An automated cooling method for a digital energy-saving pump

Abstract

The present invention belongs to the field of energy-saving pump control, and particularly relates to an automatic cooling method for a digital energy-saving pump, including: obtaining the normal working temperature range of the energy-saving pump, the water supply volume and the water supply demand curve, the temperature and energy consumption changes under different working states, the historical data of the cooling device, etc. and preprocessing them; secondly, constructing a differential energy-saving efficiency temperature prediction curve function according to the temperature changes under different working states, and constructing a comprehensive energy consumption and single energy consumption curve in combination with the energy consumption and cooling device data; thirdly, based on the cooling control optimization model, using relevant curves and intervals to construct a temperature adjustment fitness function and constraint conditions, and obtaining the cooling time point intervals and energy consumption of the air-cooling and water-cooling devices through training; fourthly, constructing a cooling control adjustment model, and inputting the above parameters to train to obtain differential time point control instructions; finally, monitoring the deviation of each parameter in real time, and feeding it back to the cooling control optimization model to adjust the cooling control adjustment model, so as to realize precise cooling control of the digital energy-saving pump.

Description

Technical Field

[0001] The present invention belongs to the field of energy-saving pump control, and particularly relates to an automatic cooling method for a digital energy-saving pump. Background Art

[0002] Digital energy-saving pumps are widely used in industries, water supply, heating ventilation, etc. When operating, the pump body generates heat due to mechanical friction and motor operation. If heat dissipation is not timely, it will affect performance, lifespan, and safety. Existing cooling technologies have deficiencies. Forced air cooling relies on a fan to accelerate air flow for heat dissipation, but its heat dissipation ability is limited in high-temperature environments; although water cooling has high heat dissipation efficiency, it consumes high costs and is complex to maintain. Therefore, how to combine cooling methods and integrate their advantages has become an important issue in current research. For example, the cooling system disclosed in the Chinese patent with the authorization announcement number CN107850355B, which has direct expansion cooling and energy-saving cooling of pumped refrigerant, can switch between energy-saving cooling of pumped refrigerant and direct expansion cooling according to the external air temperature. However, it is mainly for a specific cooling system and does not fully consider the special working conditions of digital energy-saving pumps, such as the unique distribution and variation law of heat generated by the mechanical structure of the pump body and motor operation; moreover, this system does not comprehensively balance the cooling effect and energy-saving effect, and it is difficult to be directly applied to digital energy-saving pumps to meet the heat dissipation requirements of high efficiency, low cost, and easy maintenance.

[0003] The above existing technologies have the following problems: There are significant problems in the cooling of digital energy-saving pumps in the existing technologies. Most existing methods only focus on cooling and temperature reduction operations of energy-saving pumps in isolation, but fail to effectively achieve the coordinated control of the working state, temperature of the energy-saving pump, the time point of the cooling system operation, and the cooling consumption, and cannot accurately control the cooling energy consumption. Therefore, the present invention provides an automatic cooling method and system for digital energy-saving pumps. Summary of the Invention

[0004] In view of the deficiencies of the existing technologies, the present invention proposes an automatic cooling method and system for digital energy-saving pumps, including: obtaining the normal working temperature range, water supply volume, and water supply demand curve of the energy-saving pump, temperature and energy consumption changes under different working states, historical data of the cooling device, etc., and preprocessing them; secondly, constructing a differential energy-saving efficiency temperature prediction curve function according to the temperature changes under different working states, and constructing a comprehensive energy consumption curve and a single energy consumption curve in combination with energy consumption and cooling device data; thirdly, constructing a cooling control optimization model based on the genetic algorithm, using relevant curves and intervals to construct a temperature adjustment fitness function and constraint conditions, and obtaining the cooling time point intervals and energy consumption of the air-cooling and water-cooling devices through training; fourthly, constructing a cooling control adjustment model, inputting the above parameters to train to obtain differential time point control instructions; finally, controlling the cooling device according to the instructions, monitoring the deviation of each parameter in real time, and feeding it back to the optimization model to adjust the cooling control adjustment model to achieve automatic and precise cooling control of the digital energy-saving pump.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An automated cooling method for a digital energy-saving pump, comprising:

[0007] Obtain a differential energy-saving efficiency temperature prediction curve function, a comprehensive energy consumption curve and a single energy consumption curve function under differential energy-saving efficiency;

[0008] Utilize the comprehensive energy consumption curve, the single energy consumption curve function, the temperature prediction curve function, the water supply volume change curve function, and the normal working temperature range to construct a temperature regulation fitness function and constraint conditions;

[0009] Build the temperature regulation fitness function and constraint conditions into the constructed cooling control optimization model for training to obtain the predicted cooling time point interval of the air-cooling and water-cooling devices, the single cooling energy consumption, and the comprehensive energy consumption;

[0010] The predicted cooling time point interval is obtained by obtaining the predicted temperature change rate of the energy-saving pump and the ideal temperature change rate per unit time of the energy-saving pump;

[0011] Input the predicted cooling time point interval of the air-cooling and water-cooling devices, the single cooling energy consumption, and the comprehensive energy consumption into the constructed cooling control adjustment model for training to obtain the differential time point control command of the cooling device and perform differential cooling time point control of the cooling device.

[0012] Specifically, the construction process of the comprehensive energy consumption curve includes:

[0013] Obtain a temperature prediction curve function according to the historical temperature change data of the energy-saving pump under different working conditions;

[0014] According to the cooling efficiency per unit time of the air-cooling or water-cooling device corresponding to the temperature in the temperature prediction curve function and the cooling energy consumption per unit time under the corresponding cooling efficiency, and obtain the single energy consumption curve of the corresponding cooling device;

[0015] Construct a working energy consumption change curve and a corresponding change function under different working conditions according to the working energy consumption data of the energy-saving pump under different working conditions.

[0016] Specifically, the construction process of the comprehensive energy consumption curve further includes:

[0017] Align the single energy consumption curves corresponding to the air-cooling and water-cooling devices with the working energy consumption data under the corresponding working conditions in the time dimension, and superimpose the water supply energy consumption and the air-cooling or water-cooling cooling energy consumption at the same time point to obtain the water supply and cooling comprehensive energy consumption and the comprehensive energy consumption curve of the energy-saving pump at the corresponding time point, and determine the energy consumption level at the corresponding time point.

[0018] Specifically, the working state of the energy-saving pump is the water supply flow rate of the energy-saving pump per unit time, and the construction process of the temperature adjustment fitness function and the constraint conditions includes:

[0019] According to the required water supply and the working status of the energy-saving pump, the target water supply demand time interval is obtained. [ T s ， T e ] ,in represents the initial water supply time point corresponding to the demand water supply, Indicates the end water supply time point corresponding to the demand water supply;

[0020] According to the target water supply demand time length and normal working temperature range, the ideal temperature change rate per unit time of the energy-saving pump is obtained. ;

[0021] According to the temperature prediction curve function, the predicted temperature change rate of the energy-saving pump is obtained. .

[0022] Specifically, the process of constructing the temperature adjustment fitness function and constraint conditions also includes:

[0023] According to the historical operation order of air cooling and water cooling devices, the corresponding temperature adjustment range and the corresponding energy consumption per unit time, the initial cooling device is selected for cooling;

[0024] According to the predicted temperature change rate of the energy-saving pump and the ideal temperature change rate per unit time of the energy-saving pump, we can obtain First time greater than The corresponding time point , and select the initial cooling device to start cooling down, so that .

[0025] Specifically, the process of constructing the temperature adjustment fitness function and constraint conditions also includes:

[0026] According to the predicted temperature change rate of the energy-saving pump, the ideal temperature change rate per unit time of the energy-saving pump, and the cooling efficiency of the initial cooling device corresponding to the rated power, the cooling efficiency of the initial cooling device at the rated power is obtained. The corresponding time point , and select the initial cooling device and the remaining cooling device to cool down the energy-saving pump at the same time;

[0027] According to the cooling efficiency, cooling energy consumption, energy consumption of energy-saving pumps and the difference between supply and demand of energy-saving pumps corresponding to the initial cooling device and the remaining cooling device , construct the temperature adjustment fitness function and constraints of the initial cooling device and the remaining cooling devices, so that and Under this condition, the comprehensive energy consumption of water supply and cooling is minimal.

[0028] Specifically, the construction process of the temperature adjustment fitness function and constraint conditions for the initial cooling device and the remaining cooling device includes:

[0029] Based on the corresponding output power, cooling efficiency, and air-cooling energy consumption of the historical air-cooling device, obtain the air-cooling energy consumption fitting function through a non-linear fitting function ;

[0030] Based on the corresponding output power, cooling flow rate and velocity, cooling efficiency, and water-cooling energy consumption of the historical water-cooling device, obtain the water-cooling energy consumption fitting function through a non-linear fitting function ;

[0031] Based on the flow rate, rotational speed, and corresponding working energy consumption of the energy-saving pump under the corresponding working state, obtain the working energy consumption fitting function of the energy-saving pump through a non-linear fitting function ;

[0032] According to , and Construct the temperature adjustment fitness function and the corresponding constraint conditions, and at the time point when the energy consumption value of the temperature adjustment fitness function is the smallest and , Under the state of, solve the corresponding predicted pre-joint operation parameter sets of water cooling, air cooling, and energy-saving pumps through the linear programming algorithm;

[0033] When cooling is performed simultaneously by the air-cooling and water-cooling devices, when the temperature at the current moment is less than or equal to The temperature corresponding to the moment, set the remaining cooling device to the silent state corresponding to the lowest energy consumption, and at the same time adjust the temperature of the energy-saving pump through the previous joint operation parameter set of the cooling control optimization model, so that and .

[0034] Specifically, the pre-joint operation parameter set includes a differential adjustment factor, and the differential adjustment factor is used to adjust the ratio of the output powers corresponding to the air-cooling and water-cooling devices at different time points within ( t 2 ， T e ] The time interval; the acquisition steps of the differential adjustment factor include:

[0035] Based on the attribute data of the air-cooling and water-cooling devices, obtain the effective cooling efficiencies corresponding to the air-cooling and water-cooling devices at different output powers and ;

[0036] According to , , the current moment and The difference, through the temperature adjustment fitness function and the corresponding constraint conditions, to obtain and the corresponding output powers of the air-cooling and water-cooling devices;

[0037] According to and the corresponding output powers of the air-cooling and water-cooling devices and the and at the corresponding time points, through the support vector machine, to obtain the function of the differential adjustment factor, and build the function of the differential adjustment factor into the cooling control adjustment model.

[0038] Specifically, the specific steps for building the cooling control adjustment model include:

[0039] According to [ T s ， T e ] , , , , , and , the function of the differential adjustment factor, and the temperature at the current moment less than or equal to at the corresponding moment to build the input state of the cooling control adjustment model;

[0040] According to the input state of the cooling control adjustment model, build the execution action trigger information, specifically:

[0041] When appears for the first time, at the corresponding time point , trigger the first action execution information, that is, call the initial cooling device and use and the difference to obtain the initial cooling device operation parameter set, and control the initial cooling device to cool down according to the initial cooling device operation parameter set;

[0042] When still satisfying under the rated power cooling efficiency of the initial cooling device, at the corresponding time point , trigger the second action execution information, that is, call the initial cooling device and the remaining cooling devices simultaneously to cool down.

[0043] Specifically, the specific steps for building the cooling control adjustment model further include:

[0044] When triggering the second action execution information, trigger the third action execution information simultaneously, that is, according to the temperature adjustment fitness function and the corresponding constraint conditions, through the linear programming algorithm, to obtain and Pre - combined operation parameter sets corresponding to water - cooled, air - cooled, and energy - saving pumps under certain conditions. Adjust the output power ratio of the initial cooling device and the remaining cooling device according to the function of the differential adjustment factor in the pre - combined operation parameter set;

[0045] Obtain the operation parameter sets corresponding to the current moment of the initial cooling device and the remaining cooling device according to the output power ratio of the initial cooling device and the remaining cooling device and the attribute data of the initial cooling device and the remaining cooling device;

[0046] When the temperature at the current moment is less than or equal to the temperature corresponding to the moment, trigger the fourth action execution information, that is, at the current time point, set the remaining cooling device to the silent state corresponding to the lowest energy consumption through the differential adjustment factor, and adjust the operation parameter set corresponding to the initial cooling device for cooling down so that ;

[0047] Construct the execution actions corresponding to the trigger action execution information according to the execution action trigger information;

[0048] When triggering the corresponding execution action information, input the operation parameter sets of the air - cooled, water - cooled, or energy - saving pump obtained from the corresponding execution action information into the fuzzy control algorithm to obtain the operation control instructions of the air - cooled device, water - cooled device, or energy - saving pump at the corresponding time point, so that the predicted temperature change rate curve of the energy - saving pump is always below the ideal temperature change rate curve.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] Aiming at the deficiencies of the prior art, the present invention optimizes the temperature - adjustment fitness function and constraint conditions of the cooling system by constructing differential energy - saving efficiency temperature prediction curves and comprehensive energy consumption curves, combining temperature prediction and water supply volume changes, and accurately predicting the cooling time points and energy consumption of the air - cooled and water - cooled devices. At the same time, the differential adjustment factor is obtained through the temperature - adjustment fitness function and constraint conditions to achieve the optimal ratio adjustment of the output power of the cooling device at different time points, significantly improving the response speed, cooling effect, and energy consumption management of the system, extending the equipment life, and realizing the dual optimization of temperature control and energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flowchart of an automatic cooling method for a digital energy - saving pump in Embodiment 1 of the present invention;

[0052] Figure 2 It is a structural diagram of an automatic cooling method for a digital energy - saving pump in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] Embodiment 1

[0054] Please refer toFigures 1 to 2 An embodiment provided by the present invention: An automatic cooling method for a digital energy-saving pump, which is applied to an AAB digital energy-saving centrifugal pump. Further, the cooling device in this embodiment is arranged on the digital integration controller of the digital energy-saving pump to perform data acquisition, processing, and cooling control. The specific process is as follows:

[0055] S1. Obtain the normal working temperature range corresponding to the energy-saving pump, the corresponding water supply volume change curve and water supply demand curve, the temperature change data and energy consumption change status under different working states, the historical start working time point of the configured cooling device, the temperature starting point, and the cooling energy consumption of different cooling forms under different working states, and perform preprocessing;

[0056] Further, the cooling device in this embodiment includes an air-cooling device and a water-cooling device; further, the different working states in this embodiment refer to the working states of the energy-saving pump at different output flow rate levels; for example, 5m 3 / h, 10m 3 / h, etc.; further, the energy-saving pump in this embodiment corresponds to three energy efficiency levels at different output flow rate levels. For example, in the single-stage single-suction pump unit of the energy-saving pump, when the flow rate ≤ 300 , it corresponds to the third, second, and first energy efficiency levels; further, the energy efficiency level in this embodiment is represented and obtained by the specified point efficiency value of the energy-saving pump unit; when the single-stage single-suction pump unit has a flow rate ≤ 300 , the specified point efficiency values of the energy-saving pump unit corresponding to the third, second, and first energy efficiency levels are specifically , and , where represents the specified point efficiency value of the energy-saving pump unit.

[0057] S2. According to the temperature change data corresponding to different working states, obtain the differential energy-saving efficiency temperature prediction curve function. At the same time, according to the energy consumption prediction curve under different working states, the historical start running time point of the cooling device, the corresponding temperature starting point, and the cooling energy consumption of different cooling forms under different working states, obtain the comprehensive energy consumption curve and single energy consumption curve corresponding to different cooling devices under different energy-saving efficiencies;

[0058] Further, the construction process of the comprehensive energy consumption curve in this embodiment includes:

[0059] Obtain the temperature prediction curve function according to the historical temperature change data of the energy-saving pump under different working states;

[0060] According to the cooling efficiency per unit time of the air-cooling or water-cooling device corresponding to the temperature in the temperature prediction curve function and the cooling energy consumption per unit time at the corresponding cooling efficiency, and obtain the single energy consumption curve of the corresponding cooling device;

[0061] According to the working energy consumption data of the energy-saving pump in different working states, construct the working energy consumption change curve and the corresponding change function in different working states;

[0062] Align the single energy consumption curve corresponding to the air-cooling and water-cooling devices with the working energy consumption data in the corresponding working state in the time dimension, and superimpose the water supply energy consumption and the air-cooling or water-cooling cooling energy consumption at the same time point to obtain the comprehensive water supply and cooling energy consumption and the comprehensive energy consumption curve of the energy-saving pump at the corresponding time point, and determine the energy consumption level at the corresponding time point.

[0063] This process first constructs a differential energy-saving efficiency temperature prediction curve function, which can accurately predict the temperature change trend of the energy-saving pump at different output flow levels, ensuring that it always maintains within the normal working temperature range. At the same time, the construction of the comprehensive energy consumption curve and the single energy consumption curve enables the cooling system to minimize energy consumption while ensuring the efficient operation of the energy-saving pump; secondly, based on the temperature prediction curve function and the energy consumption curve, the system can predict the working state and cooling requirements of the energy-saving pump in advance, select the optimal cooling method (air-cooling or water-cooling), and precisely control the start time and cooling intensity of the cooling device, avoiding energy waste caused by over-cooling or under-cooling in traditional methods; thirdly, by aligning and superimposing the single energy consumption curve and the comprehensive energy consumption curve of the cooling device in the time dimension, the collaborative optimization of the water supply energy consumption and the cooling energy consumption is achieved, reducing unnecessary cooling operations and significantly improving the energy utilization efficiency; finally, by constructing the working energy consumption change curve and the corresponding change function in different working states, the system can dynamically adjust the cooling strategy, flexibly respond to various working conditions changes, and ensure that the energy-saving pump can maintain an efficient and energy-saving working state at different output flow levels.

[0064] S3. Construct an optimization model for cooling control based on the genetic algorithm, and use the comprehensive energy consumption curve and the single energy consumption curve function, the differential energy-saving efficiency temperature prediction curve function, the error of the water supply volume change curve function and the water supply demand curve, and the normal working temperature range to construct a temperature adjustment fitness function and constraint conditions, and embed the temperature adjustment fitness function and constraint conditions into the cooling control optimization model for training to obtain the predicted cooling time point interval, the single cooling energy consumption, and the comprehensive energy consumption of the air-cooling and water-cooling devices;

[0065] Furthermore, the construction process of the temperature adjustment fitness function and constraint conditions in this embodiment includes:

[0066] According to the required water supply volume and the working state of the energy-saving pump, obtain the target water supply demand time length interval [ T s ， T e ] , where represents the initial water supply time point corresponding to the required water supply, and represents the ending water supply time point corresponding to the required water supply;

[0067] Furthermore, in this embodiment, the working state of the energy-saving pump, that is, the water supply flow rate per unit time of the energy-saving pump set and adjusted at the current moment, sets the initial water supply time point according to the digital integrated controller on the digital energy-saving pump, and sets the total water supply according to the user's demand. Assuming that the working state remains unchanged during the water supply process, the final ending water supply time point is calculated and obtained.

[0068] Obtain the ideal temperature change rate per unit time of the energy-saving pump according to the target water supply demand time length and the normal working temperature range ; furthermore, the normal working temperature range in this embodiment is the temperature range corresponding to the loop device in the energy-saving pump;

[0069] Obtain the predicted temperature change rate of the energy-saving pump according to the temperature prediction curve function .

[0070] Select the initial cooling device for cooling according to the historical operation sequence law, the corresponding temperature adjustment range, and the corresponding energy consumption value per unit time of the air-cooled and water-cooled devices;

[0071] Furthermore, in this embodiment, select the cooling device with the minimum energy consumption value per unit time and suitable for mechanical energy cooling in the low-temperature state. For example, in this embodiment, the air-cooled device is suitable for the cooling process in the low-temperature state, so the air-cooled device is selected for initial cooling.

[0072] Furthermore, the specific process of selecting the cooling device in this embodiment includes:

[0073] First, collect the historical operation data of the air-cooled and water-cooled devices, including the start time, operation duration, and temperature change range (temperature adjustment range);

[0074] Second, count the start-up times of each cooling device in the past period of time, understand its usage frequency, record the initial temperature and final temperature at each start-up, determine the effective temperature adjustment range of each cooling device, calculate the total energy consumption during each operation, and divide it by the operation time to obtain the energy consumption value per unit time;

[0075] Third, analyze and evaluate the air-cooled and water-cooled devices according to the above statistical data, and it is obtained that the air-cooled device usually has higher efficiency in the low-temperature state because the air-cooled depends on air flow to take away heat, and the low-temperature environment helps to improve the heat dissipation effect, while the water-cooled system performs better in the high-temperature state because the water-cooled system can more effectively handle a large amount of heat load;

[0076] Fourth, set the selection criteria, specifically: preferentially select the cooling device with the minimum energy consumption per unit time. Secondly, considering the operation requirements of the energy-saving pump in the low-temperature state, select the cooling device that is more suitable for the low-temperature environment;

[0077] Fifth, based on the set selection criteria and the specific working state and temperature state of the energy-saving pump, select the specific cooling device. For example, in this embodiment, the initial cooling device is selected as the air-cooling device.

[0078] Based on the predicted temperature change rate of the energy-saving pump and the ideal temperature change rate per unit time of the energy-saving pump, obtain the first time greater than the corresponding time point , and select the initial cooling device to start cooling down, so that ;

[0079] Furthermore, t 1 = f min{ t ∈ [ T s ， T e ]| γ t 1 > γ t 1 } , this expression represents the time point when the condition is first satisfied in [ T s ， T e ] . Further, in this embodiment, f represents the function for solving the time point when the condition of is first satisfied. the corresponding time point ;

[0080] Based on the predicted temperature change rate of the energy-saving pump, the ideal temperature change rate per unit time of the energy-saving pump, and the temperature reduction efficiency corresponding to the rated power of the initial cooling device, obtain the time point corresponding to the temperature reduction efficiency at the rated power of the initial cooling device , and select the initial cooling device and the remaining cooling devices to cool down the energy-saving pump simultaneously;

[0081] Furthermore, in this embodiment, at the time point corresponding to the temperature reduction efficiency at the rated power of the initial cooling device , that is, at the maximum cooling power of the initial cooling device, the first cooling time point that still cannot meet the condition of ; at this moment, the single initial cooling device can no longer meet the corresponding cooling requirements, so that the condition of is satisfied; then call the remaining cooling devices for cooling. In this embodiment, if the initial cooling device is an air-cooling device, the remaining cooling device is a water-cooling device.

[0082] Based on the temperature reduction efficiency, temperature reduction energy consumption, working energy consumption of the energy-saving pump, and supply-demand difference of the energy-saving pump corresponding to the initial cooling device and the remaining cooling devices , construct the temperature adjustment fitness function and constraint conditions of the initial cooling device and the remaining cooling devices, so that and Under the state of , the comprehensive energy consumption of water supply and cooling is the smallest.

[0083] Furthermore, the construction process of the temperature regulation fitness function and constraint conditions of the initial cooling device and the remaining cooling device in this embodiment includes:

[0084] According to the corresponding output power, cooling efficiency and air-cooled cooling energy consumption of the historical air-cooled device, through the non-linear fitting function, the air-cooled cooling energy consumption fitting function is obtained ;

[0085] Through non-linear fitting of the data of the corresponding output power, cooling efficiency and air-cooled cooling energy consumption of the historical air-cooled device, a function that can accurately describe the relationship between the energy consumption of the air-cooled device and factors such as output power is obtained. This enables accurate prediction of the energy consumption of the air-cooled device under different operating parameters based on the current demand and equipment status in subsequent work deployment, providing basic data support for overall energy consumption optimization. For example, when a certain cooling effect is required from the air-cooled device, the most energy-efficient output power setting can be quickly determined according to this fitting function.

[0086] Similarly, according to the corresponding output power, cooling flow rate and velocity, cooling efficiency and water-cooled cooling energy consumption of the historical water-cooled device, through the non-linear fitting function, the water-cooled cooling energy consumption fitting function is obtained ;

[0087] Furthermore, the fitting function constructed based on the data such as the corresponding output power, cooling flow rate and velocity, cooling efficiency and water-cooled cooling energy consumption of the historical water-cooled device in this embodiment can clearly reflect the energy consumption characteristics of the water-cooled device. In actual operation, the system can calculate the energy consumption under different water-cooled operating parameters using this function according to the current temperature conditions and cooling requirements, so as to select the optimal water-cooled operation plan in the temperature regulation fitness function to achieve coordinated energy-saving operation with the air-cooled device and the energy-saving pump.

[0088] According to the flow rate, rotational speed and corresponding working energy consumption of the energy-saving pump under the corresponding working state, through the non-linear fitting function, the working energy consumption fitting function of the energy-saving pump is obtained ;

[0089] According to , and Construct the temperature regulation fitness function and the corresponding constraint conditions, and when the energy consumption value of the temperature regulation fitness function is the smallest at the corresponding time point and , Under the state of, the predicted pre-joint operation parameter sets corresponding to the water-cooled, air-cooled and energy-saving pump are obtained by solving through the linear programming algorithm;

[0090] Among them, the specific expression formulas of the temperature regulation fitness function and the corresponding constraint conditions are:

[0091] l t = min( ∑ i = 1 2 w i × f ti + λ j f tj ) ， w i = η i × e − α ( t − t i ) ∑ i = 1 2 e − α ( t − t i ) + λ j + η 1 + η 2 e t = 0 ， t ∈ ( t 2 ， T e ] ， γ t 1 ≤ γ t 0 ， f t 1 ≤ f 0 ， f t 2 ≤ f 1

[0092] wherein represents the energy consumption weighted coefficient of the i-th cooling device, represents the energy efficiency grade coefficient corresponding to the energy-saving pump operating in the j-th working state, represents the energy consumption fitting function of the i-th cooling device, represents the weight adjustment coefficient, t represents the current time, represents the starting time point of the i-th cooling device; wherein represents the effective cooling efficiency corresponding to different output powers of the air-cooling device, represents the effective cooling efficiency corresponding to different output powers of the water-cooling device; represents the effective cooling efficiency corresponding to the current time of the i-th cooling device, represents the minimum value function, represents the working energy consumption fitting function corresponding to the energy-saving pump operating in the j-th working state, and successively represent the energy consumption upper limit thresholds corresponding to the air-cooling and water-cooling devices per unit time.

[0093] Furthermore, in this embodiment, by constructing corresponding minimum optimization functions and constraint conditions for the energy consumption fitting functions respectively corresponding to the air-cooling device, water-cooling device and energy-saving pump, and searching for the operating parameter sets of the air-cooling, water-cooling and energy-saving pump at the corresponding time points to overall optimize the output energy consumption among the three, under the state of meeting the user requirements and normal working temperature requirements, the operating states of the three are adjusted so that the operating states of the air-cooling, water-cooling and energy-saving pump reach the optimal state simultaneously.

[0094] Furthermore, in this embodiment, the weighted coefficients corresponding to the air-cooling and water-cooling are used to regulate the proportional coefficient of the corresponding energy consumption through the corresponding effective cooling efficiency, the energy efficiency grade corresponding to the operation of the energy-saving pump and the length of the operation time, so that in the minimum optimization function, the corresponding cooling device can adjust the overall proportion of the corresponding energy consumption according to its energy consumption within the corresponding time length, and the introduction of the energy efficiency grade coefficient is used to adjust the weight relationship between the cooling device and the energy-saving pump, so that the optimization function can adjust the corresponding weighted coefficient according to different energy consumption levels, so that the overall adjustment process can continuously adjust and change according to the change of the working state in the real-time work of the energy-saving pump; for example, when the effective cooling efficiency of the air-cooling device is relatively high within a certain period of time, its corresponding weighted coefficient will increase accordingly, so as to be more inclined to use the air-cooling device to achieve cooling in the work allocation, so as to achieve the goal of minimizing the overall energy consumption.

[0095] Further, in this embodiment, the pre - joint operation parameter set includes a differential adjustment factor, which is used to adjust the ratio of the output powers of the air - cooled and water - cooled devices corresponding to different time points within ( t 2 ， T e ] a time interval; the steps for obtaining the differential adjustment factor include:

[0096] According to the attribute data of the air - cooled and water - cooled devices, obtain the effective cooling efficiency corresponding to the air - cooled and water - cooled devices at different output powers and ;

[0097] Further, the effective cooling efficiency in this embodiment is the ratio of the actual temperature drop value to the theoretical temperature drop value per unit time;

[0098] According to , , the current moment and the difference, through the temperature - adjustment fitness function and the corresponding constraint conditions, obtain the output powers of the air - cooled and water - cooled devices that satisfy and ;

[0099] According to the output powers of the air - cooled and water - cooled devices that satisfy and and the difference between the corresponding time points and and , through the support vector machine, obtain the function of the differential adjustment factor, and build the obtained function of the differential adjustment factor into the cooling control adjustment model.

[0100] During the operation of the energy - saving pump, as the temperature rises and the energy consumption increases, the cooling demand of the system also increases accordingly; however, if the working intensity of the cooling device (such as power or flow rate) is blindly increased, it may lead to over - cooling, thus wasting energy. By reducing the weight adjustment coefficient, the over - use of the cooling device can be inhibited, avoiding unnecessary energy consumption.

[0101] Therefore, the weight adjustment coefficient in this embodiment decreases as the energy consumption and temperature continuously increase.

[0102] Further, the weight adjustment coefficient in this embodiment is obtained by fitting through the correlation coefficient algorithm according to the energy consumption data and the cooling temperature related to the height corresponding to each time point of the cooling device height.

[0103] Further, the energy - efficiency grade coefficient corresponding to the j - th working state in this embodiment is specifically the energy - efficiency grade of the j - th unit - time output flow state, where the unit time is 1 hour.

[0104] After cooling down simultaneously through the air-cooling and water-cooling devices, when the temperature at the current moment is less than or equal to the temperature corresponding to the moment, set the remaining cooling devices to the silent state corresponding to the lowest energy consumption. Meanwhile, adjust the temperature of the energy-saving pump through the previous combined operation parameter set of the cooling control optimization model in the past, so that and .

[0105] Furthermore, in this process, when the temperature at the current moment is less than or equal to the temperature corresponding to the moment, it indicates that the temperature has dropped within the controllable range of the initial cooling device and can be controlled by the initial cooling device. Setting the remaining cooling devices to the silent state corresponding to the lowest energy consumption is because the switching of the cooling devices consumes a large amount of energy, and frequent switching will affect the lifespan of the cooling devices. Therefore, setting the remaining cooling devices to the silent state corresponding to the lowest energy consumption here can not only reduce energy consumption but also avoid affecting the lifespan of the cooling devices.

[0106] This process uses a cooling control optimization model constructed based on the genetic algorithm, combined with a temperature adjustment fitness function and constraint conditions. In this embodiment, precise temperature control, efficient energy consumption management, and flexible cooling strategies of the energy-saving pump cooling system are realized. Specifically, the system can adaptively adjust the cooling scheme under different working conditions to ensure that the temperature is always maintained within the normal working range, significantly improving the stability and reliability of the equipment. At the same time, the genetic algorithm and linear programming algorithm are used to minimize the comprehensive energy consumption, greatly reducing the operating cost. The introduction of a cooperative operation adjustment factor enables the air-cooling and water-cooling devices to cooperate flexibly under different temperature conditions, further optimizing the system performance and extending the equipment lifespan. In addition, by reasonably controlling the start and stop of the cooling devices, the mechanical stress and unnecessary energy consumption caused by frequent switching are reduced, realizing more refined energy management.

[0107] S4. Construct a cooling control adjustment model, input the time points corresponding to the predicted start of cooling, cooling energy consumption, and comprehensive energy consumption of devices with different cooling forms into the cooling control adjustment model for training to obtain the differential time point control instructions for the cooling devices;

[0108] Furthermore, the specific steps for constructing the cooling control adjustment model in this embodiment include:

[0109] According to [ T s ， T e ] , , , , , and , the function of the differential adjustment factor, when the temperature at the current moment is less than or equal to The temperature corresponding to the moment constructs the input state of the cooling control adjustment model;

[0110] Construct the execution action trigger information according to the input state of the cooling control adjustment model, specifically:

[0111] When it first appears at the corresponding time point , trigger the first action execution information, that is, call the initial cooling device and use and the difference to obtain the initial cooling device operation parameter set, and control the initial cooling device to cool down according to the initial cooling device operation parameter set;

[0112] Furthermore, assume that in the current process, an air-cooling device is used to perform to the cooling process of the corresponding energy-saving pump within the time point, so that , then the process of obtaining the corresponding air-cooling device operation parameter set includes:

[0113] According to the predicted temperature change rate of the energy-saving pump and the ideal temperature change rate per unit time of the energy-saving pump, obtain the difference between the predicted temperature change rate of the energy-saving pump and the ideal temperature change rate per unit time of the energy-saving pump ;

[0114] According to the cooling efficiency corresponding to the historical air-cooling device, and the parameters corresponding to the air-cooling equipment, obtain the air-cooling temperature reduction effect function through support vector machine fitting;

[0115] According to the obtained in real time, the cooling efficiency and the air-cooling temperature reduction effect function, obtain the operation parameter set corresponding to the air-cooling device at the current time point; the operation parameter set corresponding to the air-cooling device includes output power, fan speed, start time and duration.

[0116] When it still satisfies under the rated power cooling efficiency of the initial cooling device, at the corresponding time point , trigger the second action execution information, that is, call the initial cooling device and the remaining cooling devices simultaneously to cool down.

[0117] While triggering the second action execution information, trigger the third action execution information, that is, according to the temperature adjustment fitness function and the corresponding constraint conditions, obtain the minimum value through the linear programming algorithm, and obtain the pre-set joint operation parameter set corresponding to the water-cooling, air-cooling and energy-saving pump that satisfies and conditions, and adjust the output power ratio of the initial cooling device and the remaining cooling devices according to the function of the differential adjustment factor in the pre-set joint operation parameter set;

[0118] Obtain the operation parameter sets corresponding to the initial cooling device and the remaining cooling device at the current moment according to the output power ratio of the initial cooling device and the remaining cooling device and the attribute data of the initial cooling device and the remaining cooling device;

[0119] Furthermore, the operation parameter sets corresponding to the air-cooling and water-cooling devices include: output power, fan speed, air-cooling device startup time and duration, water-cooling device startup time and duration, water-cooling device output power, output cooling flow rate and velocity, and the time length of the coordinated operation of air-cooling and water-cooling.

[0120] When the temperature at the current moment is less than or equal to the temperature corresponding to the moment, trigger the fourth action execution information, that is, at the current time point, set the remaining cooling device to the silent state corresponding to the lowest energy consumption through the differential adjustment factor, and adjust the operation parameter set corresponding to the initial cooling device for cooling so that .

[0121] Construct the execution actions corresponding to the trigger action execution information according to the execution action trigger information;

[0122] When the corresponding execution action information is triggered, input the operation parameter sets of the air-cooling, water-cooling or energy-saving pump obtained by the corresponding execution action information into the fuzzy control algorithm to obtain the operation control instructions of the air-cooling device, water-cooling device or energy-saving pump at the corresponding time point, so that the predicted temperature change rate curve of the energy-saving pump is always below the ideal temperature change rate curve.

[0123] S5. Use the differential time point control instruction to control the differential cooling of the cooling device, and real-time monitor the energy-saving pump temperature, cooling energy consumption, comprehensive energy consumption and water supply volume, obtain the corresponding cooling temperature deviation, cooling energy consumption deviation, comprehensive energy consumption deviation and water supply volume deviation, and feedback all the obtained deviations to the cooling control optimization model to optimize and adjust the cooling control regulation model.

[0124] This process realizes the efficient and intelligent management of the energy-saving pump cooling system by constructing a cooling control adjustment model. Specifically, first, the system can dynamically adjust the working parameters of the cooling device (such as the output power of the air-cooling device, the fan speed, etc.) according to the difference between the real-time temperature change rate and the ideal temperature change rate, ensuring that the temperature of the energy-saving pump is always maintained within the set ideal range and improving the accuracy of temperature control. Second, the linear programming algorithm is used to optimize the collaborative work of the air-cooling and water-cooling devices to minimize the comprehensive energy consumption, enabling the system to reduce energy consumption while meeting the cooling requirements. The introduction of the differential adjustment factor enables the air-cooling and water-cooling devices to cooperate flexibly under different temperature conditions, further optimizing the energy consumption performance. When the temperature change rate exceeds the standard for the first time, the system quickly triggers the first action execution information and calls the initial cooling device for cooling. If a single cooling device cannot meet the demand, the second and third action execution information is triggered in a timely manner, and the air-cooling and water-cooling devices are called to cool down collaboratively to ensure the continuity and reliability of the cooling effect, reasonably control the start and stop of the cooling device, reduce mechanical stress, and extend the equipment life. After the temperature reaches the control target, the remaining cooling devices are set to the lowest energy consumption silent state to reduce unnecessary energy consumption. Finally, the system monitors the temperature, cooling energy consumption, comprehensive energy consumption, and water supply of the energy-saving pump in real time, obtains the corresponding deviation data, and feeds these deviations back into the cooling control optimization model for dynamic adjustment to achieve closed-loop control, improving the adaptive ability and robustness of the system. The application of the fuzzy control algorithm ensures that the predicted temperature change rate curve of the energy-saving pump is always below the ideal temperature change rate curve, enhancing the stability and response speed of the system. Ultimately, this model not only improves the response speed and accuracy of the system but also ensures that the energy-saving pump always operates efficiently in the most energy-saving state, achieving the dual optimization of temperature control and energy management.

[0125] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make changes, modifications, substitutions, and variations to the above embodiments without departing from the purpose and scope protected by the claims of the present invention. These all fall within the protection scope of the present invention.

[0126] If the technical solution of the present disclosure involves personal information, before the product applying the technical solution of the present disclosure processes personal information, it has clearly informed the personal information processing rules and obtained the individual's independent consent. If the technical solution of the present disclosure involves sensitive personal information, before the product applying the technical solution of the present disclosure processes sensitive personal information, it has obtained the individual's separate consent and at the same time meets the requirements of "express consent". For example, at a personal information collection device such as a camera, a clear and prominent sign is set up to inform that the personal information collection scope has been entered and personal information will be collected. If an individual voluntarily enters the collection scope, it is regarded as consenting to the collection of their personal information; or on the device for personal information processing, when the personal information processing rules are informed by obvious signs / information, personal authorization is obtained through pop-up messages or by asking the individual to upload their personal information by themselves; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

Claims

1. An automatic cooling method for a digital energy-saving pump, characterized in that: include: Obtain differential energy-saving efficiency temperature prediction curve function and comprehensive energy consumption curve and single energy consumption curve function under differential energy-saving efficiency; The temperature adjustment adaptability function and constraint conditions are constructed using the comprehensive energy consumption curve and single energy consumption curve function, temperature prediction curve function, water supply change curve function, and normal working temperature range; The temperature adjustment fitness function and constraint conditions are built into the constructed cooling control optimization model for training, and the predicted cooling time interval, single cooling energy consumption and comprehensive energy consumption of air cooling and water cooling devices are obtained; The predicted cooling time intervals, single cooling energy consumption and comprehensive energy consumption of air cooling and water cooling devices are input into the constructed cooling control and regulation model for training, and differential time point control instructions of cooling devices are obtained to perform differential cooling time point control of cooling devices; The construction process of the comprehensive energy consumption curve includes: According to the historical temperature change data of the energy-saving pump under different working conditions, the temperature prediction curve function is obtained; According to the cooling efficiency of the air cooling or water cooling device per unit time at the corresponding temperature in the temperature prediction curve function and the cooling energy consumption per unit time under the corresponding cooling efficiency, a single energy consumption curve of the corresponding cooling device is obtained; According to the working energy consumption data of energy-saving pumps under different working conditions, the working energy consumption change curve and corresponding change function under different working conditions are constructed; The single energy consumption curve corresponding to the air cooling and water cooling devices is aligned with the working energy consumption data under the corresponding working state in the time dimension, and the water supply energy consumption at the same time point is superimposed with the air cooling or water cooling energy consumption to obtain the comprehensive energy consumption of water supply and cooling and the comprehensive energy consumption curve of the energy-saving pump at the corresponding time point, and determine the energy consumption level at the corresponding time point.

2. The automatic cooling method of a digital energy-saving pump according to claim 1, characterized in that: The working state of the energy-saving pump is the water supply flow rate of the energy-saving pump per unit time. The construction process of the temperature adjustment fitness function and the constraint conditions includes: According to the required water supply and the working status of the energy-saving pump, the target water supply demand time interval is obtained. ,in represents the initial water supply time point corresponding to the demand water supply, Indicates the end water supply time point corresponding to the demand water supply; According to the target water supply demand time length and normal working temperature range, the ideal temperature change rate per unit time of the energy-saving pump is obtained. ; According to the temperature prediction curve function, the predicted temperature change rate of the energy-saving pump is obtained. .

3. The automatic cooling method of a digital energy-saving pump according to claim 2, characterized in that: The process of constructing the temperature adjustment fitness function and the constraint conditions also includes: According to the historical operation order of air cooling and water cooling devices, the corresponding temperature adjustment range and the corresponding energy consumption per unit time, the initial cooling device is selected for cooling; According to the predicted temperature change rate of the energy-saving pump and the ideal temperature change rate per unit time of the energy-saving pump, we can obtain First time greater than The corresponding time point , and select the initial cooling device to start cooling down, so that .

4. The automatic cooling method of a digital energy-saving pump according to claim 3, characterized in that: The process of constructing the temperature adjustment fitness function and the constraint conditions also includes: According to the predicted temperature change rate of the energy-saving pump, the ideal temperature change rate per unit time of the energy-saving pump, and the cooling efficiency of the initial cooling device corresponding to the rated power, the cooling efficiency of the initial cooling device at the rated power is obtained. The corresponding time point , and select the initial cooling device and the remaining cooling device to cool down the energy-saving pump at the same time; According to the cooling efficiency, cooling energy consumption, energy consumption of energy-saving pumps and the difference between supply and demand of energy-saving pumps corresponding to the initial cooling device and the remaining cooling device , construct the temperature adjustment fitness function and constraints of the initial cooling device and the remaining cooling devices, so that and Under this condition, the comprehensive energy consumption of water supply and cooling is minimal.

5. The automatic cooling method of a digital energy-saving pump according to claim 4, characterized in that: The process of constructing the temperature adjustment fitness function and constraint conditions of the initial cooling device and the remaining cooling devices includes: According to the historical output power, cooling efficiency and air cooling energy consumption of the air cooling device, the air cooling energy consumption fitting function is obtained through the nonlinear fitting function. ; According to the historical water cooling device corresponding output power, cooling flow and flow rate, cooling efficiency and water cooling energy consumption, the water cooling energy consumption fitting function is obtained through nonlinear fitting function. ; According to the flow rate, speed and corresponding working energy consumption of the energy-saving pump under the corresponding working state, the energy-saving pump working energy consumption fitting function is obtained through the nonlinear fitting function. ; according to , and Constructing the temperature adjustment fitness function and the corresponding constraints, and the energy consumption value of the temperature adjustment fitness function is the minimum at the corresponding time point and , Under this condition, the linear programming algorithm is used to solve the predicted pre-combined operation parameter set corresponding to water cooling, air cooling and energy-saving pumps; After cooling by air cooling and water cooling at the same time, the current temperature is less than or equal to When the temperature corresponding to the moment is reached, the remaining cooling devices are set to the silent state corresponding to the lowest energy consumption, and the energy-saving pump is adjusted to cool down through the previous joint operation parameter set of the cooling control optimization model, so that and .

6. The automatic cooling method of a digital energy-saving pump according to claim 5, characterized in that: The pre-joint operation parameter set includes a difference adjustment factor, and the difference adjustment factor is used to adjust the Adjust the output power ratio of the air cooling device to the water cooling device at different time points within the time interval; The step of obtaining the difference adjustment factor comprises: According to the attribute data of air cooling and water cooling devices, the effective cooling efficiency of air cooling and water cooling devices at different output powers is obtained and ; according to , , current moment and The difference between and The corresponding output power of air-cooling and water-cooling devices; According to satisfaction and The corresponding air cooling and water cooling device output power and the corresponding time point and The difference is used to obtain the function of the difference adjustment factor through the support vector machine, and the function of the difference adjustment factor obtained is built into the cooling control adjustment model.

7. The automatic cooling method of a digital energy-saving pump according to claim 6, characterized in that: The specific steps of constructing the cooling control adjustment model include: according to , , , , , and , the function of the difference adjustment factor, the current temperature is less than or equal to The temperature corresponding to the moment constructs the input state of the cooling control regulation model; The execution action trigger information is constructed according to the input state of the cooling control adjustment model, specifically: When it first appeared At the corresponding time point , triggering the first action execution information, that is, calling the initial cooling device and using and , obtaining an initial cooling device operating parameter set, and controlling the initial cooling device to cool down according to the initial cooling device operating parameter set; When the cooling efficiency of the initial cooling device is still met At the corresponding time point , triggering the second action execution information, that is, calling the initial cooling device and the remaining cooling devices at the same time to cool down.

8. The automatic cooling method of a digital energy-saving pump according to claim 7, characterized in that: The specific steps of constructing the cooling control adjustment model also include: The third action execution information is triggered at the same time as the second action execution information is triggered, that is, according to the temperature adjustment fitness function And the corresponding constraints, through the linear programming algorithm, obtain and The output power ratio of the initial cooling device and the remaining cooling devices is adjusted according to the function of the difference adjustment factor in the pre-joint operation parameter set corresponding to the water cooling, air cooling and energy-saving pump under the conditions; According to the output power ratio of the initial cooling device and the remaining cooling devices and the attribute data of the initial cooling device and the remaining cooling devices, the operating parameter set corresponding to the initial cooling device and the remaining cooling devices at the current moment is obtained; When the current temperature is less than or equal to When the temperature corresponding to the moment is reached, the fourth action execution information is triggered, that is, at the current time point, the remaining cooling devices are set to the silent state corresponding to the lowest energy consumption through the difference adjustment factor, and the operating parameter set corresponding to the initial cooling device is adjusted to reduce the temperature so that ; According to the execution action trigger information, construct the execution action corresponding to the trigger action execution information; When the corresponding execution action information is triggered, the air cooling, water cooling or energy-saving pump operating parameter set obtained by triggering the corresponding execution action information is input into the fuzzy control algorithm to obtain the operation control instructions of the air cooling device, water cooling device or energy-saving pump at the corresponding time point, so that the predicted temperature change rate curve of the energy-saving pump is always below the ideal temperature change rate curve.

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