimal solution. It is suitable for various places that need to optimize the distribution of lighting fixtures, such as mines, tunnels, warehouses, large indoor venues, etc. The algorithm parameters and optimization targets can be adjusted according to the characteristics and needs of different places to obtain the best lighting effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0042] Figure 2 is a flow chart of a method for determining distribution parameters of lighting fixtures provided by an embodiment of the present invention;

[0043] Figure 3 is a schematic diagram of the structure of a device for determining distribution parameters of lighting fixtures provided by an embodiment of the present invention;

[0044] Figure 4 This is a basic structural block diagram of the electronic device of this embodiment. DETAILED DESCRIPTION

[0045] The method for determining the distributed parameters of lighting fixtures provided in the embodiment of the present invention is applied to the device for determining the distributed parameters of lighting fixtures. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0046] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0047] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0048] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0049] The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. In some embodiments, various communication client applications can be installed on the terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social online platform software, etc. The terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3) players, MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop portable computers and desktop computers, etc.

[0050] It should be noted that the method for determining the distributed parameters of lighting fixtures provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the apparatus for determining the distributed parameters of lighting fixtures is generally arranged in the server / terminal device.

[0051] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0052] Good lighting conditions are essential to ensure worker safety and improve production efficiency. However, the current lighting fixtures installed in mines and tunnels mainly rely on experience and trial and error to determine the distribution of lighting fixtures. This method is not only inefficient, but also difficult to ensure the optimization of lighting effects.

[0053] Continue to refer Figure 2 , shows a flow chart of a method for determining the distribution parameters of lighting fixtures proposed in the present application. A method for determining the distribution parameters of lighting fixtures provided in an embodiment of the present invention can be applied to a server, such as Figure 2 As shown, a method for determining lighting fixture distribution parameters provided by an embodiment of the present invention includes the following steps:

[0054] S210, randomly generating an initial population of lamp distribution in a digital twin space, wherein the initial population includes a first number of initial solutions, each initial solution corresponds to a first distribution parameter, and the digital twin space is a digital space constructed based on a target place;

[0055] In this step, the digital twin space is a digital space built based on the target location (such as an actual mine, tunnel, etc.), which can accurately simulate the various physical characteristics and conditions of the target location. In the problem of determining the distribution parameters of lamps, the digital twin space can consider factors such as the shape, size, and wall reflectivity of the target location, so as to more realistically reflect the lighting effect of the lamps in the actual environment. By optimizing the distribution parameters of lamps in the digital twin space, the lighting effects of different lamp distribution schemes can be quickly calculated without the need for tedious installation and testing in the actual location, thereby improving the optimization efficiency.

[0056] The initial population can be generated in a random manner. In other words, there may be no specific preset value for the distribution parameters of the lamps, but they are randomly selected within a certain range of values. This ensures that the initial population is diverse, thereby increasing the possibility of the algorithm finding the optimal solution. Exemplarily, for the position parameters of the lamps, the x, y and z coordinate values ​​can be randomly generated within the coordinate range of the digital twin space; for the height parameters of the lamps, the values ​​can be randomly selected within a reasonable height range; for the angle parameters of the lamps, the values ​​can be randomly determined within a certain angle range. The initial population includes a first number of initial solutions, which can be a preset value that determines the size of the initial population. Generally, a larger initial population size can increase the search space of the algorithm and increase the probability of finding the global optimal solution, but it will also increase the calculation time and complexity. For example, if the first number is set to N, then N different lamp distribution schemes will be randomly generated, and each scheme will be used as an initial solution. Each initial solution corresponds to a first distribution parameter, that is, each first distribution parameter is a specific lamp distribution scheme, which can contain detailed information such as the position, height, angle, etc. of the lamp in the digital twin space. For example, the first distribution parameter corresponding to an initial solution may be the coordinates (x1, y1, z1) of lamp A in the digital twin space, the height is h1, and the angle is θ1; lamp B is located at the coordinates (x2, y2, z2), the height is h2, the angle is θ2, and so on.

[0057] In this way, in the process of determining the distribution parameters of lamps in the digital twin space based on the differential evolution algorithm, an initial population needs to be randomly generated first. This initial population is the basis for subsequent algorithm optimization. The digital twin space is a digital space built based on the target place. It can simulate various conditions and phenomena in the real environment to optimize the distribution parameters of lamps more efficiently.

[0058] S220, cross-processing the initial solution of the first quantity and the first variation solution of the second quantity to obtain the first cross-solution of the third quantity, wherein the first variation solution is obtained after variation processing according to the initial solution, the second quantity is greater than or equal to the first quantity, the third quantity is greater than or equal to the sum of the first quantity and the second quantity, each first variation solution corresponds to a second distribution parameter, and each first cross-solution corresponds to a third distribution parameter, the first distribution parameter, the second distribution parameter and the third distribution parameter are all used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter and the third distribution parameter;

[0059] In this step, the initial solution and the variant solution can be combined to generate a new candidate solution (i.e., the first crossover solution) to explore a better lamp distribution solution. The initial solution is a randomly generated combination of lamp distribution parameters, while the variant solution is obtained by performing a mutation operation on the initial solution. The crossover process can combine the characteristics of the two, increase the diversity of solutions, and improve the possibility of finding the optimal lamp distribution parameters.

[0060] The first variant solution is obtained after the initial solution is mutated. By performing certain transformations on the initial solution, new individuals (i.e., variant solutions) are generated to introduce more diversity. The number of first variant solutions is the second number, and the second number is greater than or equal to the first number, so that there are enough variant solutions to cross with the initial solution, further expanding the search space. Each first variant solution corresponds to a second distribution parameter, which is also used to describe the deployment of lamps in the digital twin space.

[0061] Then, the first number of initial solutions and the second number of first variant solutions are cross-processed to obtain the third number of first cross-solutions. Each first cross-solution corresponds to a third distribution parameter, which is also the deployment information of the lamp in the digital twin space. The third number is greater than or equal to the sum of the first number and the second number. A new solution is generated through the cross-operation, which increases the number of cross-solutions.

[0062] It is required that there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter and the third distribution parameter. This ensures that the new solution (first crossover solution) generated in the crossover process has a certain difference from the original initial solution and the first variation solution, avoids the generation of repeated or similar solutions, and ensures that the algorithm can effectively search the entire solution space.

[0063] S230. When the change in fitness values ​​between the third number of first cross solutions is less than or equal to a preset threshold, the third distribution parameter corresponding to the first target solution in the third number of first cross solutions is determined as the lighting fixture distribution parameter of the digital twin space, and the fitness value of the first target solution is greater than the fitness values ​​of other cross solutions in the third number of first cross solutions.

[0064] In this step, the fitness value can be used to measure the quality of each solution (i.e., the lamp distribution scheme). The fitness value can be calculated based on a specific optimization goal. For example, in the lighting fixture distribution problem, factors such as the average illumination, illumination uniformity, and lighting energy consumption may be considered. The higher the fitness value, the better the solution performs in meeting these optimization goals. The preset threshold is a pre-set value used to determine whether the algorithm converges. The size of this threshold depends on the specific problem and requirements. If the threshold is set too small, the algorithm may converge too early and miss a better solution; if the threshold is set too large, the algorithm may run for a long time without converging. After obtaining the third number of first cross solutions, the fitness value changes between these cross solutions are calculated. Exemplarily, it can be to compare the absolute value of the difference between the fitness values ​​of two adjacent cross solutions, or to calculate the standard deviation of the fitness values ​​of all cross solutions. If the changes in these fitness values ​​are less than or equal to the preset threshold, it means that the algorithm may have converged to a better solution. Then, the third distribution parameter corresponding to the first target solution is determined as the lighting fixture distribution parameter of the digital twin space. The third distribution parameter corresponding to the first target solution contains detailed information such as the position, height, and angle of the lamps in the digital twin space, and is the optimal lamp distribution solution obtained after algorithm optimization. In this way, the optimization capability of the differential evolution algorithm is utilized, and in the process of continuous iteration and cross-mutation, the optimal lamp distribution solution is gradually approached to meet the lighting needs and optimization goals in the digital twin space.

[0065] In an embodiment of the present invention, an initial population of lamp distribution in a digital twin space is randomly generated, the initial population includes a first number of initial solutions, each initial solution corresponds to a first distribution parameter, and the digital twin space is a digital space constructed based on a target place; the first number of initial solutions and the second number of first variant solutions are cross-processed to obtain a third number of first cross solutions, the first variant solutions are obtained after the initial solutions are mutated, the second number is greater than or equal to the first number, the third number is greater than or equal to the sum of the first number and the second number, each first variant solution corresponds to a second distribution parameter, each first cross solution corresponds to a third distribution parameter, the first distribution parameter, the second distribution parameter and the third distribution parameter are all used to indicate the deployment information of the lighting fixtures in the digital twin space, and there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter and the third distribution parameter; when the change in fitness values ​​between the third number of first cross solutions is less than or equal to a preset threshold, the third distribution parameter corresponding to the first target solution in the third number of first cross solutions is determined as the lighting fixture distribution parameter of the digital twin space, and the fitness value of the first target solution is greater than the fitness values ​​of other cross solutions in the third number of first cross solutions. In this way, the digital twin space can be used to simulate and evaluate different lighting distribution schemes without actually installing the lighting fixtures, greatly improving the optimization efficiency; and the differential evolution algorithm can find the optimal lighting fixture distribution parameters in a complex search space through steps such as randomly generating the initial population, mutation and crossover operations, and fitness value evaluation, thereby improving the lighting effect and energy efficiency. In addition, it is required that there is at least one different deployment information between different distribution parameters, which ensures that the algorithm can fully explore the solution space and avoid falling into the local optimal solution. It is suitable for various places where the distribution of lighting fixtures needs to be optimized, such as mines, tunnels, warehouses, large indoor venues, etc. The parameters and optimization goals of the algorithm can be adjusted according to the characteristics and needs of different places to obtain the best lighting effect.

[0066] Optionally, after cross-processing the first number of initial solutions and the second number of first variant solutions to obtain a third number of first cross-solutions, the method further includes:

[0067] When the change in fitness value between the third number of first crossover solutions is greater than a preset threshold, the third number of first crossover solutions and the fourth number of second variation solutions are cross-processed to obtain a fifth number of second crossover solutions, wherein the second variation solution is obtained after variation processing according to the first crossover solution, the fourth number is greater than or equal to the third number, the fifth number is greater than or equal to the sum of the third number and the fourth number, each second variation solution corresponds to a fourth distribution parameter, each second crossover solution corresponds to a fifth distribution parameter, the fourth distribution parameter and the fifth distribution parameter are both used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter;

[0068] When the change in fitness values ​​between the fifth number of second cross solutions is less than or equal to the preset threshold, the fifth distribution parameter corresponding to the second target solution in the fifth number of second cross solutions is determined as the lighting fixture distribution parameter of the digital twin space, and the fitness value of the second target solution is greater than the fitness values ​​of other cross solutions in the fifth number of second cross solutions.

[0069] In this embodiment, if the change in fitness value between the third number of first crossover solutions is greater than a preset threshold, it means that the algorithm has not converged to the vicinity of a better solution. At this time, the third number of first crossover solutions and the fourth number of second variation solutions can be cross-processed. The second variation solution is obtained after variation processing based on the first crossover solution, and the fourth number is greater than or equal to the third number in quantity to ensure that there are enough variation solutions to perform crossover operations with the first crossover solution. After the crossover process, the fifth number of second crossover solutions is obtained, and the fifth number is greater than or equal to the sum of the third number and the fourth number. Each second variation solution corresponds to a fourth distribution parameter, and each second crossover solution corresponds to a fifth distribution parameter. These distribution parameters are used to indicate the deployment information of the lighting fixtures in the digital twin space. And it is required that there is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter to ensure the diversity of the solution.

[0070] Then, the fitness value change between the fifth number of second crossover solutions is calculated. If the fitness value change is less than or equal to the preset threshold, it means that the algorithm may have converged to a better solution. Then, the second target solution with a fitness value greater than other crossover solutions can be selected from the fifth number of second crossover solutions. This solution is considered to be the optimal solution in the current iteration process. And the fifth distribution parameter corresponding to the second target solution is determined as the lighting fixture distribution parameter of the digital twin space.

[0071] In this way, by increasing the steps of loop iteration, the algorithm can continue to explore the solution space when it has not converged, increasing the possibility of finding a better distribution parameter of lighting fixtures. In the process of continuous variation and cross-operation processing, the diversity of solutions is guaranteed to avoid the algorithm from falling into the local optimal solution. The optimal solution can be gradually approached, providing a more accurate and reliable solution for actual lighting design and optimization. At the same time, using digital twin space for simulation and optimization can greatly reduce the cost and time of actual debugging and improve work efficiency.

[0072] In some other optional embodiments, the algorithm can also be stopped when the algorithm reaches a preset maximum number of iterations. For example, the maximum number of iterations is set to N. max , record the current number of iterations n during the iteration process, when n = N max When , it can be considered that the termination condition is met; the second target solution can be determined from the cross solution after the nth iteration, so as to obtain the fifth distribution parameter corresponding to the second target solution to determine the lighting fixture distribution parameters in the digital twin space. Similarly, a more accurate and reliable solution can be provided for actual lighting design and optimization.

[0073] Optionally, the variation processing is solved according to the following formula:

[0074] V i =X i +F(X j -X k );

[0075] Among them, V i is any of the first variant solutions, X i , X j and X k are any three solutions among the initial solutions, and F is the variation factor.

[0076] In this embodiment, the optimization objective function is determined based on the lighting target. For example, the average illumination can be maximized, the illumination uniformity can be maximized, or the lighting energy consumption can be minimized as the optimization objective function. And according to the actual situation of the underground mine operation, the constraint conditions are determined, such as the installation location, quantity, power, etc. of the lamps. Then a set of distribution parameters of lighting fixtures are randomly generated as the initial population, and each solution represents a lamp distribution scheme, including parameters such as the location, height, and angle of the lamps; each solution in the initial population is mutated to generate a new individual, that is, a variant solution. The specific method is to randomly select three different initial solutions (X i , X j and X k ), then follow V i =X i +F(X j -X k) to perform mutation operation and obtain the mutated solution V i Among them, F is the variation factor, which is used to control the amplitude of variation.

[0077] Then, the mutated solution and the initial solution are cross-operated to generate a new cross-solution. The cross-processing process can be described as follows:

[0078] Optionally, the crossover process is solved according to the following formula:

[0079] X i =(x i1 , x i2 , …, x id );

[0080] V i =(v i1 , v i2 , …, v id );

[0081] U i =(u i1 ,u i2 ,…,u id );

[0082]

[0083] Among them, rand(0,1) is a random number generator function uniformly distributed in the interval [0,1], CR is the crossover probability, and the value range of CR is [0,1]. rand is a random integer in [1,d], X i is any one of the initial solutions, V i is any one of the first variant solutions, i, d and j are all positive integers, and j is less than or equal to d.

[0084] In this embodiment, each initial solution and each variant solution can be randomly cross-linked, and a solution can be selected from the variant individuals or from the original individuals with a certain probability to obtain a cross-linked solution. For example, any solution X in the initial solution i =(x i1 , x i2 , …, x id ), where d represents the dimension of the individual, that is, the number of distribution parameters of the lighting fixtures (for example, including the position coordinates z, y, z, height h, angle θ and other parameters of the lighting fixtures), and there is also the first variant solution V after the variant i =(v i1 , v i2 , …, v id). A crossover probability CR needs to be determined, which is used to control the frequency of selecting parameters from the mutated individuals. In addition, a random integer J between 1 and d needs to be generated. rand .

[0085] Dimension-by-dimensional crossover generates the first crossover solution U i , U i =(u i1 ,u i2 ,…,u id ) for each parameter u ij (j=1,2,…,d), can be determined according to the following rules:

[0086] If rand(0,1)≤CR, or j=jrand, then u ij =v ij , that is, from the first variation solution V i Otherwise, u ij =x ij , that is, from the initial solution X i Get this parameter from .

[0087] For example, suppose X i Represents a lighting fixture distribution scheme, whose parameters include the fixture position x i1 =3m,x i2 =4m, lamp height x i3 =2.5m, lighting angle x i4 =30°; V i The corresponding parameter is v i1 =3.2m,v i2 =3.8m,v i3 =2.4m,v i4 =32°. Assume the crossover probability CR = 0.7, and the random integer j rand =3.

[0088] For j=1, generate a random number r1, assuming r1=0.8 (because 0.8>0.7), then u i1 =x i1 =3m.

[0089] For j = 2, generate a random number r2, assuming r2 = 0.6 (because 0.6 ≤ 0.7), then u i2 =v i2 =3.8m.

[0090] For j=3, since j=j rand , so u i3 =v i3 =2.4m.

[0091] For j=4, generate a random number r4, assuming r4=0.9 (because 0.9>0.7), then u i4 =x i4 =30°.

[0092] In this way, a new crossover solution Ui = (3m, 3.8m, 2.4m, 30°) is generated through the crossover operation, which combines the initial solution X i and the first variant solution V i Through the crossover operation, information can be exchanged between the initial solution and the variant solution, so that the newly generated crossover solution can inherit some excellent characteristics of the initial solution, and may also obtain new characteristics explored in the variant solution. This combination method helps the algorithm to explore more comprehensively in the search space, avoid the algorithm from converging to the local optimal solution too early, and thus increase the possibility of finding the global optimal solution (that is, the optimal lighting fixture distribution parameters).

[0093] Furthermore, according to the optimization objective function, the fitness value of each solution is calculated, which can be seen in the following expression:

[0094] The fitness value is solved according to the following formula:

[0095] f(X)=w1E avg (X)+w2U(X)+P(X);

[0096] Among them, f(X) is the fitness function, which is used to evaluate the fitness value of any first crossover solution X, E avg (X) is the average illumination, U(X) is the illumination uniformity, P(X) is the penalty function, and w1 and w2 are weight coefficients.

[0097] In this implementation of the method for determining the distribution parameters of lighting fixtures based on the differential evolution algorithm, the fitness value is an indicator used to measure the degree of excellence of each solution (representing a lighting fixture distribution scheme) in meeting the lighting objectives and constraints. By comparing the individual fitness values, it is determined which solutions can enter the next generation of populations, thereby guiding the population to evolve in a better direction.

[0098] In one example, if the optimization goal is to maximize the average illumination, the calculation of the fitness value can take into account the average illumination that can be generated by the lamp distribution scheme represented by the target solution. For example, for a lamp distribution scheme, the average illumination value of the working area under the mine under the scheme is calculated through the lighting model and the relevant lighting calculation method. The higher the average illumination value, the higher the fitness value of the individual in the lighting target dimension of average illumination.

[0099] In another example, for the illumination uniformity lighting goal, if it is to be maximized, the fitness value will be related to the calculation result of the illumination uniformity. The illumination uniformity under the lamp distribution scheme corresponding to the individual is calculated. The higher the uniformity, the greater the contribution to the fitness value in the illumination uniformity dimension.

[0100] In another example, if minimizing lighting energy consumption is also one of the optimization goals, the fitness value will take energy consumption factors into consideration. For example, the lighting energy consumption under the lighting distribution scheme is calculated based on factors such as the power, quantity, and expected working time of the lamps. The lower the energy consumption, the more favorable the fitness value of the individual in the energy consumption dimension.

[0101] Specifically, the fitness function f(X) is determined to evaluate the fitness value of any first crossover solution X, and the optimization goal is to maximize the average illumination and the illumination uniformity, and at the same time, the installation height of the lamp is not less than h min First, calculate the average illumination E avg (X) and illumination uniformity U(X); then define a penalty function P(X), if the installation height of the luminaire is lower than h min , then P(X) = -C, C is a large integer, indicating the penalty intensity; if the installation height of the lamp meets the conditions, then P(X) = 0. The fitness function f(X) can be expressed as f(X) = w1E avg (X)+w2U(X)+P(X), through this calculation method, the fitness value can comprehensively reflect the performance of the individual in terms of lighting goals and constraints, thereby providing a reasonable basis for the selection operation of the differential evolution algorithm. It is suitable for various places that need to optimize the distribution of lighting fixtures and improves the lighting effect.

[0102] Further references Figure 3 , as a response to the above Figure 3 In order to realize the method shown in the figure, the present application provides an embodiment of a lighting fixture distribution parameter determination device 300. The device embodiment is similar to Figure 3 Corresponding to the method embodiment shown, the device can be specifically applied in a server.

[0103] An embodiment of the present invention provides a lighting fixture distribution parameter determination device, the lighting fixture distribution parameter determination device 300 comprising:

[0104] A generation module 310 is used to randomly generate an initial population of lamp distribution in a digital twin space, wherein the initial population includes a first number of initial solutions, each initial solution corresponds to a first distribution parameter, and the digital twin space is a digital space constructed based on a target place;

[0105] A first processing module 320 is used to cross-process the initial solution of the first quantity and the first variation solution of the second quantity to obtain a first cross-solution of the third quantity, wherein the first variation solution is obtained after variation processing is performed on the initial solution, the second quantity is greater than or equal to the first quantity, the third quantity is greater than or equal to the sum of the first quantity and the second quantity, each first variation solution corresponds to a second distribution parameter, and each first cross-solution corresponds to a third distribution parameter, the first distribution parameter, the second distribution parameter, and the third distribution parameter are all used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter, and the third distribution parameter;

[0106] The first determination module 330 is used to determine the third distribution parameter corresponding to the first target solution in the third number of first cross solutions as the lighting fixture distribution parameter of the digital twin space when the change in the fitness value between the third number of first cross solutions is less than or equal to a preset threshold, and the fitness value of the first target solution is greater than the fitness values ​​of other cross solutions in the third number of first cross solutions.

[0107] Optionally, the lighting fixture distribution parameter determination device 300 further includes:

[0108] A second processing module is used for, when the change of the fitness value between the first crossover solutions of the third number is greater than a preset threshold, performing crossover processing on the first crossover solutions of the third number and the second variation solutions of the fourth number to obtain the second crossover solutions of the fifth number, wherein the second variation solution is obtained after the variation processing is performed according to the first crossover solution, the fourth number is greater than or equal to the third number, the fifth number is greater than or equal to the sum of the third number and the fourth number, each second variation solution corresponds to a fourth distribution parameter, each second crossover solution corresponds to a fifth distribution parameter, the fourth distribution parameter and the fifth distribution parameter are both used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter;

[0109] A second determination module is used to determine the fifth distribution parameter corresponding to the second target solution in the fifth number of second cross solutions as the lighting fixture distribution parameter of the digital twin space when the change in fitness values ​​between the fifth number of second cross solutions is less than or equal to the preset threshold, and the fitness value of the second target solution is greater than the fitness values ​​of other cross solutions in the fifth number of second cross solutions.

[0110] Optionally, the variation processing is solved according to the following formula:

[0111] V i =X i +F(X j -X k );

[0112] Among them, V i is any of the first variant solutions, X i , X j and X k are any three solutions among the initial solutions, and F is the variation factor.

[0113] Optionally, the cross processing is solved according to the following formula:

[0114] X i =(x i1 , x i2 , …, x id );

[0115] V i =(v i1 , v i2 , …, v id );

[0116] U i =(u i1 ,u i2 ,…,u id );

[0117]

[0118] Among them, rand(0,1) is a random number generator function uniformly distributed in the interval [0,1], CR is the crossover probability, and the value range of CR is [0,1]. rand is a random integer in [1,d], X i is any one of the initial solutions, V i is any one of the first variant solutions, i, d and j are all positive integers, and j is less than or equal to d.

[0119] Optionally, the fitness value is solved according to the following formula:

[0120] f(X)=w1E avg (X)+w2U(X)+P(X);

[0121] Among them, f(X) is the fitness function, which is used to evaluate the fitness value of any first crossover solution X, E avg (X) is the average illumination, U(X) is the illumination uniformity, P(X) is the penalty function, and w1 and w2 are weight coefficients.

[0122] To solve the above technical problems, the present application also provides an electronic device. Figure 4 , Figure 4 This is a basic structural block diagram of the electronic device of this embodiment.

[0123] The electronic device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows an electronic device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0124] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0125] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. In other embodiments, the memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk equipped on the electronic device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 41 can also include both the internal storage unit of the electronic device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the electronic device 4, such as the program code of the method for determining the distribution parameters of lighting fixtures, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0126] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the electronic device 4. In this embodiment, the processor 42 is used to run the program code stored in the memory 41 or process data, such as running the program code of the method for determining the distributed parameters of the lighting fixtures.

[0127] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the electronic device 4 and other electronic devices.

[0128] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores a program of the method for determining the distribution parameters of lighting fixtures, and the program of the method for determining the distribution parameters of lighting fixtures can be executed by at least one processor so that the at least one processor performs the steps of the method for determining the distribution parameters of lighting fixtures as described above.

[0129] The present application also provides another implementation manner, that is, providing a computer program product, wherein the computer program product includes computer instructions, and when the computer instructions are executed by a processor, the steps of the above-mentioned method for determining the distribution parameters of lighting fixtures are implemented.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware online platform, and of course, by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0131] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0132] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for determining distribution parameters of lighting fixtures, characterized in that: The method comprises: Randomly generate an initial population of lamp distribution in a digital twin space, wherein the initial population includes a first number of initial solutions, each initial solution corresponds to a first distribution parameter, and the digital twin space is a digital space constructed based on a target place; Perform cross processing on the initial solution of the first quantity and the first variation solution of the second quantity to obtain the first cross solution of the third quantity, wherein the first variation solution is obtained after variation processing is performed on the initial solution, the second quantity is greater than or equal to the first quantity, the third quantity is greater than or equal to the sum of the first quantity and the second quantity, each first variation solution corresponds to a second distribution parameter, and each first cross solution corresponds to a third distribution parameter, the first distribution parameter, the second distribution parameter and the third distribution parameter are all used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter and the third distribution parameter; When the change in fitness values ​​between the third number of first cross solutions is less than or equal to a preset threshold, the third distribution parameter corresponding to the first target solution in the third number of first cross solutions is determined as the lighting fixture distribution parameter of the digital twin space, and the fitness value of the first target solution is greater than the fitness values ​​of other cross solutions in the third number of first cross solutions.

2. The method according to claim 1, characterized in that After cross-processing the first number of initial solutions and the second number of first variant solutions to obtain a third number of first cross-solutions, the method further includes: When the change in fitness value between the third number of first crossover solutions is greater than a preset threshold, the third number of first crossover solutions and the fourth number of second variation solutions are cross-processed to obtain a fifth number of second crossover solutions, wherein the second variation solution is obtained after variation processing according to the first crossover solution, the fourth number is greater than or equal to the third number, the fifth number is greater than or equal to the sum of the third number and the fourth number, each second variation solution corresponds to a fourth distribution parameter, each second crossover solution corresponds to a fifth distribution parameter, the fourth distribution parameter and the fifth distribution parameter are both used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter; When the change in fitness values ​​between the fifth number of second cross solutions is less than or equal to the preset threshold, the fifth distribution parameter corresponding to the second target solution in the fifth number of second cross solutions is determined as the lighting fixture distribution parameter of the digital twin space, and the fitness value of the second target solution is greater than the fitness values ​​of other cross solutions in the fifth number of second cross solutions.

3. The method according to claim 1, characterized in that The variation process is solved according to the following formula: V i =X i +F(X j -X k ); Among them, V i is any of the first variant solutions, X i , X j and X k are any three solutions among the initial solutions, and F is the variation factor.

4. The method according to claim 1, characterized in that The crossover process is solved according to the following formula: X i =(x i1 ,x i2 ,…,x id ); V i =(v i1 ,v i2 ,…,v id ); IN i =(in i1 ,in i2 ,…,in id ); Among them, rand(0,1) is a random number generator function uniformly distributed in the interval [0,1], CR is the crossover probability, and the value range of CR is [0,1]. rand is a random integer in [1,d], X i is any one of the initial solutions, V i is any one of the first variant solutions, i, d and j are all positive integers, and j is less than or equal to d.

5. The method according to claim 1, characterized in that The fitness value is solved according to the following formula: f(X)=w1E avg (X)+w2U(X)+P(X); Among them, f(X) is the fitness function, which is used to evaluate the fitness value of any first crossover solution X, E avg (X) is the average illumination, U(X) is the illumination uniformity, P(X) is the penalty function, and w1 and w2 are weight coefficients.

6. A device for determining distribution parameters of lighting fixtures, characterized in that: The device comprises: A generation module, used to randomly generate an initial population of lamp distribution in a digital twin space, wherein the initial population includes a first number of initial solutions, each initial solution corresponds to a first distribution parameter, and the digital twin space is a digital space constructed based on a target place; A first processing module is used to cross-process the initial solution of the first quantity and the first variation solution of the second quantity to obtain the first cross-solution of the third quantity, wherein the first variation solution is obtained after variation processing is performed on the initial solution, the second quantity is greater than or equal to the first quantity, the third quantity is greater than or equal to the sum of the first quantity and the second quantity, each first variation solution corresponds to a second distribution parameter, and each first cross-solution corresponds to a third distribution parameter, the first distribution parameter, the second distribution parameter and the third distribution parameter are all used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between any two of the first distribution parameter, the second distribution parameter and the third distribution parameter; The first determination module is used to determine the third distribution parameter corresponding to the first target solution in the third number of first cross solutions as the lighting fixture distribution parameter of the digital twin space when the change in fitness values ​​between the third number of first cross solutions is less than or equal to a preset threshold, and the fitness value of the first target solution is greater than the fitness values ​​of other cross solutions in the third number of first cross solutions.

7. The device according to claim 6, characterized in that Also includes: A second processing module is used for, when the change of the fitness value between the first crossover solutions of the third number is greater than a preset threshold, performing crossover processing on the first crossover solutions of the third number and the second variation solutions of the fourth number to obtain the second crossover solutions of the fifth number, wherein the second variation solution is obtained after the variation processing is performed according to the first crossover solution, the fourth number is greater than or equal to the third number, the fifth number is greater than or equal to the sum of the third number and the fourth number, each second variation solution corresponds to a fourth distribution parameter, each second crossover solution corresponds to a fifth distribution parameter, the fourth distribution parameter and the fifth distribution parameter are both used to indicate the deployment information of the lighting fixture in the digital twin space, and there is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter; A second determination module is used to determine the fifth distribution parameter corresponding to the second target solution in the fifth number of second cross solutions as the lighting fixture distribution parameter of the digital twin space when the change in fitness values ​​between the fifth number of second cross solutions is less than or equal to the preset threshold, and the fitness value of the second target solution is greater than the fitness values ​​of other cross solutions in the fifth number of second cross solutions.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for determining the distribution parameters of lighting fixtures according to any one of claims 1 to 5 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for determining the distributed parameters of lighting fixtures according to any one of claims 1 to 5 are implemented.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method for determining the distributed parameters of lighting fixtures as claimed in any one of claims 1 to 5.

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