Water chilling unit load distribution method and system based on improved dung beetle optimization algorithm

Abstract

The invention discloses a water chilling unit load distribution method and system based on an improved dung beetle optimization algorithm, and relates to the technical field of refrigerating systems. The method comprises the steps of establishing a parallel water chilling unit system power consumption model; establishing an improved dung beetle optimization algorithm based on an objective function of the model; an initial dung beetle population is generated in a Logistic chaotic mapping mode, and the position of the dung beetle population is continuously iteratively updated through an improved dung beetle optimization algorithm until an iteration termination condition is met; and determining an optimal water chilling unit load distribution scheme according to the global optimal solution. According to the improved dung beetle optimization algorithm, a biological metabolism mechanism is fused, the searching process of dung beetles is more dynamic and flexible, the searching direction can be dynamically adjusted, and therefore a real global optimal solution is found out instead of falling into a local optimal solution. The finally obtained optimal water chilling unit load distribution scheme can be directly applied to an actual water chilling unit system, the operation efficiency of the system is improved, and the power consumption and the operation cost of the system are saved.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigeration systems, and particularly to a load distribution method and system for a water chiller based on an improved dung beetle optimization algorithm. Background Art

[0002] With the sharp increase in building energy consumption, as the core equipment of a central air-conditioning system, the load distribution strategy of water chillers in parallel directly affects the system energy efficiency and operation stability. To reduce energy consumption, it is necessary to minimize the total system power by optimizing the part load ratio (PLR) of each chiller on the premise of meeting the terminal load demand. Traditional load distribution methods are mostly based on fixed ratios or empirical rules, which are difficult to adapt to dynamic working conditions and cause energy waste.

[0003] In recent years, heuristic optimization algorithms (such as genetic algorithms and particle swarm algorithms) have been introduced into this field. However, they are prone to falling into local optima in complex nonlinear systems, resulting in non-global optimal load distribution schemes and slower convergence speeds. The dung beetle optimization algorithm (DBO algorithm), as a new type of bionic algorithm, optimizes by simulating the behaviors of dung beetles rolling balls and foraging, and has the characteristics of simple structure and fast convergence speed.

[0004] However, when facing high-dimensional nonlinear problems such as parallel chiller load distribution, the basic dung beetle optimization algorithm still has some limitations, including: insufficient diversity of the initial population, imbalance between global search and local development capabilities, and premature convergence in constrained load distribution problems.

[0005] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a load distribution method and system for a water chiller based on an improved dung beetle optimization algorithm in view of the above-mentioned defects of the existing technology, aiming to solve the problem that the imbalance between global search and local development capabilities of the existing dung beetle optimization algorithm leads to poor search effects.

[0007] The technical solution adopted by the present invention to solve the problem is as follows: In a first aspect, an embodiment of the present invention provides a load distribution method for a water chiller based on an improved dung beetle optimization algorithm, and the method includes: Establish a power consumption model for a parallel water chiller system, and the power consumption model for the parallel water chiller system includes: an objective function with the minimum total system power consumption as the optimization target and system constraint conditions; An improved dung beetle optimization algorithm is established based on the objective function. The improved dung beetle optimization algorithm includes: setting the positions and search strategies of different types of dung beetles, where the position of each dung beetle represents a set of solutions to the objective function, which is used to reflect a load distribution scheme of a chiller; and dynamically adjusting the search strategies and individual energies of each dung beetle through a biological energy metabolism mechanism. An initial dung beetle population is generated by using the Logistic chaotic mapping method. The positions of the dung beetle population are continuously iteratively updated through the improved dung beetle optimization algorithm until the iterative termination condition is satisfied, and the global optimal solution is output. An optimal chiller load distribution scheme is determined according to the global optimal solution.

[0008] In a second aspect, an embodiment of the present invention further provides a chiller load distribution system based on an improved dung beetle optimization algorithm. The system includes: A model establishment module, which is used to establish a power consumption model of a parallel chiller system. The power consumption model of the parallel chiller system includes: an objective function with the minimum total system power consumption as the optimization target and system constraint conditions. An algorithm establishment module, which is used to establish an improved dung beetle optimization algorithm based on the objective function. The improved dung beetle optimization algorithm includes: setting the positions and search strategies of different types of dung beetles, where the position of each dung beetle represents a set of solutions to the objective function, which is used to reflect a load distribution scheme of a chiller; and dynamically adjusting the search strategies and individual energies of each dung beetle through a biological energy metabolism mechanism. An iterative solution module, which is used to generate an initial dung beetle population by using the Logistic chaotic mapping method, and continuously iteratively update the positions of the dung beetle population through the improved dung beetle optimization algorithm until the iterative termination condition is satisfied, and output the global optimal solution. A scheme determination module, which is used to determine an optimal chiller load distribution scheme according to the global optimal solution.

[0009] In a third aspect, an embodiment of the present invention further provides a terminal. The terminal includes a memory and more than one processor; the memory stores more than one program; the program contains instructions for executing the chiller load distribution method based on the improved dung beetle optimization algorithm as described in any one of the above; the processor is used to execute the program.

[0010] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which multiple instructions are stored. The instructions are suitable for being loaded and executed by a processor to implement the steps of the chiller load distribution method based on the improved dung beetle optimization algorithm as described in any one of the above.

[0011] Advantages of the present invention: The improved dung beetle optimization algorithm of the embodiments of the present invention incorporates a biological metabolism mechanism, making the search process of the dung beetle more dynamic and flexible. It can dynamically adjust the search direction to find the true global optimal solution instead of falling into a local optimal solution. The finally obtained optimal load distribution scheme of the chiller can be directly applied to the actual chiller system, improving the operating efficiency of the system and saving system power consumption and operating costs. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0013] Figure 1 It is a schematic flowchart of the chiller load distribution method based on the improved dung beetle optimization algorithm provided by the embodiments of the present invention.

[0014] Figure 2 It is a schematic flowchart of the improved dung beetle optimization algorithm provided by the embodiments of the present invention.

[0015] Figure 3 It is a test result diagram of the Sphere Function provided by the embodiments of the present invention.

[0016] Figure 4 It is a test result diagram of the Rosenbrock Function provided by the embodiments of the present invention.

[0017] Figure 5 It is a test result diagram of the Styblinski Function provided by the embodiments of the present invention.

[0018] Figure 6 It is a test result diagram of the Michalewicz Function provided by the embodiments of the present invention.

[0019] Figure 7 It is a software interface diagram for comparing the chiller load distribution optimization algorithms provided by the embodiments of the present invention.

[0020] Figure 8 It is a schematic diagram of the modules of the chiller load distribution system based on the improved dung beetle optimization algorithm provided by the embodiments of the present invention.

[0021] Figure 9 It is a schematic block diagram of the principle of the terminal provided by the embodiments of the present invention. Detailed implementation manners

[0022] The present invention discloses a load distribution method and system for a chiller based on an improved dung beetle optimization algorithm. To make the objectives, technical solutions and effects of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] Those skilled in the art of the present technology can understand that unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that the phrase "and / or" used herein includes all or any unit and all combinations of one or more of the associated listed items.

[0024] Those skilled in the art of the present technology can understand that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless specifically defined as here.

[0025] In view of the above defects of the prior art, the present invention provides a load distribution method for parallel chillers based on an improved dung beetle optimization algorithm, including: establishing a power consumption model for a parallel chiller system, where the power consumption model for the parallel chiller system includes: an objective function with the minimum total system power consumption as the optimization goal and system constraint conditions; establishing an improved dung beetle optimization algorithm based on the objective function, where the improved dung beetle optimization algorithm includes: setting the positions and search strategies of different types of dung beetles, and the position of each dung beetle represents a set of solutions to the objective function, which is used to reflect a load distribution scheme for a chiller; and dynamically adjusting the search strategy and individual energy of each dung beetle through a biological energy metabolism mechanism; generating an initial dung beetle population in the way of Logistic chaotic mapping, continuously iterating and updating the positions of the dung beetle population through the improved dung beetle optimization algorithm until the iteration termination condition is met, and outputting the global optimal solution; determining the optimal chiller load distribution scheme according to the global optimal solution. The improved dung beetle optimization algorithm of the present invention integrates a biological metabolism mechanism, making the search process of the dung beetles more dynamic and flexible, capable of dynamically adjusting the search direction, so as to be more likely to find the true global optimal solution instead of falling into a local optimal solution. The finally obtained optimal chiller load distribution scheme can be directly applied to the actual chiller system to improve the operating efficiency of the system, save system power consumption and operating costs.

[0026] As Figure 1 shown, the method specifically includes the following steps: Step S100, establish a power consumption model for a parallel chiller system, where the power consumption model for the parallel chiller system includes: an objective function with the minimum total system power consumption as the optimization goal and system constraint conditions.

[0027] Specifically, the method of this embodiment is mainly used to solve the load distribution problem of multiple parallel chillers. The parallel chiller system includes multiple chillers. First, establish a power consumption model for the parallel chiller system to describe the overall power consumption situation of the system. Set the optimization goal to achieve the refrigeration effect with the minimum total system power consumption, and establish the objective function of the model based on this optimization goal. The system constraint conditions are used to reflect some restriction rules of the system, such as chiller load rate constraints, cooling load constraints, and unit start-stop constraints. On the premise of meeting the system constraint conditions, the system can work normally and operate with the minimum total system power consumption as the goal.

[0028] For example, the method of this embodiment can be applied to a cold source system composed of five parallel chillers, such as three chillers with a rated cooling capacity of 1000 kW and two chillers with a rated cooling capacity of 500 kW. The specific objective function is: , Equation (1); , Equation (2); , Equation (3); , Equation (4); Wherein, represents the total power consumption of the system; is the actual energy consumption of the th chiller, is the number of chillers (for example, if there are 5 chillers, then = 5), is the actual refrigerating capacity of the th chiller; and are respectively the actual energy efficiency and load rate of the th chiller; is the performance coefficient of the chiller; and are respectively the return water temperature of the chiller cooling water and the chilled water outlet temperature; is the rated refrigerating capacity of the th unit.

[0029] The system constraint conditions are specifically: , Equation (5); , Equation (6); Wherein, is the total refrigerating capacity required by the terminal.

[0030] Step S200, establish an improved dung beetle optimization algorithm based on the objective function. The improved dung beetle optimization algorithm includes: setting the positions and search strategies of different types of dung beetles. The position of each dung beetle represents a set of solutions of the objective function, which is used to reflect a load distribution scheme of the chillers; and dynamically adjusting the search strategy and individual energy of each dung beetle through the biological energy metabolism mechanism.

[0031] Specifically, in this embodiment, a biological energy metabolism mechanism is used to establish an improved dung beetle optimization algorithm. This algorithm outputs a global optimization result by simulating the behavior of dung beetles. Among them, the position of each dung beetle represents a set of solutions, that is, a set of chiller load distribution schemes (or load distribution ratios). For example, if the number of chillers is five, then the position of each dung beetle represents a load distribution scheme of the five chillers. The biological energy metabolism mechanism will dynamically adjust the search strategy and individual energy of each dung beetle. For example, if the energy of the dung beetle is relatively high, the search range of the dung beetle may expand; if the energy of the dung beetle is relatively low, the search range of the dung beetle may shrink. And by simulating the energy consumption and replenishment mechanism of dung beetles in nature, the energy consumption of behaviors such as dung beetles rolling balls and foraging is quantified in the algorithm to balance the global search and local development of dung beetles.

[0032] In one implementation, the types of dung beetles include: ball-rolling dung beetles, egg-laying dung beetles, foraging dung beetles, and kleptoparasitic dung beetles.

[0033] Specifically, each dung beetle searches for the optimal load distribution scheme in the solution space. Different types of dung beetles have different search characteristics and capabilities. By reasonably setting their positions and search strategies, the algorithm can solve problems more comprehensively and efficiently. In this embodiment, four types of dung beetles are set: ball-rolling dung beetles, egg-laying dung beetles, foraging dung beetles, and kleptoparasitic dung beetles. And the positions and search strategies (or position update strategies) of these four types of dung beetles are set. Dung beetles use celestial cues (such as the sun, moon, and polarized light) to navigate during rolling. Therefore, in improving the dung beetle optimization algorithm, the random interference of celestial polarized light on the rolling direction is simulated, and at the same time, the global worst solution is used as the repulsive force to prevent the population from falling into the inferior region.

[0034] For example, for the ball-rolling dung beetle: The evolution process (i.e., the position update process) of the ball-rolling dung beetle is defined as follows: , Equation (7); , Equation (8); Where represents the position of the th dung beetle at time t, is a fixed step size factor, is the path deviation coefficient, simulating the random deviation of the path; is the perturbation intensity, a constant value belonging to (0, 1), providing the ability of the algorithm to jump out of the local optimum; is used to simulate the change of light intensity in nature, and is defined as the larger its value, the weaker the light intensity; represents the worst position in the dung beetle population. The significance of this step is that the current individual updates its position through the joint action of the historical position and the global worst solution, balancing individual experience and group information.

[0035] For the egg-laying dung beetle: In the reproductive behavior, in order to provide a safe living environment for the offspring, the egg-laying dung beetle needs to move the brood ball to a safe area to lay eggs and store them. Therefore, the egg-laying dung beetle adopts the following boundary selection strategy to protect the offspring, that is, the evolution process of the egg-laying dung beetle is defined as follows: , Equation (9); , Equation (10); , Equation (11); , Equation (12); Where is the current local optimal solution; and Respectively represent the upper and lower boundaries of the overall search space; and They represent the upper and lower boundaries of the safe area for dung beetles to dig and lay eggs, and the upper and lower boundaries are random. R The value size changes dynamically; ,in Indicates the maximum number of iterations in the evolution process; For the The position information of the brooding balls in the tth iteration; and are all random vectors of size 1×D, where D is the dimension of the problem to be solved.

[0036] For foraging dung beetles: When the young dung beetles grow up in the brooding balls and mature, they will crawl out of the breeding area to forage for food. These young dung beetles are defined as foraging dung beetles. At this time, it is necessary to construct the optimal foraging area to plan the movement trajectory of these young dung beetles so that they can obtain food as quickly as possible. The evolutionary process of foraging dung beetles is defined as follows: , formula (13); , formula (14); , formula (15); in, is a random number that follows a normal distribution. is a random vector on (0,1); represents the global optimal solution, and are the upper and lower bounds of the optimal foraging area, respectively.

[0037] For thieving dung beetles: Setting the best food source previously searched will attract more individuals to compete. The evolutionary process of thieving dung beetles is defined as follows: , formula (16); in, represents a random vector of size 1×D and follows a normal distribution; is a constant; and Represent the current local optimal solution and global optimal solution respectively.

[0038] In one implementation, the search strategy and individual energy of each dung beetle are dynamically adjusted through a biological energy metabolism mechanism, including: For each dung beetle, dynamically adjusting the search strategy of the dung beetle according to the individual energy of the dung beetle, and determining the update position of the dung beetle based on the adjusted search strategy; Update the individual energy of the dung beetle; during the search process, the individual energy of the dung beetle will be consumed and reduced; when a better solution is found, the individual energy of the dung beetle will be replenished.

[0039] Specifically, to improve the flexibility of the algorithm, this embodiment uses a biological energy metabolism mechanism to simulate the change of the individual energy of each dung beetle, and the individual energy can be quantified by an energy value. In addition, it is also necessary to dynamically adjust the behavior of the dung beetle according to the individual energy of the dung beetle, which is specifically reflected in: dividing different search strategies for dung beetles with different energy levels, so as to dynamically adjust the exploration and development behavior of the algorithm. For example, the dung beetle will consume energy during the search process, and the lower the energy, the more inclined it is to a conservative strategy (local development); when the dung beetle finds a better solution, it will recover some energy and stimulate the global exploration ability.

[0040] In one implementation, different search strategies correspond to different combinations of search parameters; the combination of search parameters includes: search step size and search radius.

[0041] Specifically, a search strategy is a different method or approach for finding a target solution. Each search strategy corresponds to a specific combination of search parameters. The combination of search parameters includes but is not limited to two key parameters, namely the search step size and the search radius. The search step size is the distance of each movement, and the search radius is the size of the range used to define the search. In the improved dung beetle optimization algorithm, the energy state of the dung beetle will directly affect key parameters such as the search step size and the search radius.

[0042] Step S300: Generate an initial population of dung beetles in the way of Logistic chaotic mapping, and continuously update the positions of the dung beetle population through the improved dung beetle optimization algorithm until the iteration termination condition is met, and output the global optimal solution.

[0043] Specifically, this embodiment uses the Logistic chaotic mapping method to generate the initial population and improve the uniformity of the solution space distribution. Map the chaotic sequence to the feasible solution space of the unit load rate to generate the initial cooling machine load distribution plan. During the iterative solution process, the improved dung beetle algorithm continuously updates the position of each dung beetle according to the set search strategy and biological energy metabolism mechanism. Through continuous exploration and adjustment, the dung beetle population gradually approaches the optimal solution. Each iteration is an optimization process of the current solution. By comparing the quality of the solutions represented by different dung beetles, a better solution is selected and the position of the dung beetle is updated. When the improved dung beetle optimization algorithm meets the iteration termination condition, the global optimal solution is output. The global optimal solution corresponds to the minimum value of the objective function, that is, the state with the minimum total power consumption of the system.

[0044] In one implementation, the basic formula of Logistic chaotic mapping is: , Equation (17); where = 0, 1, 2,... represents the number of iterations; is the chaotic variable generated in the -th iteration; is the control parameter. When , the Logistic map is in a fully chaotic state. A small difference in the initial value will lead to significant differences in the subsequent sequence. Therefore, it is most suitable for chaotic initialization of intelligent optimization algorithms. During the individual initialization process, the Logistic chaotic map is used to generate a chaotic sequence. Given an initial value , by applying the above mapping formula iteratively multiple times, an initial chaotic sequence can be generated, where N is the population size.

[0045] In one implementation, the positions of the dung beetle population are continuously iteratively updated by the improved dung beetle optimization algorithm until the iteration termination condition is satisfied, including: In each iteration, for each dung beetle, the search strategy of the dung beetle is dynamically adjusted according to the individual energy of the dung beetle to obtain the updated position of the dung beetle; The fitness is evaluated according to the updated position of the dung beetle, and the global optimal solution is updated according to the evaluated fitness of each dung beetle; The individual energy of each dung beetle is updated, and it is judged whether the current iteration round satisfies the iteration termination condition. The iteration termination condition includes: the global average value of the dung beetle energy is lower than a preset threshold or the number of iterations reaches a preset number; If not, the next iteration is performed, and the step of dynamically adjusting the search strategy of the dung beetle according to the individual energy of the dung beetle is continued until the iteration termination condition is satisfied, and the global optimal solution is output.

[0046] Specifically, as Figure 2As shown, during each iteration, for each dung beetle, its search strategy is dynamically adjusted according to its individual energy, and then the updated position corresponding to the dung beetle is obtained. The fitness of each dung beetle is evaluated based on its updated position, and the fitness is used to reflect the quality of the position where the dung beetle is located. The global optimal solution is updated according to the calculated fitness. The global optimal solution is the optimal solution found in the current iteration round, and a better solution is updated as the global optimal solution each time an iteration is performed, so as to gradually approach the true global optimal solution. Then, the individual energy of each dung beetle is updated, and it is judged whether the current iteration round reaches the iteration termination condition. The iteration termination condition can be set based on the number of iterations or the requirements of biological energy. For example, the global average value of the dung beetle energy is lower than a preset threshold or the number of iterations reaches a preset number. If the current iteration round does not meet the iteration termination condition, the next iteration is performed. In short, the process of iterative solution is to repeatedly execute steps such as "calculating the energy value of each dung beetle through the biological energy metabolism mechanism", "performing dynamic behavior regulation according to the individual energy of the dung beetle, and dividing different search strategies according to the energy state", and "judging whether the biological energy meets the requirements". After each round of iteration, the global optimal solution is updated until the iteration termination condition is met, and the optimal position is output.

[0047] In one implementation, dynamically adjusting the search strategy of the dung beetle according to the individual energy of the dung beetle includes: When the individual energy of the dung beetle is at the first level, adjust the search strategy of the dung beetle to the Levy flight strategy; When the individual energy of the dung beetle is at the second level, adjust the search strategy of the dung beetle to the rolling ball and / or reproduction strategy based on energy weighting; When the individual energy of the dung beetle is at the third level, adjust the search strategy of the dung beetle to the quantum rotation gate strategy; Wherein, the first level is greater than the second level, and the second level is greater than the third level; the higher the level, the higher the individual energy of the dung beetle.

[0048] Specifically, in this embodiment, different search strategies are divided for dung beetles with different energy levels, including but not limited to the Levy flight strategy, the rolling ball and / or reproduction strategy based on energy weighting, and the quantum rotation gate strategy, which can efficiently generate feasible solutions and avoid invalid searches, effectively balance the exploration and development capabilities of the algorithm, and solve the load distribution problem of parallel chillers more efficiently and accurately.

[0049] For example, assume that the initial energy of each dung beetle individual is the maximum value , and during each iteration, the individual energy decays exponentially as follows in a simulated natural state consumption: , Equation (18); Among them, is the current energy value of the individual; is the energy decay coefficient (default ). This energy decay mechanism helps to avoid the long-term ineffective search of individuals and accelerate the convergence speed.

[0050] If the individual fails to find the optimal solution, the energy will continue to decrease, while when the individual discovers a better solution, the energy will be partially restored: , Equation (19); Among them, is the maximum energy supplement (default ); is the current fitness value of the individual; and are the current best and worst fitnesses respectively. The energy supplement mechanism proposed in this embodiment helps to encourage the dung beetle to jump out of the local optimum and maintain the diversity of the population.

[0051] According to the current energy value of the individual , the following dynamically adjusted search strategy is set: (1) When the individual energy of the dung beetle is at the first level, use the Levy flight strategy to perform a large-scale global search (the Levy flight strategy is a random search strategy based on the heavy-tailed probability distribution). For example, the individual energy corresponding to the first level is high energy, is the threshold for judging whether it is in high energy. When , it means it is in high energy.

[0052] (2) When the individual energy of the dung beetle is at the second level, perform the rolling ball and reproduction behaviors of the standard dung beetle optimization algorithm (DBO algorithm), and the step size is weighted according to the dung beetle energy. Replace the fixed step size factor and perturbation intensity in Equation (7) with energy-dependent dynamic parameters. For example, the individual energy corresponding to the second level is medium energy, is the threshold for judging whether it is in low energy. When , it means it is in medium energy.

[0053] , Equation (20); , Equation (21); Among them, is the adaptive inertia weight; is the perturbation intensity. Then the updated formula for the position of the dung beetle in the rolling ball and reproduction stage becomes: , Equation (22); (3)When the individual energy of the dung beetle is at the third level, the quantum rotation gate strategy is adopted, that is, it is converted to local development through the quantum rotation gate model. For example, the individual energy corresponding to the third level is low energy. When it indicates low energy.

[0054] In each iteration, according to the threshold value (such as setting , ) can adjust the corresponding search strategy until the global average value of the dung beetle energy is lower than the threshold value ( ) and the population is considered to converge and the iteration terminates.

[0055] In one implementation, the Levy flight search strategy is specifically as follows: The step size of the Levy flight strategy is generated by the following formula: , formula (23); where is the Levy index, which determines the shape of the Levy distribution (usually taken as 1.5); is the step size scaling factor (such as ); and are both random variables that follow the standard normal distribution, following the standard normal distribution , , , respectively represent the scale parameters of the standard normal distribution; the scale parameters are calculated by the following formula: , formula (24); where the symbol represents the gamma function (Gamma function). In the improved dung beetle optimization algorithm, the Levy flight strategy is integrated into the high-energy state behavior of the dung beetle to dynamically balance the exploration and development of the algorithm. When an individual is in a high-energy state, the Levy flight strategy is triggered: , formula (25); where is the global step size coefficient (default ); is the current individual energy value; represents element-wise multiplication to ensure that the direction is related to the global optimal solution ; represents the updated position.

[0056] In one implementation, the quantum rotation gate search strategy is specifically as follows: The position of each dung beetle individual is encoded with quantum bits and represented as: , Equation (26); where is the qubit state of the individual; is the rotation angle, which determines the candidate state of the solution. During the exploration process of the dung beetle, the rotation angle is dynamically adjusted according to the difference between the current solution and the global optimal solution : , Equation (27); , Equation (28); where is the change in the rotation angle; is the learning rate used to control the rotation amplitude; is the rotation angle of the individual after update; is the fitness value of the individual; is the fitness value of the individual at the optimal position in the dung beetle population.

[0057] Observe the updated quantum state and generate a new solution: , Equation (29); where is the contraction factor, which can control the local search range.

[0058] Step S400, determine the optimal load distribution plan of the chiller according to the global optimal solution.

[0059] Specifically, after each iteration, update the global optimal solution and adjust the energy of the dung beetle individuals until the average energy of the population or reaches the set number of iterations, then end the search and output the optimal individual, that is, obtain the final global optimal solution. Through the final global optimal solution, the optimal load distribution plan of the chiller can be obtained, and this plan can be directly applied to the actual chiller system to improve the operating efficiency of the system, save system power consumption and operating costs.

[0060] To facilitate the understanding of the technical solution of this embodiment, the specific algorithm flow of the improved dung beetle optimization algorithm is as follows: Algorithm input: population size N, maximum number of iterations T, energy parameters , , threshold , ; Algorithm output: global optimal solution ; 1: Initialize the quantum rotation angle ; 2: Generate the initial population using the Logistic chaotic map; 3: Calculate the fitness f(X) of each individual in the initial population; 4: Initialize the individual initial energy state vector E = [the maximum energy value of individual 1, the maximum energy value of individual 2,..., the maximum energy value of individual N]; 5: Select the individual with the optimal fitness as the current global optimal solution ; 6: For the iteration round t, execute from 1 to the maximum number of iterations T (for represents a for loop): 7: For each individual i in the population, execute from 1 to N: 8: If the fitness of the current individual is better than that of the previous generation: 9: Update the dung beetle energy using Equation (19); 10: Otherwise: 11: The dung beetle performs energy decay according to Equation (18); 12: For each individual i in the population, execute from 1 to N: 13: If the individual energy E[i] is higher than the high energy threshold : 14: Execute the global exploration strategy based on the Levy flight strategy using Equation (25); 15: Otherwise when the energy is in the medium energy (medium - low range): 16: If it is currently in the ball - rolling stage: 17: Update the position of the ball - rolling dung beetle using Equation (7); 18: Otherwise: 19: Execute the reproduction and evolution strategy using Equation (16); 20: Otherwise when the energy is in the low energy state: 21: Execute the quantum rotation gate exploration strategy using Equation (29); 22: Perform value boundary constraint processing on the new individuals; 23: If the fitness of the new individual is better than that of the original individual; 24: Perform the individual update operation; 25: Update the global optimal solution ; 26: Output the global optimal solution .

[0061] To prove the technical effects of the present invention, the method of the present invention is applied to a cold source system composed of five chillers. At the same time, several relatively well-known intelligent optimization algorithms in recent years are used as controls, including the Dung Beetle Optimization (DBO), Salp Swarm Algorithm (SSA), Seagull Optimization Algorithm (SOA), Moth Flame Optimization (MFO), and Whale Optimization Algorithm (WOA). The return water temperature of the chiller cooling water and the chilled water outlet temperature are fixed at the same 33°C and 7°C respectively. Each algorithm runs independently 30 times to obtain the optimal unit load distribution results under different cooling loads, as shown in Table 1 (the algorithm proposed in the present invention, that is, the improved dung beetle optimization algorithm is defined as the BEODBO algorithm).

[0062] Table 1. Optimal unit load distribution results under different cooling loads

[0063] By comparing the unit load distribution results and the corresponding energy consumption under different cooling loads solved by different intelligent optimization algorithms, the improved dung beetle optimization algorithm proposed in the present invention has achieved better total energy consumption, that is, better unit load distribution results, in five of the six load scenarios.

[0064] After verifying the effectiveness of the algorithm proposed in the present invention in engineering, the algorithm proposed in the present invention is tested using 23 classical test functions for intelligent optimization algorithms, and several relatively well-known intelligent optimization algorithms in recent years are used as controls. Among them, the fitness change results of some functions are as Figures 3 to 6 shown, where Figure 3 shows the test results of the sphere function, Figure 4 shows the test results of the Rosenbrock function, Figure 5 shows the test results of the Styblinski-Tang function, Figure 6 shows the test results of the Michalewicz function. The test results show that the algorithm proposed in the present invention presents higher precision and faster search efficiency in the optimization process of most functions, and for the few test functions that fail to obtain the optimal solution, the algorithm proposed in the present invention can also obtain relatively reasonable results.

[0065] For the convenience of calculation comparison and subsequent development, the optimization model and various intelligent optimization algorithms can also be packaged and written into optimization software, and the main interface is as Figure 7 shown. Among them, the MFO algorithm is the Moth Flame Optimization, an optimization algorithm inspired by the light-seeking characteristics of moths in nature; RT is the Refrigeration Ton, a unit of refrigeration capacity; COP is the Coefficient of Performance, representing the ratio of refrigeration capacity to input power.

[0066] In summary, the advantages of the present invention are as follows: 1. Dynamic regulation of the biological energy metabolism mechanism: By simulating the energy consumption and replenishment mechanism of cockroaches, the search strategy is dynamically divided according to the energy value, balancing the global search and local development capabilities. As a result, the algorithm is superior to traditional algorithms in terms of global search ability, convergence speed, and robustness, avoiding premature convergence and enhancing the algorithm's robustness. Moreover, when an individual discovers a better solution, it replenishes energy according to the fitness ratio, motivating the population to jump out of the local optimum. The exponentially decaying energy avoids ineffective searches and quickly eliminates inefficient solutions.

[0067] 2. Initializing the population using the Logistic chaotic map: The initial solution is generated using the Logistic chaotic map to replace the traditional random initialization, ensuring that the cold machine load distribution scheme is evenly distributed in the solution space, that is, improving the uniformity of the solution space coverage and enhancing the diversity of the initial population. Chaotic initialization expands the range of the solution space covered by the algorithm in the initial stage, significantly reducing the blind area.

[0068] 3. Fusion of multiple search strategies: The present invention combines the cold machine load rate constraint, as well as various search strategies such as the Levy flight strategy and the quantum rotation gate strategy, which can efficiently generate feasible solutions and avoid ineffective searches, effectively balancing the exploration and development capabilities of the algorithm, and solving the parallel cold machine load distribution problem more efficiently and accurately. Among them, high-energy individuals trigger the Levy flight for large-scale exploration, exploring the potential optimal region of the cold machine load combination through long-step jumps, increasing the probability of finding the global optimal solution in complex multimodal problems; low-energy individuals perform local development through the quantum rotation gate, encoding the cold machine PLR value as the phase of a quantum bit, and approaching the global optimal solution by dynamically adjusting the rotation angle, enhancing the global optimization ability of complex nonlinear problems and improving the local search accuracy at the same time.

[0069] 4. Dynamic parameter adaptive adjustment based on energy: The step size factor and perturbation intensity are bound to the individual energy value to achieve dynamic parameter weighting.

[0070] Possible technical deformations of the present invention: 1. Replacement of the chaotic strategy: Replace the Logistic chaotic map with the Tent map (a piecewise linear map) or the Chebyshev map (one of the commonly used chaotic maps) to optimize the generation of the initial population; 2. Replacement of the search strategy: Replace the Levy flight strategy with simulated annealing or differential evolution; replace the quantum rotation gate strategy with gradient descent or the Nelder-Mead method (an algorithm for finding the local minimum of a multivariate function); 3. Simplification of behavior classification: Combine cockroach behavior categories (such as only retaining ball rolling and foraging) to reduce the computational complexity.

[0071] Based on the above embodiments, the present invention further provides a load distribution system for a chiller based on an improved dung beetle optimization algorithm, as Figure 8 shown. The system includes: A model establishment module 01, configured to establish a power consumption model for a parallel chiller system. The power consumption model for the parallel chiller system includes: an objective function with the minimum total system power consumption as the optimization target and system constraint conditions; An algorithm establishment module 02, configured to establish an improved dung beetle optimization algorithm based on the objective function. The improved dung beetle optimization algorithm includes: setting the positions and search strategies of different types of dung beetles. The position of each dung beetle represents a set of solutions to the objective function, which is used to reflect a load distribution scheme for a chiller; and dynamically adjusting the search strategy and individual energy of each dung beetle through a biological energy metabolism mechanism; An iterative solution module 03, configured to generate an initial dung beetle population in a way of Logistic chaotic mapping, and continuously iteratively update the positions of the dung beetle population through the improved dung beetle optimization algorithm until the iterative termination condition is met, and output the global optimal solution; A scheme determination module 04, configured to determine an optimal load distribution scheme for the chiller according to the global optimal solution.

[0072] Based on the above embodiments, the present invention further provides a terminal, and its principle block diagram can be as Figure 9 shown. The terminal includes a processor, a memory, a network interface, and a display screen connected through a system bus. Among them, the processor of the terminal is used to provide computing and control capabilities. The memory of the terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the terminal is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a load distribution method for a chiller based on an improved dung beetle optimization algorithm. The display screen of the terminal can be a liquid crystal display screen or an electronic ink display screen. Those skilled in the art can understand that Figure 9 the principle block diagram shown in

[0073] In one implementation, more than one program is stored in the memory of the terminal, and is configured to be executed by more than one processor. The more than one program includes instructions for performing a load distribution method for a chiller based on an improved dung beetle optimization algorithm. Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0074] In summary, the present invention discloses a load distribution method and system for a chiller based on an improved dung beetle optimization algorithm, which relates to the technical field of refrigeration systems. The method includes: establishing a power consumption model for a parallel chiller system, the power consumption model of the parallel chiller system including: an objective function with the minimum total system power consumption as the optimization target and system constraint conditions; establishing an improved dung beetle optimization algorithm based on the objective function, the improved dung beetle optimization algorithm including: setting the positions and search strategies of different types of dung beetles, the position of each dung beetle representing a set of solutions to the objective function, which is used to reflect a chiller load distribution scheme; and dynamically adjusting the search strategy and individual energy of each dung beetle through a biological energy metabolism mechanism; generating an initial dung beetle population in the manner of Logistic chaotic mapping, and continuously iteratively updating the positions of the dung beetle population through the improved dung beetle optimization algorithm until the iterative termination condition is met, and outputting the global optimal solution; determining the optimal chiller load distribution scheme according to the global optimal solution. The improved dung beetle optimization algorithm of the present invention incorporates a biological metabolism mechanism, making the search process of the dung beetles more dynamic and flexible, capable of dynamically adjusting the search direction, and thus more likely to find the true global optimal solution instead of falling into a local optimal solution. The finally obtained optimal chiller load distribution scheme can be directly applied to an actual chiller system to improve the operating efficiency of the system, save system power consumption and operating costs.

[0075] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or modifications can be made according to the above description, and all such improvements and modifications shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A chiller load distribution method based on an improved dung beetle optimization algorithm, characterized in that: The method comprises: Establishing a parallel chiller system power consumption model, wherein the parallel chiller system power consumption model includes: an objective function with minimum total system power consumption as an optimization goal and system constraints; An improved dung beetle optimization algorithm is established based on the objective function, and the improved dung beetle optimization algorithm includes: setting positions and search strategies of different types of dung beetles, where the position of each dung beetle represents a set of solutions of the objective function, which is used to reflect a load distribution plan of a chiller; and dynamically adjusting the search strategy and individual energy of each dung beetle through a biological energy metabolism mechanism; The initial dung beetle population is generated by using a Logistic chaotic mapping method, and the position of the dung beetle population is continuously updated by the improved dung beetle optimization algorithm until the iteration termination condition is met, and the global optimal solution is output; An optimal chiller load distribution scheme is determined according to the global optimal solution.

2. The chiller load distribution method based on the improved dung beetle optimization algorithm according to claim 1 is characterized in that: The system constraints include: chiller load rate constraints, cooling load constraints, and unit start and stop constraints.

3. The chiller load distribution method based on the improved dung beetle optimization algorithm according to claim 1 is characterized in that: Types of dung beetles include: ball-rolling dung beetles, egg-laying dung beetles, foraging dung beetles, and stealing dung beetles.

4. The chiller load distribution method based on the improved dung beetle optimization algorithm according to claim 1 is characterized in that: The search strategy and individual energy of each dung beetle are dynamically adjusted through bioenergetic metabolism mechanisms, including: For each dung beetle, dynamically adjusting the search strategy of the dung beetle according to the individual energy of the dung beetle, and determining the update position of the dung beetle based on the adjusted search strategy; The individual energy of the dung beetle is updated; wherein, during the search process, the individual energy of the dung beetle will be consumed and reduced; when a better solution is found, the individual energy of the dung beetle will be replenished.

5. The chiller load distribution method based on the improved dung beetle optimization algorithm according to claim 4 is characterized in that: Dynamically adjusting the search strategy of the dung beetle according to the individual energy of the dung beetle, including: When the individual energy of the dung beetle is at the first level, adjusting the search strategy of the dung beetle to a Levy flight strategy; When the individual energy of the dung beetle is at the second level, adjusting the search strategy of the dung beetle to a rolling ball and / or breeding strategy based on energy weighting; When the individual energy of the dung beetle is at the third level, adjusting the search strategy of the dung beetle to a quantum revolving door strategy; Among them, the first level is greater than the second level, and the second level is greater than the third level; the higher the level, the higher the individual energy of the dung beetle.

6. The chiller load distribution method based on the improved dung beetle optimization algorithm according to claim 5 is characterized in that: Different search strategies correspond to different search parameter combinations; the search parameter combination includes: search step length and search radius.

7. The chiller load distribution method based on the improved dung beetle optimization algorithm according to claim 1 is characterized in that: The position of the dung beetle population is continuously updated iteratively by the improved dung beetle optimization algorithm until the iteration termination condition is met, including: In each iteration, for each dung beetle, the search strategy of the dung beetle is dynamically adjusted according to the individual energy of the dung beetle to obtain an updated position of the dung beetle; The fitness is evaluated according to the updated position of the dung beetle, and the global optimal solution is updated according to the evaluated fitness of each dung beetle; Update the individual energy of each dung beetle, and determine whether the current iteration round meets the iteration termination condition, wherein the iteration termination condition includes: the global average value of the dung beetle energy is lower than a preset threshold or the number of iterations reaches a preset number; If not, the next iteration is performed to continue the step of dynamically adjusting the search strategy of the dung beetle according to the individual energy of the dung beetle until the iteration termination condition is met and the global optimal solution is output.

8. A chiller load distribution system based on an improved dung beetle optimization algorithm, characterized in that: The system comprises: A model building module is used to build a power consumption model of a parallel chiller system, wherein the power consumption model of the parallel chiller system includes: an objective function and system constraints with the minimum total power consumption of the system as the optimization goal; An algorithm establishment module is used to establish an improved dung beetle optimization algorithm based on the objective function, wherein the improved dung beetle optimization algorithm includes: setting positions and search strategies of different types of dung beetles, wherein the position of each dung beetle represents a set of solutions of the objective function, which is used to reflect a load distribution plan of a chiller; and dynamically adjusting the search strategy and individual energy of each dung beetle through a biological energy metabolism mechanism; An iterative solution module is used to generate an initial dung beetle population by means of Logistic chaotic mapping, and continuously iteratively update the position of the dung beetle population by means of the improved dung beetle optimization algorithm until an iterative termination condition is met, and output a global optimal solution; A scheme determination module is used to determine the optimal chiller load distribution scheme according to the global optimal solution.

9. A terminal, characterized in that: The terminal includes a memory and one or more processors; the memory stores one or more programs; the program contains instructions for executing the chiller load distribution method based on the improved dung beetle optimization algorithm as described in any one of claims 1-7; the processor is used to execute the program.

10. A computer-readable storage medium having a plurality of instructions stored thereon, characterized in that: The instructions are suitable for being loaded and executed by a processor to implement the steps of the chiller load distribution method based on the improved dung beetle optimization algorithm as described in any one of claims 1 to 7.

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