Real-time optimization control methods, systems, and equipment for cooling tower outlet water temperature in ice machine systems.

Abstract

A real-time optimization control method for cooling tower outlet water temperature in an ice machine system includes the following steps: determining whether to execute the cooling tower start-up phase; if so, querying the ice machine system experience database; if the determination result is that the cooling tower start-up phase is not required, executing the cooling tower operation phase, running the cooling tower outlet water temperature model, and outputting and updating the optimal cooling water outlet temperature; running the control parameter optimization model, optimizing and outputting the control model parameters and corresponding thresholds based on the current operating conditions and control effect; determining whether to switch in or out of the fan; if the determination result is that it is required, executing the switch-in or switch-out action before proceeding to the next step; if the determination result is that it is not required, directly executing the next step; running the cooling tower fan frequency adjustment model to determine the optimal operating frequency; and executing the number of fans and frequency control.

Description

Technical Field

[0001] This invention relates to the field of computers, and in particular to a method, system, and device for real-time optimization control of the outlet water temperature of a cooling tower in an ice machine system. Background Technology

[0002] Ice machine systems are widely used in industrial and building applications, and the market potential for energy-saving products targeting these systems is enormous. Cooling towers are crucial components of ice machine systems, responsible for cooling the circulating water. Their operation not only affects the energy consumption of the cooling tower itself but also significantly impacts the overall energy consumption of the ice machine system.

[0003] The optimization goal of cooling tower control is to minimize the total energy consumption of the cooling tower fan while meeting the cooling water outlet temperature requirements. Current cooling tower control mainly relies on manual operation by workers, which is prone to instability and inefficiency, making it difficult to achieve the most economical operating mode.

[0004] This invention develops a multivariable model predictive control method for the cooling tower of an ice machine system, based on real-time data collected from the ice machine system and the user terminal, to optimize and control the cooling tower outlet water temperature in real time. This advanced control method not only saves equipment manufacturers on control manpower costs but also contributes to energy conservation in the operation of the ice machine system. Summary of the Invention

[0005] One of the objectives of this invention is to provide a method, system, and device for real-time optimization control of the cooling tower outlet water temperature in an ice machine system. This method enables multivariate model predictive control of the cooling tower in the ice machine system and real-time optimization control of the cooling tower outlet water temperature. This not only saves on manpower costs for control but also contributes to energy conservation in the operation of the ice machine system.

[0006] One of the objectives of this invention is to provide a real-time optimization control method, system, and device for the outlet water temperature of a cooling tower in an ice machine system. Based on the historical operating data of the ice machine system, the system continuously maintains an experience database module, so that each time the cooling tower is started, it can output recommended values ​​of the current operating parameters according to the operating environment.

[0007] One of the objectives of this invention is to provide a method, system, and device for real-time optimization control of cooling tower outlet water temperature in an ice machine system. This method can perform data mining on historical operating data and optimize the cooling tower outlet water temperature setpoint in real time while meeting the safe operation of the ice machine system and the cooling capacity requirements of the terminal equipment. This ensures that the system maintains the most economical operating state under different operating conditions.

[0008] One of the objectives of this invention is to provide a real-time optimization control method, system, and device for the outlet water temperature of a cooling tower in an ice machine system. Based on the current operating conditions and control effect, the control model parameters and related thresholds can be optimized in real time, enabling the control model to adapt to the control conditions, enhancing the robustness of the model, and making the control effect more stable by continuously optimizing the control range thresholds.

[0009] One of the objectives of this invention is to provide a real-time optimized control method, system, and equipment for the outlet water temperature of a cooling tower in an ice machine system. This method can optimize the overall fan operating efficiency by considering the economic operating frequency range of each cooling tower fan while meeting the overall output of the cooling tower. At the same time, it avoids the problems of frequent start-stop of the same fan and excessively long fan operation time in the selection of start-stop fans.

[0010] One of the objectives of this invention is to provide a method, system, and device for real-time optimization control of the cooling tower outlet water temperature in an ice machine system, which can adjust the operating frequency of the fan within an optimized frequency range to finely control the cooling tower outlet water temperature.

[0011] To achieve at least one objective of this invention, the present invention provides a real-time optimization control method for the outlet water temperature of a cooling tower in an ice machine system, the real-time optimization control method for the outlet water temperature of a cooling tower in an ice machine system comprising the following steps:

[0012] Determine whether to execute the cooling tower start-up phase. If the cooling tower start-up phase is executed, based on the heat load and atmospheric dry and wet bulb temperature information, query the ice machine system experience database to determine: the cooling tower outlet water set temperature, the number of cooling towers to be started, and the cooling tower fan operating frequency, as the initial start-up set values.

[0013] When the judgment result is that the cooling tower start-up phase does not need to be executed, the cooling tower operation phase is executed. The circulating water heat exchange load, the cooling tower inlet dry and wet bulb temperatures, and the cooling water flow rate data are input into the cooling tower outlet water temperature model. The cooling tower outlet water temperature model is run, and the optimal cooling water outlet temperature is output and updated.

[0014] Run the control parameter optimization model, optimize and output the control model parameters and corresponding thresholds based on the current operating conditions and control effect;

[0015] Determine whether it is necessary to switch in or out of the fan. If the result is yes, perform the switch-in or switch-out action before proceeding to the next step. If the result is no, proceed directly to the next step.

[0016] Run the cooling tower fan frequency regulation model to determine the optimal operating frequency; and

[0017] Performs unit number control and frequency control.

[0018] In some embodiments, the unit control step further includes the following steps: keeping the cooling tower fans running at the same frequency; when the number of operating fans has not reached the upper limit, and the fan operating frequency exceeds the optimal operating frequency of the fan and the duration exceeds the set time limit, randomly adding one fan from the standby fans to start operation; when more than one cooling tower is running, and the cooling tower fan operating frequency is less than the lower limit of the economic operating frequency range and the duration exceeds the set time limit, shutting down the earliest operating fan.

[0019] In some embodiments, the frequency control step further includes the following step: activating the cooling tower fan frequency adjustment model based on the deviation between the actual outlet water temperature and the set outlet water temperature.

[0020] In some embodiments, the algorithm model for the cooling tower outlet water temperature model is selected from fully connected neural networks, extreme gradient boosting decision trees, random forests, support vector machines, and linear regression.

[0021] In some embodiments, the algorithm model for the control parameter optimization model is selected from genetic algorithm, evolutionary algorithm, particle swarm optimization algorithm, simulated annealing algorithm, PID parameter tuning algorithm, convex optimization algorithm, and regression algorithm.

[0022] In some embodiments, the optimized model parameters of the control parameter optimization model are selected from system identification model parameters, PID control model parameters, upper and lower limits of the number of fans started under the current operating condition, and upper and lower limits of frequency control.

[0023] In some embodiments, the real-time optimization control method for the outlet water temperature of the cooling tower in the ice machine system further includes the following steps: based on the historical operating data of the ice machine system, maintaining an experience database module, and outputting recommended values ​​of the current operating parameters according to the operating environment each time the cooling tower is started.

[0024] In some embodiments, the real-time optimization control method for the cooling tower outlet water temperature in the ice machine system further includes the following steps: performing data mining on historical operating data and performing real-time optimization on the cooling tower outlet water temperature setpoint.

[0025] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, performs the steps of the real-time optimization control method for the outlet water temperature of the cooling tower in the ice machine system.

[0026] According to another aspect of the present invention, a real-time optimization control device for the outlet water temperature of a cooling tower in an ice machine system is also provided, comprising: a memory for storing a software application, and a processor for executing the software application, wherein each program portion of the software application correspondingly executes a step in the real-time optimization control method for the outlet water temperature of a cooling tower in the ice machine system.

[0027] According to another aspect of the present invention, a real-time optimization control system for the outlet water temperature of the cooling tower in an ice machine system is also provided. The real-time optimization control system for the outlet water temperature of the cooling tower in the ice machine system acquires multiple control models of the cooling tower of the ice machine system, performs real-time optimization control on the outlet water temperature of the cooling tower, and each control model of the cooling tower of the ice machine system correspondingly executes the steps in the real-time optimization control method for the outlet water temperature of the cooling tower in the ice machine system. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the steps of a real-time optimization control method for the outlet water temperature of a cooling tower in an ice machine system according to an embodiment of the present invention.

[0029] Figure 2 This is a schematic diagram of the structure of a real-time optimization control system for the outlet water temperature of a cooling tower in an ice machine system according to an embodiment of the present invention.

[0030] Figure 3 This is an example of the control interface of the advanced control system for the ice machine cooling tower according to the above embodiments. Detailed Implementation

[0031] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0032] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0033] This invention relates to computer programs. For example... Figure 1The diagram shows a flowchart of a real-time optimization control method for the cooling tower outlet water temperature in an ice machine system based on the present invention. It illustrates a solution to the problems proposed in this invention, based on a computer program processing flow. This solution involves executing a computer program compiled according to the above flow to control or process external or internal objects of the computer. Through the real-time optimization control method for the cooling tower outlet water temperature in the ice machine system described in this invention, an advanced control model for the cooling tower of the ice machine system can be constructed using big data and artificial intelligence methods, supplemented by model predictive control (MPC), industrial mechanism models, and other technologies. This allows for real-time optimization control of the cooling tower outlet water temperature, ensuring optimal operation under different industrial control conditions, thereby achieving system energy saving.

[0034] The optimization objective of cooling tower control is to reduce the total energy consumption of cooling tower fans. The operating parameters related to the constructed cooling tower control model mainly include: manipulated variables, controlled variables, and feedforward variables. Manipulated variables include the number of cooling towers in operation and the frequency of cooling tower fans; controlled variables include the cooling water outlet temperature; and feedforward variables include atmospheric dry-bulb and wet-bulb temperatures and the circulating water heat exchange load. The two feedforward variables that have the greatest impact on the cooling tower operation are the atmospheric dry-bulb and wet-bulb temperatures and the circulating water heat exchange load. By modeling the manipulated and controlled variables under different feedforward variable scenarios, the mapping relationship between the two is obtained. Then, AI data mining is performed to obtain the optimal operating values ​​of the manipulated variables under different scenarios.

[0035] The real-time optimization control method for the cooling tower outlet water temperature in the ice machine system controls the cooling tower outlet water temperature. This mainly requires determining the set temperature of the cooling tower outlet water. At the operational level, it requires adjusting the number of cooling towers started and the operating frequency of the cooling tower fans.

[0036] Specifically, such as Figure 1 As shown, the real-time optimization control method for the cooling tower outlet water temperature in the ice machine system includes the following steps:

[0037] S100: Determine whether to execute the cooling tower start-up phase. When the cooling tower start-up phase is executed, based on the heat load and atmospheric dry and wet bulb temperature information, query the ice machine system experience database to determine: the cooling tower outlet water set temperature, the number of cooling towers to be started, and the cooling tower fan operating frequency, as the initial start-up set values.

[0038] S200: When the judgment result is that the cooling tower start-up phase does not need to be executed, the cooling tower operation phase is executed. The circulating water heat exchange load, the cooling tower inlet dry and wet bulb temperatures, and the cooling water flow rate data are input into the cooling tower outlet water temperature model. The cooling tower outlet water temperature model is run, and the optimal cooling water outlet temperature is output and updated. The algorithm model of the cooling tower outlet water temperature model is selected from fully connected neural network (FCN), extreme gradient boosting decision tree (Xgboost), random forest (Randomforest), support vector machine (SVM), linear regression (LR), etc.

[0039] S300: Runs a control parameter optimization model. Based on the current operating conditions and control effects, it optimizes and outputs the control model parameters and corresponding thresholds. Depending on the selected control model, the model parameters to be optimized in this step include: system identification model parameters, PID (Proportional, Integral, Differential) control model parameters, upper and lower limits of the number of wind turbines started under the current operating conditions, and upper and lower limits of frequency control, etc. Optimization algorithm models can be selected from: genetic algorithm, evolutionary algorithm, particle swarm optimization algorithm, simulated annealing algorithm, PID parameter tuning algorithm (such as critical proportional method, decay curve method, empirical tuning method, etc.), convex optimization algorithm, regression algorithm, etc.

[0040] S400: Determine whether it is necessary to switch in or out of the fan. If the determination result is yes, execute the switch in or switch out action and then proceed to the next step. If the determination result is no, directly execute the next step.

[0041] S500: Runs the cooling tower fan frequency regulation model to determine the optimal operating frequency. Based on the target optimal outlet water temperature and the current actual outlet water temperature, it adjusts the number of cooling towers in operation and the fan operating frequency, and performs unit number control and frequency control.

[0042] Furthermore, the real-time optimization control method for the cooling tower outlet water temperature in the ice machine system also includes a unit control step, comprising the following steps: keeping the cooling tower fans running at the same frequency; when the number of operating fans has not reached the upper limit, and the fan operating frequency exceeds the optimal operating frequency of the fan and the duration exceeds the set time limit, randomly adding one fan from the standby fans; when more than one cooling tower is running, and the cooling tower fan operating frequency is less than the lower limit of the economic operating frequency range and the duration exceeds the set time limit, shutting down the earliest operating fan. It is worth noting that during the unit start-stop control operation, the number of operating cooling towers must be within the cooling tower operating number range optimized in step S300.

[0043] Furthermore, the real-time optimization control method for the cooling tower outlet water temperature in the ice machine system also includes a frequency control step, comprising the following steps: based on the deviation between the actual outlet water temperature and the set outlet water temperature, the cooling tower fan frequency adjustment model is activated, and the operating frequency of the cooling tower fan is adjusted within the frequency adjustment range optimized in step S300. The frequency adjustment algorithm can employ proportional-integral-derivative controller (PID), dynamic matrix control (DMC), fuzzy control (FZ), recurrent neural network (RNN), and integrated algorithm models of multiple methods.

[0044] It is worth mentioning the design of the experience database model in the ice machine system experience database: based on the historical operating data of the ice machine system, the experience database module is continuously maintained to build an "expert system" so that when the cooling tower is started, it can output recommended values ​​of the current operating parameters according to the operating environment.

[0045] It is worth mentioning that the cooling tower outlet water temperature model uses AI-based methods to mine historical operating data and optimize the cooling tower outlet water temperature setpoint in real time while meeting the safe operation of the ice machine system and the cooling capacity requirements of the terminal equipment. This ensures that the system maintains the most economical operating state under different working conditions.

[0046] It is worth mentioning that the control parameter optimization model optimizes the control model parameters and related thresholds in real time based on the current operating conditions and control effect, enabling the control model to adapt to the control conditions and enhancing the robustness of the model. By continuously optimizing the control range threshold, the control effect is also made more stable.

[0047] It is worth mentioning that the fan operation control model considers the economic operating frequency range of each cooling tower fan while meeting the overall output of the cooling tower, so as to achieve the best overall fan operation efficiency. At the same time, the selection of start-up and shutdown of the fans also avoids the problems of frequent start-up and shutdown of the same fan and excessively long fan operation time.

[0048] It is worth mentioning that the cooling tower fan frequency regulation model: based on the temperature difference, and using a control model designed according to the AI ​​coupled model predictive control method, the operating frequency of the fan is adjusted within the optimized frequency range to finely regulate the cooling tower outlet water temperature.

[0049] Those skilled in the art will understand that embodiments of the present invention can be provided in the form of methods, systems, or computer program products. Therefore, the present invention can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware. A typical combination of hardware and software can be a general-purpose computer system with a computer program that, when loaded and executed, controls the computer system to perform the methods disclosed in this invention.

[0050] This invention can be embedded in a computer program product, which includes all the features that enable the methods described herein to be implemented. The computer program product is contained in one or more computer-readable storage media having computer-readable program code contained therein. According to another aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, is capable of performing the steps of the methods of the invention. A computer storage medium is a medium in a computer memory used to store some discontinuous physical quantity. Computer storage media include, but are not limited to, semiconductors, disk drives, magnetic cores, magnetic drums, magnetic tapes, laser disks, etc. Those skilled in the art will understand that computer storage media are not limited to the foregoing examples, which are merely illustrative and not intended to limit the invention.

[0051] According to another aspect of the present invention, a real-time optimization control device for the outlet water temperature of a cooling tower in an ice machine system is also provided. This device includes: a software application, a memory for storing the software application, and a processor for executing the software application. Each program portion of the software application is capable of correspondingly executing the steps in the real-time optimization control method for the outlet water temperature of the cooling tower in the ice machine system of the present invention.

[0052] Those skilled in the art will understand that the method of the present invention can be implemented by hardware, software, or a combination of both. The present invention can be implemented centrally in at least one computer system, or distributed in a decentralized manner by different parts distributed across several interconnected computer systems. Any computer system or other device capable of implementing the method is applicable. A common combination of hardware and software can be a general-purpose computer system with computer programs installed, controlling the computer system to operate according to the method by installing and executing the programs.

[0053] Corresponding to an embodiment of the method of the present invention, another aspect of the present invention provides a real-time optimization control system for the cooling tower outlet water temperature in an ice machine system. This system is an application of the real-time optimization control method for the cooling tower outlet water temperature in an ice machine system of the present invention in the form of computer program improvement. Each subsystem of the real-time optimization control system for the cooling tower outlet water temperature in the ice machine system correspondingly executes the steps of the real-time optimization control method for the cooling tower outlet water temperature in an ice machine system of the present invention. Preferably, in a specific embodiment, the experience database model subsystem of the real-time optimization control system for the cooling tower outlet water temperature in the ice machine system is configured to: maintain an experience database module based on historical operating data of the ice machine system, and output recommended values ​​of current operating parameters according to the operating environment each time the cooling tower is started. The cooling tower outlet water temperature model subsystem of the real-time optimization control system for the cooling tower outlet water temperature in the ice machine system is configured to: perform data mining on historical operating data, and optimize the cooling tower outlet water temperature setpoint in real time while meeting the safe operation of the ice machine system equipment and the cooling capacity requirements of the terminal equipment, thereby ensuring that the system maintains the most economical operating state under different operating conditions. The control parameter optimization model subsystem of the real-time optimization control system for cooling tower outlet water temperature in the ice machine system is configured to: perform real-time optimization of control model parameters and related thresholds based on the current operating conditions and control effects, enabling the control model to adapt to the control conditions, enhancing the model's robustness, and making the control effect more stable through continuous optimization of the control range thresholds. The fan operation number control model subsystem of the real-time optimization control system for cooling tower outlet water temperature in the ice machine system is configured to: control the overall fan operation at its optimal efficiency based on the economic operating frequency range of each cooling tower fan, while ensuring the overall output of the cooling tower is met, and simultaneously execute fan start-up and shutdown to avoid frequent start-up and shutdown of the same fan and excessively long fan operation time. The cooling tower fan frequency adjustment model subsystem of the real-time optimization control system for cooling tower outlet water temperature in the ice machine system is configured to: adjust the fan operating frequency within the optimized frequency range based on the temperature difference and the control model designed using the AI ​​coupled model predictive control method, thereby finely controlling the cooling tower outlet water temperature.

[0054] like Figure 2 As shown, corresponding to an embodiment of the method of the present invention, according to another aspect of the present invention, a real-time optimization control system for the cooling tower outlet water temperature in an ice machine system is also provided. Preferably, the real-time optimization control system for the cooling tower outlet water temperature in the ice machine system is set in an algorithm server. All data involved in the ice machine system are collected by a PLC. The PLC communicates with the algorithm server, and the control commands output by the algorithm control the actuators via the PLC. Simultaneously, a host computer is configured at the equipment end of the ice machine system to facilitate operators monitoring the equipment's operating status. A remote management station is configured in the cloud to remotely iterate and upgrade the algorithms and related software in the algorithm server.

[0055] like Figure 3 The image shows a partial system control interface example of the real-time optimization control system for the cooling tower outlet water temperature in the ice machine system during specific implementation. For example, an APC (Advanced Process Control Program) is designed for each device to select whether to use advanced control program control. The interface also displays whether the device is currently in operation and its current operating frequency. The device's start / stop and frequency adjustment can also be manually controlled from the interface. Furthermore, to reduce frequency fluctuations and ensure the device operates within a safe and economical frequency range, the control panel also supports setting upper and lower limits for the device's operating frequency.

[0056] Those skilled in the art will understand that the invention has been described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to the invention. Each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can obviously be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, thereby instructing (the instructions via the processor of the computer or other programmable data processing apparatus) to generate means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or block diagrams.

[0057] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and any modifications or variations of the embodiments of the present invention may be made without departing from these principles.

Claims

1. A method for real-time optimized control of cooling tower outlet water temperature in an ice machine system, characterized in that, The real-time optimization control method for the cooling tower outlet water temperature in the ice machine system includes the following steps: Determine whether to execute the cooling tower start-up phase. If the cooling tower start-up phase is executed, based on the heat load and atmospheric dry and wet bulb temperature information, query the ice machine system experience database to determine: the cooling tower outlet water set temperature, the number of cooling towers to be started, and the cooling tower fan operating frequency, as the initial start-up set values. When the judgment result is that the cooling tower start-up phase does not need to be executed, the cooling tower operation phase is executed. The circulating water heat exchange load, the cooling tower inlet dry and wet bulb temperatures, and the cooling water flow rate data are input into the cooling tower outlet water temperature model. The cooling tower outlet water temperature model is run, and the optimal cooling water outlet temperature is output and updated. Run the control parameter optimization model, optimize and output the control model parameters and corresponding thresholds based on the current operating conditions and control effect; Determine whether it is necessary to switch in or out of the fan. If the result is yes, perform the switch-in or switch-out action before proceeding to the next step. If the result is no, proceed directly to the next step. Run the cooling tower fan frequency regulation model to determine the optimal operating frequency; and Performs unit number control and frequency control.

2. The real-time optimization control method for cooling tower outlet water temperature in the ice machine system as described in claim 1, wherein the unit number control step further includes the following steps: Keep the cooling tower fans running at the same frequency. When the number of fans in operation has not reached the upper limit, and the fan operating frequency exceeds the optimal operating frequency and the duration exceeds the set time limit, randomly add one fan from the standby fans to start operation. When more than one cooling tower is in operation, and the cooling tower fan operating frequency is less than the lower limit of the economic operating frequency range and the duration exceeds the set time limit, shut down the earliest fan that was put into operation.

3. The real-time optimization control method for cooling tower outlet water temperature in the ice machine system as described in claim 1, wherein the frequency control step further includes the following steps: Based on the deviation between the actual outlet water temperature and the set outlet water temperature, the cooling tower fan frequency adjustment model is activated.

4. The real-time optimization control method for cooling tower outlet water temperature in the ice machine system as described in claim 1, wherein the algorithm model for the cooling tower outlet water temperature model is selected from fully connected neural networks, extreme gradient boosting decision trees, random forests, support vector machines, and linear regression.

5. The real-time optimization control method for cooling tower outlet water temperature in the ice machine system as described in claim 1, wherein the algorithm model for the control parameter optimization model is selected from genetic algorithm, evolutionary algorithm, particle swarm optimization algorithm, simulated annealing algorithm, PID parameter tuning algorithm, convex optimization algorithm, and regression algorithm.

6. The real-time optimization control method for cooling tower outlet water temperature in the ice machine system as described in claim 1, wherein the optimization model parameters of the control parameter optimization model are selected from system identification model parameters, PID control model parameters, upper and lower limits of the number of fans started under the current operating condition, and upper and lower limits of frequency control.

7. The real-time optimization control method for the cooling tower outlet water temperature in an ice machine system as described in any one of claims 1-6, wherein the real-time optimization control method for the cooling tower outlet water temperature in the ice machine system further includes the following steps: Based on the historical operating data of the ice machine system, the maintenance experience database module outputs recommended values ​​for the current operating parameters according to the operating environment each time the cooling tower is started.

8. The real-time optimization control method for the cooling tower outlet water temperature in an ice machine system as described in any one of claims 1-6, wherein the real-time optimization control method for the cooling tower outlet water temperature in the ice machine system further includes the following steps: Data mining is performed on historical operating data to optimize the cooling tower outlet water temperature setpoint in real time.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it performs the steps of the real-time optimization control method for the outlet water temperature of the cooling tower in the ice machine system according to any one of claims 1 to 8.

10. A real-time optimization control device for the outlet water temperature of a cooling tower in an ice machine system, characterized in that, include: A memory for storing a software application, and a processor for executing the software application, wherein each program portion of the software application correspondingly performs a step in the real-time optimization control method for the cooling tower outlet water temperature in any of claims 1 to 8.

11. A real-time optimization control system for the outlet water temperature of a cooling tower in an ice machine system, characterized in that, The real-time optimization control system for the cooling tower outlet water temperature in the ice machine system acquires multiple cooling tower control models of the ice machine system, performs real-time optimization control on the cooling tower outlet water temperature, and each of the ice machine system cooling tower control models correspondingly executes the steps in the real-time optimization control method for the cooling tower outlet water temperature in the ice machine system according to any one of claims 1 to 8.

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