Air conditioning system test method, system and equipment based on genetic algorithm and storage medium

Abstract

The invention discloses an air conditioning system test method, system and equipment based on a genetic algorithm and a storage medium, and belongs to the technical field of automobiles, and the method comprises the steps that a thermodynamic model of a condenser is established based on basic parameters of the condenser; calculating the refrigerating capacity of the condenser based on the thermodynamic model of the condenser; establishing a fitness function based on the thermodynamic model of the condenser; optimizing the fitness function of the thermodynamic model of the condenser by using a genetic algorithm to obtain optimized parameters; and an energy-saving optimization control strategy of the air conditioning system is determined according to the optimization parameters. Through a targeted optimization algorithm, the refrigeration performance of the condenser is remarkably improved after implementation, it is ensured that the condenser can reach or exceed the design target in practical application, and therefore the overall energy efficiency ratio of an air conditioning system is improved; and by optimizing the design of the condenser, the energy consumption can be effectively reduced on the premise of ensuring the refrigeration effect, so that the energy expenditure of a user is reduced, and meanwhile, the influence on the environment is also reduced.

Description

Technical Field

[0001] The present invention belongs to the field of automobile technology, and in particular relates to an air conditioning system test method, system, equipment and storage medium based on a genetic algorithm. Background Art

[0002] Air conditioning systems are indispensable equipment in modern buildings and transportation. In recent years, with the improvement of energy consumption and environmental protection awareness, the requirements for air conditioning system performance have become increasingly stringent. Especially in terms of cooling efficiency, energy consumption and user experience, R&D personnel and engineers are constantly exploring to achieve higher performance indicators.

[0003] With the development of computer science and intelligent algorithms, researchers have gradually introduced optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) into the optimization design of air-conditioning systems to improve energy efficiency and system performance. Among the existing technical solutions, many studies focus on the optimization of the coefficient of performance (COP) and cooling capacity of air-conditioning systems. For example, the design of the condenser is optimized by simulating the coolant flow and heat exchange process through computational fluid dynamics (CFD).

[0004] In the prior art, numerical simulation and optimization algorithms are used to improve the performance of air conditioning systems. These solutions increase the cooling capacity by optimizing the geometry and operating parameters of the condenser.

[0005] However, they usually have the following obvious shortcomings:

[0006] 1. Accuracy problem:

[0007] Many current numerical simulation-based solutions rely on complex models and assumptions, which may lead to calculation results that are inconsistent with actual application conditions. Especially when the condenser works under extreme conditions or emergencies, the model error may increase, affecting the reliability of the system;

[0008] 2. Low flexibility and applicability:

[0009] Existing technologies are often limited to air conditioning systems of specific models and working conditions, and lack universal solutions. For example, some methods are only applicable to household air conditioning and are difficult to extend to commercial or industrial air conditioning systems, limiting their scope of application;

[0010] 3. High cost:

[0011] Many optimization solutions require expensive computing resources and professional modeling tools, which puts small and medium-sized enterprises under great cost pressure in the process of technology introduction, which, to a certain extent, limits the promotion and application of new technologies;

[0012] 4. Slow response:

[0013] Existing optimization algorithms, especially standard implementations of genetic algorithms, often require long computation times to find a satisfactory solution, which is particularly inappropriate in real-time control systems and cannot adapt to dynamically changing environmental conditions;

[0014] 5. Insufficient experimental verification:

[0015] Although theoretical models and simulation results can provide guidance, the lack of effective experimental verification means that many research results cannot be effectively applied in practical environments, further affecting their market promotion. Summary of the invention

[0016] In view of the significant problems existing in the prior art such as insufficient accuracy, poor flexibility, high cost, slow response speed and insufficient experimental verification, the present invention provides an air-conditioning system test method, system, equipment and storage medium based on genetic algorithm, aiming to improve the overall performance and efficiency of the air-conditioning system.

[0017] The present invention is achieved through the following technical solutions:

[0018] In a first aspect, the present invention provides an air conditioning system test method based on a genetic algorithm, which specifically comprises the following steps:

[0019] Step 1: Establish a thermodynamic model of the condenser based on the basic parameters of the condenser;

[0020] Step 2: Calculate the cooling capacity of the condenser based on the thermodynamic model of the condenser;

[0021] Step 3: Establish the fitness function based on the thermodynamic model of the condenser;

[0022] Step 4: Using genetic algorithm to optimize the fitness function of the thermodynamic model of the condenser to obtain the optimized parameters;

[0023] Step 5: Determine the energy-saving optimization control strategy of the air-conditioning system according to the optimization parameters.

[0024] Furthermore, in step 1, the basic parameters of the condenser include: inlet temperature (T in )、Outlet temperature (T out )、Inlet pressure (P in )、Outlet pressure (P out ), cooling water temperature (T water ) and cooling water flow (m water ).

[0025] Furthermore, in step 2, the cooling capacity Qc of the condenser is obtained by the following formula:

[0026] Qc=m*(h out -h in )

[0027] Where Qc is the cooling capacity of the condenser, m is the mass flow rate of the refrigerant, h out is the enthalpy based on the condenser outlet state, h in is the enthalpy based on the condenser inlet state.

[0028] Furthermore, in step 3, the fitness function F(i) is as follows:

[0029] F(i)=1-|Qc-Qc(i)|

[0030] Where: Qc is the target cooling capacity, Qc(i) is the cooling capacity calculated for individual i.

[0031] Furthermore, step 4 specifically includes the following contents:

[0032] S41, individual coding: binary coding or real number coding is used to represent different operating parameters of the condenser;

[0033] S42, population generation: set the population size and randomly form the initial group;

[0034] S43, calculating the cooling capacity and fitness value of the group;

[0035] S44, optimizing the population with a genetic algorithm using an improved optimization strategy to generate a new population after genetic evolution;

[0036] S45, conduct multiple generations of crossover, selection and mutation to continuously optimize the population;

[0037] S46, determine whether the iteration termination condition is met. If the termination condition is not met, return to step S45 and enter the next round of population evolution optimization. If the termination condition is met, terminate the population evolution and output the global optimal solution.

[0038] Furthermore, in step S41, the different operating parameters include: flow rate, cooling water temperature, and condenser effective area.

[0039] Furthermore, in step S46, the iteration termination condition includes reaching a maximum number of iterations or the fitness reaching a preset threshold.

[0040] In a second aspect, the present invention provides an air conditioning system test system based on a genetic algorithm, which is used to implement the test method described in the first aspect, comprising:

[0041] A thermodynamic model building module is used to build a thermodynamic model of the condenser based on basic parameters of the condenser;

[0042] A calculation module, used for calculating the cooling capacity of the condenser based on a thermodynamic model of the condenser;

[0043] A fitness function building module is used to build a fitness function based on a thermodynamic model of a condenser;

[0044] An optimization module is used to optimize the fitness function of the thermodynamic model of the condenser by using a genetic algorithm to obtain optimized parameters;

[0045] The control module is used to determine the energy-saving optimization control strategy of the air-conditioning system according to the optimization parameters.

[0046] In a third aspect, the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an air conditioning system testing method based on a genetic algorithm as described in any one of the present invention is implemented.

[0047] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the genetic algorithm-based air conditioning system testing methods described in the present invention.

[0048] Compared with the prior art, the advantages of the present invention are as follows:

[0049] 1. Improve the accuracy of cooling capacity: Through targeted optimization algorithms, the cooling performance of the condenser is significantly improved after implementation, ensuring that it can meet or exceed the design target in actual application, thereby improving the overall energy efficiency ratio (COP) of the air-conditioning system.

[0050] 2. Enhance the applicability of the system: Since the present invention has good adaptability, it can be applied to a variety of air-conditioning systems, so that different types of air-conditioning equipment can achieve performance optimization, reducing the user's selection cost.

[0051] 3. Reduce energy consumption: The present invention can effectively reduce energy consumption by optimizing the condenser design while ensuring the refrigeration effect, thereby reducing the user's energy expenditure and reducing the impact on the environment.

[0052] 4. Shorten the optimization cycle and improve efficiency: The improved genetic algorithm can find the best solution in a shorter time, shorten the optimization process, improve R&D efficiency, and make enterprises more competitive in the product design process.

[0053] 5. Simple operation and control: The present invention lowers the threshold for technology application by providing a concise operation interface and easy-to-execute experimental steps, allowing relevant personnel to easily master and apply this new technology and reducing training costs.

[0054] 6. Reliability of practical verification: Since the present invention incorporates sufficient experimental verification links into the solution design, the effectiveness of the optimization suggestions is ensured, thereby better promoting users' trust in new technologies and increasing market acceptance. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0056] Figure 1 A schematic flow chart of an air conditioning system test method based on a genetic algorithm according to the present invention;

[0057] Figure 2 It is a structural block diagram of an air conditioning system test system based on a genetic algorithm of the present invention;

[0058] Figure 3 This is a schematic diagram of the structure of an electronic device in Example 3 of the present invention. DETAILED DESCRIPTION

[0059] In order to clearly and completely describe the technical solution and its specific working process of the present invention, the specific implementation methods of the present invention are as follows in conjunction with the accompanying drawings of the specification:

[0060] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0062] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0063] Example 1

[0064] like Figure 1 As shown, this embodiment provides an air conditioning system test method based on a genetic algorithm, which specifically includes the following steps:

[0065] Step 1: Establish a thermodynamic model of the condenser based on the basic parameters of the condenser;

[0066] The basic parameters of the condenser include: inlet temperature (T in )、Outlet temperature (T out )、Inlet pressure (P in )、Outlet pressure (P out ), cooling water temperature (T water ) and cooling water flow (m water ).

[0067] Step 2: Calculate the cooling capacity of the condenser based on the thermodynamic model of the condenser;

[0068] The cooling capacity Qc of the condenser is obtained by the following formula:

[0069] Qc=m*(h out -h in )

[0070] Where Qc is the cooling capacity of the condenser, m is the mass flow rate of the refrigerant, h out is the enthalpy based on the condenser outlet state, h in is the enthalpy based on the condenser inlet state. Step 3: Establish a fitness function based on the thermodynamic model of the condenser;

[0071] The fitness function F(i) is as follows:

[0072] F(i)=1-|Qc-Qc(i)|

[0073] Where: Qc is the target cooling capacity, Qc(i) is the cooling capacity calculated by individual i. Step 4: Use genetic algorithm to optimize the fitness function of the thermodynamic model of the condenser to obtain the optimized parameters, which specifically include the following contents:

[0074] S41, individual coding: binary coding or real number coding is used to represent different operating parameters of the condenser;

[0075] The different operating parameters include: flow rate, cooling water temperature, and condenser effective area.

[0076] S42, population generation: set the population size and randomly form the initial group;

[0077] S43, calculating the cooling capacity and fitness value of the group;

[0078] S44, optimizing the population with a genetic algorithm using an improved optimization strategy to generate a new population after genetic evolution;

[0079] S45, conduct multiple generations of crossover, selection and mutation to continuously optimize the population;

[0080] S46, determine whether the iteration termination condition is met. If the termination condition is not met, return to step S45 and enter the next round of population evolution optimization. If the termination condition is met, terminate the population evolution and output the global optimal solution.

[0081] The iteration termination condition includes reaching a maximum number of iterations or the fitness reaching a preset threshold.

[0082] Step 5: Determine the energy-saving optimization control strategy of the air-conditioning system according to the optimization parameters.

[0083] In this embodiment, the maximum number of iterations is set to 100; the fitness reaching a preset threshold is set to reach 95% of the target cooling capacity.

[0084] In this example, a common automotive air-conditioning condenser is selected as the research object for performance optimization. First, the condenser working data under different seasons and temperature conditions are collected through the field data collection method, including cooling capacity, power consumption and ambient temperature. Then, an optimization model based on genetic algorithm is constructed, the collected data is input, and the fitness function is set to achieve the goal of optimal cooling capacity.

[0085] Data collection: Use sensors to record the actual operating data of the condenser under different working conditions.

[0086] Model establishment: Multiple linear regression analysis was used to establish a relationship model between cooling capacity and condenser design parameters (such as fin arrangement, pipe diameter, etc.).

[0087] Algorithm optimization: Apply the improved genetic algorithm to solve the problem, set the appropriate crossover rate, mutation rate and selection strategy, and finally output the optimal design parameters.

[0088] The obtained optimized parameters were applied to the actual condenser for experiments, and the actual cooling capacity was measured and compared with the theoretical value.

[0089] The genetic algorithm optimization results are verified through experimental data, and the corresponding parameters are adjusted to obtain the best performance.

[0090] Analyze the experimental results to confirm whether the cooling capacity of the condenser is improved, whether the overall energy efficiency ratio is improved, etc.

[0091] The experimental data are fed back to the genetic algorithm for further optimization and correction of the model.

[0092] Example 2

[0093] This embodiment provides an air conditioning system test system based on a genetic algorithm, which is used to implement the test method described in the first aspect, including:

[0094] A thermodynamic model building module is used to build a thermodynamic model of the condenser based on basic parameters of the condenser;

[0095] A calculation module, used for calculating the cooling capacity of the condenser based on a thermodynamic model of the condenser;

[0096] A fitness function building module is used to build a fitness function based on a thermodynamic model of a condenser;

[0097] An optimization module is used to optimize the fitness function of the thermodynamic model of the condenser by using a genetic algorithm to obtain optimized parameters;

[0098] The control module is used to determine the energy-saving optimization control strategy of the air-conditioning system according to the optimization parameters.

[0099] Example 3

[0100] Figure 3 This is a schematic diagram of the structure of a computer device in Example 3 of the present invention. Figure 3 A block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 3 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0101] like Figure 3As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0102] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0103] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0104] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 3 not shown, usually called a "hard drive"). Although Figure 3 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0105] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0106] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), may also communicate with one or more devices that enable a user to interact with the computer device 12, and / or communicate with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. In addition, the computer device 12 in this embodiment, the display 24 does not exist as an independent individual, but is embedded in the mirror surface, and when the display surface of the display 24 is not displayed, the display surface of the display 24 and the mirror surface are visually integrated. In addition, the computer device 12 may also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet) through a network adapter 20. As shown in the figure, the network adapter 20 communicates with other modules of the computer device 12 through a bus 18. It should be understood that although not shown in the figures, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0107] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, for example, implementing an air conditioning system test method based on a genetic algorithm provided in an embodiment of the present invention.

[0108] Example 4

[0109] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, a genetic algorithm-based air conditioning system test method as provided in all the embodiments of the present application is implemented.

[0110] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0111] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0112] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0113] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An air conditioning system test method based on genetic algorithm, characterized in that: The specific steps include: Step 1: Establish a thermodynamic model of the condenser based on the basic parameters of the condenser; Step 2: Calculate the cooling capacity of the condenser based on the thermodynamic model of the condenser; Step 3: Establish the fitness function based on the thermodynamic model of the condenser; Step 4: Using genetic algorithm to optimize the fitness function of the thermodynamic model of the condenser to obtain the optimized parameters; Step 5: Determine the energy-saving optimization control strategy of the air-conditioning system according to the optimization parameters.

2. The air conditioning system test method based on genetic algorithm according to claim 1, characterized in that: In step 1, the basic parameters of the condenser include: inlet temperature (T in )、Outlet temperature (T out )、Inlet pressure (P in )、Outlet pressure (P out ), cooling water temperature (T water ) and cooling water flow (m water ).

3. The air conditioning system test method based on genetic algorithm according to claim 1, characterized in that: In step 2, the cooling capacity Qc of the condenser is obtained by the following formula: Qc=m*(h out -h in ) Where Qc is the cooling capacity of the condenser, m is the mass flow rate of the refrigerant, h out is the enthalpy based on the condenser outlet state, h in is the enthalpy based on the condenser inlet state.

4. The air conditioning system test method based on genetic algorithm according to claim 1, characterized in that: In step 3, the fitness function F(i) is as follows: F(i)=1-|Qc-Qc(i)| Where: Qc is the target cooling capacity, Qc(i) is the cooling capacity calculated for individual i.

5. The air conditioning system test method based on genetic algorithm according to claim 1, characterized in that: Step 4 specifically includes the following: S41, individual coding: binary coding or real number coding is used to represent different operating parameters of the condenser; S42, population generation: set the population size and randomly form the initial group; S43, calculating the cooling capacity and fitness value of the group; S44, optimizing the population with a genetic algorithm using an improved optimization strategy to generate a new population after genetic evolution; S45, conduct multiple generations of crossover, selection and mutation to continuously optimize the population; S46, determine whether the iteration termination condition is met. If the termination condition is not met, return to step S45 and enter the next round of population evolution optimization. If the termination condition is met, terminate the population evolution and output the global optimal solution.

6. The air conditioning system test method based on genetic algorithm as claimed in claim 5, characterized in that: In step S41, the different operating parameters include: flow rate, cooling water temperature, and condenser effective area.

7. The air conditioning system test method based on genetic algorithm as claimed in claim 5, characterized in that: In step S46, the iteration termination condition includes reaching a maximum number of iterations or the fitness reaching a preset threshold.

8. An air conditioning system test system based on genetic algorithm, used to implement the test method according to any one of claims 1 to 7, characterized in that: include: A thermodynamic model building module is used to build a thermodynamic model of the condenser based on basic parameters of the condenser; A calculation module, used for calculating the cooling capacity of the condenser based on a thermodynamic model of the condenser; A fitness function building module is used to build a fitness function based on a thermodynamic model of a condenser; An optimization module is used to optimize the fitness function of the thermodynamic model of the condenser by using a genetic algorithm to obtain optimized parameters; The control module is used to determine the energy-saving optimization control strategy of the air-conditioning system according to the optimization parameters.

9. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the air conditioning system test method based on the genetic algorithm as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the air conditioning system test method based on a genetic algorithm as described in any one of claims 1 to 7 is implemented.

Images