Optimization method and device, medium and electronic equipment of intelligent circulating cooling water system

Abstract

The present application provides an optimization method and device, medium and electronic device for an intelligent circulating cooling water system. The method includes: obtaining the real-time working parameters of the circulating cooling water system and preprocessing the real-time working parameters; the real-time working parameters after preprocessing include: the temperature value of the water inlet, the water flow rate and the pressure value of the water inlet; predicting the working parameters of the water outlet based on the temperature value of the water inlet, the water flow rate of the water inlet and the pressure value, and obtaining the predicted data of the water outlet temperature and water flow rate; obtaining the optimization data corresponding to the predicted data of the water outlet temperature and water flow rate, for the optimization of the circulating cooling water system. The present application is a circulating cooling water method that determines the flow rate change trend based on the Internet and machine learning, and uses big data analysis to determine the water temperature change trend, so as to achieve the purpose of optimizing the cooling effect and saving energy.

Description

Technical Field

[0001] The present application relates to the technical field of industrial cooling water treatment, and in particular to an optimization method and device, medium and electronic equipment for an intelligent circulating cooling water system. Background Art

[0002] The industrial circulating cooling water system is an important public engineering device that is indispensable for industrial production enterprises to handle the heat load of process equipment. It has the characteristics of large water consumption and high electricity load. At the same time, the industrial circulating cooling water system also plays a pivotal role in the production of the factory: it takes away the heat load generated during the operation of the production equipment and keeps it within the allowable working temperature. No matter how expensive the production equipment is, if there is no sufficient cooling water supply to cool it down, it cannot be put into production normally.

[0003] Most of the current circulating cooling water systems use fixed flow and temperature control methods, which fail to fully adapt to fluctuations in actual operation, resulting in deficiencies in energy consumption and cooling efficiency. Summary of the invention

[0004] The purpose of this application is to provide an optimization method and device, medium and electronic equipment for an intelligent circulating cooling water system, which is used to solve the technical problem that the circulating cooling water system fails to fully adapt to fluctuations in actual operation, resulting in insufficient energy consumption and cooling efficiency.

[0005] In a first aspect, the present application provides an optimization method for an intelligent circulating cooling water system. The method comprises: obtaining real-time operating parameters of the circulating cooling water system and preprocessing the real-time operating parameters; the preprocessed real-time operating parameters include: a temperature value of a water inlet, a water flow rate and a pressure value of a water inlet; predicting the operating parameters of a water outlet based on the temperature value of the water inlet, the water flow rate and the pressure value of the water inlet, and obtaining predicted data of the water outlet temperature and water flow rate; obtaining optimization data corresponding to the predicted data of the water outlet temperature and water flow rate, for use in optimizing the circulating cooling water system.

[0006] In some embodiments of the first aspect of the present application, the working parameters of the water outlet are predicted based on the temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value, and the outlet temperature and water flow velocity prediction data are obtained, including: performing feature extraction on the pre-processed temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value, respectively, to obtain temperature gradient characteristics, flow velocity change characteristics and fluctuation characteristics; inputting the temperature gradient characteristics, the flow velocity change characteristics and the fluctuation characteristics into a trained cooling water demand prediction model to output the outlet temperature and water flow velocity prediction data.

[0007] In some embodiments of the first aspect of the present application, the cooling water demand prediction model adopts a linear regression model, and training the cooling water demand prediction model includes: obtaining a historical data set of the inlet and outlet of the circulating cooling water system; preprocessing the historical data set and performing feature extraction to obtain a historical feature data set for model training; the historical feature data set includes: historical temperature gradient feature data, historical flow rate change feature data and historical fluctuation feature data; wherein each group of the historical feature data sets includes a historical temperature gradient feature, a historical flow rate change feature and a historical fluctuation feature; and using the historical feature data set to train the cooling water demand prediction model.

[0008] In some embodiments of the first aspect of the present application, preprocessing the real-time operating parameters includes: deleting abnormal data and noise data in the real-time operating parameters to obtain cleaned real-time operating parameters; normalizing the cleaned real-time operating parameters to map the real-time operating parameters of different data levels to the same scale.

[0009] In some embodiments of the first aspect of the present application, the method also includes: before obtaining the optimization data corresponding to the water outlet temperature and water flow rate prediction data, determining whether the water outlet temperature and water flow rate prediction data meet the preset threshold value of the circulating cooling water system; if the water outlet temperature and water flow rate prediction data meet the preset threshold value, the circulating cooling water system is not optimized; otherwise, the optimization data corresponding to the water outlet temperature and water flow rate prediction data is obtained to optimize the circulating cooling water system.

[0010] In some embodiments of the first aspect of the present application, adjusting sludge treatment parameters based on the real-time analysis data to adjust the intensity of sludge treatment includes: determining whether the real-time analysis data is greater than a preset threshold; if the real-time analysis data is greater than the preset threshold, increasing the intensity of sludge treatment; if the real-time analysis data is not greater than the preset threshold, reducing the intensity of sludge treatment.

[0011] In some embodiments of the first aspect of the present application, the method further includes: acquiring the external conditions of the circulating cooling water system in real time; and dynamically adjusting the water flow rate and temperature of the outlet of the circulating cooling water system according to the external climate.

[0012] In a second aspect, the present application provides an optimization device for an intelligent circulating cooling water system, the device comprising: an acquisition module, used to acquire real-time operating parameters of the circulating cooling water system and preprocess the real-time operating parameters; the preprocessed real-time operating parameters include: a temperature value of a water inlet, a water flow rate and a pressure value of a water inlet; a data processing module, used to predict the working parameters of a water outlet based on the temperature value of the water inlet, the water flow rate and the pressure value of the water inlet, and obtain predicted data of the water outlet temperature and water flow rate; an optimization module, used to acquire optimization data corresponding to the predicted data of the water outlet temperature and water flow rate, for use in optimizing the circulating cooling water system.

[0013] In some embodiments of the second aspect of the present application, the device also includes: an adaptive module for acquiring the external conditions of the circulating cooling water system in real time and dynamically adjusting the temperature of the water outlet of the circulating cooling water system according to the external climate.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by an electronic device, the optimization method of the intelligent circulating cooling water system described in any one of the first aspects of the present application is implemented.

[0015] In a fourth aspect, the present application provides an electronic device comprising: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device performs the optimization method of the intelligent circulating cooling water system described in any one of the first aspects of the present application.

[0016] According to the optimization method and device, medium and electronic equipment of the intelligent circulating cooling water system of the present application, the present application uses a circulating cooling water method based on the Internet and machine learning to determine the flow rate change trend, and uses big data analysis to determine the water temperature change trend, so as to achieve the purpose of optimizing the cooling effect and saving energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a schematic diagram of a scene of an electronic device in an embodiment of the present application.

[0018] Figure 2 Shown is a schematic structural diagram of an intelligent circulating cooling water system described in the present application in one embodiment.

[0019] Figure 3 Shown is a schematic flow chart of an optimization method for an intelligent circulating cooling water system described in the present application in one embodiment.

[0020] Figure 4 Shown is a flow chart of obtaining outlet temperature and water flow velocity prediction data in one embodiment of the present application.

[0021] Figure 5 Shown is a flow chart of another optimization method of an intelligent circulating cooling water system described in the present application in one embodiment.

[0022] Figure 6 Shown is a schematic diagram of the structure of an optimization device for the intelligent circulating cooling water system described in the present application in one embodiment.

[0023] Figure 7 Shown is a schematic diagram of the structure of an electronic device described in an embodiment of the present application.

[0024] Component number description

[0025] 11 cell phone 12 Tablet 13 Laptop 200 Intelligent circulating cooling water system 201 Water tank 202 Water Pump 203 heat sink 204 Pressure relief valve 205 Water outlet 600 Optimization device for intelligent circulating cooling water system 610 Get Module 620 Data processing module 630 Optimization Module 640 Adaptive Module 650 Judgment module 71 Processing Unit 72 Memory 721 Random Access Memory 722 Cache memory 723 Storage System 724 Programs / Utilities 725 Program Modules 73 bus 74 Input / Output Interface 75 Network adapters DETAILED DESCRIPTION

[0026] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0027] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0028] Industrial circulating cooling water refers to water used to absorb the heat generated during the operation of equipment and release the heat through heat exchange equipment during the industrial production process. Its main function is to take away the excess heat generated by the equipment during operation to prevent the equipment from overheating, thereby ensuring the normal operation and production efficiency of the equipment. Most commonly used circulating cooling water systems use fixed flow and temperature control methods, which cannot fully adapt to the fluctuations in flow and temperature during actual operation, resulting in insufficient cooling effect and further increasing energy consumption.

[0029] At least for the above problems, the present application provides an optimization method and device, medium and electronic device for an intelligent circulating cooling water system. The method is based on the Internet and machine learning to determine the flow rate change trend, and uses big data analysis to determine the water temperature change trend of the circulating cooling water method, so as to achieve the purpose of optimizing the cooling effect and saving energy. This effectively solves the technical problem that the circulating cooling water system fails to fully adapt to the fluctuations in actual operation, resulting in insufficient energy consumption and cooling efficiency.

[0030] The optimization method of the intelligent circulating cooling water system of the present application can be applied to Figure 1 The electronic device shown. The electronic device described in this application may include a mobile phone 11 with wireless charging function, a tablet computer 12, a laptop computer 13, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc. The embodiment of this application does not impose any restrictions on the specific type of the electronic device.

[0031] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next-generation communication system, etc.

[0032] For example, the electronic device can communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobilecommunication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), BT, GNSS, WLAN, NFC, FM, and / or IR technology.

[0033] The following will describe in detail the principles and implementation methods of the optimization method and device, medium and electronic equipment of the intelligent circulating cooling water system described in the embodiments of the present application in conjunction with the accompanying drawings, so that those skilled in the art can understand the optimization method and device, medium and electronic equipment of the intelligent circulating cooling water system of the present embodiment without creative work.

[0034] To facilitate understanding of the embodiments of the present application, first Figure 2 The intelligent circulating cooling water system of the present application is described in detail. Figure 2 As shown, the intelligent circulating cooling water system 200 includes: a water storage tank 201, a water pump 202, a radiator 203, a pressure relief valve 204, and a water outlet 205.

[0035] The water outlet 205 is connected to the water tank 201, and the cooling water passes through the water tank 201, the water pump 202, the radiator 203, the pressure relief valve 204 and the water outlet 205 and then returns to the water tank 201, thereby realizing the recycling of cooling water.

[0036] In the present application, in the process of cooling water flowing from the water pump 202 to the radiator 203, a first-level temperature sensor and a first-level flow rate sensor are set to obtain the first-level temperature value and the first-level water flow rate value of the water inlet. In the process of cooling water flowing from the radiator 203 to the pressure relief valve 204, a second-level temperature sensor and a second-level flow rate sensor are set to obtain the second-level temperature value and the second-level water flow rate value of the water inlet. In the process of cooling water flowing from the pressure relief valve 204 to the water outlet 205, a pressure sensor is set to obtain the pressure value of the cooling water at the pressure relief valve.

[0037] The predicted water flow velocity value and the temperature value of the water outlet can be obtained based on the first-level temperature value and the first-level water flow velocity value of the water inlet, the second-level temperature value and the second-level water flow velocity value of the water inlet, and the pressure value of the cooling water at the pressure relief valve. The obtained predicted values ​​can be used to further determine whether the intelligent circulating cooling water system can achieve the preset cooling effect of the cooling water. If the preset cooling effect cannot be achieved, adaptive calculation is performed based on the obtained predicted values ​​to obtain optimization data corresponding to the predicted values, and parameters of the intelligent circulating cooling water system are adjusted based on the optimization data to improve the cooling effect of the intelligent circulating cooling water system.

[0038] See also Figure 3 , which is a schematic diagram of the process flow of the optimization method of the intelligent circulating cooling water system described in the embodiment of the present application. Figure 3 As shown, the optimization method of the intelligent circulating cooling water system includes the following steps S1 to S3.

[0039] Step S1, obtaining real-time operating parameters of a circulating cooling water system and preprocessing the real-time operating parameters.

[0040] Specifically, the real-time working parameters after preprocessing include: a temperature value of the water inlet, a water flow velocity and a pressure value of the water inlet.

[0041] Specifically, it includes: obtaining the first-level temperature value and the first-level water flow velocity value of the water inlet measured in real time by the first-level temperature sensor and the flow velocity sensor arranged between the water pump and the radiator; obtaining the second-level temperature value and the second-level water flow velocity value of the water inlet measured by the second-level temperature sensor and the flow velocity sensor arranged between the radiator and the pressure relief valve; obtaining the pressure value of the cooling water measured in real time by the pressure sensor arranged between the pressure relief valve and the water outlet.

[0042] In some implementations, preprocessing the real-time operating parameters includes: deleting abnormal data and noise data in the real-time operating parameters to obtain cleaned real-time operating parameters; normalizing the cleaned real-time operating parameters to map the real-time operating parameters of different data levels to the same scale.

[0043] Specifically, the first-level temperature value and the first-level water flow velocity value of the water inlet, the second-level temperature value and the second-level water flow velocity value of the water inlet and the pressure value of the cooling water are analyzed to obtain the abnormal data and noise data therein, and the abnormal data and noise data obtained by analysis are deleted to improve the accuracy of the data and further ensure the accuracy of the predicted data of the water outlet temperature and water flow velocity.

[0044] Furthermore, the data from which abnormal data and noise data have been deleted are normalized so that the first-level temperature value and the first-level water flow velocity value of the water inlet, the second-level temperature value and the second-level water flow velocity value of the water inlet and the pressure value of the cooling water are mapped to the [0, 1] interval based on the maximum-minimum normalization, thereby mapping the real-time working parameters of different data levels to the same scale.

[0045] Among them, the formula for maximum-minimum normalization is:

[0046] Among them, x is the original data, min is the minimum value of the data, and max is the maximum value of the data.

[0047] Step S2: predicting the working parameters of the water outlet based on the temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value, and obtaining the predicted data of the water outlet temperature and the water flow velocity.

[0048] In some implementations, obtaining the predicted data of the water outlet temperature and water flow velocity includes: performing feature extraction on the preprocessed temperature value of the water inlet, the water flow velocity of the water inlet, and the pressure value, respectively, to obtain temperature gradient characteristics, flow velocity change characteristics, and fluctuation characteristics; inputting the temperature gradient characteristics, the flow velocity change characteristics, and the fluctuation characteristics into a trained cooling water demand prediction model to output the predicted data of the water outlet temperature and water flow velocity.

[0049] like Figure 4 As shown, obtaining the predicted data of the water outlet temperature and water flow velocity includes the following steps S21 to S22.

[0050] Step S21, performing feature extraction on the pre-processed temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value, respectively, to obtain temperature gradient characteristics, flow velocity change characteristics and fluctuation characteristics.

[0051] Specifically, the temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value are stored as a DataFrame object, and the temperature gradient feature, flow velocity change feature and fluctuation feature are extracted using a dictionary feature extraction method.

[0052] Step S22: input the temperature gradient characteristics, the flow velocity change characteristics and the fluctuation characteristics into the trained cooling water demand prediction model to output the outlet temperature and water flow velocity prediction data.

[0053] In some implementations, the cooling water demand prediction model adopts a linear regression model, and training the cooling water demand prediction model includes: obtaining a historical data set of the inlet and outlet of the circulating cooling water system; preprocessing the historical data set and performing feature extraction to obtain a historical feature data set for model training; the historical feature data set includes: historical temperature gradient feature data, historical flow rate change feature data and historical fluctuation feature data; wherein each group of the historical feature data sets includes a historical temperature gradient feature, a historical flow rate change feature and a historical fluctuation feature; and using the historical feature data set to train the cooling water demand prediction model.

[0054] Specifically, a historical data set of the inlet and outlet of the circulating cooling water system is obtained, and the historical data set of the inlet and outlet is imported into a pandas library, and a historical feature data set of the historical data set of the inlet and outlet is extracted by using a dictionary feature extraction method, and the historical feature data set is divided into a historical feature test data set and a historical feature training data set by using a train_test_split function; the historical feature training data set is input into a created linear regression model to train the linear regression model, and the linear regression model is verified by using the historical feature test data set to obtain a trained cooling water demand prediction model.

[0055] Step S3: Obtain optimization data corresponding to the water outlet temperature and water flow velocity prediction data for use in optimizing the circulating cooling water system.

[0056] Specifically, based on the acquired water outlet temperature and water flow rate prediction data, the speed of the water pump and the power of the radiator of the circulating cooling water system are dynamically adjusted to optimize the circulating cooling water system, so as to further improve the cooling effect of the circulating cooling water system and reduce the energy consumption of the circulating cooling water system.

[0057] In some implementations, before obtaining the optimization data corresponding to the water outlet temperature and water flow rate prediction data, it also includes determining whether the water outlet temperature and water flow rate prediction data meet the preset threshold of the circulating cooling water system.

[0058] If the predicted data of the water outlet temperature and water flow velocity meet the preset threshold, the circulating cooling water system will not be optimized; conversely, if the predicted data of the water outlet temperature and water flow velocity do not meet the preset threshold, the optimization data corresponding to the predicted data of the water outlet temperature and water flow velocity are obtained to optimize the circulating cooling water system.

[0059] In some implementations, the optimization method of the intelligent circulating cooling water system further includes: acquiring the external conditions of the circulating cooling water system in real time; and dynamically adjusting the water flow rate and temperature of the outlet of the circulating cooling water system according to the external climate.

[0060] See also Figure 5 , which is a flow chart of another optimization method of an intelligent circulating cooling water system according to an embodiment of the present application. Figure 5 As shown, the optimization method of the intelligent circulating cooling water system includes:

[0061] Step S51, obtaining real-time operating parameters of the circulating cooling water system and preprocessing the real-time operating parameters; the real-time operating parameters after preprocessing include: a temperature value of a water inlet, a water flow rate and a pressure value of the water inlet;

[0062] Step S52, predicting the working parameters of the water outlet based on the temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value, and obtaining the predicted data of the water outlet temperature and the water flow velocity;

[0063] Step S53, determining whether the water outlet temperature and water flow rate prediction data meet the preset thresholds of the circulating cooling water system;

[0064] If the predicted data of the water outlet temperature and the water flow velocity meet the preset threshold, the circulating cooling water system is not optimized, and after a preset time interval, the process returns to step S51 to obtain the real-time working parameters of the circulating cooling water system and pre-process the real-time working parameters;

[0065] On the contrary, if the predicted data of the water outlet temperature and the water flow rate cannot meet the preset threshold, that is, if the predicted data of the water outlet temperature and the water flow rate cannot achieve the required cooling effect, continue to step S54;

[0066] Step S54, obtaining optimization data corresponding to the water outlet temperature and water flow velocity prediction data for use in optimizing the circulating cooling water system; and

[0067] Step S51 ’ , obtaining the external conditions of the circulating cooling water system in real time;

[0068] Step S52 ’ , dynamically adjust the water flow rate and temperature of the outlet of the circulating cooling water system according to the external conditions.

[0069] In some implementations, the external conditions include: external climate conditions and external load conditions.

[0070] Among them, the external climate conditions include: external climate temperature and external climate humidity; if the external temperature is low, the cooling water temperature setting value of the circulating cooling water system can be increased to reduce the cooling operation load and further reduce energy consumption.

[0071] External load conditions include: the cooling load demand of the circulating cooling water system; adjusting the speed of the water pump of the circulating cooling water system and the speed of the cooling tower fan in real time according to changes in the cooling load to save energy; when the cooling load is low, the water flow rate of the circulating cooling water system and the speed of the cooling tower fan can be reduced to reduce unnecessary energy consumption; in scenarios where the production load changes rapidly, such as power stations or factories, the water flow rate and cooling water temperature of the intelligent circulating cooling water system can be dynamically adjusted to ensure that the cooling capacity of the intelligent circulating cooling water system is maintained during peak load periods.

[0072] It should be noted that the protection scope of the optimization method of the intelligent circulating cooling water system described in the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0073] See also Figure 6 , showing a structural schematic diagram of the optimization device of the intelligent circulating cooling water system described in an embodiment of the present application.

[0074] like Figure 6 As shown, the optimization device 600 of the intelligent circulating cooling water system includes: an acquisition module 610, a data processing module 620, and an optimization module 630.

[0075] The acquisition module 610 is used to acquire the real-time operating parameters of the circulating cooling water system and preprocess the real-time operating parameters; the real-time operating parameters after preprocessing include: the temperature value of the water inlet, the water flow rate and the pressure value of the water inlet;

[0076] The data processing module 620 is used to predict the working parameters of the water outlet based on the temperature value of the water inlet, the water flow rate of the water inlet and the pressure value, and obtain the predicted data of the water outlet temperature and water flow rate;

[0077] The optimization module 630 is used to obtain optimization data corresponding to the water outlet temperature and water flow velocity prediction data for use in optimizing the circulating cooling water system.

[0078] In some implementations, the optimization device 600 of the intelligent circulating cooling water system also includes an adaptive module 640, which is used to obtain the external conditions of the circulating cooling water system in real time and dynamically adjust the temperature of the outlet of the circulating cooling water system according to the external conditions.

[0079] In some implementations, the optimization device 600 of the intelligent circulating cooling water system also includes a judgment module 650, which is used to judge whether the water outlet temperature and water flow rate prediction data meet the preset threshold value of the circulating cooling water system; if the water outlet temperature and water flow rate prediction data meet the preset threshold value, the circulating cooling water system is not optimized; otherwise, the optimization data corresponding to the water outlet temperature and water flow rate prediction data is obtained to optimize the circulating cooling water system.

[0080] It should be noted that the manner in which each module of the optimization device for the intelligent circulating cooling water system described in this embodiment implements the corresponding function corresponds one-to-one to the optimization method for the intelligent circulating cooling water system described in the embodiment of the present application, and therefore will not be repeated here.

[0081] It should be noted that it should be understood that the division of the above modules is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the x module can be a separately established processing element, or it can be integrated in a chip of the above-mentioned device for implementation. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by a processing element of the above-mentioned device. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0082] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more microprocessors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0083] The embodiment of the present application also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0084] An embodiment of the present application also provides an electronic device, including: a processor and a memory.

[0085] Specifically, the memory is used to store computer programs; the memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[0086] The processor is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned optimization method of the intelligent circulating cooling water system.

[0087] like Figure 7As shown, the electronic device of the present application is in the form of a general computing device. The components of the electronic device may include but are not limited to: one or more processors or processing units 71, a memory 72, and a bus 73 connecting different system components (including the memory 72 and the processing unit 71).

[0088] Bus 73 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0089] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0090] The memory 72 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 721 and / or cache memory 722. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 723 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 not shown, usually called a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 73 via one or more data medium interfaces. The memory 72 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0091] A program / utility 724 having a set (at least one) of program modules 725 may be stored, for example, in the memory 72, such program modules 725 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 725 generally perform the functions and / or methods of the embodiments described herein.

[0092] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 74. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 75. Figure 7 As shown, the network adapter 75 communicates with other modules of the electronic device via the bus 73. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, redundant arrays of independent disks (RAID systems), tape drives, and data backup storage systems, etc.

[0093] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0094] In summary, a circulating cooling water method that determines the flow rate change trend based on the Internet and machine learning, and uses big data analysis to determine the water temperature change trend, can achieve the purpose of optimizing the cooling effect and saving energy. Therefore, this application effectively overcomes the various shortcomings of the prior art and has a high industrial utilization value.

[0095] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. An optimization method for an intelligent circulating cooling water system, characterized in that: The method comprises: Acquiring real-time operating parameters of a circulating cooling water system and preprocessing the real-time operating parameters; The real-time working parameters after preprocessing include: the temperature value of the water inlet, the water flow velocity and the pressure value of the water inlet; Based on the temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value, the working parameters of the water outlet are predicted to obtain the predicted data of the water outlet temperature and the water flow velocity; including: respectively extracting the features of the pre-processed temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value to obtain the temperature gradient feature, the flow velocity change feature and the fluctuation feature, inputting the temperature gradient feature, the flow velocity change feature and the fluctuation feature into the trained cooling water demand prediction model to output the predicted data of the water outlet temperature and the water flow velocity; Acquiring optimization data corresponding to the water outlet temperature and water flow velocity prediction data for use in optimizing the circulating cooling water system; The temperature value of the water inlet includes: the primary temperature value between the water pump and the radiator and the secondary temperature value between the radiator and the pressure relief valve; the water flow rate of the water inlet includes: the primary water flow rate between the water pump and the radiator and the secondary water flow rate between the radiator and the pressure relief valve; the pressure value is the pressure value between the pressure relief valve and the water outlet.

2. The optimization method of the intelligent circulating cooling water system according to claim 1, characterized in that: The cooling water demand prediction model adopts a linear regression model, and training the cooling water demand prediction model includes: Acquire a historical data set of the water inlet and outlet of the circulating cooling water system; Preprocessing the historical data set and performing feature extraction to obtain a historical feature data set for model training; the historical feature data set includes: historical temperature gradient feature data, historical flow velocity change feature data and historical fluctuation feature data; wherein each group of the historical feature data sets includes a historical temperature gradient feature, a historical flow velocity change feature and a historical fluctuation feature; The cooling water demand prediction model is trained using the historical feature data set.

3. The optimization method of the intelligent circulating cooling water system according to claim 1, characterized in that: Preprocessing the real-time working parameters includes: Deleting abnormal data and noise data in the real-time working parameters to obtain the real-time working parameters after cleaning; The cleaned real-time operating parameters are normalized to map the real-time operating parameters at different data levels to the same scale.

4. The optimization method of the intelligent circulating cooling water system according to claim 1, characterized in that: The method further comprises: before obtaining the optimization data corresponding to the water outlet temperature and water flow velocity prediction data, determining whether the water outlet temperature and water flow velocity prediction data meet a preset threshold value of the circulating cooling water system; If the predicted data of the water outlet temperature and the water flow rate meet the preset threshold, the circulating cooling water system is not optimized; Otherwise, the optimization data corresponding to the water outlet temperature and water flow velocity prediction data is obtained to optimize the circulating cooling water system.

5. The optimization method of the intelligent circulating cooling water system according to claim 1, characterized in that: The method further comprises: Acquiring the external conditions of the circulating cooling water system in real time; The water flow rate and temperature of the water outlet of the circulating cooling water system are dynamically adjusted according to the external conditions.

6. An optimization device for an intelligent circulating cooling water system, characterized in that: The device comprises: An acquisition module is used to acquire the real-time working parameters of the circulating cooling water system and preprocess the real-time working parameters; the real-time working parameters after preprocessing include: the temperature value of the water inlet, the water flow rate and the pressure value of the water inlet; A data processing module, used to predict the working parameters of the water outlet based on the temperature value of the water inlet, the water flow velocity of the water inlet and the pressure value, and obtain the predicted data of the water outlet temperature and the water flow velocity; An optimization module, used to obtain optimization data corresponding to the water outlet temperature and water flow velocity prediction data, for use in optimizing the circulating cooling water system; The temperature value of the water inlet includes: the primary temperature value between the water pump and the radiator and the secondary temperature value between the radiator and the pressure relief valve; the water flow rate of the water inlet includes: the primary water flow rate between the water pump and the radiator and the secondary water flow rate between the radiator and the pressure relief valve; the pressure value is the pressure value between the pressure relief valve and the water outlet.

7. The optimization device for the intelligent circulating cooling water system according to claim 6, characterized in that: The device also includes: The adaptive module is used to obtain the external conditions of the circulating cooling water system in real time and dynamically adjust the temperature of the water outlet of the circulating cooling water system according to the external conditions.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the optimization method of the intelligent circulating cooling water system according to any one of claims 1 to 5 is implemented.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory so that the electronic device executes the optimization method of the intelligent circulating cooling water system according to any one of claims 1 to 5.

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