Chiller load distribution method and system based on improved dung beetle optimization algorithm

Abstract

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; establishing an improved dung beetle optimization algorithm based on the objective function of the model; generating an initial dung beetle population by means of Logistic chaotic mapping, and continuously iteratively updating the positions of the dung beetle population through the improved dung beetle optimization algorithm until the iteration termination condition is satisfied; 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, thereby finding 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.

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 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 the central air-conditioning system, the load distribution strategy of parallel chillers 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, but they are prone to falling into local optima in complex nonlinear systems, resulting in non-global optimal load distribution schemes and slow 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 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:

[0008] In the first aspect, an embodiment of the present invention provides a load distribution method for a chiller based on an improved dung beetle optimization algorithm, and the method includes:

[0009] Establish a power consumption model for a parallel chiller system, and 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;

[0010] 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, 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.

[0011] The 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 iteration termination condition is met, and the global optimal solution is output.

[0012] The optimal chiller load distribution scheme is determined according to the global optimal solution.

[0013] 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:

[0014] A model establishment module for establishing 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 goal and system constraint conditions.

[0015] An algorithm establishment module for establishing 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, 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.

[0016] An iterative solution module for generating the initial dung beetle population by using the Logistic chaotic mapping method, and continuously iteratively 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.

[0017] A scheme determination module for determining the optimal chiller load distribution scheme according to the global optimal solution.

[0018] 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.

[0019] 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.

[0020] 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 for the chiller can be directly applied to the actual chiller system, improving the operating efficiency of the system, saving system power consumption and operating costs. Description of the Drawings

[0021] 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.

[0022] 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.

[0023] Figure 2 It is a schematic flowchart of the improved dung beetle optimization algorithm provided by the embodiments of the present invention.

[0024] Figure 3 It is a test result diagram of the Sphere Function provided by the embodiments of the present invention.

[0025] Figure 4 It is a test result diagram of the Rosenbrock Function provided by the embodiments of the present invention.

[0026] Figure 5 It is a test result diagram of the Styblinski Function provided by the embodiments of the present invention.

[0027] Figure 6 It is a test result diagram of the Michalewicz Function provided by the embodiments of the present invention.

[0028] Figure 7 It is a software interface diagram for comparing the chiller load distribution optimization algorithms provided by the embodiments of the present invention.

[0029] 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.

[0030] Figure 9It is a schematic block diagram of the terminal provided by an embodiment of the present invention. Detailed implementation manners

[0031] 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.

[0032] 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 any and all combinations of one or more of the associated listed items.

[0033] 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 commonly understood by those of ordinary skill in the field 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.

[0034] In view of the above defects of the prior art, the present invention provides a load distribution method for a parallel chiller system based on an improved dung beetle optimization algorithm, including: establishing a power consumption model for the 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 manner 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 beetle 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.

[0035] As Figure 1 shown, the method specifically includes the following steps:

[0036] Step S100, establish a power consumption model for the 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.

[0037] 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 limitation 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.

[0038] 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:

[0039] , Equation (1);

[0040] , Equation (2);

[0041] , Equation (3);

[0042] , Equation (4);

[0043] 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, for 5 chillers, then = 5), is the actual cooling capacity of the th chiller; and are respectively the actual energy efficiency and load ratio 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 cooling capacity of the th unit.

[0044] The system constraint conditions are specifically:

[0045] , Equation (5);

[0046] , Equation (6);

[0047] Wherein, is the total cooling capacity required at the end.

[0048] 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 to the objective function, which is used to reflect a load distribution scheme of the chillers; and dynamically adjusting the search strategies and individual energies of each dung beetle through the biological energy metabolism mechanism.

[0049] Specifically, in this embodiment, a bio - energy metabolism mechanism is adopted to establish an improved dung beetle optimization algorithm, which outputs the 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 load distribution schemes for chillers (or load distribution ratios). For example, if there are five chillers, the position of each dung beetle represents a load distribution scheme for the five chillers. The bio - energy metabolism mechanism will dynamically adjust the search strategy and individual energy of each dung beetle. For example, if the energy of a dung beetle is relatively high, the search range of this dung beetle may expand; if the energy of a dung beetle is relatively low, the search range of this 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.

[0050] In one implementation, the types of dung beetles include: ball - rolling dung beetles, egg - laying dung beetles, foraging dung beetles, and stealing dung beetles.

[0051] 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 stealing dung beetles. And the positions and search strategies (or position update strategies) of these four types of dung beetles are set. Dung beetles will use celestial clues (such as the sun, moon, and polarized light) to navigate during the rolling process. Therefore, in the improved 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.

[0052] For example, for ball - rolling dung beetles: The evolution process (i.e., the position update process) of ball - rolling dung beetles is defined as follows:

[0053] , Equation (7);

[0054] , Equation (8);

[0055] Among them, 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, which is a constant value belonging to (0, 1), providing the ability for 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 passes through the historical position Update the position by jointly acting with the global worst solution to balance individual experience and group information.

[0056] For ovipositing dung beetles: During the reproductive behavior, in order to provide a safe living environment for their offspring, ovipositing dung beetles need to move the brood balls to a safe area for oviposition and storage. Therefore, the following boundary selection strategy is adopted by ovipositing dung beetles to protect their offspring, that is, the evolutionary process of ovipositing dung beetles is defined as follows:

[0057] , Equation (9);

[0058] , Equation (10);

[0059] , Equation (11);

[0060] , Equation (12);

[0061] Among them, is the current local optimal solution; and respectively represent the upper and lower boundaries of the overall search space; and respectively represent the upper and lower boundaries of the safe oviposition area dug by ovipositing dung beetles, and the upper and lower boundaries change dynamically with the R value; , where represents the maximum number of iterations during the evolutionary process; is the position information of the and are both random vectors of size 1×D, and D is the dimension of the problem to be solved.

[0062] For foraging dung beetles: The young dung beetles that grow up in the brood balls will emerge from the breeding area to forage after they mature. These young dung beetles are defined as foraging dung beetles. At this time, an optimal foraging area needs to be constructed to plan the movement trajectories of these young dung beetles so that they can obtain food as soon as possible. The evolutionary process of foraging dung beetles is defined as follows:

[0063] , Equation (13);

[0064] , Equation (14);

[0065] , Equation (15);

[0066] Among them, 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.

[0067] For kleptoparasitic dung beetles: It is assumed that the previously found optimal food source will attract more individuals to compete. The evolutionary process of kleptoparasitic dung beetles is defined as follows:

[0068] , Equation (16);

[0069] where represents a random vector of size 1×D and follows a normal distribution; is a constant; and represent the current local optimal solution and the global optimal solution, respectively.

[0070] In one implementation, the search strategy and individual energy of each dung beetle are dynamically adjusted through a bio - energy metabolism mechanism, including:

[0071] For each dung beetle, the search strategy of the dung beetle is dynamically adjusted according to the individual energy of the dung beetle, and the updated position of the dung beetle is determined based on the adjusted search strategy;

[0072] Update the individual energy of the dung beetle; where the individual energy of the dung beetle will be consumed and reduced during the search process; when a better solution is found, the individual energy of the dung beetle will be replenished.

[0073] Specifically, in order to improve the flexibility of the algorithm, this embodiment uses a bio - energy metabolism mechanism to simulate the change of the individual energy of each dung beetle, and the individual energy can be quantified using an energy value. In addition, it is also necessary to perform dynamic behavior regulation on the dung beetle according to the individual energy of the dung beetle, which is specifically reflected in: different search strategies are divided for dung beetles with different energy levels, so as to dynamically adjust the exploration and exploitation behaviors of the algorithm. For example, dung beetles consume energy during the search process, and tend to be more conservative (local exploitation) when the energy is lower; when a dung beetle finds a better solution, it will recover some energy and stimulate the global exploration ability.

[0074] 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.

[0075] 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 directly affects key parameters such as the search step size and the search radius.

[0076] Step S300: Generate an initial population of dung beetles using the Logistic chaotic mapping method, 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.

[0077] Specifically, in this embodiment, the Logistic chaotic mapping method is used to generate the initial population to improve the uniformity of the solution space distribution. The chaotic sequence is mapped to the feasible solution space of the unit load rate to generate the initial chilled water load distribution plan. In the process of iterative solution, 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.

[0078] In one implementation, the basic formula of the Logistic chaotic mapping is:

[0079] , Equation (17);

[0080] 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 mapping is in a completely chaotic state, and a small difference in the initial value will lead to a significant difference in the subsequent sequence. Therefore, it is most suitable for chaotic initialization of intelligent optimization algorithms. In the process of individual initialization, the Logistic chaotic mapping is used to generate a chaotic sequence. Given an initial value , by applying the above mapping formula multiple times, the initial chaotic sequence can be generated, where N is the population size.

[0081] In one implementation, the positions of the dung beetle population are continuously iteratively updated by the improved dung beetle optimization algorithm until the iterative termination condition is met, including:

[0082] 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;

[0083] 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;

[0084] The individual energy of each dung beetle is updated, and it is judged whether the current iteration round meets the iterative termination condition. The iterative termination condition includes: the global average value of the dung beetle energy is lower than the preset threshold or the number of iterations reaches the preset number;

[0085] 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 iterative termination condition is met, and the global optimal solution is output.

[0086] Specifically, as Figure 2 shown, in 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 according to the updated position of each dung beetle, 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 in each iteration, 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 iterative termination condition. The iterative termination condition can be set based on the number of iterations or the requirement of biological energy. For example, the global average value of the dung beetle energy is lower than the preset threshold or the number of iterations reaches the preset number. If the current iteration round does not meet the iterative termination condition, the next iteration is performed. In short, the process of iterative solution is to repeat the steps of "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", "judging whether the biological energy meets the requirements", etc. The global optimal solution is updated after each iteration until the iterative termination condition is met, and the optimal position is output.

[0087] In one implementation, dynamically adjusting the search strategy of the dung beetle according to the individual energy of the dung beetle includes:

[0088] When the individual energy of the dung beetle is at the first level, the search strategy of the dung beetle is adjusted to the Levy flight strategy;

[0089] When the individual energy of the dung beetle is at the second level, adjust the search strategy of the dung beetle to a ball-rolling and / or reproduction strategy based on energy weighting;

[0090] When the individual energy of the dung beetle is at the third level, adjust the search strategy of the dung beetle to a quantum rotation gate strategy;

[0091] 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.

[0092] Specifically, in this embodiment, different search strategies are assigned to dung beetles with different energy levels, including but not limited to Levy flight strategy, ball-rolling and / or reproduction strategy based on energy weighting, and quantum rotation gate strategy, which can efficiently generate feasible solutions and avoid ineffective searches, effectively balance the exploration and development capabilities of the algorithm, and solve the load distribution problem of parallel chillers more efficiently and accurately.

[0093] For example, set the initial energy of each dung beetle individual to the maximum value , and in each iteration, the individual energy simulates the following exponential decay during natural state consumption:

[0094] , Equation (18);

[0095] Wherein, is the current energy value of the individual; is the energy decay coefficient (default ). This energy decay mechanism helps to avoid long-term ineffective searches by individuals and speeds up the convergence rate.

[0096] 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 partially recover:

[0097] , Equation (19);

[0098] Wherein, is the maximum energy replenishment amount (default ); is the current fitness value of the individual; and are the current best and worst fitness values respectively. The energy replenishment mechanism proposed in this embodiment helps to encourage dung beetles to jump out of local optima and maintain the diversity of the population.

[0099] According to the current energy value of the individual, set the following dynamically adjusted search strategy:

[0100] (1) When the individual energy of the dung beetle is at the first level, a large-scale global search is performed using the Levy flight strategy (the Levy flight strategy is a random search strategy based on a 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.

[0101] (2) When the individual energy of the dung beetle is at the second level, the rolling ball and reproduction behaviors of the standard dung beetle optimization algorithm (DBO algorithm) are executed, and the step size is weighted according to the dung beetle energy. The fixed step size factor and perturbation intensity in Equation (7) are replaced 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.

[0102] , Equation (20);

[0103] , Equation (21);

[0104] 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:

[0105] , Equation (22);

[0106] (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 means it is in low energy.

[0107] In each iteration, according to the threshold value (such as setting , ) the corresponding search strategy can be adjusted 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.

[0108] In one implementation, the Levy flight search strategy is specifically:

[0109] The step size of the Levy flight strategy is generated by the following formula:

[0110] , Equation (23);

[0111] Among them, 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 according to the following formula:

[0112] , Equation (24);

[0113] where the symbol represents the 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 exploitation of the algorithm. When an individual is in a high-energy state, the Levy flight strategy is triggered:

[0114] , Equation (25);

[0115] where is the global step size coefficient (default ); is the current individual energy value; represents element-wise multiplication to ensure the direction is related to the global optimal solution ; represents the updated position.

[0116] In one implementation, the quantum rotation gate search strategy is specifically as follows:

[0117] The position of each dung beetle individual is encoded with quantum bits and represented as:

[0118] , Equation (26);

[0119] where is the quantum bit 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:

[0120] , Equation (27);

[0121] , Equation (28);

[0122] where is the change in the rotation angle; is the learning rate used to control the rotation amplitude; is the rotation angle after the individual is updated; is the individual fitness value; is the fitness value of the individual at the optimal position in the dung beetle population.

[0123] Observe the updated quantum state and generate a new solution:

[0124] , Equation (29);

[0125] where is the contraction factor, which can control the local search range.

[0126] Step S400, determine the optimal chiller load distribution plan according to the global optimal solution.

[0127] Specifically, update the global optimal solution after each iteration and adjust the energy of the dung beetle individuals until the average energy of the population or when the set number of iterations is reached, end the search, output the optimal individual, that is, obtain the final global optimal solution. Through the final global optimal solution, the optimal chiller load distribution plan can be obtained, and this plan can be directly applied to the actual chiller system to improve the operation efficiency of the system and save system power consumption and operation costs.

[0128] To facilitate understanding of the technical solution of this embodiment, the specific algorithm flow of the improved dung beetle optimization algorithm is as follows:

[0129] Algorithm input: population size N, maximum number of iterations T, energy parameters , , threshold , ;

[0130] Algorithm output: global optimal solution ;

[0131] 1: Initialize the quantum rotation angle ;

[0132] 2: Use the Logistic chaotic map to generate the initial population;

[0133] 3: Calculate the fitness f(X) of each individual in the initial population;

[0134] 4: Initialize the individual initial energy state vector E = [maximum energy value of individual 1, maximum energy value of individual 2,..., maximum energy value of individual N];

[0135] 5: Select the individual with the optimal fitness as the current global optimal solution ;

[0136] 6: For iteration round t, execute from 1 to the maximum number of iterations T (for represents a for loop):

[0137] 7: For each individual i in the population, execute from 1 to N:

[0138] 8: If the fitness of the current individual is better than that of the previous generation:

[0139] 9: Update the energy of the dung beetle using Equation (19);

[0140] 10: Otherwise:

[0141] 11: The dung beetle performs energy decay according to Equation (18);

[0142] 12: For each individual i in the population, execute from 1 to N:

[0143] 13: If the individual energy E[i] is higher than the high energy threshold :

[0144] 14: Execute the global exploration strategy based on the Levy flight strategy using Equation (25);

[0145] 15: Otherwise when the energy is in the medium energy range (medium - low interval):

[0146] 16: If it is currently in the ball - rolling stage:

[0147] 17: Update the position of the ball - rolling dung beetle using Equation (7);

[0148] 18: Otherwise:

[0149] 19: Execute the reproductive evolution strategy using Equation (16);

[0150] 20: Otherwise when the energy is in the low energy range:

[0151] 21: Execute the quantum rotation gate exploration strategy using Equation (29);

[0152] 22: Perform value boundary constraint processing on the newly born individuals;

[0153] 23: If the fitness of the new individual is better than that of the original individual;

[0154] 24: Execute the individual update operation;

[0155] 25: Update the global optimal solution ;

[0156] 26: Output the global optimal solution 。

[0157] 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 well-known intelligent optimization algorithms in recent years are used as controls, including the Dung Beetle Optimization Algorithm (DBO), the Salp Swarm Algorithm (SSA), the Seagull Optimization Algorithm (SOA), the Moth-Flame Optimization Algorithm (MFO), and the 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).

[0158] Table 1. Optimal unit load distribution results under different cooling loads

[0159]

[0160] 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 in five of the six load scenarios, that is, better unit load distribution results.

[0161] 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 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 accuracy and faster search efficiency in the optimization process of most functions, and for a few test functions that fail to obtain the optimal solution, the algorithm proposed in the present invention can also obtain relatively reasonable results.

[0162] 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 7As shown in the figure. Among them, the MFO algorithm is the Moth Flame Optimization algorithm, 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.

[0163] In summary, the advantages of the present invention are as follows:

[0164] 1. Dynamic regulation of the biological energy metabolism mechanism: Simulate the energy consumption and replenishment mechanism of cockroaches, dynamically divide the search strategy through energy values, balance the global search and local development capabilities, making the algorithm superior to traditional algorithms in terms of global search ability, convergence speed, and robustness, avoiding premature convergence, and enhancing the robustness of the algorithm. And 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 energy exponentially decays to avoid ineffective searches and quickly eliminate inefficient solutions.

[0165] 2. Initializing the population with Logistic chaotic mapping: Use Logistic chaotic mapping to generate initial solutions, replacing traditional random initialization, ensuring that the cold machine load distribution scheme is evenly distributed in the solution space, that is, improving the uniformity of 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.

[0166] 3. Fusion of multiple search strategies: The present invention combines constraints on the cold machine load rate and 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 more efficiently and accurately solving the parallel cold machine load distribution problem. Among them, high-energy individuals trigger Levy flight for large-scale exploration, exploring potential optimal regions of cold machine load combinations 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 for complex nonlinear problems and improving the local search accuracy at the same time.

[0167] 4. Energy-based dynamic parameter adaptive adjustment: The step size factor and perturbation intensity are bound to the individual energy value to achieve dynamic parameter weighting.

[0168] Possible technical deformations of the present invention:

[0169] 1. Chaotic strategy replacement: Replace the Logistic chaotic map with the Tent map (a piecewise linear map) and the Chebyshev map (one of the common chaotic maps) to optimize the generation of the initial population.

[0170] 2. Search strategy replacement: Replace the Levy flight strategy with simulated annealing and differential evolution; replace the quantum rotation gate strategy with gradient descent and the Nelder-Mead method (an algorithm for finding the local minimum of a multivariate function).

[0171] 3. Simplification of behavior classification: Combine cockroach behavior categories (such as only retaining ball rolling and foraging) to reduce the computational complexity.

[0172] Based on the above embodiments, the present invention also provides a chiller load distribution system based on an improved dung beetle optimization algorithm, as Figure 8 shown. The system includes:

[0173] A model establishment module 01 for establishing 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 goal and system constraint conditions.

[0174] An algorithm establishment module 02 for establishing 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 chiller load distribution scheme; and dynamically adjusting the search strategy and individual energy of each dung beetle through a biological energy metabolism mechanism.

[0175] An iterative solution module 03 for generating an initial dung beetle population in the manner of a Logistic chaotic map, 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.

[0176] A scheme determination module 04 for determining the optimal chiller load distribution scheme according to the global optimal solution.

[0177] Based on the above embodiments, the present invention also provides a terminal, and its principle block diagram can be as Figure 9As shown. The terminal includes a processor, a memory, a network interface, and a display screen connected via 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 via 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 it is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0178] 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 in the methods of 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 memory. 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 (DDRSDRAM), enhanced SDRAM (ESDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0179] 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, 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 chiller load distribution scheme; and dynamically adjusting the search strategies and individual energies 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 incorporates 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 an actual chiller system to improve the operation efficiency of the system, save system power consumption and operation costs.

[0180] 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 transformations can be made according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A load distribution method for a chiller based on an improved dung beetle optimization algorithm, characterized in that The method includes: 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; Based on the objective function, an improved dung beetle optimization algorithm is established. 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 chiller load distribution scheme; and dynamically adjusting the search strategies and individual energies of each dung beetle 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: when the individual energy of the dung beetle is at the first level, adjusting 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, adjusting the search strategy of the dung beetle to a rolling ball and / or reproduction 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 the quantum rotation gate strategy; where 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; and determining the updated position of the dung beetle based on the adjusted search strategy; updating the individual energy of the dung beetle; where the individual energy of the dung beetle will be consumed and reduced during the search process; when a better solution is found, the individual energy of the dung beetle will be replenished; Generating an initial dung beetle population in the way of Logistic chaotic mapping, and continuously iteratively 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.

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

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

4. The load distribution method of the chiller based on the improved dung beetle optimization algorithm according to claim 1, wherein Different search strategies correspond to different combinations of search parameters; the combination of search parameters includes: search step size and search radius.

5. The load distribution method of the chiller based on the improved dung beetle optimization algorithm according to claim 1, characterized in that Continuously iteratively updating the positions of the dung beetle population through the improved dung beetle optimization algorithm until the iteration termination condition is met, including: During each iteration, for each dung beetle, dynamically adjusting the search strategy of the dung beetle according to the individual energy of the dung beetle to obtain the updated position of the dung beetle; Evaluating the fitness according to the updated position of the dung beetle, and updating the global optimal solution according to the evaluated fitness of each dung beetle; Updating the individual energy of each dung beetle, and determining whether the current iteration round meets 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, then perform the next iteration, and continue to execute 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 output the global optimal solution.

6. A chiller load distribution system based on an improved dung beetle optimization algorithm, characterized in that, The system includes: A model establishment module for establishing a power consumption model of a parallel chiller system, where the power consumption model of the parallel chiller system includes an objective function with the minimum total system power consumption as the optimization goal and system constraint conditions; An algorithm establishment module for establishing 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 chiller load distribution plan. And dynamically adjusting the search strategies and individual energies of each dung beetle 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: when the individual energy of the dung beetle is at the first level, adjusting 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, adjusting the search strategy of the dung beetle to a rolling ball and / or reproduction 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 the quantum rotation gate strategy; where 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; and determining the updated position of the dung beetle based on the adjusted search strategy; updating the individual energy of the dung beetle; where the individual energy of the dung beetle will be consumed and reduced during the search process; when a better solution is found, the individual energy of the dung beetle will be replenished; An iterative solution module for generating an initial dung beetle population in the way 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; A scheme determination module for determining the optimal chiller load distribution scheme according to the global optimal solution.

7. A terminal, characterized in that, 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 claims 1-5; the processor is used to execute the program.

8. 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 the 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 1-5.

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