Ice maker group model scheduling optimization method based on ice maker efficiency curved surface

Abstract

The invention relates to an ice maker group model scheduling optimization method based on an ice maker efficiency curved surface, and belongs to the technical field of ice maker cluster systems. The method comprises the steps that basic ice maker efficiency curve data are acquired, an ice maker control system is initialized, and the start-stop state of the ice maker control system is adjusted according to a model scheduling optimization period; constructing a public model based on the ice maker type according to the ice maker type; detecting the starting state of the ice maker control system, setting a sampling period, reading chilled water temperature data and cooling water temperature data through the ice maker control system according to the sampling period, and updating the public model according to the chilled water temperature data and the cooling water temperature data; integrating and fusing the updated common model into a cluster model, and ending a scheduling optimization mode according to the start-stop state of the ice maker control system to export ice maker efficiency curve data or dynamically updating basic ice maker efficiency curve data according to the cluster model. And efficient optimization of the operation energy efficiency of the ice maker under different operation conditions is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of ice machine cluster systems, and in particular relates to an ice machine cluster model scheduling optimization method based on an ice machine efficiency surface. Background Art

[0002] The operating performance of the ice machine is not only affected by its own factors, but also by the operating conditions in which it is located. The main influencing factors include the type of ice machine, manufacturing level, matching of compressors, type of refrigerant and charging amount; it is also restricted by external factors, including factors affecting evaporation temperature and condensation temperature such as chilled water temperature and cooling water temperature; although the performance of the cooling system under different working conditions can be understood through the energy efficiency curve, regular maintenance and optimization of system design still cannot completely eliminate the fluctuation and fluctuation range of the energy efficiency curve. This means that in actual operation, the energy efficiency of the ice machine may be affected by changes in the external environment, resulting in unsatisfactory optimization effect. In a cold source system with multiple chillers running in parallel, as long as the chillers are controlled by a fixed chilled water outlet temperature, the load rate of multiple chillers depends on the chilled water return temperature, that is, the load rate is consistent. This consistency may cause the system to fail to achieve optimal configuration under certain working conditions, thereby affecting the overall energy efficiency and energy saving performance. The COP curve is used to describe the energy efficiency performance of the refrigerator, but under changing load conditions, changes in COP may affect the optimization effect of the system. This means that in practical applications, simply relying on the COP curve for optimization may not be able to adapt to all changes in working conditions, resulting in inaccurate optimization results. The relationship between the energy efficiency ratio and temperature of the screw ice machine shows that in actual operation, the operating parameters need to be adjusted according to the current temperature conditions to improve efficiency. However, this adjustment requires real-time monitoring and control, which increases the complexity and operating costs of the system. Therefore, the current problems with the ice machine group model scheduling optimization method based on the ice machine efficiency surface mainly include the fluctuation of the energy efficiency curve, the consistency of the load rate, the application limitations of the COP curve, and the impact of temperature conditions on the operating parameters. These problems need to be solved through further research and technological innovation to achieve more efficient and energy-saving ice machine group scheduling optimization. At present, the commonly used system optimization control calculation method mainly establishes mathematical models of each device in the system and uses global optimal calculation to study its optimal control problem. However, due to the complex nonlinearity of the system and the high coupling relationship between the components, this method cannot achieve satisfactory results according to traditional methods from model establishment to the final optimization calculation. The optimization control method based on artificial intelligence does not need to establish a complex mathematical model, but only needs to adaptively train and calculate the program parameters according to the system operation status results. The disadvantage of this optimization control method is that it requires a large parameter sample support. When the data sample size does not meet the requirements, the optimization calculation results will have a large deviation. Summary of the invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides an ice machine group model scheduling optimization method based on ice machine efficiency surface.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A method for optimizing ice machine group model scheduling based on ice machine efficiency surface, comprising:

[0006] Setting a model scheduling optimization cycle, obtaining basic ice machine efficiency curve data, initializing an ice machine control system according to the basic ice machine efficiency curve data, and adjusting a start and stop state of the ice machine control system according to the model scheduling optimization cycle;

[0007] Building a common model based on ice machine types according to the ice machine types of the ice machine control system;

[0008] By detecting the power-on state of the ice machine control system, if the power-on state is the start-up state, setting a sampling period, reading the chilled water temperature data and the cooling water temperature data according to the sampling period through the ice machine control system, updating the common model according to the chilled water temperature data and the cooling water temperature data, and if the power-on state is the off state, re-performing the power-on state detection;

[0009] The updated public model is integrated into a cluster model to detect whether the ice machine control system is turned off. If the system is turned off, the scheduling optimization mode is ended and the ice machine efficiency surface data is exported. If the system is in the startup state, the basic ice machine efficiency curve data is dynamically updated according to the cluster model.

[0010] Specifically, the basic ice machine efficiency curve data includes a refrigeration capacity curve, a working condition coefficient and operation state curve and a working condition coefficient, a chiller load factor relationship curve, and a water pump performance curve.

[0011] Specifically, the common model is composed of a refrigeration unit sub-model, a water pump sub-model, an optimization objective function, and a combination of input and output variables.

[0012] Specifically, the input-output variable combination includes controllable variable input data and target control output data; the controllable variable input data is the chilled water temperature and the cooling water temperature; the target control output data is the cooling water pump control power and the chilled water pump control power.

[0013] Specifically, the refrigeration unit sub-model calculates the maximum refrigeration capacity under rated conditions according to the refrigeration capacity curve in the basic refrigeration machine efficiency curve data and the chilled water temperature and the cooling water temperature, and the calculation formula is:

[0014]

[0015] Among them, Q a is the maximum cooling capacity, Q ref is the rated cooling capacity, a, b, c, d, e, f are the fitting coefficients of the cooling capacity curve, T1 is the chilled water temperature, and T2 is the cooling water temperature;

[0016] The maximum cooling capacity Qa under rated conditions calculated can further determine the efficiency and performance of the ice machine in actual operation. The calculation results of the refrigeration unit submodel will serve as an important input parameter of the optimization scheduling algorithm to ensure that the ice machine group achieves the optimal energy efficiency ratio while meeting the load demand.

[0017] The operating coefficient under maximum load is calculated according to the operating coefficient and the operating state curve in the basic ice machine efficiency curve data and the freezing water temperature and the cooling water temperature. The calculation formula is:

[0018]

[0019] Among them, E a is the operating coefficient under maximum load, a1, b1, c1, d1, e1, f1 are the fitting coefficients of the operating coefficient and the operation state curve, T1 is the chilled water temperature, and T2 is the cooling water temperature;

[0020] The calculated operating coefficient Ea under the maximum load can be used to further evaluate the operating efficiency and performance of the ice machine under different operating conditions. Combined with the real-time operating data of the ice machine fleet, the ice machine fleet can be dynamically optimized to adapt to changing load requirements and environmental conditions. The optimization scheduling algorithm will use these parameters to simulate and predict the operating status of the ice machine fleet under different operating conditions, thereby achieving efficient management of the ice machine fleet and ensuring that the energy efficiency ratio is maximized and energy consumption is reduced while meeting the refrigeration needs.

[0021] The relationship curve between the operating coefficient and the chiller load factor is used to express the relationship curve of the operating coefficient as the load of different parts changes. The calculation formula is:

[0022]

[0023] Among them, f E is the corresponding operating factor under system load conditions, Q is the actual system load, Q a is the maximum cooling capacity, the fitting coefficient of the relationship curve between the operating conditions coefficients a2, b2, c2, d2, e2, f2, g2 and the load factor of the chiller;

[0024] The power of the refrigeration host is calculated according to the maximum cooling capacity under the rated working conditions, the working condition coefficient under the maximum load and the corresponding working condition coefficient under the system load conditions. The calculation formula is:

[0025] P c =Q a f E (E a ),

[0026] Among them, P c is the power of the refrigeration host.

[0027] Specifically, the water pump sub-model obtains the fitting coefficient of the relationship between the power and flow rate of the water pump at different speeds and flow rates according to the water pump performance curve, and calculates the flow rate according to the flow characteristics of cooling water and chilled water in the open pipe system. The formula of the fitted water pump sub-model is:

[0028] P=a3+b3V+c3V 2 ,

[0029] Among them, P is the pump power, a2, b2, c2 are the pump performance curve fitting parameters, and V is the flow rate.

[0030] The operating efficiency of the ice machine group can be further optimized through the power and flow relationship of the water pump sub-model. In practical applications, by real-time monitoring of the flow of cooling water and chilled water, combined with the fitting parameters of the water pump performance curve, the power consumption of the water pump under different working conditions can be accurately calculated. This calculation method not only improves the energy efficiency ratio of the system, but also helps to reduce unnecessary energy consumption, thereby achieving the goal of energy conservation and emission reduction. In addition, by dynamically adjusting the speed of the water pump, the cooling water and chilled water flow can be accurately controlled, further improving the operating efficiency and response speed of the entire ice machine group.

[0031] Specifically, the optimization objective function constructs a performance objective function according to the power of cooling water and chilled water and the power of the refrigeration host, and the calculation formula is:

[0032] P min =P c +P1+P2,

[0033] Among them, P min Optimize the target power for performance, P c is the power of the refrigeration unit, P1 is the power of the chilled water pump, and P2 is the power of the cooling water pump;

[0034] The optimization objective function uses the outlet water temperature and mass flow rate of the chilled water and the cooling water, and the water volume range of the chilled water and the cooling water as the constraints of the optimization range.

[0035] By minimizing the performance objective function P min, which can optimize the overall operating efficiency of the ice machine group. In actual operation, this objective function can guide the system to reduce energy consumption as much as possible while meeting the cooling demand. In order to achieve this goal, the system will dynamically adjust the working status of the refrigeration host and water pump according to the real-time monitored outlet water temperature, mass flow rate and water volume range of the chilled water and cooling water. This dynamic adjustment mechanism can ensure that the ice machine group can operate at the best efficiency under different environmental and load conditions, thereby achieving the dual goals of energy conservation and emission reduction and improving economic benefits.

[0036] The beneficial effects of the present invention are:

[0037] The basic ice machine efficiency curve data is used to establish a mathematical model based on the energy consumption power of the ice machine in its operating state. Factors such as the power consumption of auxiliary equipment and the total load of the system are considered. The entire system is taken as the control target to optimize the ratio of the system's output cooling capacity to energy consumption. This can achieve more accurate energy consumption calculations and more efficient energy management. According to the characteristics of numerical calculations, ice machine models of different models can be integrated into the ice machine cluster, which can truly reflect the operating energy efficiency of the chiller under different operating conditions, avoid the more complicated calculation process when facing a large number of ice machine models, and solve the complex nonlinearity of the system and the high coupling relationship between the components, making this method more concise and efficient from model establishment to the final optimization calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0039] Figure 1 It is a flow chart of an ice machine group model scheduling optimization method based on an ice machine efficiency surface according to the present invention.

[0040] Figure 2 It is a structural schematic diagram of an ice machine group model scheduling optimization method based on an ice machine efficiency surface of the present invention. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0042] See also Figure 1 , a scheduling optimization method for ice machine group model based on ice machine efficiency surface:

[0043] Setting a model scheduling optimization cycle, obtaining basic ice machine efficiency curve data, initializing an ice machine control system according to the basic ice machine efficiency curve data, and adjusting a start and stop state of the ice machine control system according to the model scheduling optimization cycle;

[0044] Building a common model based on ice machine types according to the ice machine types of the ice machine control system;

[0045] By detecting the power-on state of the ice machine control system, if the power-on state is the start-up state, setting a sampling period, reading the chilled water temperature data and the cooling water temperature data according to the sampling period through the ice machine control system, updating the common model according to the chilled water temperature data and the cooling water temperature data, and if the power-on state is the off state, re-performing the power-on state detection;

[0046] The updated public model is integrated into a cluster model to detect whether the ice machine control system is turned off. If the system is turned off, the scheduling optimization mode is ended and the ice machine efficiency surface data is exported. If the system is in the startup state, the basic ice machine efficiency curve data is dynamically updated according to the cluster model.

[0047] In this embodiment, Figure 2 The specific steps of implementing scheduling optimization are as follows: Select cooling water inlet temperature, chilled water return temperature and cold load disturbance variables, and establish an optimization control strategy based on the performance curve model obtained by fitting the sample data provided by the chiller manufacturer or the standard data; set the limit range of water pump variable flow, cooling water inlet temperature and chilled water return temperature to prevent potential accidents during operation. Ensure that the flow and head of the chilled water pump and the cooling water pump are within a reasonable range to avoid excessive energy consumption. Using the refrigeration host performance curve model, combined with the cooling water and chilled water temperatures, calculate the maximum cooling capacity and COP of the refrigeration host under different working conditions. According to the actual operation data of the refrigeration host, perform quadratic curve fitting to establish a relationship model between the power consumption of the refrigeration host and the operating load. Calculate the power of the water pump: According to the preset water pump operation frequency calculation model and water pump operation efficiency calculation model, perform real-time power calculation of the chilled water pump and the cooling water pump. Combine the flow, head and efficiency of the chilled water pump and the cooling water pump to calculate the predicted power. Use the MATLAB optimization toolbox for programming calculations to simulate the operating status of the system under different load rates and find the operating parameter combination with the lowest energy consumption. The simulated annealing optimization algorithm is used to find the parameter values ​​that can achieve the minimum energy consumption and optimal performance of the system under a specific load. The total power of the system is calculated by combining the power of the refrigeration host, the power of the chilled water pump, and the power of the cooling water pump. The working state of each device is adjusted through the optimization control strategy to optimize the operating efficiency of the entire system. The chilled water outlet temperature and the cooling water temperature are dynamically adjusted to improve the overall energy efficiency ratio (COP) of the system. After implementing the optimization control scheme, the system operation data is continuously monitored, and the optimization parameters are adjusted according to the actual operation conditions to ensure that the system is always in the optimal operating state. The optimization effect is regularly evaluated, and the model parameters are readjusted to adapt to the new operating conditions when necessary.

[0048] Specifically, the basic ice machine efficiency curve data includes a refrigeration capacity curve, a working condition coefficient and operation state curve and a working condition coefficient, a chiller load factor relationship curve, and a water pump performance curve.

[0049] Specifically, the common model is composed of a refrigeration unit sub-model, a water pump sub-model, an optimization objective function, and a combination of input and output variables.

[0050] Specifically, the input-output variable combination includes controllable variable input data and target control output data; the controllable variable input data is the chilled water temperature and the cooling water temperature; the target control output data is the cooling water pump control power and the chilled water pump control power.

[0051] Specifically, the refrigeration unit sub-model calculates the maximum refrigeration capacity under rated conditions according to the refrigeration capacity curve in the basic refrigeration machine efficiency curve data and the chilled water temperature and the cooling water temperature, and the calculation formula is:

[0052]

[0053] Among them, Q a is the maximum cooling capacity, Q ref is the rated cooling capacity, a, b, c, d, e, f are the fitting coefficients of the cooling capacity curve, T1 is the chilled water temperature, and T2 is the cooling water temperature;

[0054] The operating coefficient under maximum load is calculated according to the operating coefficient and the operating state curve in the basic ice machine efficiency curve data and the freezing water temperature and the cooling water temperature. The calculation formula is:

[0055]

[0056] Among them, E a is the operating coefficient under maximum load, a1, b1, c1, d1, e1, f1 are the fitting coefficients of the operating coefficient and the operation state curve, T1 is the chilled water temperature, and T2 is the cooling water temperature;

[0057] The relationship curve between the operating coefficient and the chiller load factor is used to express the relationship curve of the operating coefficient as the load of different parts changes. The calculation formula is:

[0058]

[0059] Among them, f E is the corresponding operating factor under system load conditions, Q is the actual system load, Q a is the maximum cooling capacity, the fitting coefficient of the relationship curve between the operating conditions coefficients a2, b2, c2, d2, e2, f2, g2 and the load factor of the chiller;

[0060] The power of the refrigeration host is calculated according to the maximum cooling capacity under the rated working conditions, the working condition coefficient under the maximum load and the corresponding working condition coefficient under the system load conditions. The calculation formula is:

[0061] P c =Q a f E (E a ),

[0062] Among them, P c is the power of the refrigeration host.

[0063] Specifically, the water pump sub-model obtains the fitting coefficient of the relationship between the power and flow rate of the water pump at different speeds and flow rates according to the water pump performance curve, and calculates the flow rate according to the flow characteristics of cooling water and chilled water in the open pipe system. The formula of the fitted water pump sub-model is:

[0064] P=a3+b3V+c3V 2 ,

[0065] Among them, P is the pump power, a2, b2, c2 are the pump performance curve fitting parameters, and V is the flow rate.

[0066] Specifically, the optimization objective function constructs a performance objective function according to the power of cooling water and chilled water and the power of the refrigeration host, and the calculation formula is:

[0067] P min =P c +P1+P2,

[0068] Among them, P min Optimize the target power for performance, P c is the power of the refrigeration unit, P1 is the power of the chilled water pump, and P2 is the power of the cooling water pump;

[0069] The optimization objective function uses the outlet water temperature and mass flow rate of the chilled water and the cooling water, and the water volume range of the chilled water and the cooling water as the constraints of the optimization range.

[0070] In this embodiment, the relationship curve data between the refrigeration efficiency and energy consumption of the ice machine under different working conditions and the basic attribute data of the chiller are provided, and the basic attributes include total installed capacity, number of units, type, fixed frequency, chiller capacity, design working condition, type of refrigerant, working condition coefficient and chiller load factor. A database of the ice machine group model is constructed, which can store and call the refrigeration efficiency and energy consumption data under different working conditions. By analyzing these data, the optimal operating state of the ice machine group under specific working conditions can be determined, thereby maximizing energy efficiency. In addition, the basic attribute data of the chiller provides the necessary parameters for the model to ensure that the actual operating conditions and limitations of the chiller can be taken into account when performing scheduling optimization. The integration of these attribute data enables the model to more accurately simulate the performance of the ice machine group in actual work, providing solid data support for the efficient operation of the ice machine group. The cold source uses York centrifugal chillers, including 6 2675RT variable frequency ice machines, 1 2650RT variable frequency ice machine, 7 2350RT fixed frequency ice machines, 4 2350RT variable frequency ice machines, 1 2200RT fixed frequency machine, 3 2350RT fixed frequency ice machines with heat recovery and 3 1400RT fixed frequency ice machines with heat recovery. The heat source is provided by ice machines with heat recovery. In the experiment, 14 ice machines were used, with a rated supply and return water temperature of 7 / 12°C for chilled water, and a supply and return water temperature of 35 / 30°C for cooling water; 9 ice machines had a rated supply and return water temperature of 6 / 12°C, and a supply and return water temperature of 37 / 32°C for chilled water; the chilled water used a closed circulation water system with a secondary pump and variable flow rate; the primary pumps were all installed with frequency converters, running in series with the ice machine 1 to 1, and manually controlled to start and stop by the on-site operator, with an operating frequency of 35-47HZ; there were a total of 18 secondary pumps, all of which were installed with frequency converters, which were connected in parallel and then in series with the ice machine, and automatically changed frequency and added pumps according to the pressure of the water supply main, and the reduction of pumps was manually controlled by the on-site operator, with an operating frequency of 35-45HZ; the cooling pumps were all installed with frequency converters, which were first connected in parallel and then in series with the ice machine, running in series with the ice machine 1 to 1, and manually controlled to start and stop by the on-site operator, and the cooling pumps were ... The water supply pressure is automatically controlled by frequency conversion, and the pressure is set to 2.6bar. The operating frequency of the cooling pump is 35-45HZ. Two cooling pumps are turned on all year round for CDA cooling. Medium-temperature water is obtained by mixing chilled water return water and supply water. There are a total of 6 groups of medium-temperature mixing water pumps, of which 2 groups are used for exhaust heat recovery circulating water. Each group has 3 water pumps, 2 in use and 1 in reserve, and the water supply pressure is used to control the pump frequency conversion. The return water temperature of the exhaust heat recovery system is basically higher than 28°C, resulting in too high a chilled water return temperature. The cooling tower adopts an open countercurrent tower, a total of 39 units, installed on the roof of the power station, of which 3 fans have been installed with frequency converters. The on-site operator controls the tower according to the cooling water outlet temperature, giving priority to the operation of the variable frequency fan, and the operating frequency is automatically adjusted according to the cooling water outlet temperature. The operating range is 45-50HZ, and the cooling tower outlet temperature is set to 20-32°C.

[0071] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The 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 of the above. More specific examples (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 of the above. 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 combination with an instruction execution system, device or device.

[0072] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry 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.

[0073] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can 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 it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0074] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A scheduling optimization method for ice machine group model based on ice machine efficiency surface, characterized in that: include: Setting a model scheduling optimization cycle, obtaining basic ice machine efficiency curve data, initializing an ice machine control system according to the basic ice machine efficiency curve data, and adjusting a start and stop state of the ice machine control system according to the model scheduling optimization cycle; Building a common model based on ice machine types according to the ice machine types of the ice machine control system; By detecting the power-on state of the ice machine control system, if the power-on state is the start-up state, setting a sampling period, reading the chilled water temperature data and the cooling water temperature data according to the sampling period through the ice machine control system, updating the common model according to the chilled water temperature data and the cooling water temperature data, and if the power-on state is the off state, re-performing the power-on state detection; The updated public model is integrated into a cluster model to detect whether the ice machine control system is turned off. If the system is turned off, the scheduling optimization mode is ended and the ice machine efficiency surface data is exported. If the system is in the startup state, the basic ice machine efficiency curve data is dynamically updated according to the cluster model.

2. The method according to claim 1, characterized in that The basic ice machine efficiency curve data includes a refrigeration capacity curve, a working condition coefficient and an operating state curve and a working condition coefficient, a chiller load factor relationship curve, and a water pump performance curve.

3. The method according to claim 1, characterized in that The common model is composed of a refrigeration unit sub-model, a water pump sub-model, an optimization objective function and a combination of input and output variables.

4. The method according to claim 3, characterized in that The input-output variable combination includes controllable variable input data and target control output data; the controllable variable input data is the chilled water temperature and the cooling water temperature; the target control output data is the cooling water pump control power and the chilled water pump control power.

5. The method according to claim 4, characterized in that The refrigeration unit sub-model calculates the maximum refrigeration capacity under rated conditions according to the refrigeration capacity curve in the basic refrigeration machine efficiency curve data and the chilled water temperature and cooling water temperature, and the calculation formula is: Among them, Q a is the maximum cooling capacity, Q ref is the rated cooling capacity, a, b, c, d, e, f are the fitting coefficients of the cooling capacity curve, T1 is the chilled water temperature, and T2 is the cooling water temperature; The operating coefficient under maximum load is calculated according to the operating coefficient and the operating state curve in the basic ice machine efficiency curve data and the freezing water temperature and the cooling water temperature. The calculation formula is: Among them, E a is the operating coefficient under maximum load, a1, b1, c1, d1, e1, f1 are the fitting coefficients of the operating coefficient and the operation state curve, T1 is the chilled water temperature, and T2 is the cooling water temperature; The relationship curve between the operating coefficient and the chiller load factor is used to express the relationship curve of the operating coefficient as the load of different parts changes. The calculation formula is: Among them, fE is the corresponding operating factor under system load conditions, Q is the actual system load, Q a is the maximum cooling capacity, the fitting coefficient of the relationship curve between the operating conditions coefficients a2, b2, c2, d2, e2, f2, g2 and the load factor of the chiller; The power of the refrigeration host is calculated according to the maximum cooling capacity under the rated working conditions, the working condition coefficient under the maximum load and the corresponding working condition coefficient under the system load conditions. The calculation formula is: P c =Q a f E (E a ), Among them, P c is the power of the refrigeration host.

6. The method according to claim 3, characterized in that The water pump sub-model obtains the fitting coefficient of the relationship between the power and flow rate of the water pump at different speeds and flow rates according to the water pump performance curve, and calculates the flow rate according to the flow characteristics of cooling water and chilled water in the open pipe system. The formula of the fitted water pump sub-model is: <h2 style=";text-align:left;direction:ltr">P = a3 + b3V + c3V<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> , Among them, P is the pump power, a2, b2, c2 are the pump performance curve fitting parameters, and V is the flow rate.

7. The method according to claim 3, characterized in that The optimization objective function constructs a performance objective function according to the power of cooling water and chilled water and the power of the refrigeration host, and the calculation formula is: P min =P c +P1+P2, Among them, P min Optimize the target power for performance, P c is the power of the refrigeration unit, P1 is the power of the chilled water pump, and P2 is the power of the cooling water pump; The optimization objective function uses the outlet water temperature and mass flow rate of the chilled water and the cooling water, and the water volume range of the chilled water and the cooling water as the constraints of the optimization range.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the ice machine group model scheduling optimization method based on the ice machine efficiency surface as described in any one of claims 1 to 7 is implemented.

9. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to execute the ice machine group model scheduling optimization method based on the ice machine efficiency surface as described in any one of claims 1 to 7.

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