A lighting fixture distribution parameter determination method and related device

Abstract

The application provides a lighting lamp distribution parameter determination method and related equipment, and the method comprises the following steps: randomly generating an initial population of lamp distribution in a digital twin space, the initial population comprising a first number of initial solutions, each initial solution corresponding to a first distribution parameter; performing cross processing on the first number of initial solutions and a second number of first mutation solutions to obtain a third number of first cross solutions, the first mutation solutions being obtained by mutation processing on the initial solutions; in the case that the change of fitness values between the third number of first cross solutions is less than or equal to a preset threshold, determining the third distribution parameter corresponding to a first target solution in the third number of first cross solutions as the lighting lamp distribution parameter of the digital twin space. In this way, the optimal lighting lamp distribution parameter can be found in a complex search space, and the lighting effect and energy utilization efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of mining engineering technology, and in particular to a method for determining the distribution parameters of lighting fixtures and related equipment. Background Technology

[0002] In the field of mining engineering, especially in underground and tunnel environments, insufficient light can significantly impact work safety and efficiency due to geological limitations. Lighting fixtures are typically installed in underground mines and tunnels to improve worker efficiency and safety, and reduce the risk of accidents.

[0003] Currently, lighting fixtures are installed in mines and tunnels, and their distribution is mainly determined by experience and trial and error. For example, a fixture is placed at regular intervals, and the number or power of fixtures is increased at corners to illuminate the mines and tunnels. However, after the fixtures are deployed, in actual operation, some areas may have poor lighting due to long spacing between fixtures or a small number of fixtures, or some areas may have redundant fixtures, resulting in high lighting costs. Adjusting the distribution of the fixtures in these situations is time-consuming, labor-intensive, and may even lead to safety accidents.

[0004] It is evident that existing lighting fixtures suffer from poor lighting performance. Summary of the Invention

[0005] The main objective of this invention is to provide a method and related equipment for determining the distribution parameters of lighting fixtures, aiming to solve the problem of poor lighting effect of existing lighting fixtures.

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

[0007] 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 of which corresponds to a first distribution parameter. The digital twin space is a digital space constructed based on the target location.

[0008] The first number of initial solutions and the second number of first mutated solutions are cross-processed to obtain a third number of first cross-solutions. The first mutated solutions are obtained by mutating the initial solutions. The second number is greater than or equal to the first number, and the third number is greater than or equal to the sum of the first number and the second number. Each first mutated 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 lighting fixtures in the digital twin space. At least one of the first distribution parameter, the second distribution parameter, and the third distribution parameter is different from the deployment information.

[0009] If the change in fitness value among 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 value of other cross solutions in the third number of first cross solutions.

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

[0011] If the change in fitness value among the third number of first cross solutions is greater than a preset threshold, the third number of first cross solutions and the fourth number of second mutated solutions are cross-processed to obtain a fifth number of second cross solutions. The second mutated solutions are obtained by mutating the first cross solutions. The fourth number is greater than or equal to the third number, and the fifth number is greater than or equal to the sum of the third number and the fourth number. Each second mutated solution corresponds to a fourth distribution parameter, and each second cross solution corresponds to a fifth distribution parameter. Both the fourth distribution parameter and the fifth distribution parameter are used to indicate the deployment information of lighting fixtures in the digital twin space. There is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter.

[0012] If the change in fitness value among 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 value of other cross solutions in the fifth number of second cross solutions.

[0013] Optionally, the mutation process is solved according to the following formula:

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

[0015] Among them, V i Let X be any one of the solutions in the first mutation. i X j and X k Let F be any three solutions in the initial solution, and let F be the mutation factor.

[0016] Optionally, the crossover process 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] Where rand(0,1) is a random number generator uniformly distributed in the interval [0,1], CR is the crossover probability, and the value of CR ranges from [0,1]. rand Let X be a random integer in the range [1, d]. i Let V be any one of the initial solutions. i Let i be any solution in the first variant solution, where i, d, and j are all positive integers, and j is less than or equal to d.

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

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

[0024] Where f(X) is the fitness function, used to evaluate the fitness value of any first crossover solution X, E avg (X) represents the average illuminance, U(X) represents the illuminance uniformity, P(X) represents the penalty function, and w1 and w2 are weighting coefficients.

[0025] Furthermore, to achieve the above objectives, the present invention also provides a device for determining the distribution parameters of lighting fixtures, the device comprising:

[0026] A generation module is used to randomly generate an initial population of lamp distribution in a digital twin space. The initial population includes a first number of initial solutions, each initial solution corresponding to a first distribution parameter. The digital twin space is a digital space constructed based on the target location.

[0027] A first processing module is used to perform cross-processing on the first number of initial solutions and the second number of first mutated solutions to obtain a third number of first cross-solutions. The first mutated solutions are obtained by mutating the initial solutions. The second number is greater than or equal to the first number, and the third number is greater than or equal to the sum of the first number and the second number. Each first mutated 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 lighting fixtures in the digital twin space. At least one of the first distribution parameter, the second distribution parameter, and the third distribution parameter is different from the deployment information.

[0028] The first determining module is configured 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 value among the third number of first cross solutions is less than or equal to a preset threshold, wherein the fitness value of the first target solution is greater than the fitness value of other cross solutions in the third number of first cross solutions.

[0029] Optionally, the device further includes:

[0030] The second processing module is configured to, when the change in fitness value among the third number of first cross solutions is greater than a preset threshold, perform cross processing on the third number of first cross solutions and the fourth number of second mutated solutions to obtain a fifth number of second cross solutions. The second mutated solutions are obtained by mutating the first cross solutions. The fourth number is greater than or equal to the third number, and the fifth number is greater than or equal to the sum of the third number and the fourth number. Each second mutated solution corresponds to a fourth distribution parameter, and each second cross solution corresponds to a fifth distribution parameter. Both the fourth distribution parameter and the fifth distribution parameter are used to indicate the deployment information of lighting fixtures in the digital twin space. At least one of the deployment information is different between the fourth distribution parameter and the fifth distribution parameter.

[0031] The second determining 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 value among the fifth number of second cross solutions is less than or equal to the preset threshold, wherein the fitness value of the second target solution is greater than the fitness value of other cross solutions in the fifth number of second cross solutions.

[0032] To address the aforementioned technical problems, this application also provides an electronic device that employs the following technical solution:

[0033] The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of any of the lighting fixture distribution parameter determination methods proposed in the embodiments of this application.

[0034] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0035] The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the lighting fixture distribution parameter determination methods proposed in the embodiments of this application.

[0036] To address the aforementioned technical problems, this application also provides a computer program product that employs the following technical solution:

[0037] The computer program product includes computer instructions that, when executed by a processor, implement the steps of any of the lighting fixture distribution parameter determination methods proposed in the embodiments of this application.

[0038] Compared with the prior art, the embodiments of this application have the following main advantages:

[0039] This invention provides a method and related equipment for determining the distribution parameters of lighting fixtures. The method includes: randomly generating an initial population of lighting fixture distributions in a digital twin space, the initial population including a first number of initial solutions, each initial solution corresponding to a first distribution parameter, the digital twin space being a digital space constructed based on a target location; cross-processing the first number of initial solutions with a second number of first mutated solutions to obtain a third number of first cross-processed solutions, the first mutated solutions being obtained after mutating 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 and second numbers, each first mutated solution corresponding to a second distribution parameter. Each first crossover solution corresponds to a third distribution parameter. The first, second, and third distribution parameters all indicate the deployment information of lighting fixtures in the digital twin space. At least one of the first, second, and third distribution parameters must be different. If the change in fitness values ​​among a third number of first crossover solutions is less than or equal to a preset threshold, the third distribution parameter corresponding to the first target solution among the third number of first crossover solutions is determined as the lighting fixture distribution parameter in the digital twin space. The fitness value of the first target solution is greater than the fitness values ​​of other crossover solutions among the third number of first crossover solutions. In this embodiment of the invention, the digital twin space allows for the simulation and evaluation of different lighting fixture distribution schemes without actual installation of lighting fixtures, greatly improving optimization efficiency. Furthermore, the differential evolution algorithm, through steps such as random initial population generation, mutation and crossover operations, and fitness value evaluation, can find the optimal lighting fixture distribution parameters in a complex search space, improving lighting effect and energy utilization efficiency. In addition, requiring at least one different deployment information among different distribution parameters ensures that the algorithm can fully explore the solution space and avoid getting trapped in local optima. It is suitable for various locations that require optimized lighting fixture distribution, such as mines, tunnels, warehouses, and large indoor stadiums. The algorithm parameters and optimization objectives can be adjusted according to the characteristics and needs of different locations to achieve the best lighting effect. Attached Figure Description

[0040] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0042] Figure 2 This is a flowchart of the method for determining the distribution parameters of lighting fixtures provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of the lighting fixture distribution parameter determination device provided in an embodiment of the present invention;

[0044] Figure 4 This is a basic structural block diagram of the electronic device in this embodiment. Detailed Implementation

[0045] The lighting fixture distribution parameter determination method provided in this invention is applied to a lighting fixture distribution parameter determination device. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the application. The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or accompanying drawings of this application are used to distinguish different objects, not to describe a specific order.

[0046] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

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

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

[0049] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. In some embodiments, various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social online platform software, etc. Terminal devices 101, 102, and 103 can be various electronic devices with displays and supporting web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptops, and desktop computers, etc.

[0050] It should be noted that the method for determining the distribution parameters of lighting fixtures provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the device for determining the distribution parameters of lighting fixtures is generally installed in the server / terminal device.

[0051] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0052] Good lighting conditions are crucial for ensuring worker safety and improving production efficiency. However, the current method of installing lighting fixtures in mines and tunnels relies mainly on experience and trial and error to determine their distribution. This approach is not only inefficient but also makes it difficult to ensure optimal lighting effects.

[0053] Continue to refer to Figure 2 A flowchart illustrating a method for determining the distribution parameters of lighting fixtures according to this application is shown. The method for determining the distribution parameters of lighting fixtures provided in this embodiment of the invention can be applied to servers, such as... Figure 2 As shown, the method for determining the distribution parameters of lighting fixtures provided in this embodiment of the invention includes the following steps:

[0054] S210. Randomly generate an initial population of lamp distribution in the digital twin space, the initial population including a first number of initial solutions, each initial solution corresponding to a first distribution parameter, the digital twin space being a digital space constructed based on the target location;

[0055] In this step, the digital twin space is a digital space constructed based on the target location (such as an actual mine or tunnel), capable of accurately simulating various physical characteristics and conditions of the target location. In determining the luminaire distribution parameters, the digital twin space can consider factors such as the shape, size, and wall reflectivity of the target location, thus more realistically reflecting the lighting effect of the luminaires in the actual environment. By optimizing the luminaire distribution parameters in the digital twin space, the lighting effects of different luminaire distribution schemes can be quickly calculated without the need for tedious installation and testing in the actual location, improving optimization efficiency.

[0056] The initial population can be generated randomly. In other words, the distribution parameters of the lamps do not have specific preset values, but are randomly selected within a certain range. This ensures the diversity of the initial population, thereby increasing the probability of the algorithm finding the optimal solution. For example, for the position parameters of the lamps, 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, values ​​can be randomly selected within a reasonable height range; and for the angle parameters of the lamps, 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 improve the probability of finding the global optimum, but it will also increase the computation time and complexity. For example, if the first number is set to N, then N different lamp distribution schemes will be randomly generated, each scheme serving 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, and angle of the lamps in the digital twin space. For example, the first distribution parameter corresponding to an initial solution might be that lamp A is located at coordinates (x1, y1, z1) in the digital twin space, with a height of h1 and an angle of θ1; lamp B is located at coordinates (x2, y2, z2), with a height of h2 and an angle of θ2, and so on.

[0057] Thus, in determining the lighting fixture distribution parameters in the digital twin space based on the differential evolution algorithm, an initial population needs to be randomly generated first. This initial population forms the basis for subsequent algorithm optimization. The digital twin space is a digital space constructed based on the target location, capable of simulating various conditions and phenomena in the real environment to more efficiently optimize the lighting fixture distribution parameters.

[0058] S220. The first number of initial solutions and the second number of first mutated solutions are cross-processed to obtain a third number of first cross solutions. The first mutated solutions are obtained by mutating the initial solutions. The second number is greater than or equal to the first number, and the third number is greater than or equal to the sum of the first number and the second number. Each first mutated 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 lighting fixtures in the digital twin space. At least one of the first distribution parameter, the second distribution parameter, and the third distribution parameter is different from the deployment information.

[0059] In this step, the initial solution and the mutated solution can be combined to generate new candidate solutions (i.e., the first cross solution) to explore a better lamp distribution scheme. The initial solution is a randomly generated combination of lamp distribution parameters, while the mutated solution is obtained by mutating the initial solution. By cross-processing, the characteristics of both can be combined, increasing the diversity of solutions and improving the probability of finding the optimal lamp distribution parameters.

[0060] The first mutated solution is obtained by mutating the initial solution. By transforming the initial solution, new individuals (i.e., mutated solutions) are generated to introduce greater diversity. The number of first mutated solutions is the second number, and the second number is greater than or equal to the first number. This ensures that a sufficient number of mutated solutions intersect with the initial solution, further expanding the search space. Each first mutated solution corresponds to a second distribution parameter, which is also used to describe the deployment of the lighting fixtures in the digital twin space.

[0061] Then, the first number of initial solutions and the second number of first mutated 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 lighting fixtures in the digital twin space. The third number is greater than or equal to the sum of the first and second numbers, and new solutions are generated through the cross-processing, increasing the number of cross-solutions.

[0062] This requires that at least one of the first, second, and third distribution parameters must be different. This ensures that the new solution generated during the crossover process (the first crossover solution) differs from the original initial solution and the first mutation solution, avoiding the generation of duplicate or similar solutions, thus guaranteeing that the algorithm can effectively search the entire solution space.

[0063] S230, if the change in fitness value among 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 value of other cross solutions in the third number of first cross solutions.

[0064] In this step, the fitness value is used to measure the quality of each solution (i.e., the lighting distribution scheme). The fitness value can be calculated based on specific optimization objectives. For example, in the lighting distribution problem, factors such as average illuminance, illuminance uniformity, and lighting energy consumption may be considered. The higher the fitness value, the better the solution performs in meeting these optimization objectives. The preset threshold is a pre-set value used to determine whether the algorithm has converged. The size of this threshold depends on the specific problem and requirements. If the threshold is set too small, the algorithm may converge prematurely and miss better solutions; 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 change in fitness values ​​between these cross solutions is calculated. For example, this could be comparing the absolute value of the difference between the fitness values ​​of two adjacent cross solutions, or calculating the standard deviation of the fitness values ​​of all cross solutions. If the change in these fitness values ​​is less than or equal to the preset threshold, it indicates that the algorithm may have converged to a better solution. Then, the third distribution parameter corresponding to the first objective solution is determined as the lighting distribution parameter in the digital twin space. The third distribution parameter corresponding to the first objective solution contains detailed information such as the position, height, and angle of the luminaires in the digital twin space. It is the optimal luminaire distribution scheme obtained after algorithm optimization. In this way, by utilizing the optimization capability of the differential evolution algorithm, the optimal luminaire distribution scheme is gradually approached through continuous iteration and crossover mutation to meet the lighting needs and optimization objectives in the digital twin space.

[0065] In this embodiment of the invention, an initial population of lighting fixture distribution in a digital twin space is randomly generated. The initial population includes a first number of initial solutions, each of which corresponds to a first distribution parameter. The digital twin space is a digital space constructed based on a target location. The first number of initial solutions and a second number of first mutated solutions are cross-processed to obtain a third number of first cross-solutions. The first mutated solutions are obtained by mutating the initial solutions. The second number is greater than or equal to the first number, and the third number is greater than or equal to the sum of the first and second numbers. Each first mutated solution corresponds to a second distribution parameter, and each first cross-solution corresponds to a third distribution parameter. The first, second, and third distribution parameters are all used to indicate the deployment information of lighting fixtures in the digital twin space. At least one of the first, second, and third distribution parameters has different deployment information. When the change in fitness value among 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. 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, by utilizing a digital twin space, different lighting fixture distribution schemes can be simulated and evaluated without actually installing the fixtures, significantly improving optimization efficiency. Furthermore, the differential evolutionary algorithm, through steps such as randomly generating an initial population, mutation and crossover operations, and fitness value evaluation, can find the optimal lighting fixture distribution parameters in a complex search space, improving lighting effect and energy utilization efficiency. In addition, it requires at least one distinct deployment information between different distribution parameters, ensuring that the algorithm can fully explore the solution space and avoid getting trapped in local optima. It is suitable for various locations requiring optimized lighting fixture distribution, such as mines, tunnels, warehouses, and large indoor stadiums. The algorithm parameters and optimization objectives can be adjusted according to the characteristics and needs of different locations to achieve the best lighting effect.

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

[0067] If the change in fitness value among the third number of first cross solutions is greater than a preset threshold, the third number of first cross solutions and the fourth number of second mutated solutions are cross-processed to obtain a fifth number of second cross solutions. The second mutated solutions are obtained by mutating the first cross solutions. The fourth number is greater than or equal to the third number, and the fifth number is greater than or equal to the sum of the third number and the fourth number. Each second mutated solution corresponds to a fourth distribution parameter, and each second cross solution corresponds to a fifth distribution parameter. Both the fourth distribution parameter and the fifth distribution parameter are used to indicate the deployment information of lighting fixtures in the digital twin space. There is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter.

[0068] If the change in fitness value among 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 value of other cross solutions in the fifth number of second cross solutions.

[0069] In this embodiment, if the fitness value change among the third number of first cross solutions exceeds a preset threshold, it indicates that the algorithm has not yet converged to a near-optimal solution. At this point, the third number of first cross solutions can be cross-processed with the fourth number of second mutated solutions. The second mutated solutions are obtained by mutating the first cross solutions, and the fourth number is greater than or equal to the third number to ensure sufficient mutated solutions to cross-process with the first cross solutions. After cross-processing, a fifth number of second cross solutions is obtained, which is greater than or equal to the sum of the third and fourth numbers. Each second mutated solution corresponds to a fourth distribution parameter, and each second cross solution corresponds to a fifth distribution parameter. These distribution parameters are used to indicate the deployment information of lighting fixtures in the digital twin space. Furthermore, it is required that there is at least one different deployment information between the fourth and fifth distribution parameters to ensure solution diversity.

[0070] Next, the fitness value change among the fifth number of second crossover solutions is calculated. If the fitness value change is less than or equal to a preset threshold, it indicates that the algorithm may have converged to a better solution. Then, among the fifth number of second crossover solutions, the second objective solution with a fitness value greater than other crossover solutions is selected. This solution is considered the optimal solution in the current iteration. The fifth distribution parameter corresponding to the second objective solution is then determined as the lighting fixture distribution parameter in the digital twin space.

[0071] In this way, by adding iterative steps, the algorithm can continuously explore the solution space even before convergence, increasing the likelihood of finding better lighting fixture distribution parameters. Through continuous mutation and cross-operation processes, the diversity of solutions is ensured, preventing the algorithm from getting trapped in local optima. This allows the algorithm to gradually approach the optimal solution, providing more accurate and reliable solutions for practical lighting design and optimization. Simultaneously, utilizing digital twin space for simulation and optimization can significantly reduce the cost and time of actual debugging, improving work efficiency.

[0072] In some alternative embodiments, the algorithm may stop running when it reaches a preset maximum number of iterations. For example, the maximum number of iterations may be set to N. max During the iteration process, record the current iteration number n. When n = N max When the termination condition is met, the second objective solution can be determined from the cross-solutions after the nth iteration, thus obtaining the fifth distribution parameter corresponding to the second objective solution, and determining the lighting fixture distribution parameters in the digital twin space. Similarly, this can provide a more accurate and reliable solution for actual lighting design and optimization.

[0073] Optionally, the mutation process is solved according to the following formula:

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

[0075] Among them, V i Let X be any one of the solutions in the first mutation. i X j and X k Let F be any three solutions in the initial solution, and let F be the mutation factor.

[0076] In this embodiment, the objective function is determined based on the lighting objective. For example, maximizing average illuminance, maximizing illuminance uniformity, or minimizing lighting energy consumption can be used as the objective function. Constraints are determined according to the actual conditions of underground mining operations, such as the installation location, quantity, and power of the lighting fixtures. Then, a set of distribution parameters for the lighting fixtures is randomly generated as an initial population. Each solution represents a lighting fixture distribution scheme, including parameters such as the location, height, and angle of the fixtures. A mutation operation is performed on each solution in the initial population to generate new individuals, i.e., mutated solutions. Specifically, three different initial solutions (Xi, Xj, Xi) are randomly selected from the current population. i X j and X k ), then follow V i =X i +F(X j -X kPerform a mutation operation to obtain the mutated solution V. i Where F is the variation factor, used to control the magnitude of variation.

[0077] Then, a crossover operation is performed between the mutated solution and the initial solution to generate a new crossover solution. The crossover process can be described as follows:

[0078] Alternatively, the cross-processing 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] Where rand(0,1) is a random number generator uniformly distributed in the interval [0,1], CR is the crossover probability, and the value of CR ranges from [0,1]. rand Let X be a random integer in the range [1, d]. i Let V be any one of the initial solutions. i Let i be any solution in the first variant solution, where 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 mutated solution can be randomly crossed. A solution is selected from the mutated individuals or from the original individuals with a certain probability, resulting in a crossover solution. For example, any solution X in the initial solutions... 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 fixture (e.g., including the position coordinates z, y, z of the fixture, height h, angle θ, etc.), and also has the first mutated solution V after mutation. i =(v i1 v i2 , ..., v idA crossover probability CR needs to be determined, which controls the frequency of parameter selection from the mutated individuals. Additionally, a random integer J between 1 and d needs to be generated. rand .

[0085] Generation of the first cross solution U by dimension-wise cross i U i =(u i1 u i2 ,…,u id Each parameter u in ) 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 mutation solution V i Get this parameter from the source. Otherwise, u ij =x ij That is, from the initial solution X i The parameter can be obtained from [the source].

[0087] For example, suppose X i This represents a lighting fixture distribution scheme, whose parameters include the fixture positions x. i1 =3m, x i2 =4m, lamp height x i3 = 2.5m, lamp 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°. Let 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, because j=j rand Therefore, 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 mutation solution V i Some characteristics of the initial solution. Through crossover, information can be exchanged between the initial solution and the mutated solution, allowing the newly generated crossover solution to inherit some of the good characteristics of the initial solution, while also potentially acquiring new characteristics explored in the mutated solution. This combination method helps the algorithm explore the search space more comprehensively, avoiding premature convergence to local optima, thereby increasing the likelihood of finding the global optimum (i.e., the optimal lighting fixture distribution parameters).

[0093] Furthermore, based on the optimization objective function, the fitness value of each solution is calculated, as described below:

[0094] The fitness value is calculated using the following formula:

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

[0096] Where f(X) is the fitness function, used to evaluate the fitness value of any first crossover solution X, E avg (X) represents the average illuminance, U(X) represents the illuminance uniformity, P(X) represents the penalty function, and w1 and w2 are weighting coefficients.

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

[0098] In one example, if the optimization objective is to maximize average illuminance, then the fitness value can be calculated considering the average illuminance produced by the luminaire distribution scheme represented by the objective solution. For instance, for a luminaire distribution scheme, the average illuminance value of the working area in the mine under that scheme is calculated using a lighting model and related illumination calculation methods. The higher this average illuminance value, the higher the fitness value of that individual is likely to be in the dimension of average illuminance as the lighting objective.

[0099] In another example, for the lighting objective of illuminance uniformity, the fitness value is related to the calculated illuminance uniformity to maximize it. The higher the uniformity, the greater its contribution to the fitness value in the illuminance uniformity dimension.

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

[0101] Specifically, a fitness function f(X) is determined to evaluate the fitness value of any first crossover solution X, and the optimization objective is to maximize the average illuminance and the illuminance uniformity, while ensuring that the luminaire installation height is not less than h. min The constraints are as follows. First, calculate the average illuminance E. avg (X) and illuminance uniformity U(X); then define a penalty function P(X) if the luminaire installation height is less than h. min If the light fixture installation height meets the condition, then P(X) = -C, where C is a large integer representing the penalty strength; if the light fixture installation height meets the condition, then P(X) = 0. The fitness function f(X) can be expressed as f(X) = w1E avg The fitness value, calculated as (X) + w2U(X) + P(X), comprehensively reflects an individual's performance in terms of lighting objectives and constraints, thus providing a reasonable basis for the selection operation of the differential evolution algorithm. It is applicable to various locations requiring optimized lighting fixture distribution, improving lighting effects.

[0102] Further reference Figure 3 As a response to the above Figure 3 To implement the method shown, this application provides an embodiment of a lighting fixture distribution parameter determination device 300, which is similar to... Figure 3 Corresponding to the method embodiment shown, the device can be specifically applied to a server.

[0103] This invention provides a device for determining the distribution parameters of lighting fixtures. The device 300 includes:

[0104] The generation module 310 is used to randomly generate an initial population of lamp distribution in the digital twin space. The initial population includes a first number of initial solutions, each initial solution corresponding to a first distribution parameter. The digital twin space is a digital space constructed based on the target location.

[0105] The first processing module 320 is used to perform cross-processing on the first number of initial solutions and the second number of first mutated solutions to obtain a third number of first cross-solutions. The first mutated solutions are obtained by mutating the initial solutions. The second number is greater than or equal to the first number, and the third number is greater than or equal to the sum of the first number and the second number. Each first mutated 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 lighting fixtures in the digital twin space. At least one of the first distribution parameter, the second distribution parameter, and the third distribution parameter is different from the deployment information.

[0106] The first determining 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 fitness value among the third number of first cross solutions is less than or equal to a preset threshold, wherein the fitness value of the first target solution is greater than the fitness value 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] The second processing module is configured to, when the change in fitness value among the third number of first cross solutions is greater than a preset threshold, perform cross processing on the third number of first cross solutions and the fourth number of second mutated solutions to obtain a fifth number of second cross solutions. The second mutated solutions are obtained by mutating the first cross solutions. The fourth number is greater than or equal to the third number, and the fifth number is greater than or equal to the sum of the third number and the fourth number. Each second mutated solution corresponds to a fourth distribution parameter, and each second cross solution corresponds to a fifth distribution parameter. Both the fourth distribution parameter and the fifth distribution parameter are used to indicate the deployment information of lighting fixtures in the digital twin space. At least one of the deployment information is different between the fourth distribution parameter and the fifth distribution parameter.

[0109] The second determining 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 value among the fifth number of second cross solutions is less than or equal to the preset threshold, wherein the fitness value of the second target solution is greater than the fitness value of other cross solutions in the fifth number of second cross solutions.

[0110] Optionally, the mutation process is solved according to the following formula:

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

[0112] Among them, V i Let X be any one of the solutions in the first mutation. i X j and X k Let F be any three solutions in the initial solution, and let F be the mutation factor.

[0113] Optionally, the crossover process 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] Where rand(0,1) is a random number generator uniformly distributed in the interval [0,1], CR is the crossover probability, and the value of CR ranges from [0,1]. rand Let X be a random integer in the range [1, d]. i Let V be any one of the initial solutions. i Let i be any solution in the first variant solution, where i, d, and j are all positive integers, and j is less than or equal to d.

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

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

[0121] Where f(X) is the fitness function, used to evaluate the fitness value of any first crossover solution X, E avg (X) represents the average illuminance, U(X) represents the illuminance uniformity, P(X) represents the penalty function, and w1 and w2 are weighting coefficients.

[0122] To address the aforementioned technical problems, embodiments of this application also provide an electronic device. Please refer to [link / reference needed] for details. Figure 4 , Figure 4 This is a basic structural block diagram of the electronic device in this embodiment.

[0123] The electronic device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only electronic device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing 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 (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0124] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0125] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), 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, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the electronic device 4, such as the hard disk or memory of the electronic device 4. In other embodiments, the memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 4. Of course, the memory 41 may also include both the internal storage unit and the external storage device of the electronic device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the electronic device 4, such as the program code for a method to determine the distribution parameters of lighting fixtures. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

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

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

[0128] This application also provides another embodiment, namely, providing a computer-readable storage medium storing a program for the method of determining the distribution parameters of lighting fixtures, the program of which can be executed by at least one processor to cause the at least one processor to perform the steps of the method of determining the distribution parameters of lighting fixtures as described above.

[0129] This application also provides another implementation method, namely, a computer program product, which includes computer instructions that, when executed by a processor, implement the steps of the above-described method for determining the distribution parameters of lighting fixtures.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware online platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0131] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: 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, and distributed computing environments including any of the above systems or devices. This 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. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0132] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for determining the distribution parameters of lighting fixtures, characterized in that, The method includes: 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 of which corresponds to a first distribution parameter. The digital twin space is a digital space constructed based on the target location. The first number of initial solutions and the second number of first mutated solutions are cross-processed to obtain a third number of first cross-solutions. The first mutated solutions are obtained by mutating the initial solutions. The second number is greater than or equal to the first number, and the third number is greater than or equal to the sum of the first number and the second number. Each first mutated 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 lighting fixtures in the digital twin space. At least one of the first distribution parameter, the second distribution parameter, and the third distribution parameter is different from the deployment information. If the change in fitness value among 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 value of other cross solutions in the third number of first cross solutions.

2. The method according to claim 1, characterized in that, After performing cross-processing on the first number of initial solutions and the second number of first mutated solutions to obtain a third number of first cross-solutions, the method further includes: If the change in fitness value among the third number of first cross solutions is greater than a preset threshold, the third number of first cross solutions and the fourth number of second mutated solutions are cross-processed to obtain a fifth number of second cross solutions. The second mutated solutions are obtained by mutating the first cross solutions. The fourth number is greater than or equal to the third number, and the fifth number is greater than or equal to the sum of the third number and the fourth number. Each second mutated solution corresponds to a fourth distribution parameter, and each second cross solution corresponds to a fifth distribution parameter. Both the fourth distribution parameter and the fifth distribution parameter are used to indicate the deployment information of lighting fixtures in the digital twin space. There is at least one different deployment information between the fourth distribution parameter and the fifth distribution parameter. If the change in fitness value among 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 value of other cross solutions in the fifth number of second cross solutions.

3. The method according to claim 1, characterized in that, The mutation treatment is solved according to the following formula: V i =X i +F(X j -X k ); Among them, V i Let X be any one of the solutions in the first mutation. i X j and X k Let F be any three solutions in the initial solution, and let F be the mutation 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 ); Where rand(0,1) is a random number generator uniformly distributed in the interval [0,1], CR is the crossover probability, and the value of CR ranges from [0,1]. rand Let X be a random integer in the range [1, d]. i Let V be any one of the initial solutions. i For any solution in the first mutation, U i For any solution in the first cross solution, u ij For U i The parameter x ij For X i The parameter in, v ij For V i In the parameters, i, d, and j are all positive integers, and j is less than or equal to d, where d represents the dimension of the individual.

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

6. A device for determining the distribution parameters of lighting fixtures, characterized in that, The device includes: A generation module is used to randomly generate an initial population of lamp distribution in a digital twin space. The initial population includes a first number of initial solutions, each initial solution corresponding to a first distribution parameter. The digital twin space is a digital space constructed based on the target location. A first processing module is used to perform cross-processing on the first number of initial solutions and the second number of first mutated solutions to obtain a third number of first cross-solutions. The first mutated solutions are obtained by mutating the initial solutions. The second number is greater than or equal to the first number, and the third number is greater than or equal to the sum of the first number and the second number. Each first mutated 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 lighting fixtures in the digital twin space. At least one of the first distribution parameter, the second distribution parameter, and the third distribution parameter is different from the deployment information. The first determining module is configured 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 value among the third number of first cross solutions is less than or equal to a preset threshold, wherein the fitness value of the first target solution is greater than the fitness value of other cross solutions in the third number of first cross solutions.

7. The apparatus according to claim 6, characterized in that, Also includes: The second processing module is configured to, when the change in fitness value among the third number of first cross solutions is greater than a preset threshold, perform cross processing on the third number of first cross solutions and the fourth number of second mutated solutions to obtain a fifth number of second cross solutions. The second mutated solutions are obtained by mutating the first cross solutions. The fourth number is greater than or equal to the third number, and the fifth number is greater than or equal to the sum of the third number and the fourth number. Each second mutated solution corresponds to a fourth distribution parameter, and each second cross solution corresponds to a fifth distribution parameter. Both the fourth distribution parameter and the fifth distribution parameter are used to indicate the deployment information of lighting fixtures in the digital twin space. At least one of the deployment information is different between the fourth distribution parameter and the fifth distribution parameter. The second determining 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 value among the fifth number of second cross solutions is less than or equal to the preset threshold, wherein the fitness value of the second target solution is greater than the fitness value of other cross solutions in the fifth number of second cross solutions.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for determining the distribution parameters of lighting fixtures as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for determining the distribution parameters of lighting fixtures as described in any one of claims 1 to 5.

10. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the method for determining the distribution parameters of lighting fixtures as described in any one of claims 1 to 5.

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