Method, device, computer equipment and storage medium for controlling flow of freezing pump

Abstract

The present application discloses a method, device, computer equipment, and storage medium for controlling the flow rate of a chilled water pump, belonging to the technical field of refrigeration equipment. The method comprises: obtaining a control parameter of the chilled water pump at a current time point and a first instantaneous flow rate of chilled water at a previous time point; inputting the control parameter and the first instantaneous flow rate of chilled water into a target regression model to predict a second instantaneous flow rate of chilled water of the chilled water pump at the current time point; determining an adjustment strategy for the control parameter based on the second instantaneous flow rate of chilled water and a preset instantaneous flow rate threshold; and adjusting the control parameter according to the adjustment strategy so that the error between the second instantaneous flow rate of chilled water of the chilled water pump at the current time point and the instantaneous flow rate threshold is within a preset error threshold range. In this way, the influence of factors such as analog signals and device sensors on the chilled water pump flow prediction results can be reduced, thereby improving the control accuracy of the chilled water pump flow.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of refrigeration equipment, and in particular to a method, device, computer equipment, and storage medium for controlling the flow of a freezing pump. Background Art

[0002] A chiller pump is a chilled water circulation system commonly used in large-scale refrigeration equipment such as central air conditioners. Currently, big data and AI simulation technologies are often combined to simulate data center HVAC processes. A predictive model for the instantaneous chilled water flow rate in a chiller pump is a key model for simulating data center HVAC processes. It plays a crucial role in calculating cooling capacity to meet load heat dissipation and in cascading data center processes.

[0003] The inventors discovered that most of the equipment data stored in the data center is segmented fixed-frequency data. For example, the frequency of the chilled pump remains at a constant value for a period of time. The chilled pump flow is predicted based on the characteristic curve relationship between the chilled pump frequency and the chilled water flow. However, due to the influence of analog signals and equipment sensors, the chilled pump flow is usually an oscillating value within this time period. Therefore, the existing method only considers the characteristic curve relationship between the chilled pump frequency and the chilled water flow, and it is difficult to predict the oscillation value of the chilled pump flow within this time period, which will affect the control accuracy of the chilled pump flow. Summary of the Invention

[0004] An embodiment of the present application provides a method, device, computer equipment and storage medium for controlling the flow of a freezing pump. By considering the control parameters of the freezing pump at the current time point and the first instantaneous flow of chilled water at the previous time point, the second instantaneous flow of chilled water of the cooling pump at the current time point is predicted, so as to reduce the influence of factors such as analog signals and equipment sensors on the prediction results of the freezing pump flow, thereby improving the control accuracy of the freezing pump flow.

[0005] In order to solve the above technical problems, this application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for controlling the flow rate of a freezing pump, comprising:

[0007] Obtaining the control parameters of the freezing pump at the current time point and the first instantaneous flow rate of the chilled water at the previous time point;

[0008] Inputting the control parameter and the first instantaneous flow rate of chilled water into a target regression model to predict the second instantaneous flow rate of chilled water of the freezing pump at the current time point;

[0009] determining a control parameter adjustment strategy based on the instantaneous flow rate of the second chilled water and a preset instantaneous flow rate threshold;

[0010] The control parameter is adjusted according to the adjustment strategy so that the error between the second instantaneous flow rate of the chilled water of the freezing pump and the instantaneous flow rate threshold at the current moment is within a preset error threshold range.

[0011] In a possible implementation manner, before obtaining the control parameter of the freezing pump at the current time point and the first instantaneous flow rate of chilled water at the previous time point, the method further includes:

[0012] Acquiring historical data of the refrigeration pump within a preset time period, the historical data including instantaneous flow rates and control parameters of chilled water at multiple time points;

[0013] Inputting the historical data into a plurality of pre-established regression models, and performing parameter training on the plurality of regression models;

[0014] A target regression model is selected from a plurality of regression models according to an evaluation index of the regression model, and the target regression model is used to predict the instantaneous flow rate of the second chilled water of the freezing pump at a current time point.

[0015] Furthermore, after obtaining the historical data of the refrigeration pump within a preset time period, the historical data is input into a plurality of pre-established regression models, and before parameter training of the plurality of regression models is performed, the method further includes:

[0016] The historical data is preprocessed to remove abnormal data and data when the freezing pump is not turned on.

[0017] Furthermore, the multiple regression models include one or more of a long short-term memory network, a decision tree, an X-GBoost, a ridge regression model, and a Lasso regression model.

[0018] Furthermore, the evaluation indicators of the regression model include mean absolute error and goodness of fit; and selecting a target regression model from multiple regression models based on the evaluation indicators of the regression model includes:

[0019] The regression model with the smallest mean absolute error and a goodness of fit greater than a preset goodness of fit threshold among multiple regression models is selected as the target regression model.

[0020] Furthermore, before obtaining the control parameter of the freezing pump at the current time point and the first instantaneous flow rate of the chilled water at the previous time point, the method further includes:

[0021] Acquiring a plurality of candidate control parameters of the refrigeration pump;

[0022] For each candidate control parameter, determining the correlation coefficient between the candidate control parameter and the instantaneous flow rate of chilled water;

[0023] A candidate control parameter having a correlation coefficient greater than a preset coefficient threshold is selected as the control parameter of the freezing pump at the current moment.

[0024] In a second aspect, an embodiment of the present application further provides a device for controlling the flow rate of a freezing pump, comprising:

[0025] A first acquisition module is used to obtain the control parameter of the freezing pump at a current time point and the first instantaneous flow rate of the chilled water at a previous time point;

[0026] a flow prediction module, configured to input the control parameter and the first instantaneous flow of chilled water into a target regression model, and predict the second instantaneous flow of chilled water of the freezing pump at a current time point;

[0027] a strategy determination module, configured to determine a regulation strategy for the control parameter according to the instantaneous flow rate of the second chilled water and a preset instantaneous flow rate threshold;

[0028] The parameter adjustment module is used to adjust the control parameter according to the adjustment strategy so that the error between the second instantaneous flow rate of the chilled water of the freezing pump and the instantaneous flow rate threshold at the current moment is within a preset error threshold range.

[0029] In a possible implementation, the method further includes:

[0030] A second acquisition module is used to acquire historical data of the freezing pump within a preset time period, wherein the historical data includes instantaneous flow rates and control parameters of the chilled water at multiple time points;

[0031] A model training module is used to input the historical data into a plurality of pre-established regression models and perform parameter training on the plurality of regression models;

[0032] The model selection module is used to select a target regression model from multiple regression models according to the evaluation index of the regression model, and the target regression model is used to predict the instantaneous flow rate of the second chilled water of the freezing pump at the current time point.

[0033] In a third aspect, an embodiment of the present application further provides a computer device, comprising a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the above-mentioned method for controlling the flow of a freezing pump.

[0034] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the above-mentioned method for controlling the flow of a freezing pump.

[0035] The technical solution provided by this application may have the following beneficial effects:

[0036] The embodiment of the present application obtains the control parameter of the chilled water pump at the current time point and the first instantaneous flow rate of chilled water at the previous time point; inputs the control parameter and the first instantaneous flow rate of chilled water into a target regression model to predict the second instantaneous flow rate of chilled water of the chilled water pump at the current time point; determines an adjustment strategy for the control parameter based on the second instantaneous flow rate of chilled water and a preset instantaneous flow rate threshold; and adjusts the control parameter according to the adjustment strategy so that the error between the second instantaneous flow rate of chilled water of the chilled water pump at the current time point and the instantaneous flow rate threshold is within the preset error threshold range. In this way, by considering the control parameter of the chilled water pump at the current time point and the first instantaneous flow rate of chilled water at the previous time point to predict the second instantaneous flow rate of chilled water of the cooling pump at the current time point, the influence of factors such as analog signals and device sensors on the chilled water pump flow prediction result can be reduced, thereby improving the control accuracy of the chilled water pump flow.

[0037] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] Figure 1 A schematic flow chart of a method for controlling the flow of a freezing pump provided in an embodiment of the present application is shown;

[0040] Figure 2 A flow chart showing another method for controlling the flow of a freezing pump provided in an embodiment of the present application is shown;

[0041] Figure 3 A line graph showing the predicted value and the actual value of the instantaneous flow rate of chilled water in the existing method;

[0042] Figure 4 A line graph showing the predicted value and the actual value of the instantaneous flow rate of chilled water in the method provided in the embodiment of the present application;

[0043] Figure 5 A schematic structural diagram of a freezing pump flow control device provided in an embodiment of the present application is shown;

[0044] Figure 6 A schematic structural diagram of another freezing pump flow control device provided in an embodiment of the present application is shown;

[0045] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0046] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0047] Currently, most chiller pump data stored in data centers is segmented and fixed-frequency data. For example, if the chiller pump frequency remains constant over a period of time, the chiller pump flow rate is predicted based on the characteristic curve between chiller pump frequency and chilled water flow rate, and the chiller pump flow rate is then controlled by adjusting the chiller pump frequency. However, due to the influence of analog signals and device sensors, the chiller pump flow rate often fluctuates at a certain frequency. Existing methods have difficulty predicting this oscillation, resulting in low chiller pump control accuracy.

[0048] Based on this, an embodiment of the present application provides a method for controlling the flow of a freezing pump. The method predicts the second instantaneous flow of chilled water of the cooling pump at the current time point by considering the control parameters of the freezing pump at the current time point and the first instantaneous flow of chilled water at the previous time point, so as to reduce the influence of factors such as analog signals and equipment sensors on the prediction results of the freezing pump flow, thereby helping to improve the control accuracy of the freezing pump flow.

[0049] Figure 1 The flowchart of a method for controlling the flow of a freezing pump provided by an embodiment of the present application is shown. The method can be executed by an electronic device, such as a terminal device or a server device. In other words, the method can be executed by software or hardware installed on the terminal device or the server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster. As shown in the figure, the method 100 may include the following steps:

[0050] S110: Obtaining control parameters of the chiller pump at the current time point and the first chilled water instantaneous flow rate at the previous time point.

[0051] Typically, the control parameters of a cooling pump correspond to the instantaneous chilled water flow rate. However, considering the influence of analog signals and device sensors, the instantaneous chilled water flow rate oscillates under the current control parameters. To more accurately predict the instantaneous chilled water flow rate of a cooling pump in a multi-segmented fixed-frequency data scenario, this embodiment of the present application obtains the control parameters of the cooling pump at the current time point t and the first instantaneous chilled water flow rate at the previous time point t-1 to predict the second instantaneous chilled water flow rate of the cooling pump at the current time point t. Here, the control parameters include the cooling pump frequency, head, etc.

[0052] S120: Inputting the control parameter and the first instantaneous flow rate of chilled water into a target regression model to predict the second instantaneous flow rate of chilled water of the freezing pump at the current time point.

[0053] In a specific implementation, the control parameter obtained in step S110 and the first instantaneous chilled water flow rate are input into a target regression model to predict the second instantaneous chilled water flow rate of the chiller pump at the current time point t. The target regression model is typically open source and includes models such as long short-term memory networks, decision trees, X-GBoost, ridge regression models, and lasso regression models.

[0054] In this way, by considering the control parameters of the freezing pump at the current time point and the instantaneous flow rate of the first chilled water at the previous time point, the instantaneous flow rate of the second chilled water of the cooling pump at the current time point is predicted. During the prediction process, the influence of factors such as analog signals and equipment sensors on the freezing pump flow prediction results can be reduced, thereby improving the prediction accuracy of the second chilled water instantaneous flow rate.

[0055] S130: Determine an adjustment strategy for the control parameter according to the second chilled water instantaneous flow rate and a preset instantaneous flow rate threshold.

[0056] In a specific implementation, the adjustment strategy of the control parameter, such as increasing / decreasing the control parameter, can be determined by comparing the second chilled water instantaneous flow rate with a preset instantaneous flow rate threshold.

[0057] S140: Adjust the control parameter according to the adjustment strategy so that the error between the second instantaneous flow rate of the chilled water of the freezing pump at the current moment and the instantaneous flow rate threshold is within a preset error threshold range.

[0058] In specific implementation, by adjusting the control parameters according to the regulation strategy, the instantaneous flow rate of the second chilled water of the cooling pump can be quickly controlled to reach the desired instantaneous flow rate threshold. Moreover, since the influence of factors such as analog signals and equipment sensors on the prediction results of the freezing pump flow rate is taken into account during the prediction process, the control accuracy of the freezing pump flow rate is higher.

[0059] Compared with the existing methods, the control method of the freezing pump flow provided in the embodiment of the present application predicts the second instantaneous chilled water flow of the cooling pump at the current time point by considering the control parameters of the freezing pump at the current time point and the first instantaneous chilled water flow at the previous time point. This can reduce the influence of factors such as analog signals and equipment sensors on the freezing pump flow prediction results, thereby improving the control accuracy of the freezing pump flow.

[0060] Figure 2 FIG. 1 is a flow chart showing another method for controlling the flow of a freezing pump provided in an embodiment of the present application. Figure 2As shown, the embodiment of the present application also provides a method for controlling the flow of a freezing pump. The method 200 includes the following steps:

[0061] S210: Acquire historical data of the refrigeration pump within a preset time period, wherein the historical data includes instantaneous flow rates and control parameters of chilled water at multiple time points.

[0062] In a specific implementation, historical data of the refrigeration pump in a preset time period, such as the last month, week, or day, is obtained. The historical data includes the instantaneous flow rate and control parameters of the chilled water at multiple time points. The instantaneous flow rate and control parameters of the chilled water at each time point are one-to-one corresponding.

[0063] S220: Inputting the historical data into a plurality of pre-established regression models, and performing parameter training on the plurality of regression models.

[0064] In a specific implementation, historical data is input into multiple pre-established regression models for model training. Specifically, the control parameters of the chiller pump at time t0 and the instantaneous chilled water flow rate of the chiller pump at time t0-1 can be used as inputs to the regression model to predict the instantaneous chilled water flow rate of the chiller pump at time t0. A loss function for the regression model is constructed based on the predicted instantaneous chilled water flow rate at t0 and the actual instantaneous chilled water flow rate. By continuously adjusting the training parameters of the regression model, the loss function converges to a preset threshold range, thereby obtaining multiple trained regression models.

[0065] Here, the multiple regression models include but are not limited to one or more of long short-term memory network, decision tree, X-GBoost, ridge regression model, and Lasso regression model.

[0066] As an optional implementation, after obtaining the historical data of the freezing pump within a preset time period, the historical data is input into multiple pre-established regression models. Before parameter training of the multiple regression models, it also includes: preprocessing the historical data to eliminate data outliers and data when the freezing pump is not turned on.

[0067] In this way, the reliability of the data can be guaranteed, which is conducive to improving the training speed of the regression model and the accuracy of the prediction results.

[0068] S230: Selecting a target regression model from a plurality of regression models according to an evaluation index of the regression model, wherein the target regression model is used to predict the instantaneous flow rate of the second chilled water of the freezing pump at a current time point.

[0069] In the specific implementation, according to the evaluation index of the regression model, the optimal regression model in the training results is selected and saved, and the last saved regression model is used to perform real-time prediction of the instantaneous traffic model.

[0070] Optionally, the evaluation indicators of the regression model include mean absolute error and goodness of fit; selecting a target regression model from multiple regression models based on the evaluation indicators of the regression model includes: selecting a regression model with the smallest mean absolute error and a goodness of fit greater than a preset goodness of fit threshold among the multiple regression models as the target regression model.

[0071] Among them, the mean absolute error (MAE) is a commonly used regression loss function. It is the sum of the absolute values of the differences between the true value and the predicted value. It is used to characterize the average error margin of the predicted value. The smaller the value, the better. Goodness of fit R 2 It can approximately represent the amount of useful information learned by the regression model. The calculation formula is as follows:

[0072]

[0073] Among them, SSR represents the regression sum of squares, SST represents the total deviation sum of squares, y i Indicates the actual instantaneous flow rate of chilled water, Indicates the average value of the actual instantaneous flow rate of chilled water, Represents the predicted instantaneous chilled water flow rate. A larger value is better. In specific applications, the preset goodness-of-fit threshold can be set to 0.9.

[0074] Figure 3 and Figure 4 The line graphs respectively show the predicted value and the actual value of the instantaneous flow rate of chilled water in the existing method and the method provided in the embodiment of the present application, as shown in FIG. Figure 3 As shown in FIG, the existing method only considers the characteristic curve relationship between the freezing pump frequency and the chilled water flow rate, and the predicted value of the instantaneous chilled water flow rate is a fixed value, and the oscillation value of the freezing pump flow rate cannot be predicted. Figure 4 As shown, the method provided in the embodiment of the present application can predict the oscillation value of the freezing pump flow, and the error between the predicted value and the true value is smaller and the accuracy is higher, which is beneficial to improving the control accuracy of the freezing pump flow.

[0075] Here, it should be noted that the real value is the instantaneous chilled water flow of the chilled water pump collected by the sensor.

[0076] S240: Obtaining control parameters of the chiller pump at the current time point and the first chilled water instantaneous flow rate at the previous time point;

[0077] S250: Inputting the control parameter and the first instantaneous flow rate of chilled water into a target regression model to predict the second instantaneous flow rate of chilled water of the freezing pump at the current time point;

[0078] S260: Determining a control strategy for the control parameter according to the second chilled water instantaneous flow rate and a preset instantaneous flow rate threshold;

[0079] S270: Adjust the control parameter according to the adjustment strategy so that the error between the second instantaneous flow rate of the chilled water of the freezing pump at the current moment and the instantaneous flow rate threshold is within a preset error threshold range.

[0080] The description of steps S240 to S270 may refer to the description of steps S110 to S140 , and the same effects can be achieved, so they will not be repeated here.

[0081] As an optional implementation, before obtaining the control parameters of the refrigeration pump at the current time point and the first instantaneous flow rate of chilled water at the previous time point, it also includes: obtaining multiple candidate control parameters of the refrigeration pump; for each candidate control parameter, determining the correlation coefficient between the candidate control parameter and the instantaneous flow rate of chilled water; selecting the candidate control parameter with a correlation coefficient greater than a preset coefficient threshold as the control parameter of the refrigeration pump at the current moment.

[0082] In this way, a control parameter having a strong correlation with the instantaneous flow rate of the refrigeration pump can be obtained, thereby further improving the control accuracy of the flow rate of the refrigeration pump.

[0083] Figure 5 FIG. 5 is a schematic diagram showing a structure of a freezing pump flow control device provided in an embodiment of the present application. The device 500 includes:

[0084] A first acquisition module 510 is used to acquire the control parameter of the freezing pump at the current time point and the first instantaneous flow rate of the chilled water at the previous time point;

[0085] A flow prediction module 520 is configured to input the control parameter and the first instantaneous flow of chilled water into a target regression model to predict the second instantaneous flow of chilled water of the freezing pump at the current time point;

[0086] a strategy determination module 530, configured to determine a regulation strategy for the control parameter according to the instantaneous flow rate of the second chilled water and a preset instantaneous flow rate threshold;

[0087] The parameter adjustment module 540 is configured to adjust the control parameter according to the adjustment strategy so that the error between the second instantaneous flow rate of the chilled water of the freezing pump and the instantaneous flow rate threshold at the current moment is within a preset error threshold range.

[0088] The control device for the flow of the freezing pump provided in the embodiment of the present application can be executed Figure 1 The method described above is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail here.

[0089] Figure 6 FIG. 5 is a schematic structural diagram of another freezing pump flow control device provided in an embodiment of the present application. The device 500 includes:

[0090] A second acquisition module 550 is configured to acquire historical data of the refrigeration pump within a preset time period, wherein the historical data includes instantaneous flow rates and control parameters of the chilled water at multiple time points;

[0091] A model training module 560 is used to input the historical data into a plurality of pre-established regression models and perform parameter training on the plurality of regression models;

[0092] A model selection module 570 is configured to select a target regression model from a plurality of regression models based on an evaluation index of the regression model, wherein the target regression model is used to predict the instantaneous flow rate of the second chilled water of the freezing pump at a current time point;

[0093] A first acquisition module 510 is used to acquire the control parameter of the freezing pump at the current time point and the first instantaneous flow rate of the chilled water at the previous time point;

[0094] A flow prediction module 520 is configured to input the control parameter and the first instantaneous flow of chilled water into a target regression model to predict the second instantaneous flow of chilled water of the freezing pump at the current time point;

[0095] a strategy determination module 530, configured to determine a regulation strategy for the control parameter according to the instantaneous flow rate of the second chilled water and a preset instantaneous flow rate threshold;

[0096] The parameter adjustment module 540 is configured to adjust the control parameter according to the adjustment strategy so that the error between the second instantaneous flow rate of the chilled water of the freezing pump and the instantaneous flow rate threshold at the current moment is within a preset error threshold range.

[0097] The device 500 provided in the embodiment of the present application can execute the various methods described in the above method embodiments and realize the functions and beneficial effects of the various methods described in the above method embodiments, which will not be repeated here.

[0098] Figure 7 A schematic diagram of the hardware structure of a computer device that implements the embodiments of the present application is shown. Referring to this diagram, at the hardware level, the computer device includes a processor and, optionally, an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), and may also include non-volatile memory, such as at least one disk drive. Of course, the computer device may also include other hardware required for its services.

[0099] The processor, network interface, and memory can be interconnected via an internal bus, such as an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. These buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the diagram uses only one bidirectional arrow, but this does not imply a single bus or type of bus.

[0100] The memory stores programs. Specifically, the programs may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0101] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, forming a device for locating the target user at the logical level. The processor executes the program stored in the memory and specifically performs the following: Figure 1-2 The methods disclosed in the illustrated embodiments implement the functions and beneficial effects of the various methods described in the foregoing method embodiments, which will not be described in detail here.

[0102] The above application Figure 1-2The methods disclosed in the illustrated embodiments can be implemented in or by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be performed by hardware integrated logic circuits in the processor or by software instructions. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of this application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0103] The electronic device can also execute the methods described in the above method embodiments and realize the functions and beneficial effects of the methods described in the above method embodiments, which will not be described in detail here.

[0104] Of course, in addition to software implementation, the computer device of this application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0105] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, which, when executed by an electronic device including multiple application programs, enables the electronic device to execute Figure 1-2 The methods disclosed in the illustrated embodiments implement the functions and beneficial effects of the various methods described in the foregoing method embodiments, which will not be described in detail here.

[0106] The computer-readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0107] Furthermore, an embodiment of the present application provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions. When the program instructions are executed by a computer, the following process is implemented: Figure 1-2 The methods disclosed in the illustrated embodiments implement the functions and beneficial effects of the various methods described in the foregoing method embodiments, which will not be described in detail here.

[0108] In short, the above description is only a preferred embodiment of the present application and does not limit the scope of protection of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0109] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0110] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0111] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0112] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

Claims

1. A method for controlling the flow rate of a freezing pump, characterized in that: include: Obtaining the control parameters of the freezing pump at the current time point and the first instantaneous flow rate of the chilled water at the previous time point; Inputting the control parameter and the first instantaneous flow rate of chilled water into a target regression model to predict the second instantaneous flow rate of chilled water of the freezing pump at the current time point; determining a control parameter adjustment strategy based on the instantaneous flow rate of the second chilled water and a preset instantaneous flow rate threshold; The control parameter is adjusted according to the adjustment strategy so that the error between the second instantaneous flow rate of the chilled water of the freezing pump and the instantaneous flow rate threshold at the current moment is within a preset error threshold range.

2. The method according to claim 1, characterized in that Before obtaining the control parameter of the freezing pump at the current time point and the first instantaneous flow rate of the chilled water at the previous time point, the method further includes: Acquiring historical data of the refrigeration pump within a preset time period, the historical data including instantaneous flow rates and control parameters of chilled water at multiple time points; Inputting the historical data into a plurality of pre-established regression models, and performing parameter training on the plurality of regression models; A target regression model is selected from a plurality of regression models according to an evaluation index of the regression model, and the target regression model is used to predict the instantaneous flow rate of the second chilled water of the freezing pump at a current time point.

3. The method according to claim 2, characterized in that After acquiring historical data of the freezing pump within a preset time period, the historical data is input into a plurality of pre-established regression models, and before parameter training of the plurality of regression models is performed, the method further includes: The historical data is preprocessed to remove abnormal data and data when the freezing pump is not turned on.

4. The method according to claim 2, characterized in that The multiple regression models include one or more of a long short-term memory network, a decision tree, an X-GBoost, a ridge regression model, and a Lasso regression model.

5. The method according to claim 2, characterized in that The evaluation indicators of the regression model include mean absolute error and goodness of fit; The method of selecting a target regression model from multiple regression models based on the evaluation index of the regression model includes: The regression model with the smallest mean absolute error and a goodness of fit greater than a preset goodness of fit threshold among multiple regression models is selected as the target regression model.

6. The method according to any one of claims 1 to 5, characterized in that Before obtaining the control parameter of the freezing pump at the current time point and the first instantaneous flow rate of the chilled water at the previous time point, the method further includes: Acquiring a plurality of candidate control parameters of the refrigeration pump; For each candidate control parameter, determining the correlation coefficient between the candidate control parameter and the instantaneous flow rate of chilled water; A candidate control parameter having a correlation coefficient greater than a preset coefficient threshold is selected as the control parameter of the freezing pump at the current moment.

7. A freezing pump flow control device, characterized in that: include: A first acquisition module is used to obtain the control parameter of the freezing pump at a current time point and the first instantaneous flow rate of the chilled water at a previous time point; a flow rate prediction module, configured to input the control parameter and the first instantaneous flow rate of chilled water into a target regression model, and predict the second instantaneous flow rate of chilled water of the freezing pump at a current time point; a strategy determination module, configured to determine a regulation strategy for the control parameter according to the instantaneous flow rate of the second chilled water and a preset instantaneous flow rate threshold; The parameter adjustment module is used to adjust the control parameter according to the adjustment strategy so that the error between the second instantaneous flow rate of the chilled water of the freezing pump and the instantaneous flow rate threshold at the current moment is within a preset error threshold range.

8. The device according to claim 7, characterized in that Also includes: A second acquisition module is used to acquire historical data of the freezing pump within a preset time period, wherein the historical data includes instantaneous flow rate and control parameters of the chilled water at multiple time points; A model training module, configured to input the historical data into a plurality of pre-established regression models and perform parameter training on the plurality of regression models; The model selection module is used to select a target regression model from multiple regression models according to the evaluation index of the regression model, and the target regression model is used to predict the instantaneous flow rate of the second chilled water of the freezing pump at the current time point.

9. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method for controlling the flow rate of the freezing pump according to any one of claims 1 to 6 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the method for controlling the flow rate of a freezing pump according to any one of claims 1 to 6 are implemented.

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