CN115682357B

CN115682357B - Cooling water optimizing method taking approximation degree as center and independent control system

Info

Publication number: CN115682357B
Application number: CN202211345034.5A
Authority: CN
Inventors: 夏金瑞; 阳红军; 袁玉玲; 林光; 黄维
Original assignee: Guangzhou Sjest Energy Saving Technology Co ltd
Current assignee: Guangzhou Sjest Energy Saving Technology Co ltd
Priority date: 2022-10-31
Filing date: 2022-10-31
Publication date: 2023-07-25
Anticipated expiration: 2042-10-31
Also published as: CN115682357A

Abstract

The application relates to the technical field of cooling water system energy conservation, and discloses a cooling water optimizing method taking approximation degree as a center and an independent control system, wherein the cooling water optimizing method taking approximation degree as the center comprises the following steps: acquiring load demand data, system equipment parameters and historical meteorological data of a target project, and generating project information; creating a system optimizing model based on system equipment parameters, wherein the system optimizing model comprises an approximation model, a cooling tower model and a cooling pump model; acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimizing model, matching an optimal control scheme, generating a annual control strategy based on the optimal control scheme, and sending the annual control strategy to a control system; inputting the current load demand value into a control system, matching the current optimal cooling approximation degree to correct the cooling water inlet temperature target value, and executing PID frequency conversion regulation; the energy-saving control method has the effect of optimizing the energy-saving control of the cooling water system.

Description

Cooling water optimizing method taking approximation degree as center and independent control system

Technical Field

The application relates to the technical field of cooling water system energy conservation, in particular to a cooling water optimizing method taking approximation degree as a center and an independent control system.

Background

The cooling water system is a water supply system which takes water as a cooling medium, exchanges heat with the cooling water, is cooled and recycled, and mainly comprises cooling equipment, a water pump and a pipeline; taking a central air-conditioning cold station as an example, the optimal control of the cold station comprising a chilled water system and a cooling water system plays a very important role in the overall energy saving of the cold station, and the equipment comprises a cooling water pump, a cooling tower and auxiliary equipment thereof;

in order to improve the energy-saving effect of the cooling water system, on one hand, how to match the load and high efficiency is considered in the system design, such as a frequency conversion system matched with equipment, and on the other hand, how to operate and control the system in high efficiency is considered after the system is built; the existing cooling water system has a large lifting space in the aspect of system control; the existing control strategy of the cooling water system of the central air conditioning cold station has two modes: one is that when the host computer carries out the number adjustment, the number of the cooling tower and the water pump changes along with the change, and the traditional control mode of frequency conversion adjustment is carried out by manually giving corresponding target values; one is the direction of the reciprocal miscellaneous algorithm, apply fuzzy algorithm, neural algorithm, genetic algorithm, colony-ant colony algorithm, exhaustion method, etc. to carry on various complex calculations, get the complex algorithm control method of the fine calculation result; in practical application, the traditional control method only realizes automatic control from the functional point of view, the equipment is basically controlled independently, the whole energy consumption is not comprehensively considered, and the energy-saving potential of the system is excavated; the complex algorithm control method can be well realized in a theoretical level, but in practical control application, the data monitoring precision, especially the refrigerating capacity data, is high in requirement, and the problems of high control stability difficulty, high hardware cost, high maintenance difficulty and the like exist.

With respect to the above related art, the inventors consider that the existing cooling water system energy-saving control strategy has a problem of poor effect.

Disclosure of Invention

In order to optimize the energy-saving control effect of the cooling water system, the application provides a cooling water optimizing method taking approximation degree as a center and an independent control system.

The first technical scheme adopted by the invention of the application is as follows:

a cooling water optimizing method taking approximation degree as center comprises the following steps:

acquiring load demand data, system equipment parameters and historical meteorological data of a target project, and generating project information;

creating a system optimizing model based on system equipment parameters, wherein the system optimizing model comprises an approximation model, a cooling tower model and a cooling pump model;

acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimizing model, matching an optimal control scheme, generating a annual control strategy based on the optimal control scheme, and sending the annual control strategy to a control device;

and inputting the current load demand value into a control device, matching the current optimal cooling approximation degree to correct the cooling outlet water temperature target value, and executing PID frequency conversion regulation.

By adopting the technical scheme, the load demand data, the system equipment parameters and the historical meteorological data of the target project are obtained to form project information, so that the equipment condition and the meteorological condition of the cooling water system of the target project and the use requirement of the target project on the cooling water system can be conveniently obtained; respectively creating an approximation model, a cooling tower model and a cooling pump model based on parameters of each device in the cooling water system, so as to summarize and generate a system optimizing model, and then calculating corresponding electricity consumption values based on different operation parameters; acquiring outdoor wet bulb temperature values all the year around according to historical meteorological data, and inputting the outdoor wet bulb temperature values and load demand data into a system optimizing model so as to match a corresponding optimal control scheme from the system optimizing model based on the environmental temperature and the load demand, thereby improving the energy efficiency of a cooling water system; generating an annual control strategy based on each optimal control scheme and sending the annual control strategy to a control device so as to plan annual operation control of the cooling water system in advance; when the control device is used for regulating and controlling the cooling water system, the current load demand value is input into the control device and matched with the current optimal cooling approximation degree, so that the target values of the cooling water outlet temperature and the cooling tower temperature difference are further corrected, the cooling tower and the cooling pump are controlled to execute PID frequency conversion regulation, and the energy-saving control effect of the cooling water system is optimized.

In a preferred example, the present application: creating a system optimizing model based on system equipment parameters, wherein the system optimizing model comprises an approximation model, a cooling tower model and a cooling pump model, and the method comprises the following steps:

setting an approximation degree initial value for each wet bulb temperature interval based on the cooling tower equipment parameters, and creating an approximation degree model;

the method comprises the steps of evaluating the outlet water temperature and the electricity consumption value of a cooling tower under various inlet water temperatures, wet bulb temperatures, fan frequencies and cooling flow based on cooling tower equipment parameters, and creating a cooling tower model;

and evaluating the power consumption value of the cooling pump at different flow rates based on the cooling pump equipment parameters, and creating a cooling pump model.

By adopting the technical scheme, the cooling water system comprises the cooling tower and the cooling pump, the equipment parameters of the cooling tower and the cooling pump can be obtained from specifications provided by manufacturers, the historical wet bulb temperature change range, the time distribution condition and the change trend of the area are calculated based on historical meteorological data of the area where the cooling water system to be subjected to optimizing control, the historical wet bulb temperature change range is divided into a plurality of wet bulb temperature intervals, and corresponding initial values of the approximation degree are set for each wet bulb temperature interval based on the equipment parameters of the cooling tower, so that an approximation degree model is created, and the initial values of approximation degree adjustment can be rapidly determined based on the wet bulb temperature values in the follow-up process; the water outlet temperature and the electricity consumption value of the cooling tower at different water inlet temperatures, wet bulb temperatures, fan frequencies and cooling flow rates are evaluated based on the cooling tower equipment parameters, so that a cooling tower model is created, and electricity consumption of the cooling tower in various running states can be calculated conveniently when the cooling station is subjected to optimizing control in the follow-up process; based on the cooling pump equipment parameters, evaluating the power consumption values of the cooling pump under different flow rates, thereby creating a cooling pump model, and facilitating the subsequent evaluation of the energy consumption condition of the cooling pump; and creating a system optimizing model based on the approximation model, the cooling tower model and the cooling pump model.

In a preferred example, the present application: acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimizing model, and matching an optimal control scheme, wherein the method comprises the following steps:

acquiring an outdoor wet bulb temperature value, matching a corresponding initial approximation degree value, calculating a cooling water outlet temperature required value and inputting the cooling water outlet temperature required value into a cooling tower model;

calculating the heat dissipation capacity of the system according to a preset calculation coefficient based on the load demand data, obtaining a control temperature difference value, calculating a cooling water flow and a cooling water inlet temperature demand value, and inputting the cooling water flow and the cooling water inlet temperature demand value into a cooling tower model;

and matching the optimal operation scheme of the cooling tower and the cooling pump in the initial value of the corresponding approximation degree based on the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature.

By adopting the technical scheme, the outdoor wet bulb temperature value is obtained and input into the approximation degree model, the corresponding approximation degree initial value is matched, and then the corresponding cooling outlet water temperature is calculated and input into the cooling tower model; based on the load demand data, calculating the system heat dissipation capacity according to a preset calculation coefficient, so that the accuracy of calculating the system heat dissipation capacity is improved, a control temperature difference is obtained, and then the cooling water flow and the cooling water outlet temperature are calculated according to the control temperature difference and the system heat dissipation capacity; the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature are input into the cooling tower model and the cooling pump model, and then the optimal operation scheme is matched from the cooling tower model and the cooling pump model according to each approximation initial value corresponding to the outdoor wet bulb temperature value.

In a preferred example, the present application: based on the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature, after the step of matching the optimal operation scheme of the cooling tower and the cooling pump corresponding to the initial value of the approximation degree, the method further comprises the following steps:

defining an optimal operation scheme of the cooling tower and the cooling pump at the initial value of the corresponding approximation degree as a reference scheme, and obtaining a corresponding electricity consumption value;

setting a positive optimizing approximation degree value and a negative optimizing approximation degree value in positive and negative directions of an approximation degree initial value based on a preset optimizing resolution, and determining a positive optimizing scheme, a negative optimizing scheme and corresponding power consumption values based on the positive optimizing approximation degree value and the negative optimizing approximation degree value;

and defining the approximation degree value of the scheme with the lowest power consumption value in the reference scheme, the positive optimizing scheme and the negative optimizing scheme as a new approximation degree initial value.

By adopting the technical scheme, based on the initial approximation degree value, the optimal operation schemes of the cooling tower and the cooling pump under the current initial approximation degree value are matched from the cooling tower model and the cooling pump model to serve as reference schemes, and the power consumption values corresponding to the optimal operation schemes are obtained, so that the power consumption values of the reference schemes and the power consumption values of the optimizing schemes can be conveniently compared in the follow-up process; based on a preset optimizing resolution and an initial value of approximation, determining a positive optimizing approximation value and a negative optimizing approximation value, respectively inputting the positive optimizing approximation value and the negative optimizing approximation value into a cooling tower model and a cooling pump model, generating an optimal operation scheme based on the positive optimizing approximation value as a positive optimizing scheme, generating an optimal operation scheme based on the negative optimizing approximation value as a negative optimizing scheme, and calculating power consumption values corresponding to the positive optimizing scheme and the negative optimizing scheme; and comparing the power consumption values of the reference scheme, the positive optimizing scheme and the negative optimizing scheme, and defining the approximation degree value corresponding to the scheme with the lowest power consumption value as a new approximation degree initial value so as to facilitate the subsequent execution of a further approximation degree optimizing program.

In a preferred example, the present application: after the step of defining the approximation degree value of the scheme with the lowest power consumption value in the reference scheme, the positive optimizing scheme and the negative optimizing scheme as the new approximation degree initial value, the method further comprises the following steps:

if the electricity consumption value of the reference scheme is the lowest, generating an optimal control scheme based on the reference scheme;

and if the power consumption value of the reference scheme is not the lowest, executing a new approximation optimizing program based on the new approximation initial value.

By adopting the technical scheme, an approximation degree optimizing program is executed to generate an approximation degree optimizing result, if the electricity consumption value of the reference scheme is lower than the electricity consumption value corresponding to the positive optimizing scheme and the negative optimizing scheme, the energy efficiency of the reference scheme is considered to be higher than that of the positive optimizing scheme and the negative optimizing scheme, and an optimal control scheme is generated based on the reference scheme, so that the follow-up control of the operation of the cooling water system according to the optimal control scheme is facilitated; if the power consumption value of the reference scheme is higher than the power consumption value corresponding to the positive optimizing scheme or the negative optimizing scheme, the energy efficiency of the reference scheme is considered to be lower than that of the positive optimizing scheme or the negative optimizing scheme, and the next round of approximation optimizing program is executed based on the new initial value of approximation in the approximation optimizing result until the optimal control scheme is determined.

In a preferred example, the present application: further comprises:

recording the operation parameters of the cooling tower, the operation parameters of the cooling pump, the actual load demands, the outdoor weather parameters and the heat dissipation data in real time, and generating historical data;

and correcting parameters of the system optimizing model and the annual control strategy according to the historical data.

By adopting the technical scheme, the operation parameters of the cooling tower, the operation parameters of the cooling pump, the actual load demands, the outdoor weather parameters and the heat dissipation data are recorded in real time to generate historical data, so that the subsequent rapid judgment of the abnormality cause when the cooling water system is abnormal in operation is facilitated; and correcting parameters of the system optimizing model and the annual control strategy according to the historical data according to the period so as to reduce the deviation between the optimal control scheme and the actual optimal control parameters and further improve the optimizing effect of the optimal control scheme on the energy efficiency of the cooling water system.

The second object of the present application is achieved by the following technical scheme:

an independent control system for optimizing cooling water with approximation as center, comprising:

the project information acquisition module is used for acquiring load demand data, system equipment parameters and historical meteorological data of a target project and generating project information;

The system optimizing model creation module is used for creating a system optimizing model based on system equipment parameters, and the system optimizing model comprises an approximation model, a cooling tower model and a cooling pump model;

the optimal control scheme generation module is used for acquiring the outdoor wet bulb temperature value and the load demand data, inputting the data into the system optimizing model, matching the optimal control scheme, generating a annual control strategy based on the optimal control scheme and sending the strategy to the control device;

and the cooling water system control module is used for inputting the current load demand value into the control device, matching the current optimal cooling approximation degree so as to correct the cooling outlet water temperature target value and executing PID frequency conversion regulation.

In a preferred example, the present application: the system optimizing model creation module comprises:

the approximation degree model creation sub-module is used for creating a plurality of wet bulb temperature intervals based on historical meteorological data of a target area, setting an approximation degree initial value for each wet bulb temperature interval based on cooling tower equipment parameters, and creating an approximation degree model;

the cooling tower model creation submodule is used for evaluating the water outlet temperature and the power consumption value of the cooling tower under the conditions of each water inlet temperature, wet bulb temperature, fan frequency and cooling flow based on the cooling tower equipment parameters, and creating a cooling tower model;

and the cooling pump model creation submodule is used for evaluating the power consumption value of the cooling pump at different flow rates based on the cooling pump equipment parameters and creating a cooling pump model.

The third object of the present application is achieved by the following technical scheme:

a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above-described proximity-centric cooling water optimizing method when executing the computer program.

The fourth object of the present application is achieved by the following technical scheme:

a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the above-described approximation-centric cooling water optimizing method.

In summary, the present application includes at least one of the following beneficial technical effects:

1. acquiring load demand data, system equipment parameters and historical meteorological data of a target project to form project information, so that equipment conditions and meteorological conditions of a cooling water system of the target project and the use demands of the target project on the cooling water system are conveniently acquired; respectively creating an approximation model, a cooling tower model and a cooling pump model based on parameters of each device in the cooling water system, so as to summarize and generate a system optimizing model, and then calculating corresponding electricity consumption values based on different operation parameters; acquiring outdoor wet bulb temperature values all the year around according to historical meteorological data, and inputting the outdoor wet bulb temperature values and load demand data into a system optimizing model so as to match a corresponding optimal control scheme from the system optimizing model based on the environmental temperature and the load demand, thereby improving the energy efficiency of a cooling water system; generating an annual control strategy based on each optimal control scheme and sending the annual control strategy to a control device so as to plan annual operation control of the cooling water system in advance; when the control device is used for regulating and controlling the cooling water system, the current load demand value is input into the control device and matched with the current optimal cooling approximation degree, so that the target values of the cooling water outlet temperature and the cooling tower temperature difference are further corrected, the cooling tower and the cooling pump are controlled to execute PID frequency conversion regulation, and the energy-saving control effect of the cooling water system is optimized.

2. The cooling water system comprises a cooling tower and a cooling pump, equipment parameters of the cooling tower and the cooling pump can be obtained from specifications provided by manufacturers, historical wet bulb temperature change ranges, time distribution conditions and change trends of the areas are calculated based on historical meteorological data of the areas where the cooling water system to be subjected to optimizing control are located, the historical wet bulb temperature change ranges are divided into a plurality of wet bulb temperature intervals, corresponding approximation degree initial values are set for the wet bulb temperature intervals based on the equipment parameters of the cooling tower, so that an approximation degree model is created, and the initial values of approximation degree adjustment can be conveniently and rapidly determined based on the wet bulb temperature values; the water outlet temperature and the electricity consumption value of the cooling tower at different water inlet temperatures, wet bulb temperatures, fan frequencies and cooling flow rates are evaluated based on the cooling tower equipment parameters, so that a cooling tower model is created, and electricity consumption of the cooling tower in various running states can be calculated conveniently when the cooling station is subjected to optimizing control in the follow-up process; based on the cooling pump equipment parameters, evaluating the power consumption values of the cooling pump under different flow rates, thereby creating a cooling pump model, and facilitating the subsequent evaluation of the energy consumption condition of the cooling pump; and creating a system optimizing model based on the approximation model, the cooling tower model and the cooling pump model.

3. Acquiring an outdoor wet bulb temperature value, inputting the outdoor wet bulb temperature value into an approximation degree model, matching a corresponding initial approximation degree value, further calculating a corresponding cooling outlet water temperature, and inputting the cooling outlet water temperature into a cooling tower model; based on the load demand data, calculating the system heat dissipation capacity according to a preset calculation coefficient, so that the accuracy of calculating the system heat dissipation capacity is improved, a control temperature difference is obtained, and then the cooling water flow and the cooling water inflow temperature are calculated according to the control temperature difference and the system heat dissipation capacity; the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature are input into the cooling tower model and the cooling pump model, and then the optimal operation scheme is matched from the cooling tower model and the cooling pump model according to each approximation initial value corresponding to the outdoor wet bulb temperature value.

Drawings

Fig. 1 is a schematic diagram of an information transmission flow of cooling water system control in the present application.

Fig. 2 is a flowchart of a cooling water optimizing method centering on the approximation degree in the first embodiment of the present application.

Fig. 3 is a flowchart of step S20 in the cooling water optimizing method centering on the approximation degree of the present application.

Fig. 4 is a flowchart of step S30 in the cooling water optimizing method centering on the approximation degree of the present application.

Fig. 5 is another flowchart of step S30 in the cooling water optimizing method centering on the approximation degree of the present application.

Fig. 6 is another flowchart of step S30 in the cooling water optimizing method centering on the approximation degree of the present application.

Fig. 7 is another flowchart of a cooling water optimizing method centering on the approximation degree in the second embodiment of the present application.

Fig. 8 is a schematic block diagram of a cooling water optimizing independent control system centering on the approximation degree in the third embodiment of the present application.

Fig. 9 is a schematic view of an apparatus in a fourth embodiment of the present application.

Detailed Description

The present application is described in further detail below in conjunction with figures 1 to 9.

The application discloses a cooling water optimizing method taking approximation degree as a center, which can be used for controlling the working states of a cooling tower and a cooling pump in a cooling water system, wherein the cooling water system refers to a cooling water system taking the cooling tower as a cooling mode, and related application scenes comprise, but are not limited to, a cooling water system of a central air conditioner cooling station, a cooling water system of an industrial air compressor, a cooling water system of a data center and other process cooling water systems; in this embodiment, taking a central air conditioning cold station as an example, the cold station is divided into two side water systems, the chilled water system refers to the water circulation of the cold station for providing cold water to the terminal air conditioner, and the cooling water system refers to the water circulation for performing corresponding heat dissipation treatment in order to meet the refrigeration requirement.

The cooling water optimizing method taking the approximation degree as the center combines the advantages of the traditional control mode and the complex algorithm control mode, accords with the process characteristics, provides the intelligent automatic optimizing method of the automatic control system under certain deterministic load demand, has the advantages of strong practicability, definite control logic and high stability, and overcomes the defects of the current complex algorithm technology; the control objective of the cooling water optimizing strategy is to realize the objective of lowest energy consumption of cooling water equipment under various working conditions, and the core is energy saving of the system, and the method is intelligent optimizing; as shown in fig. 1, the main process of the cooling water optimizing strategy is to execute a series of matching and comparison calculation with approximation degree as the center, and finally output the scheme with the lowest energy consumption, and the scheme relates to control such as cooling tower number adjustment, cooling tower frequency conversion adjustment, cooling pump number adjustment, cooling pump frequency adjustment and the like.

In the application, the approximation degree refers to the difference between the cooling water outlet temperature and the wet bulb temperature, and the lower the approximation degree is, the closer the cooling water inlet temperature is to the wet bulb temperature, and the higher the cooling efficiency is; cooling water efficiency = cooling water system heat dissipation/cooling side power consumption, the cooling side power consumption is generally the sum of cooling tower power and cooling pump power; the load demand refers to the amount of refrigeration required by the end (e.g., cold station system) or the amount of direct heat dissipation required by the end (e.g., air pressure system).

Example 1

As shown in fig. 2, the method specifically comprises the following steps:

s10: and acquiring load demand data, system equipment parameters and historical meteorological data of the target project, and generating project information.

In this embodiment, the target item refers to an item that needs to be generated by the annual cooling water system control policy at present, and may specifically correspond to a building, an electricity unit, or a cooling water system usage unit; the load demand data refers to the annual load demand data of the target project; the system equipment parameters refer to the model number, performance parameters and the like of each equipment in the cooling water system used in the target project; the historical meteorological data is data composed of historical meteorological information of an area where a target project is located, and specifically comprises temperature data and humidity data; the project information is information formed by summarizing load demand data, system equipment parameters and historical meteorological data corresponding to a target project.

Specifically, historical meteorological data of a region where a target project is located and annual load demand values of the target project are obtained, future annual meteorological parameter prediction data are estimated based on the historical meteorological data of the region where the target project is located, wherein the load demand data can be calculated according to annual meteorological parameter prediction data and an annual temperature control target, and the load demand data and the annual meteorological parameter prediction data need to be unfolded to data points of 8760 hours in the whole year; and inquiring performance parameters of the cooling tower, the cooling pump and other equipment through the model of cooling water system equipment used in the target project and manufacturer information to form system equipment parameters, generating project information based on load demand data, system equipment parameters, historical meteorological data, annual meteorological parameter prediction data and annual temperature control targets, and storing the project information in a database, so that the required information can be conveniently called from the database by a subsequent cooling water system control strategy generation link.

S20: and creating a system optimizing model based on system equipment parameters, wherein the system optimizing model comprises an approximation model, a cooling tower model and a cooling pump model.

In this embodiment, the system optimizing model refers to a model created according to system equipment parameters, and is used for matching an optimal control scheme of the cooling water system in various scenes, where the system optimizing model includes an approximation model, a cooling tower model, and a cooling pump model.

Specifically, acquiring system equipment parameters from project information, wherein the system equipment parameters comprise cooling tower equipment parameters and cooling pump equipment parameters, and creating an approximation model based on the cooling tower equipment parameters and historical meteorological data so as to facilitate matching of corresponding approximation values according to real-time meteorological data; creating a cooling tower model based on the cooling tower equipment parameters, so that the power consumption value of the cooling tower can be conveniently estimated according to the running state of the cooling tower; creating a cooling pump model based on the cooling pump equipment parameters, so that the power consumption value of the cooling pump can be conveniently estimated according to the running state of the cooling pump; and a system optimizing model is established based on the approximation model, the cooling tower model and the cooling pump model, so that the data sharing and the data association among the approximation model, the cooling tower model and the cooling pump model are conveniently realized, and the efficiency of generating the optimal control scheme of the cooling water system is improved.

Referring to fig. 3, in step S20, the method includes:

s21: and setting an approximation degree initial value for each wet bulb temperature interval based on the cooling tower equipment parameters, and creating an approximation degree model.

Specifically, the temperature value change range and the humidity value change range of the target area are respectively judged based on historical meteorological data of the target area, and the change and distribution rule of the temperature value and the humidity value along with the quarter and the month are respectively judged; and calculating a corresponding wet bulb temperature change range based on the temperature value and the corresponding humidity value, dividing the wet bulb temperature change range into a plurality of wet bulb temperature intervals, preferably, setting a corresponding approximation degree initial value based on the cooling tower equipment parameter and a midpoint value of the corresponding wet bulb temperature value in each wet bulb temperature interval, and creating an approximation degree model based on the approximation degree initial value corresponding to each wet bulb temperature interval, so that after the wet bulb temperature value is input into the approximation degree model, the corresponding approximation degree initial value is automatically matched.

S22: and evaluating the outlet water temperature and the electricity consumption value of the cooling tower at each inlet water temperature, wet bulb temperature, fan frequency and cooling flow based on the cooling tower equipment parameters, and creating a cooling tower model.

Specifically, based on cooling tower equipment parameters, evaluating corresponding cooling water outlet temperature and electricity consumption values of the cooling tower under various cooling tower inlet water temperatures, wet bulb temperatures, fan frequencies and cooling tower flow rates, thereby creating a cooling tower model; the corresponding relation among the cooling tower water inlet temperature, the wet bulb temperature, the fan frequency, the cooling tower flow, the cooling water outlet temperature and the electricity consumption value can be obtained from historical operation parameters of cooling tower equipment, regression algorithm analysis is carried out based on the historical operation parameters of the cooling tower, so that the corresponding relation among the cooling tower water inlet temperature, the wet bulb temperature, the fan frequency, the cooling tower flow, the cooling water outlet temperature and the electricity consumption value is obtained, the reliability of a cooling tower model is improved conveniently, and the reliability of the cooling tower model can be gradually optimized along with the accumulation of the historical operation parameters.

S23: and evaluating the power consumption value of the cooling pump at different flow rates based on the cooling pump equipment parameters, and creating a cooling pump model.

Specifically, based on the parameters of the cooling pump equipment, the electricity consumption values of the cooling pump equipment under different flow rates are evaluated, so that a cooling pump model is created, wherein the corresponding relation between the cooling water flow rate and the electricity consumption values of the cooling pump equipment can be obtained from the historical operation parameters of the cooling pump equipment, regression algorithm analysis is performed based on the historical operation parameters of the cooling pump, so that the corresponding relation between the cooling water flow rate and the electricity consumption values of the cooling pump equipment is obtained, the reliability of the cooling pump model is improved conveniently, and the reliability of the cooling pump model can be optimized gradually along with the accumulation of the historical operation parameters.

S30: acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimizing model, matching an optimal control scheme, generating a annual control strategy based on the optimal control scheme, and sending the annual control strategy to a control device.

In the present embodiment, the control means refers to means for automatically controlling the cooling water system based on the annual control strategy.

Specifically, the predicted value of the outdoor wet bulb temperature value and the load demand data of each time period in the whole year are obtained, the outdoor wet bulb temperature value and the load demand data of the corresponding time period are input to the system optimizing model one by one, so that the corresponding approximation degree value, the cooling tower operation scheme and the cooling pump operation scheme are matched, the optimal control scheme of each time period is determined according to the control target with the lowest total energy consumption of the cooling water system, and the whole year control strategy is generated based on the optimal control scheme of each time period and is sent to the control device, so that the follow-up automatic control of the running state of the cooling water system through the control device is facilitated.

In this embodiment, the annual working condition may be split into 8760 time periods (i.e. 8760 hours, where the working conditions in the time periods include load requirements and meteorological parameters), and the working conditions in each time period respectively execute a single-point optimizing process, and after 8760 times of calculation are circulated, an annual control strategy is finally formed; in the actual operation of the subsequent cooling water system, the system can be matched with the nearest working condition, so as to output a control approximation degree value.

Further, after the annual control strategy generated based on the target item is transmitted to the control device, the control device performs the process of cooling water system control as follows: at a certain moment, the system acquires the current load demand, and acquires the current outdoor dry bulb temperature and relative humidity through an outdoor temperature and humidity sensor; the system acquires the approximation degree value of the optimal control scheme under the current working condition according to the annual control strategy; correcting a cooling outlet water temperature control target of cooling tower equipment according to the approximation degree value, and controlling a cooling tower fan to execute PID frequency conversion regulation according to the cooling outlet water temperature control target; the cooling pump equipment executes PID frequency conversion adjustment according to the temperature difference target value; after the cooling tower equipment and the cooling pump equipment are adjusted in place, obtaining the energy consumption value of the cooling water system in the current time period; and acquiring and recording data such as cooling tower operation parameters, cooling pump operation parameters, actual load demands, outdoor weather parameters, system heat dissipation capacity data and the like in real time.

Referring to fig. 4, in step S30, the method includes:

s31: and acquiring an outdoor wet bulb temperature value, matching the corresponding initial approximation degree value, calculating a cooling water outlet temperature required value, and inputting the cooling water outlet temperature required value into a cooling tower model.

Specifically, outdoor meteorological data in a target time period are obtained, and a current outdoor wet bulb temperature value is calculated according to the obtained outdoor dry bulb temperature and relative humidity; selecting a corresponding initial approximation degree value under the current outdoor wet bulb temperature value according to the approximation degree model; and calculating a cooling water temperature demand value according to the initial approximation degree value, and inputting the cooling water temperature demand value into a cooling tower model.

S32: based on the load demand data, calculating the heat dissipation capacity of the system according to a preset calculation coefficient, obtaining a control temperature difference value, calculating a cooling water flow and a cooling water outlet temperature demand value, and inputting the cooling water flow and the cooling water outlet temperature demand value into a cooling tower model.

Specifically, based on load demand data required at the end of the target time period, calculating the heat dissipation capacity of the system according to a preset calculation coefficient, wherein in the embodiment, the calculation coefficient is; in the air pressure embodiment of the present application, if the cooling water system is an air compressor cooling water system, the heat dissipation of the system may also be directly obtained; based on the system heat dissipation capacity, acquiring a control temperature difference, calculating a cooling water flow demand value and inputting the cooling water flow demand value into a cooling tower model and a cooling pump model; based on the system heat dissipation and the control temperature difference, a cooling inlet water temperature demand is calculated and input into a cooling tower model.

S33: and matching the optimal operation scheme of the cooling tower and the cooling pump in the initial value of the corresponding approximation degree based on the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature.

Specifically, based on a wet bulb temperature value, a cooling water inlet temperature, a cooling water flow and a cooling water outlet temperature which are input into a cooling tower model, matching the number of cooling towers and the cooling tower frequency corresponding to the lowest power consumption of cooling tower equipment as an optimal cooling tower operation scheme, and outputting a power consumption value corresponding to the optimal cooling tower operation scheme; based on the cooling water flow input to the cooling pump model, matching the number of cooling pumps and the cooling pump frequency corresponding to the lowest power consumption of the cooling pump equipment as an optimal cooling pump operation scheme, and outputting a power consumption value corresponding to the optimal cooling pump operation scheme; and generating an optimal operation scheme of the cooling water system based on the optimal cooling tower operation scheme and the optimal cooling pump operation scheme.

Further, in this embodiment, since the core objective of the cooling water optimizing strategy control is that the electric consumption of the cooling water system is the lowest, if the operation states of the cooling water system corresponding to the optimal cooling tower operation scheme and the optimal cooling pump operation scheme are not the operation states with the lowest energy consumption of the cooling water system, parameter adjustment is performed based on the optimal cooling tower operation scheme and the optimal cooling pump operation scheme, so that the operation state with the lowest energy consumption of the cooling water system is determined to generate the optimal operation scheme of the cooling water system.

Wherein, referring to fig. 5, after step S33, further comprising:

s34: and defining an optimal operation scheme of the cooling tower and the cooling pump at the initial value of the corresponding approximation degree as a reference scheme, and obtaining the corresponding electricity consumption value.

Specifically, in this embodiment, since the core objective of the control of the cooling water optimizing strategy is that the electric consumption of the cooling water system is the lowest, the operation states of the cooling water system corresponding to the optimal cooling tower operation scheme and the optimal cooling pump operation scheme may not be the operation scheme that is the most energy-saving for the cooling water system; after generating an optimal operation scheme of the cooling water system based on the initial approximation degree value, defining the optimal operation scheme as a reference scheme, and calculating a total power consumption value of the cooling water system corresponding to the reference scheme, so that the total power consumption value of each optimizing scheme is conveniently compared with the total power consumption value of the reference scheme in the follow-up process, and the more energy-saving cooling water system operation scheme is determined.

S35: setting a positive optimizing approximation degree value and a negative optimizing approximation degree value in positive and negative directions of an approximation degree initial value based on a preset optimizing resolution, and determining a positive optimizing scheme, a negative optimizing scheme and corresponding power consumption values based on the positive optimizing approximation degree value and the negative optimizing approximation degree value.

In this embodiment, the optimizing resolution refers to a difference between a new value of the optimizing approximation degree and an initial value of the approximation degree when the approximation degree optimizing program is executed based on the reference scheme.

Specifically, in this embodiment, the optimizing resolution is 0.2 ℃, and a positive optimizing approximation degree value and a negative optimizing approximation degree value are set in the positive and negative directions based on the initial approximation degree value and the preset optimizing resolution, for example, the initial approximation degree value is 3 ℃, the positive optimizing approximation degree value is 3.2 ℃, and the negative optimizing approximation degree value is 2.8 ℃; and determining a positive optimizing scheme and a negative optimizing scheme based on the positive optimizing approximation degree value and the negative optimizing approximation degree value.

Specifically, calculating a corresponding cooling outlet water temperature demand value based on the forward optimizing approximation degree value and inputting the cooling outlet water temperature demand value into a cooling tower model; calculating the heat dissipation capacity of the system according to a preset calculation coefficient based on the load demand data, acquiring a control temperature difference value, calculating a corresponding cooling water flow and cooling water outlet temperature demand value, and inputting the cooling tower model; based on the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature, matching an optimal operation scheme of the cooling tower and the cooling pump in corresponding forward optimizing approximation degree values as a forward optimizing scheme; and calculating a corresponding electricity consumption value based on the forward optimizing scheme.

Specifically, calculating a corresponding cooling outlet water temperature demand value based on a negative optimizing approximation degree value and inputting the cooling outlet water temperature demand value into a cooling tower model; calculating the heat dissipation capacity of the system according to a preset calculation coefficient based on the load demand data, acquiring a control temperature difference value, calculating a corresponding cooling water flow and cooling water outlet temperature demand value, and inputting the cooling tower model; based on the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature, matching an optimal operation scheme of the cooling tower and the cooling pump in the corresponding negative optimizing approximation degree value as a negative optimizing scheme; and calculating a corresponding electricity consumption value based on the negative optimizing scheme.

S36: and defining the approximation degree value of the scheme with the lowest power consumption value in the reference scheme, the positive optimizing scheme and the negative optimizing scheme as a new approximation degree initial value.

Specifically, the power consumption values of the reference scheme, the positive-direction optimizing scheme and the negative-direction optimizing scheme are compared, and the approximation degree value corresponding to the scheme with the lowest power consumption value is defined as a new approximation degree initial value, so that the next approximation degree optimizing program can be conveniently executed, and the energy consumption of the cooling water system control scheme can be further optimized.

Wherein, referring to fig. 6, after step S36, further comprising:

S37: and if the electricity consumption value of the reference scheme is the lowest, generating an optimal control scheme based on the reference scheme.

In this embodiment, the optimal control scheme refers to a scheme that is obtained based on a loop of a multiple-approximation-degree optimizing program and is finally used for controlling the cooling water system.

Specifically, after an approximation degree optimizing program is executed to generate an approximation degree optimizing result, comparing electricity consumption values corresponding to a reference scheme, a positive optimizing scheme and a negative optimizing scheme in the approximation degree optimizing result, and if the electricity consumption value of the reference scheme is lower than the electricity consumption values corresponding to the positive optimizing scheme and the negative optimizing scheme, considering that the energy efficiency of the reference scheme is higher than that of the positive optimizing scheme and the negative optimizing scheme, generating an optimal control scheme based on the reference scheme, and facilitating the follow-up control of the operation of the cooling water system according to the optimal control scheme.

S38: and if the power consumption value of the reference scheme is not the lowest, executing a new approximation optimizing program based on the new approximation initial value.

Specifically, executing an approximation degree optimizing program, after generating an approximation degree optimizing result, comparing power consumption values corresponding to a reference scheme, a positive optimizing scheme and a negative optimizing scheme in the approximation degree optimizing result, if the power consumption value of the reference scheme is higher than the power consumption value corresponding to the positive optimizing scheme, considering that the energy efficiency of the reference scheme is lower than the positive optimizing scheme, and if the power consumption value of the reference scheme is higher than the power consumption value corresponding to the negative optimizing scheme, considering that the energy efficiency of the reference scheme is lower than the negative optimizing scheme; therefore, the next round of approximation optimizing program needs to be executed based on the new initial approximation value in the approximation optimizing result until the optimal control scheme is determined, so that the subsequent control of the cooling water system according to the optimal control scheme is facilitated.

S40: and inputting the current load demand value into a control device, matching the current optimal cooling approximation degree to correct the cooling outlet water temperature target value, and executing PID frequency conversion regulation.

Specifically, when the control device executes automatic control on the cooling water system, the current load demand value and wet bulb temperature are input to the control device, and the approximation degree value of the optimal control scheme under the current working condition is obtained according to the annual control strategy so as to correct the cooling water outlet temperature control target of the cooling tower equipment, control the cooling tower fan to execute PID frequency conversion regulation, control the cooling pump equipment to execute PID frequency conversion regulation according to the temperature difference target value, and therefore the effect of optimizing the energy efficiency of the cooling water system is achieved.

Example two

On the basis of the first embodiment, referring to fig. 7, the cooling water optimizing method centering on the approximation degree further includes:

s50: and recording the cooling tower operation parameters, the cooling pump operation parameters, the actual load requirements, the outdoor weather parameters and the heat dissipation data in real time, and generating historical data.

Specifically, in the daily operation process of the cooling water system equipment, data such as cooling tower operation parameters, cooling pump operation parameters, actual load demands, outdoor weather parameters, system heat dissipation capacity data and the like are obtained in real time and recorded, and historical data are generated so that the reason of abnormality can be conveniently and rapidly judged when the cooling water system is abnormal in operation.

S60: and correcting parameters of the system optimizing model and the annual control strategy according to the historical data.

Specifically, the parameters of the system optimizing model and the annual control strategy are corrected according to the historical data in a periodic manner so as to reduce the deviation between the optimal control scheme and the actual optimal control parameters, so that the actual cooling water system running condition is matched, and the optimizing effect of the optimal control scheme on the energy efficiency of the cooling water system is further improved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.

Example III

As shown in fig. 8, the present application discloses a cooling water optimizing independent control system with approximation degree as a center, which is used for executing the steps of the cooling water optimizing method with approximation degree as a center, and corresponds to the cooling water optimizing method with approximation degree as a center in the above embodiment.

The cooling water optimizing independent control system taking the approximation degree as the center comprises a project information acquisition module, a system optimizing model creation module, an optimal control scheme generation module and a cooling water system control module. The detailed description of each functional module is as follows:

the system optimizing model creation module is used for creating a system optimizing model based on system equipment parameters, wherein the system optimizing model comprises an approximation model, a cooling tower model and a cooling pump model;

The system optimizing model creation module comprises:

The optimal control scheme generating module comprises:

the approximation degree initial value matching sub-module is used for acquiring an outdoor wet bulb temperature value, matching the corresponding approximation degree initial value, calculating a cooling water outlet temperature required value and inputting the cooling water outlet temperature required value into the cooling tower model;

the system heat dissipation amount calculation operator module is used for calculating the system heat dissipation amount according to a preset calculation coefficient based on the load demand data, obtaining a control temperature difference value, calculating the cooling water flow and the cooling water inlet temperature demand value and inputting the cooling tower model;

the optimal operation scheme matching sub-module is used for matching the optimal operation scheme of the cooling tower and the cooling pump in the corresponding approximation degree initial value based on the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature;

the reference scheme obtaining submodule is used for defining an optimal operation scheme of the cooling tower and the cooling pump at the initial value of the corresponding approximation degree as a reference scheme and obtaining a corresponding electricity consumption value;

the positive and negative optimizing sub-module is used for respectively setting a positive optimizing approximation degree value and a negative optimizing approximation degree value in the positive and negative directions of the approximation degree initial value based on the preset optimizing resolution, and determining a positive optimizing scheme, a negative optimizing scheme and corresponding power consumption values based on the positive optimizing approximation degree value and the negative optimizing approximation degree value;

The approximation degree initial value updating sub-module is used for defining approximation degree values of a scheme with the lowest power consumption value in the reference scheme, the positive optimizing scheme and the negative optimizing scheme as new approximation degree initial values;

the optimal control scheme generation sub-module is used for generating an optimal control scheme based on the reference scheme if the electricity consumption value of the reference scheme is the lowest;

and the approximation degree optimizing program executing sub-module is used for executing a new approximation degree optimizing program based on the new approximation degree initial value if the electricity consumption value of the reference scheme is not the lowest.

The cooling water optimizing independent control system taking approximation degree as center further comprises:

the historical data acquisition module is used for recording the operation parameters of the cooling tower, the operation parameters of the cooling pump, the actual load demands, the outdoor weather parameters and the heat dissipation data in real time and generating historical data;

the system optimizing model correcting module is used for correcting parameters of the system optimizing model and the annual control strategy according to the historical data according to the period.

Specific limitations regarding the independent control system for optimizing the cooling water with the approximation degree as the center can be found in the above description of the limitation of the method for optimizing the cooling water with the approximation degree as the center, and will not be described in detail herein; all or part of each module in the cooling water optimizing independent control system taking the approximation degree as the center can be realized by software, hardware and the combination thereof; the above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

Example IV

A computer device, which may be a server, may have an internal structure as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing project information, a system optimizing model, an optimal control scheme, a annual control strategy, cooling tower operation parameters, cooling pump operation parameters, actual load demands, outdoor weather parameters, heat dissipation data and other data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a method of proximity-centric cooling water optimization.

In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program:

S10: acquiring load demand data, system equipment parameters and historical meteorological data of a target project, and generating project information;

s20: creating a system optimizing model based on system equipment parameters, wherein the system optimizing model comprises an approximation model, a cooling tower model and a cooling pump model;

s30: acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimizing model, matching an optimal control scheme, generating a annual control strategy based on the optimal control scheme, and sending the annual control strategy to a control device;

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink), DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand; the technical scheme described in the foregoing embodiments can be modified or some of the features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. The cooling water optimizing method with approximation degree as center is characterized by comprising the following steps:

inputting the current load demand value to a control device, matching the cooling approximation degree of the optimal control scheme under the current working condition to correct the cooling outlet water temperature target value, and controlling a cooling tower fan and cooling pump equipment to execute PID frequency conversion adjustment according to the temperature difference target value;

the method comprises the steps of creating a system optimizing model based on system equipment parameters, wherein the system optimizing model comprises an approximation model, a cooling tower model and a cooling pump model, and the method comprises the following steps:

judging a temperature value change range and a humidity value change range of a target area based on historical meteorological data of the target area, further calculating a corresponding wet bulb temperature change range, dividing the wet bulb temperature change range into a plurality of wet bulb temperature intervals, setting approximation degree initial values for the wet bulb temperature intervals based on cooling tower equipment parameters and midpoint values of the wet bulb temperature values corresponding to the wet bulb temperature intervals, and creating an approximation degree model based on the approximation degree initial values corresponding to the wet bulb temperature intervals;

Carrying out regression algorithm analysis based on historical operation parameters of the cooling tower, so as to obtain the corresponding relation among the water inlet temperature, wet bulb temperature, fan frequency, cooling tower flow, the water outlet temperature of the cooling water and electricity consumption value, evaluating the water outlet temperature and electricity consumption value of the cooling tower under the water inlet temperature, wet bulb temperature, fan frequency and cooling tower flow based on the cooling tower equipment parameters, and creating a cooling tower model;

carrying out regression algorithm analysis based on historical operation parameters of the cooling pump so as to obtain a corresponding relation between the cooling water flow and the power consumption value of cooling pump equipment, evaluating the power consumption value of the cooling pump under different cooling water flows based on the cooling pump equipment parameters, and creating a cooling pump model;

the method comprises the steps of acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimizing model, and matching an optimal control scheme system, wherein the method comprises the following steps:

acquiring an outdoor wet bulb temperature value, selecting a corresponding approximation degree initial value under the current outdoor wet bulb temperature value according to an approximation degree model, calculating a cooling outlet water temperature required value according to the approximation degree initial value, and inputting the cooling outlet water temperature required value into a cooling tower model;

calculating the system heat dissipation capacity according to a preset calculation coefficient based on the load demand data, obtaining a control temperature difference value, calculating a cooling water flow demand value, inputting the cooling water flow demand value into a cooling tower model and a cooling pump model, calculating a cooling water inlet temperature demand value based on the system heat dissipation capacity and the control temperature difference, and inputting the cooling water inlet temperature demand value into the cooling tower model;

Based on wet bulb temperature value, cooling water inlet temperature, cooling water flow and cooling water outlet temperature input to the cooling tower model, matching the number of cooling towers corresponding to the lowest power consumption of the cooling tower equipment and the cooling tower frequency as an optimal cooling tower operation scheme, based on the cooling water flow input to the cooling pump model, matching the number of cooling pumps corresponding to the lowest power consumption of the cooling pump equipment and the cooling pump frequency as an optimal cooling pump operation scheme, and generating an optimal operation scheme of the cooling water system based on the optimal cooling tower operation scheme and the optimal cooling pump operation scheme;

based on the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature, after the step of matching the optimal operation scheme of the cooling tower and the cooling pump corresponding to the initial value of the approximation degree, the method further comprises the following steps:

2. The approximation-centered cooling water optimizing method as set forth in claim 1, wherein: after the step of defining the approximation degree value of the scheme with the lowest power consumption value in the reference scheme, the positive optimizing scheme and the negative optimizing scheme as the new approximation degree initial value, the method further comprises the following steps:

3. The approximation-centered cooling water optimizing method as set forth in claim 1, wherein: further comprises:

4. An independent control system for optimizing cooling water with approximation as center, comprising:

the cooling water system control module is used for inputting the current load demand value into the control device, matching the cooling approximation degree of the optimal control scheme under the current working condition to correct the cooling outlet water temperature target value, and controlling the cooling tower fan and the cooling pump equipment to execute PID frequency conversion adjustment according to the temperature difference target value;

wherein, the system optimizing model creation module comprises:

the approximation degree model creation submodule is used for judging a temperature value change range and a humidity value change range of a target area based on historical meteorological data of the target area, further calculating a corresponding wet bulb temperature change range, dividing the wet bulb temperature change range into a plurality of wet bulb temperature intervals, setting approximation degree initial values for the wet bulb temperature intervals based on cooling tower equipment parameters and midpoint values of the wet bulb temperature values corresponding to the wet bulb temperature intervals, and creating an approximation degree model based on the approximation degree initial values corresponding to the wet bulb temperature intervals;

The cooling tower model creation submodule is used for carrying out regression algorithm analysis based on historical operation parameters of the cooling tower so as to obtain a corresponding relation among the water inlet temperature, wet bulb temperature, fan frequency, cooling tower flow, the water outlet temperature of the cooling water and electricity consumption value, and evaluating the water outlet temperature and electricity consumption value of the cooling tower under the water inlet temperature, wet bulb temperature, fan frequency and cooling tower flow based on cooling tower equipment parameters to create a cooling tower model;

the cooling pump model creation submodule is used for carrying out regression algorithm analysis based on historical operation parameters of the cooling pump so as to obtain a corresponding relation between cooling water flow and power consumption values of cooling pump equipment, and evaluating the power consumption values of the cooling pump under different cooling water flow based on the cooling pump equipment parameters to create a cooling pump model;

the optimal control scheme generating module comprises:

the approximation degree initial value matching sub-module is used for acquiring an outdoor wet bulb temperature value, selecting a corresponding approximation degree initial value under the current outdoor wet bulb temperature value according to an approximation degree model, calculating a cooling water outlet temperature required value according to the approximation degree initial value, and inputting the cooling water outlet temperature required value into the cooling tower model;

the system heat dissipation calculation operator module is used for calculating the system heat dissipation capacity according to a preset calculation coefficient based on the load demand data, obtaining a control temperature difference value, calculating a cooling water flow demand value, inputting the cooling water flow demand value into the cooling tower model and the cooling pump model, calculating a cooling water inflow temperature demand value based on the system heat dissipation capacity and the control temperature difference, and inputting the cooling water inflow temperature demand value into the cooling tower model;

The optimal operation scheme matching sub-module is used for matching the cooling tower table number and the cooling tower frequency corresponding to the lowest power consumption of the cooling tower equipment as an optimal cooling tower operation scheme based on the wet bulb temperature value, the cooling water inlet temperature, the cooling water flow and the cooling water outlet temperature input to the cooling tower model, matching the cooling pump table number and the cooling pump frequency corresponding to the lowest power consumption of the cooling pump equipment as an optimal cooling pump operation scheme based on the cooling water flow input to the cooling pump model, and generating an optimal operation scheme of the cooling water system based on the optimal cooling tower operation scheme and the optimal cooling pump operation scheme;

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the steps of the approximation-centric cooling water optimizing method according to any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the proximity-centric cooling water optimizing method according to any one of claims 1 to 3.