Cooling water system control method, air conditioner cooling water system and efficient refrigerating machine room

Abstract

The invention discloses a cooling water system control method, an air conditioner cooling water system and an efficient refrigerating machine room. The control method comprises the steps of collecting current operation data of the cooling water system; determining a preset feedback set value for feeding back and controlling the cooling equipment frequency of the cooling water system according to the current operation data; according to the current operation data and the preset feedback set value, the energy loss amount of the cooling water system in the future preset time period is calculated and determined through a preset optimization algorithm of a preset data model, and the preset feedback set value when the energy loss amount is minimum is taken as the optimal feedback set value; and performing feedback regulation on the cooling equipment frequency of the cooling water system according to the optimal feedback set value. According to the control method, the optimal feedback set value obtained when the energy loss of the cooling water system is minimum is calculated based on the preset optimization algorithm according to the current operation data and the preset feedback set value, feedback adjustment is conducted on the cooling equipment frequency of the cooling water system, and control optimization, energy conservation and consumption reduction of the air conditioner cooling water system are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of air conditioning energy-saving control, and in particular to a cooling water system control method, an air conditioning cooling water system and a high-efficiency refrigeration room. Background Art

[0002] Water-cooled central air conditioners are usually used in large public buildings, centralized cooling energy stations, and industrial refrigeration. Water-cooled central air conditioners are large in scale and have high energy loss. The cooling water system is an important part of water-cooled central air conditioners, including cooling towers, cooling water pumps, cooling units and other equipment. The structure of the cooling water system is relatively complex, but it is very critical to the energy efficiency of the entire air conditioning system.

[0003] At present, energy-saving control of central air-conditioning cooling water systems is usually achieved by frequency conversion of cooling water pumps and cooling tower fans. However, excessive frequency conversion will lead to reduced cooling capacity and water flow, which will affect the operating efficiency of the cooling unit, resulting in poor energy-saving effects of frequency conversion of cooling tower fans or cooling water pumps in a large number of actual engineering projects.

[0004] Therefore, there is an urgent need for a control method for determining in real time the frequency that a cooling tower fan or cooling water pump should reach when the frequency is changed, so that the energy-saving effect of the cooling tower fan or cooling water pump frequency change can be truly demonstrated. Summary of the invention

[0005] In view of this, the present application aims to solve one of the problems in the related art at least to a certain extent. To this end, the purpose of the present application is to provide a cooling water system control method, an air conditioning cooling water system and an efficient refrigeration room.

[0006] The present application provides a cooling water system control method. The cooling water system control method comprises: collecting the current operation data of the cooling water system; determining the preset feedback setting value for feedback control of the cooling equipment frequency of the cooling water system according to the current operation data; calculating and determining the energy loss of the cooling water system in a future preset time period through a preset optimization algorithm of a preset data model according to the current operation data and the preset feedback setting value, taking the preset feedback setting value when the energy loss is the minimum as the optimal feedback setting value; and feedback-adjusting the cooling equipment frequency of the cooling water system according to the optimal feedback setting value.

[0007] In certain embodiments, before calculating and determining the optimal feedback set value when the energy loss of the cooling water system in a future preset time period is minimized through a preset optimization algorithm of a preset data model based on the current operating data and the preset feedback set value, the control method includes: obtaining the historical operating data of the cooling water system within the preset time period; performing model training on preset models of different cooling equipment in the cooling water system based on the historical operating data to obtain multiple preliminary data models corresponding to different cooling equipment; and merging multiple of the preliminary data models to obtain the preset data model.

[0008] In certain embodiments, after acquiring the historical operation data of the cooling water system within a preset time period, the control method includes: preprocessing the historical operation data; and saving the preprocessed historical operation data into a preset database.

[0009] In certain embodiments, the preprocessing of the historical operation data includes: classifying and arranging the historical operation data according to the type of the cooling equipment; and eliminating outliers and filling missing values ​​for each type of the historical operation data.

[0010] In certain embodiments, the energy loss of the cooling water system in a future preset time period is calculated and determined through a preset optimization algorithm of a preset data model based on the current operating data and the preset feedback setting value, and the preset feedback setting value at which the energy loss is minimized is taken as the optimal feedback setting value, including: inputting the current operating data into the preset data model, predicting the energy loss of the cooling water system in a future preset time period based on the preset feedback setting value and the preset optimization algorithm; judging whether the energy loss is less than or equal to a preset minimum value; if the energy loss is less than or equal to the preset minimum value, determining that the preset feedback setting value is the optimal feedback setting value.

[0011] In certain embodiments, the control method includes: if the energy loss is greater than a preset minimum value, resetting the preset feedback setting value based on the energy loss; predicting the new energy loss of the cooling water system in a future preset time period based on the current operating data, the reset preset feedback setting value and the preset optimization algorithm; determining whether the new energy loss is less than or equal to a preset minimum value; if the new energy loss is less than or equal to the minimum value, determining that the reset preset feedback setting value is the optimal feedback setting value.

[0012] The present application also provides a cooling water system for an air conditioner, which uses the control method described in any one of the above embodiments. The cooling water system includes a digital control device and an intelligent control device connected to the digital control device; the digital control device is used to collect the current operating data of the cooling water system; the intelligent control device is used to determine the preset feedback setting value for feedback control of the cooling equipment frequency of the cooling water system according to the current operating data; and calculate and determine the energy loss of the cooling water system in a future preset time period according to the current operating data through a preset optimization algorithm of a preset data model, and take the preset feedback setting value when the energy loss is the minimum as the optimal feedback setting value; the digital control device is used to feedback adjust the cooling equipment frequency of the cooling water system according to the optimal feedback setting value.

[0013] In certain embodiments, when a data transmission link between the intelligent control device and the digital control device fails, the preset feedback setting value in the digital control device is reset to a default value.

[0014] In some embodiments, the intelligent control device and the digital control device are connected via a switch.

[0015] The present application also provides a high-efficiency refrigeration room, which includes the cooling water system described in any one of the above embodiments.

[0016] The control method of the present application uses a direct digital control method to control the frequency of the cooling equipment of the cooling water system to ensure the safe and stable operation of the cooling water system of the air conditioner. The optimal feedback set value when the energy loss of the cooling water system is minimized is calculated based on a preset optimization algorithm according to current operating data and preset feedback set values. The cooling equipment frequency of the cooling water system is feedback-adjusted according to the optimal feedback set value determined in real time, thereby realizing control optimization of the cooling water system of the air conditioner and achieving the effect of energy saving and consumption reduction of the cooling water system of the air conditioner and improving the energy efficiency of the air conditioner.

[0017] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 is a flow chart of a control method of certain embodiments of the present application;

[0020] Figure 2is a schematic structural diagram of a cooling water system of an air conditioner in certain embodiments of the present application;

[0021] Figure 3 is a schematic structural diagram of a cooling water system of an air conditioner in certain embodiments of the present application;

[0022] Figure 4 is a flow chart of a control method of certain embodiments of the present application;

[0023] Figure 5 is a flow chart of a control method of certain embodiments of the present application;

[0024] Figure 6 It is a partial structural schematic diagram of a cooling water system of an air conditioner in certain embodiments of the present application;

[0025] Figure 7 is a flow chart of a control method of certain embodiments of the present application;

[0026] Figure 8 is a flow chart of a control method of certain embodiments of the present application;

[0027] Fig. 9 is a flow chart of a control method of certain embodiments of the present application;

[0028] Fig.10 is a flow chart of a control method of certain embodiments of the present application;

[0029] Fig.11 It is a schematic diagram of the structure of the cooling water system of the air conditioner in certain embodiments of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0031] In the description of the present application, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0032] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installation" and "connection" should be understood in a broad sense, and may refer to fixed connection, detachable connection, or integral connection; mechanical connection, electrical connection, or mutual communication; direct connection, indirect connection through an intermediate medium, internal connection between two elements, or interaction between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0033] The disclosure below provides many different embodiments or examples to realize the different structures of the present application. In order to simplify the disclosure of the present application, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present application. In addition, the present application can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed.

[0034] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0035] See also Figure 1 , the present application provides a cooling water system control method. The control method comprises:

[0036] 01: Collect the current operation data of the cooling water system;

[0037] 03: Determine the preset feedback set value of the cooling equipment frequency of the feedback control cooling water system based on the current operating data;

[0038] 05: The energy loss of the cooling water system in the future preset time period is calculated and determined by the preset optimization algorithm of the preset data model according to the current operation data and the preset feedback setting value, and the preset feedback setting value with the minimum energy loss is taken as the optimal feedback setting value;

[0039] 07: Feedback adjustment of the cooling equipment frequency of the cooling water system according to the optimal feedback setting value.

[0040] See also Figure 2 The present application also provides a cooling water system 10 for an air conditioner. The cooling water system 10 for an air conditioner includes a digital control device 11 and an intelligent control device 12 connected to the digital control device 11.

[0041] Step 01 and step 07 can be implemented by the digital control device 11, and step 03 and step 05 can be implemented by the intelligent control device 12. That is, the digital control device 11 is used to collect the current operation data of the cooling water system in the future preset time period; the intelligent control device 12 is used to determine the preset feedback setting value of the cooling equipment frequency of the cooling water system for feedback control according to the current operation data; and calculate and determine the energy loss of the cooling water system through the preset optimization algorithm of the preset data model according to the current operation data, and take the preset feedback setting value with the minimum energy loss as the optimal feedback setting value; the digital control device 11 is used to feedback adjust the cooling equipment frequency of the cooling water system according to the optimal feedback setting value.

[0042] Specifically, Figure 3 As shown, the cooling water system of the air conditioner includes cooling equipment such as cooling units, refrigeration pipes, cooling tower fans and cooling water pumps.

[0043] Collecting the current operation data of the cooling water system, that is, installing sensors for the main equipment in the cooling water system, and obtaining the current operation data collected by the sensors includes: cooling water flow, cooling main water supply temperature, cooling main return water temperature, chilled water flow, chilled main water supply temperature, chilled main return water temperature, frequency and power of each cooling tower fan, frequency and power of each cooling water pump, cooling capacity, load rate, power of each cooling unit, etc. The above current operation data can be collected into the digital control device 11, and the digital control device 11 is programmed with feedback adjustment automatic control programs for cooling tower fan frequency conversion and cooling water pump frequency conversion.

[0044] It can be understood that the control method of the present application uses a direct digital control (DDC) method to achieve the control of the frequency of the cooling water pump and the cooling tower fan, and its control method is feedback regulation, and the specific control method of feedback regulation can be a proportional-integral-differential control (PID) method. Therefore, after obtaining the current operating data of the cooling water system, the preset feedback setting value can be determined according to the current operating data, or the PID setting value can be determined according to the current operating data.

[0045] In detail, determining the preset feedback set value of the cooling equipment frequency of the feedback control cooling water system based on the current operating data means that the temperature difference between the cooling main water supply temperature and the return water temperature in the current operating data can be used as the preset feedback set value for feedback control of the cooling water pump frequency, and the cooling main return water temperature can be used as the preset feedback set value for feedback control of the cooling tower fan frequency.

[0046] The preset optimization algorithm may be a heuristic optimization algorithm or other algorithms, which are not limited here.

[0047] like Figure 4 As shown, the energy loss of the cooling water system in the future preset time period is calculated and determined by the preset optimization algorithm of the preset data model according to the current operation data and the preset feedback setting value, and the preset feedback setting value when the energy loss is the smallest is taken as the optimal feedback setting value. That is, the optimization function of the intelligent control device 12 is enabled, and it will receive the current operation data of the cooling water system in real time. The preset data model can be used to calculate: when the temperature difference between the supply water temperature and the return water temperature of the cooling main pipe is set to what value, and when the return water temperature of the cooling main pipe is set to what value, the energy loss of the cooling water system in the future preset time period can be minimized without affecting its normal operation. Among them, the future preset time period can refer to the next 1 week, the next 2 weeks or the next 3 weeks, and there is no restriction here.

[0048] The optimal feedback setting value of the cooling water system is the temperature difference between the cooling main pipe supply water temperature and the return water temperature and the cooling main pipe return water temperature corresponding to the minimum energy loss of the cooling water system.

[0049] In this way, the control method of the present application uses a direct digital control method to control the cooling equipment frequency of the cooling water system to ensure the safe and stable operation of the cooling water system of the air conditioner. According to the current operating data and the preset feedback setting value, the optimal feedback setting value when the energy loss of the cooling water system is minimized is calculated based on the preset optimization algorithm. The cooling equipment frequency of the cooling water system is feedback-adjusted according to the optimal feedback setting value determined in real time, thereby realizing the control optimization of the cooling water system of the air conditioner, achieving the effect of energy saving and consumption reduction of the cooling water system of the air conditioner and improving the energy efficiency of the air conditioner.

[0050] See also Figure 5 , before step 05, the control method includes:

[0051] 041: Obtain historical operation data of the cooling water system within a preset time period;

[0052] 043: Train the preset models of different cooling equipment in the cooling water system based on historical operation data to obtain multiple preliminary data models corresponding to different cooling equipment;

[0053] 045: Merge multiple preliminary data models to obtain a preset data model.

[0054] Please combine Figure 2, step 041, step 043 and step 045 can be implemented by the intelligent control device 12. That is, the intelligent control device 12 is used to obtain the historical operation data of the cooling water system within a preset time period; perform model training on the preset models of different cooling devices in the cooling water system according to the historical operation data to obtain multiple preliminary data models corresponding to different cooling devices; and merge the multiple preliminary data models to obtain the preset data model.

[0055] Specifically, the preset time period can be a default time period or a custom time period. For example, the preset time period can be 2 weeks. That is, the control method of the present application can connect a hardware device equipped with a preset optimization algorithm dedicated to optimization calculation to the automatic control network where the direct digital control system is located, and after the entire air-conditioning system starts to run, wait for the air-conditioning system to run normally for about 2 weeks, and the operating data of the cooling water system within 2 weeks can be saved in the database of the hardware device.

[0056] Then, if Figure 6 As shown, the control method of the present application can use an artificial intelligence (AI) algorithm to read historical operation data that has been stored in a database, and then use these historical operation data to train the preset models of different cooling equipment to obtain multiple preliminary data models of different cooling equipment in the cooling water system. The multiple preliminary data models can include, for example, preliminary data models of cooling units, cooling towers, and cooling water pumps, respectively. Figure 6 The chiller data-driven model, cooling tower data-driven model and water pump data-driven model are shown.

[0057] After obtaining multiple preliminary data models of different cooling equipment, the multiple preliminary data models can be merged into an overall preset data model, so that the preset data model can be used to more accurately predict the operating status and energy loss of the cooling equipment in the air-conditioning cooling water system of the system in the future.

[0058] In this way, the control method of the present application can use the historical operating data within a preset time period to perform model training to obtain multiple preliminary data models corresponding to different cooling equipment, and merge the multiple preliminary data models to obtain a preset data model. Through the preset data model, the operating status and energy loss of each cooling equipment can be accurately predicted, so that the moment when the energy loss of the cooling water system is the smallest can be accurately determined according to the operating status and energy loss of each cooling equipment.

[0059] See also Figure 7 In some embodiments, after step 041, the control method includes:

[0060] 0421: Preprocess historical operation data;

[0061] 0423: Save the preprocessed historical operation data to the preset database.

[0062] Please combine Figure 2 , step 0421 and step 0423 can be implemented by the intelligent control device 12. That is, the intelligent control device 12 is used to pre-process the historical operation data; and save the pre-processed historical operation data into a preset database.

[0063] Specifically, Figure 6 As shown, after obtaining the historical operation data of the cooling water system within a preset time period, the historical operation data can be preprocessed first, and the preprocessed historical operation data can be saved in a preset database to provide a data basis for subsequent model training.

[0064] In this way, the control method of the present application first preprocesses the historical operation data to obtain the preprocessed historical operation data, and saves the preprocessed historical operation data to a preset database, so that a preliminary data model corresponding to each type of cooling equipment can be obtained for accurately predicting its operating status and energy loss based on the preprocessed historical operation data.

[0065] See also Figure 8 In some embodiments, step 0421 includes:

[0066] 04211: Categorize historical operating data by type of cooling equipment; and

[0067] 04212: Eliminate outliers and fill in missing values ​​for various historical operating data.

[0068] Please combine Figure 2 , step 04211 and step 04212 can be implemented by the intelligent control device 12. That is, the intelligent control device 12 is used to classify and sort the historical operation data according to the type of cooling equipment; and to exclude abnormal values ​​and fill missing values ​​for various types of historical operation data.

[0069] Specifically, the specific process of preprocessing can be a process of first classifying and arranging the historical operation data according to the type of cooling equipment. For example, three types of cooling equipment, namely cooling units, cooling towers, and cooling water pumps, can be classified and arranged to obtain three types of historical operation data.

[0070] Then, outliers are eliminated and missing values ​​are filled in for various historical operating data.

[0071] In this way, the model training method of the present application can be applied to various types of historical operation data.

[0072] See also Fig. 9In some embodiments, step 05 includes:

[0073] 051: Input the current operation data into the preset data model, and predict the energy loss of the cooling water system in the future preset time period according to the preset feedback set value and the preset optimization algorithm;

[0074] 052: Determine whether the energy loss is less than or equal to a preset minimum value;

[0075] 053: If the energy loss amount is less than or equal to the minimum value, the preset feedback setting value is determined to be the optimal feedback setting value.

[0076] Please combine Figure 2 , step 051, step 053 and step 054 can be implemented by the intelligent control device 12. That is, the intelligent control device 12 is used to: input the current operation data into the preset data model, predict the energy loss of the cooling water system in the future preset time period according to the preset feedback setting value and the preset optimization algorithm; determine whether the energy loss reaches the preset minimum value; if the energy loss is less than or equal to the minimum value, determine the preset feedback setting value as the optimal feedback setting value.

[0077] Specifically, the future preset time period may refer to the next 1 week, the next 2 weeks, or the next 3 weeks, and there is no limitation here.

[0078] The preset optimization algorithm may be a heuristic optimization algorithm or other algorithms, which are not limited here. The heuristic optimization algorithm is a type of artificial intelligence algorithm that can solve a complex function within the range of its independent variable and the value of the independent variable when the dependent variable reaches the maximum value.

[0079] The preset minimum value may be a preset default energy loss value, for example, 10% of the energy loss amount.

[0080] In this way, the control method of the present application inputs the current operating data of the cooling water system into the established preset data model, and can predict the energy loss of the cooling water system in a future preset time period based on the preset feedback setting value and the preset optimization algorithm. If the energy loss is less than or equal to the minimum value, the current preset feedback setting value can be directly determined as the optimal feedback setting value.

[0081] See also Fig.10 In some embodiments, the control method comprises:

[0082] 054: If the energy loss is greater than the preset minimum value, reset the preset feedback set value based on the energy loss;

[0083] 055: predicting the new energy loss of the cooling water system in a future preset time period based on the current operating data, the reset preset feedback set value and the preset optimization algorithm;

[0084] 056: Determine whether the new energy consumption is less than or equal to the preset minimum value;

[0085] 057: If the new energy loss amount is less than or equal to the preset minimum value, the reset preset feedback setting value is determined to be the optimal feedback setting value.

[0086] Please combine Figure 2 , step 054, step 055, step 056 and step 057 can be implemented by the intelligent control device 12. That is, the intelligent control device 12 is used to: if the energy loss is greater than the preset minimum value, reset the preset feedback setting value based on the energy loss; predict the new energy loss of the cooling water system in the future preset time period according to the current operation data, the reset preset feedback setting value and the preset optimization algorithm; determine whether the new energy loss is less than or equal to the preset minimum value; if the new energy loss is less than or equal to the preset minimum value, determine that the reset preset feedback setting value is the optimal feedback setting value.

[0087] Specifically, by inputting the current operating data of the cooling water system into the established preset data model, the energy loss of the cooling water system in the future preset time period can be predicted based on the preset feedback setting value and the preset optimization algorithm. If the energy loss does not reach the preset minimum value, it means that the current preset feedback setting value is not the optimal feedback setting value. Therefore, it is necessary to reset the preset feedback setting value based on the current preset feedback setting value and the value of the energy loss predicted by the preset optimization algorithm. Then, the new energy loss is predicted based on the reset preset feedback setting value, and it is judged again whether the new energy loss reaches the preset minimum value, until the new energy loss reaches the preset minimum value.

[0088] For example, if the preset minimum value of energy loss is A, the temperature difference between the cooling main water supply temperature and the return water temperature in the current operation data is a1 as the preset feedback setting value for feedback control of the cooling water pump frequency, and the cooling main water return temperature b1 is used as the preset feedback setting value for feedback control of the cooling tower fan frequency. The current operation data of the cooling water system is input into the established preset data model, and the energy loss of the cooling water system in the future preset time period can be predicted to be B according to the preset feedback setting value and the preset optimization algorithm, and B is greater than A. Therefore, at this time, the temperature difference between the cooling main water supply temperature and the return water temperature can be reset to a2 as the preset feedback setting value for feedback control of the cooling water pump frequency, and the cooling main water return temperature b2 is used as the preset feedback setting value for feedback control of the cooling tower fan frequency. It is judged again whether the new energy loss reaches the preset minimum value A until the new energy loss reaches the preset minimum value.

[0089] In this way, when the energy loss amount predicted by the preset data model is greater than the preset minimum value, the control method of the present application resets the preset feedback setting value based on the energy loss amount until the predicted energy loss amount is less than or equal to the preset minimum value. In this way, the reset preset feedback setting value can be determined to be the optimal feedback setting value. The solution is simple and feasible.

[0090] See also Figure 2 and Fig.11 , the present application also provides a cooling water system for an air conditioner, applying the control method described in any one of the embodiments. The cooling water system includes a digital control device 11 and an intelligent control device 12 connected to the digital control device 11. The digital control device 11 is used to collect current operating data of the cooling water system. The intelligent control device 12 is used to determine a preset feedback set value for feedback controlling the cooling equipment frequency of the cooling water system according to the current operating data; and to calculate and determine the energy loss of the cooling water system in a preset time period in the future through a preset optimization algorithm of a preset data model according to the current operating data, and take the preset feedback set value with the minimum energy loss as the optimal feedback set value. The digital control device 11 is used to feedback adjust the cooling equipment frequency of the cooling water system according to the optimal feedback set value.

[0091] That is, the cooling water system of the air conditioner of the present application is equipped with a hardware module equipped with an AI algorithm on the basis of the direct digital control (DDC) method of the digital control device 11, that is, the cooling water system of the air conditioner of the present application is equipped with an intelligent control device 12 dedicated to the AI ​​algorithm for optimizing the cooling water system of the air conditioner.

[0092] For details, see Figure 6 and Fig.11 The intelligent control device 12 of the present application equipped with an AI algorithm dedicated to optimizing the air conditioning cooling water system may specifically include:

[0093] (1) Data processing module: used to communicate with the automatic control network, collect the real-time operation data of the system from the DDC controller of the digital control device 11, and save it in the database within the device; it is also responsible for sending the results calculated by the AI ​​algorithm (the set values ​​of "the supply and return water temperature difference of the cooling main pipe" and "the return water temperature of the cooling main pipe") to the DDC controller of the digital control device 11. Figure 6 The "DDC" in it is the abbreviation of DDC controller.

[0094] (2) AI model training module: Using the system historical operation data stored in the database, a mathematical model is trained to accurately predict the operating status and energy consumption of each major equipment (cooling unit, cooling tower, water pump, etc.). Figure 6 The "cold machine" in the text is the abbreviation of cooling unit.

[0095] (3) Energy-saving optimization calculation module: Based on the trained equipment model, the system's current operating data is used as input to predict its operating status and energy consumption in the future, thereby predicting and determining the set values ​​of the "cooling main supply and return water temperature difference" and "cooling main return water temperature" when the energy consumption of the entire system is lowest.

[0096] In this way, the cooling water system of the air conditioner in the present application applies the above-mentioned control method. On the basis of using the direct digital control method to control the cooling equipment frequency of the cooling water system to ensure the safe and stable operation of the cooling water system of the air conditioner, the optimal feedback setting value when the energy loss of the cooling water system is minimized is calculated based on the preset optimization algorithm according to the current operating data and the preset feedback setting value. The cooling equipment frequency of the cooling water system is feedback-adjusted according to the optimal feedback setting value determined in real time, thereby realizing the control optimization of the cooling water system of the air conditioner and achieving the effect of energy saving and consumption reduction of the cooling water system of the air conditioner and improving the energy efficiency of the air conditioner.

[0097] In some embodiments, when a data transmission link between the intelligent control device 12 and the digital control device 11 fails, the preset feedback setting value in the digital control device 11 is reset to a default value. The default value may be a preset feedback setting value for first determining the frequency of the cooling device of the feedback control cooling water system according to the current operation data, or may be a custom set feedback setting value, which is not limited here.

[0098] Please combine Figure 3 That is, when the data transmission link of the AI ​​control module fails, the control program in the DDC controller can reset the value of the PID set point to the default value to ensure the smooth operation of the system.

[0099] See also Fig.11 In some embodiments, the intelligent control device 12 can be connected to the digital control device 11 through a switch. The intelligent control device 12 can be connected to the control network of the cooling water system of the air conditioner through a network cable and a switch, and can automatically read all operating data of the current cooling water system.

[0100] Specifically, Fig.11As shown, the structure of the cooling water system of the air conditioner of the present application follows the current common engineering practice, and uses a DDC controller to control and collect data for each device in the system. In detail, in the cooling water pump and cooling tower fan part, the PID control method is used to feedback and adjust the frequency of the cooling water pump and the cooling tower fan. Among them, the PID set value is selected as two values: "cooling main supply and return water temperature difference" and "cooling main return water temperature". "Cooling main supply and return water temperature difference" is used to feedback and adjust the cooling water pump frequency. "Cooling main return water temperature" is used to feedback and adjust the cooling tower fan frequency.

[0101] Please combine Figure 3 That is to say, the cooling water system of the air conditioner of the present application uses a DDC controller to write a PID control program, and according to the preset cooling water temperature difference and cooling tower outlet water temperature, the cooling water pump and cooling tower fan frequency are respectively feedback-adjusted, and the AI ​​control module is connected to the automatic control network of the central air-conditioning system to automatically collect the operating data of the equipment. The built-in AI algorithm identifies the operating status of the system and calculates: the value of the above-mentioned PID set point when the total energy efficiency is the highest.

[0102] In this way, the hardware device equipped with the AI ​​algorithm of the present application is connected to the same automatic control network with the DDC system through a switch. After the hardware device enters the normal operating state, the PID setting values ​​controlled by the above two PID control methods in the DDC system can be modified in real time based on the optimization calculation results of the AI ​​algorithm, thereby realizing the control optimization of the air-conditioning cooling water system.

[0103] That is, the intelligent control device 12 and the digital control device 11 of the present application can be connected in the same automatic control network through a switch. After the intelligent control device 12 enters the normal operating state, the PID set values ​​controlled by the above two PID control methods in the DDC system of the digital control device 11 can be modified in real time based on the optimization calculation results of the AI ​​algorithm, thereby realizing the control optimization of the air-conditioning cooling water system.

[0104] For example, Figure 3 As shown in the figure, the AI ​​control module can be set to send the optimal PID setting value to the DDC controller through the automatic control network every 10 minutes, and the DDC controller will then complete the feedback adjustment of the frequency to achieve overall energy efficiency improvement on the cooling side of the central air-conditioning system.

[0105] The present application also provides an air conditioner. The air conditioner includes a cooling water system as described in any one of the above embodiments. The specific structure of the cooling water system is as described above and will not be repeated here.

[0106] In this way, the cooling water system in the air conditioner of the present application applies the control method described above. On the basis of using a direct digital control method to control the cooling equipment frequency of the cooling water system to ensure the safe and stable operation of the cooling water system of the air conditioner, the optimal feedback setting value when the energy loss of the cooling water system is minimized is calculated based on the preset optimization algorithm according to the current operating data and the preset feedback setting value. The cooling equipment frequency of the cooling water system is feedback-adjusted according to the optimal feedback setting value determined in real time, thereby realizing control optimization of the cooling water system of the air conditioner and achieving the effect of energy saving and consumption reduction of the cooling water system of the air conditioner and improving the energy efficiency of the air conditioner.

[0107] The present application also provides a non-volatile computer-readable storage medium containing a computer program. When the computer program is executed by one or more processors, the control method described in any one of the above embodiments is implemented.

[0108] In this way, the computer-readable storage medium of the present application applies the control method described above to control the cooling equipment frequency of the cooling water system using a direct digital control method to ensure the safe and stable operation of the cooling water system of the air conditioner. According to the current operating data and the preset feedback setting value, the optimal feedback setting value when the energy loss of the cooling water system is minimized is calculated based on the preset optimization algorithm. The cooling equipment frequency of the cooling water system is feedback-adjusted according to the optimal feedback setting value determined in real time, thereby realizing the control optimization of the cooling water system of the air conditioner, achieving the effect of energy saving and consumption reduction of the cooling water system of the air conditioner and improving the energy efficiency of the air conditioner.

[0109] The present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by one or more processors, the control method described in any one of the above embodiments is implemented.

[0110] In this way, the computer program product of the present application applies the control method described above to control the cooling equipment frequency of the cooling water system using a direct digital control method to ensure the safe and stable operation of the cooling water system of the air conditioner. According to the current operating data and the preset feedback setting value, the optimal feedback setting value when the energy loss of the cooling water system is minimized is calculated based on the preset optimization algorithm. The cooling equipment frequency of the cooling water system is feedback-adjusted according to the optimal feedback setting value determined in real time, thereby realizing control optimization of the cooling water system of the air conditioner and achieving the effect of energy saving and consumption reduction of the cooling water system of the air conditioner and improving the energy efficiency of the air conditioner.

[0111] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A cooling water system control method, characterized in that: The cooling water system control method comprises: Collecting current operating data of the cooling water system; Determining a preset feedback set value for feedback controlling a cooling device frequency of the cooling water system according to the current operating data; The energy loss of the cooling water system in a future preset time period is determined by calculating the preset optimization algorithm of the preset data model according to the current operating data and the preset feedback setting value, and the preset feedback setting value when the energy loss is the minimum is taken as the optimal feedback setting value; Feedback adjustment is performed on the cooling equipment frequency of the cooling water system according to the optimal feedback setting value.

2. The control method according to claim 1, characterized in that: Before calculating and determining the optimal feedback set value when the energy loss of the cooling water system in a future preset time period is minimized by a preset optimization algorithm of a preset data model according to the current operating data and the preset feedback set value, the cooling water system control method includes: Acquiring historical operation data of the cooling water system within a preset time period; Performing model training on preset models of different cooling equipment in the cooling water system according to historical operation data to obtain multiple preliminary data models corresponding to different cooling equipment; The plurality of preliminary data models are combined to obtain the preset data model.

3. The control method according to claim 2, characterized in that: After acquiring the historical operation data of the cooling water system within a preset time period, the cooling water system control method includes: Preprocessing the historical operation data; The preprocessed historical operation data is saved in the preset database.

4. The control method according to claim 3, characterized in that: The preprocessing of the historical operation data comprises: Classifying and arranging the historical operation data according to the type of the cooling equipment; and Outliers are eliminated and missing values ​​are filled in for each type of historical operating data.

5. The control method according to claim 1, characterized in that: The method of calculating and determining the energy loss of the cooling water system in a future preset time period by a preset optimization algorithm of a preset data model according to the current operating data and the preset feedback setting value, and taking the preset feedback setting value with the minimum energy loss as the optimal feedback setting value comprises: Input the current operation data into the preset data model, and predict the energy loss of the cooling water system in a future preset time period according to the preset feedback set value and the preset optimization algorithm; Determining whether the energy loss amount is less than or equal to a preset minimum value; If the energy loss amount is less than or equal to the preset minimum value, the preset feedback setting value is determined to be the optimal feedback setting value.

6. The control method according to claim 5, characterized in that: The cooling water system control method comprises: If the energy loss amount is greater than a preset minimum value, resetting the preset feedback setting value based on the energy loss amount; Predicting the new energy loss of the cooling water system in a future preset time period according to the current operating data, the reset preset feedback set value and the preset optimization algorithm; Determining whether the new energy loss amount is less than or equal to a preset minimum value; If the new energy loss amount is less than or equal to the minimum value, the reset preset feedback setting value is determined to be the optimal feedback setting value.

7. A cooling water system of an air conditioner, using the cooling water system control method according to any one of claims 1 to 6, characterized in that: The cooling water system includes a digital control device and an intelligent control device connected to the digital control device; The digital control device is used to collect current operating data of the cooling water system; The intelligent control device is used to determine the preset feedback setting value for the frequency of the cooling equipment of the cooling water system for feedback control according to the current operation data; and to calculate and determine the energy loss of the cooling water system in a future preset time period according to the current operation data through a preset optimization algorithm of a preset data model, and take the preset feedback setting value when the energy loss is the minimum as the optimal feedback setting value; The digital control device is used to perform feedback regulation on the cooling equipment frequency of the cooling water system according to the optimal feedback setting value.

8. The cooling water system according to claim 7, characterized in that: When a data transmission link between the intelligent control device and the digital control device fails, the preset feedback setting value in the digital control device is reset to a default value.

9. The cooling water system according to claim 7, characterized in that: The intelligent control device and the digital control device are connected via a switch.

10. A high-efficiency refrigeration room, characterized in that: The high-efficiency refrigeration room comprises the cooling water system according to any one of claims 7 to 9.

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