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CN119333406A - Water pump control system based on intelligent water pump data acquisition function - Google Patents

Water pump control system based on intelligent water pump data acquisition function Download PDF

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Publication number
CN119333406A
CN119333406A CN202411912626.XA CN202411912626A CN119333406A CN 119333406 A CN119333406 A CN 119333406A CN 202411912626 A CN202411912626 A CN 202411912626A CN 119333406 A CN119333406 A CN 119333406A
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water pump
pipeline
resistance
data
lift
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CN119333406B (en
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蔡立明
熊斌
李加炜
林志尚
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EIFEL PUMP (FUZHOU) CO LTD
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EIFEL PUMP (FUZHOU) CO LTD
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Abstract

The invention discloses a water pump control system based on an intelligent water pump data acquisition function, and relates to the technical field of water pump operation data analysis. The pipeline resistance evaluation module is used for combining liquid characteristic data, acquiring fluid dynamic viscosity, flow and cavitation residual data in real time, comprehensively calculating a pipeline friction loss index, a local resistance loss index and cavitation residual, accurately evaluating pipeline resistance, avoiding energy consumption increase caused by resistance increase, monitoring the cavitation residual by the liquid characteristic data acquisition module, and identifying cavitation risk in advance by combining the prediction analysis module.

Description

Water pump control system based on intelligent water pump data acquisition function
Technical Field
The invention relates to the technical field of water pump operation data analysis, in particular to a water pump control system based on an intelligent water pump data acquisition function.
Background
In the traditional water pump system, the fixed sensor is used for monitoring the lift data under the static lift or single operation state, so that the change conditions of the water suction lift, the water discharge lift and the dynamic operation lift can not be comprehensively reflected. In particular, in the case of variable operating conditions, the hysteresis and singleness of the head data tend to result in a decrease in the operating efficiency of the water pump. During operation of the water pump, the flow of liquid in the pipeline is doubly influenced by friction resistance and local resistance, and the conventional water pump control system lacks accurate evaluation and dynamic response to the pipeline resistance, so that the energy consumption is increased or the pipeline is worn easily. In the operation process of the water pump, as the local pressure is reduced below the saturated vapor pressure of the liquid, the liquid is rapidly vaporized to form bubbles, so that cavitation is formed, and the cavitation can cause serious damage to the impeller and other key components, thereby influencing the service life of equipment and the stability of a system.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a water pump control system based on an intelligent water pump data acquisition function, so as to solve the problems in the background art.
The intelligent water pump data acquisition system comprises a lift monitoring module, a liquid characteristic data acquisition module, a predictive analysis module, a dynamic lift optimization module and a pipeline resistance evaluation module;
The pump head monitoring module is used for monitoring static pump head data, water absorption pump head data and drainage pump head data in the running process of the water pump in real time to generate a first data set;
The liquid characteristic data acquisition module is used for acquiring liquid flow, fluid dynamic viscosity and cavitation allowance data in real time to generate a second data set;
The predictive analysis module is used for carrying out depth analysis and calculation according to the first data set to obtain dynamic lift coefficient And performing depth analysis calculation according to the second data set to obtain the friction loss index of the pipelineIndex of local resistance lossAnd cavitation marginAnd index the friction loss of the pipelineIndex of local resistance lossAnd cavitation marginCorrelating to obtain the coefficient of resistance of the pipeline;
The dynamic lift optimization module is used for optimizing the dynamic lift coefficientCoefficient of resistance to pipeFitting calculation to obtain a lift risk indexPresetting a lift risk threshold Y and indexing the lift riskComparing and analyzing the water pump running parameter with a lift risk threshold Y, generating corresponding adjusting instructions, and regulating and controlling the water pump running parameter in real time, wherein the adjusting instructions comprise adjusting motor power, impeller rotating speed and water pump installation angle;
The pipeline resistance evaluation module is used for presetting a resistance threshold value X and converting the pipeline resistance coefficient And comparing the resistance threshold value X with a resistance threshold value X, analyzing friction resistance and local resistance risks generated in the process of fluid passing through the pipeline, and generating a corresponding layout optimization instruction.
Preferably, the static lift data comprises the liquid level height of the suction end of the water pumpLiquid level of water pump discharge endDensity of liquidAnd gravitational acceleration;
The water absorption and lift data comprises the flow velocity of liquid absorbed by a water pump;
The drainage lift data comprises the flow velocity of fluid at the outlet of a water pump;
And after cleaning, calibrating and dimensionless processing the static lift data, the water absorption lift data and the drainage lift data, summarizing to generate a first data set.
Preferably, the liquid flow rate comprises a flow rate Q;
The fluid dynamic viscosity comprises ;
The cavitation margin data comprises fluid temperature T and liquid vapor pressure;
And after cleaning, calibrating and dimensionless processing the liquid flow, the fluid dynamic viscosity and the cavitation allowance data, summarizing to generate a second data set.
Preferably, the prediction analysis module comprises a model building unit, a lift analysis unit and a pipeline loss analysis unit;
The model building unit is used for building a convolutional neural network initial model by utilizing the convolutional neural network, training and testing the convolutional neural network initial model by using a first data set and a second data set, taking the trained convolutional neural network initial model as a water pump lift analysis model, simultaneously using the middle layer output of the water pump lift analysis model as a characteristic vector to identify characteristic information in the first data set and the second data set, training and testing the water pump lift analysis model by using the acquired characteristic information, and taking the trained water pump lift analysis model as data operation prediction;
The lift analysis unit is used for carrying out deep analysis on the first data set and the second data set through the trained water pump lift analysis model so as to obtain a dynamic lift coefficient :
The pipeline loss analysis unit is used for acquiring data of the connecting pipeline of the water pump, and performing deep analysis by combining the trained water pump lift analysis model with the first data set and the second data set to obtain a pipeline friction loss indexIndex of local resistance lossAnd cavitation marginAnd index the friction loss of the pipelineIndex of local resistance lossAnd cavitation marginCorrelating to obtain the coefficient of resistance of the pipeline。
Preferably, the dynamic lift coefficientThe acquisition mode of (a) is as follows:
s11, firstly, monitoring and obtaining the total dynamic pressure of the outlet of the water pump Total dynamic pressure of water pump inletDensity of liquidThe basic head index is calculated by the following formula:
;
In the formula,Represents the total dynamic pressure of the outlet of the water pump,Indicating the total dynamic pressure of the inlet of the water pump,Indicating the density of the liquid and,Represents gravitational acceleration, set to 9.81m/s2;
S12, collecting the flow rate of liquid sucked by the water pump Fluid flow rate at outlet of water pumpThe kinetic energy loss index is calculated by the following formula:
;
In the formula,AndThe unit is m/s;
S13, collecting the current fluid temperature T in real time, and calculating and obtaining a temperature correction coefficient through the following formula :
;
In the formula,Indicating a reference temperature, set at 25C,For correction constants, determined from experimental data;
s14, collecting the dynamic viscosity of the fluid in real time The viscosity influence coefficient is obtained by calculation according to the following formula:
;
In the formula,Representing the Reynolds number, reflecting the flow state of the fluid;
s15, combining the basic lift indexes in S11 to S14 Index of kinetic energy lossCoefficient of temperature correctionAnd viscosity influence coefficientThe dynamic lift coefficient is obtained through calculation according to the following formula:
。
Preferably, S11 specifically includes:
s111, measuring the liquid level height of the suction end of the water pump through a liquid level sensor After that, the static pressure caused by the liquid level height is obtained by the following calculation:
;
Wherein, Represents the density of the liquid, the unit kg/m3,Represents gravitational acceleration, set to 9.81m/s2;
S112, calculating dynamic pressure caused by flow velocity :
;
In the formula,Indicating the flow rate of the sucked liquid of the water pump;
S113, static pressure caused by liquid level And dynamic pressure caused by flow velocityAdding to obtain the total dynamic pressure of the inlet of the water pump as:
;
S114, combining the liquid level height of the water pump discharge end and the fluid flow rate for analysis, and calculating to obtain the total dynamic pressure of the water pump outlet through the following formula:
;
In the formula,The liquid level at the discharge end of the water pump is indicated,Representing the flow rate of the fluid at the outlet of the water pump, and is measured and obtained by a flow rate sensor.
Preferably, the pipe friction loss indexThe acquisition mode is as follows:
collecting and acquiring data of a connecting pipeline of a water pump, and calculating and acquiring a friction loss index of the pipeline through a Darcy-Weisbach formula :
;
Wherein f represents a friction factor, which is obtained through a Kelbuck-white formula or a Mordi diagram;
Wherein, Representing the average flow velocity of the pipeline, arranging a plurality of points on the pipeline, measuring the flow velocity of the points through a flow velocity sensor, and monitoring to obtain the average flow velocity;The calculation is as follows: q represents flow, NJ represents the inner diameter of the pipeline;
the local resistance loss index The acquisition mode of (a) is as follows:
Collecting pipe fitting information communicated with a water pump, wherein the pipe fitting information comprises resistance loss data of an elbow, a tee joint and a valve, and calculating and obtaining local resistance loss index through the following formula :
;
Wherein n represents the total number of pipes; The local resistance coefficient of the ith pipe fitting is represented and obtained through inquiring a pipe fitting manual, and specifically comprises 0.2-0.3 of elbow, 0.1-0.2 of main flow tee joint, 1.0-5.0 of branch flow tee joint, 0.1-0.2 of full-open valve and 1.0-5.0 of half-open valve;
the cavitation margin The acquisition mode of (a) is as follows:
using clean suction head, cavitation margin Is the difference between the pressure and vapor pressure at the inlet of the water pump, and is calculated and obtained by the following formula:
;
In the formula, Static pressure caused by liquid level,In the event of a liquid vapor pressure,For total dynamic pressure of water pump inlet;
Index of friction loss of pipelineIndex of local resistance lossAnd cavitation marginAfter dimensionless treatment, the pipeline resistance coefficient is obtained through calculation according to the following related formulas:
;
In the formula,、AndRespectively represent friction loss index of pipelineIndex of local resistance lossAnd cavitation marginAnd (2) weight of。
Preferably, the dynamic lift optimization module comprises a fitting unit, a first evaluation unit and a first optimization unit;
the fitting unit is used for utilizing the dynamic lift coefficient Coefficient of resistance to pipeFitting calculation to obtain a lift risk index;
;
In the formula,AndIs a weight value, and;
The first evaluation unit is used for presetting a lift risk threshold value Y and indexing the lift riskComparing with a lift risk threshold value Y, and obtaining a first evaluation result, wherein the first evaluation result comprises:
When the risk index of the lift < Lift risk threshold Y, representing the current lift risk index of the water pumpThe water pump is in a normal running state without deviation;
when the lift risk threshold value Y is less than or equal to the lift risk index Risk threshold of lift less than or equal toRepresenting the current lift risk index of the water pumpWhen the water pump is in an offset abnormal running state, generating a first adjusting instruction, wherein the first adjusting instruction comprises the steps of adjusting and reducing the current motor power by 3% -5%, reducing the rotating speed of the current water pump impeller by 5% -10% and adjusting the current mounting angle of the water pump by +/-2-5 degrees;
When the risk index of the lift Threshold of risk of headRepresenting the current lift risk index of the water pumpWhen the water pump is in an offset abnormal running state, a second adjusting instruction is generated, wherein the second adjusting instruction comprises the steps of adjusting and reducing the current motor power by 6% -10%, reducing the rotating speed of the current water pump impeller by 11% -15% and adjusting the current mounting angle of the water pump by +/-6-10 degrees.
Preferably, the pipeline resistance evaluation module comprises a second evaluation unit and a second optimization unit;
the second evaluation unit is used for presetting a resistance threshold value X and converting the pipeline resistance coefficient Analyzing frictional resistance and local resistance risks generated in the process of fluid passing through the pipeline in comparison with the resistance threshold X to obtain a second evaluation result, wherein the second evaluation result comprises the following steps:
Coefficient of resistance of pipeline The resistance threshold value X indicates that the current pipeline connected with the water pump is reasonable in layout, and the existing pipeline design is maintained;
when the resistance threshold value X is less than or equal to the resistance coefficient of the pipeline Resistance threshold value is less than or equal toThe method comprises the steps of indicating that the current pipeline connected with a water pump is unreasonable in layout, and marking a first resistance level;
when the resistance threshold value X is less than or equal to the resistance coefficient of the pipeline Resistance threshold value is less than or equal toThe second resistance level is marked as worse than the first resistance level, indicating that the current piping arrangement to the water pump is not reasonable.
Preferably, the second optimizing unit is used for generating a first layout optimizing instruction according to a first resistance level, and comprises the steps of cleaning an elbow, a tee joint and a valve communicated with a water pump at a frequency of 2-3 times per month, replacing 10-20% of pipe fittings, replacing the elbow and the tee joint with streamline elbow and tee joint pipe fittings, replacing a stop valve with a ball valve or a butterfly valve, and arranging 1 intermediate compensator or a shock absorber in a water pump pipeline;
Generating a second layout optimization instruction according to a second resistance level, wherein the second layout optimization instruction comprises the steps of cleaning an elbow, a tee joint and a valve communicated with a water pump at a frequency of 4-6 times per month, replacing 21-30% of pipe fittings with streamline elbow and tee joint pipe fittings, replacing a stop valve with a ball valve or a butterfly valve, arranging 1 intermediate compensator or a damper in a water pump pipeline, and adding 1 balance valve or a pressure regulating device.
The invention provides a water pump control system based on an intelligent water pump data acquisition function. The beneficial effects are as follows:
(1) According to the invention, through the lift monitoring module, static lift, water suction lift and water discharge lift data are collected in real time, a comprehensive first data set is generated, the lift changes under different running states are dynamically presented, data support is provided for accurate optimization, and the defects of data hysteresis and singleness in a traditional system are overcome.
(2) Frictional and localized drag in the pipe significantly affects the pump efficiency, and conventional systems lack accurate assessment of these drag. The invention combines the dynamic viscosity, flow and cavitation allowance data of the fluid obtained in real time by the liquid characteristic data acquisition module through the pipeline resistance evaluation module, and comprehensively analyzes and calculates the friction loss index of the pipelineIndex of local resistance lossAnd cavitation marginComprehensively obtain the resistance coefficient of the pipelineCompared with a preset resistance threshold value X, the method realizes the accurate management of the fluid resistance and avoids the increase of energy consumption caused by the increase of the resistance.
(3) Under complex and changeable working conditions, the traditional water pump system often reduces the operation efficiency due to fixed lift parameters. The invention utilizes the dynamic lift optimization module to calculate the dynamic lift coefficient in real timeCoefficient of resistance to pipeFitting calculation to obtain a lift risk indexAnd comparing and analyzing with a preset lift risk threshold Y to generate a real-time adjustment instruction. The function obviously improves the adaptability and stability of the water pump system under variable conditions.
(4) Cavitation is often difficult to predict and is prone to severe damage to critical components such as the impeller. According to the invention, the cavitation margin is monitored in real time through the liquid characteristic data acquisition module, and the depth calculation of the prediction analysis module is combined, so that whether the local pressure is lower than the liquid saturated steam pressure can be accurately judged, and the cavitation risk can be identified in advance. The system also effectively prevents cavitation by dynamically adjusting operation parameters (such as impeller rotating speed and installation angle), and avoids frequent maintenance or replacement of equipment caused by cavitation in the traditional system.
Drawings
FIG. 1 is a block flow diagram of a water pump control system based on an intelligent water pump data acquisition function.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the invention provides a water pump control system based on an intelligent water pump data acquisition function, which comprises a lift monitoring module, a liquid characteristic data acquisition module, a prediction analysis module, a dynamic lift optimization module and a pipeline resistance evaluation module;
The pump head monitoring module is used for monitoring static pump head data, water absorption pump head data and drainage pump head data in the running process of the water pump in real time to generate a first data set;
The liquid characteristic data acquisition module is used for acquiring liquid flow, fluid dynamic viscosity and cavitation allowance data in real time to generate a second data set;
The predictive analysis module is used for carrying out depth analysis and calculation according to the first data set to obtain dynamic lift coefficient And performing depth analysis calculation according to the second data set to obtain the friction loss index of the pipelineIndex of local resistance lossAnd cavitation marginAnd index the friction loss of the pipelineIndex of local resistance lossAnd cavitation marginCorrelating to obtain the coefficient of resistance of the pipeline;
The dynamic lift optimization module is used for optimizing the dynamic lift coefficientCoefficient of resistance to pipeFitting calculation to obtain a lift risk indexPresetting a lift risk threshold Y and indexing the lift riskComparing and analyzing the water pump running parameter with a lift risk threshold Y, generating corresponding adjusting instructions, and regulating and controlling the water pump running parameter in real time, wherein the adjusting instructions comprise adjusting motor power, impeller rotating speed and water pump installation angle;
The pipeline resistance evaluation module is used for presetting a resistance threshold value X and converting the pipeline resistance coefficient And comparing the resistance threshold value X with a resistance threshold value X, analyzing friction resistance and local resistance risks generated in the process of fluid passing through the pipeline, and generating a corresponding layout optimization instruction.
In this embodiment, the conventional water pump control system only monitors the static lift or the lift data in a single operation state, and cannot fully reflect the actual working condition. According to the invention, through the lift monitoring module, static lift, water suction lift and water discharge lift data are collected in real time, a comprehensive first data set is generated, the lift changes under different running states are dynamically presented, data support is provided for accurate optimization, and the defects of data hysteresis and singleness in a traditional system are overcome. Frictional and localized drag in the pipe significantly affects the pump efficiency, and conventional systems lack accurate assessment of these drag. The invention combines the dynamic viscosity, flow and cavitation allowance data of the fluid obtained in real time by the liquid characteristic data acquisition module through the pipeline resistance evaluation module, and comprehensively analyzes and calculates the friction loss index of the pipelineIndex of local resistance lossAnd cavitation marginComprehensively obtain the resistance coefficient of the pipelineCompared with a preset resistance threshold value X, the method realizes the accurate management of the fluid resistance and avoids the increase of energy consumption caused by the increase of the resistance.
Under complex and changeable working conditions, the traditional water pump system often reduces the operation efficiency due to fixed lift parameters. The invention utilizes the dynamic lift optimization module to calculate the dynamic lift coefficient in real timeCoefficient of resistance to pipeFitting calculation to obtain a lift risk indexAnd comparing and analyzing with a preset lift risk threshold Y to generate a real-time adjustment instruction. The function obviously improves the adaptability and stability of the water pump system under variable conditions.
Cavitation is often difficult to predict and is prone to severe damage to critical components such as the impeller. According to the invention, the cavitation margin is monitored in real time through the liquid characteristic data acquisition module, and the depth calculation of the prediction analysis module is combined, so that whether the local pressure is lower than the liquid saturated steam pressure can be accurately judged, and the cavitation risk can be identified in advance. The system also effectively prevents cavitation by dynamically adjusting operation parameters (such as impeller rotating speed and installation angle), and avoids frequent maintenance or replacement of equipment caused by cavitation in the traditional system.
Example 2
The embodiment is explained and illustrated in embodiment 1, referring to FIG. 1, specifically, the static lift data includes the liquid level of the suction end of the water pumpLiquid level of water pump discharge endDensity of liquidAnd gravitational acceleration;
The water absorption and lift data comprises the flow velocity of liquid absorbed by a water pump;
The drainage lift data comprises the flow velocity of fluid at the outlet of a water pump;
And after cleaning, calibrating and dimensionless processing the static lift data, the water absorption lift data and the drainage lift data, summarizing to generate a first data set.
The liquid flow rate comprises a flow rate Q;
The fluid dynamic viscosity comprises ;
The cavitation margin data comprises fluid temperature T and liquid vapor pressure;
And after cleaning, calibrating and dimensionless processing the liquid flow, the fluid dynamic viscosity and the cavitation allowance data, summarizing to generate a second data set.
The first data set and the second data set are acquired through sensors, including one or more of sensors, specifically a liquid level sensor, a density sensor, an accelerometer, a flow rate sensor, an electromagnetic flowmeter, a temperature sensor, a liquid vapor pressure sensor and a liquid viscosity sensor.
The first data set and the second data set are collected through different sensors and then subjected to cleaning, calibration and dimensionless processing. Cleaning includes outlier removal, calibration is to ensure sensor accuracy, and dimensionless processing is to eliminate differences from different dimensions. The collection of the first data set and the second data set ensures that the system can accurately monitor the running condition of the water pump in real time and provide data support for subsequent predictive analysis, dynamic optimization and pipeline resistance evaluation.
Example 3
The embodiment is explained and illustrated in embodiment 1, referring to fig. 1, specifically, the prediction analysis module includes a model building unit, a lift analysis unit, and a pipe loss analysis unit;
The model building unit is used for building a convolutional neural network initial model by utilizing the convolutional neural network, training and testing the convolutional neural network initial model by using a first data set and a second data set, taking the trained convolutional neural network initial model as a water pump lift analysis model, simultaneously using the middle layer output of the water pump lift analysis model as a characteristic vector to identify characteristic information in the first data set and the second data set, training and testing the water pump lift analysis model by using the acquired characteristic information, and taking the trained water pump lift analysis model as data operation prediction;
The lift analysis unit is used for carrying out deep analysis on the first data set and the second data set through the trained water pump lift analysis model so as to obtain a dynamic lift coefficient :
The pipeline loss analysis unit is used for acquiring data of the connecting pipeline of the water pump, and performing deep analysis by combining the trained water pump lift analysis model with the first data set and the second data set to obtain a pipeline friction loss indexIndex of local resistance lossAnd cavitation marginAnd index the friction loss of the pipelineIndex of local resistance lossAnd cavitation marginCorrelating to obtain the coefficient of resistance of the pipeline。
Specifically, the dynamic lift coefficientThe acquisition mode of (a) is as follows:
s11, firstly, monitoring and obtaining the total dynamic pressure of the outlet of the water pump Total dynamic pressure of water pump inletDensity of liquidThe basic head index is calculated by the following formula:
;
In the formula,Represents the total dynamic pressure of the outlet of the water pump,Indicating the total dynamic pressure of the inlet of the water pump,Indicating the density of the liquid and,Indicating the acceleration of gravity and setting the acceleration to 9.81m/s2 by monitoring the total dynamic pressure of the outlet of the water pumpTotal dynamic pressure of water pump inletDensity of liquidThe basic lift index of the water pump can be accurately calculated. The calculation provides key preliminary parameters for subsequent dynamic lift analysis, and is beneficial to dynamic adjustment of the operation of the water pump. The step can feed back the liquid flowing state and the pump body pressure change in real time, so that the pressure fluctuation in the water pump operation process is ensured to be captured in time, and the system control and operation strategy is optimized.
S11 specifically comprises:
s111, measuring the liquid level height of the suction end of the water pump through a liquid level sensor After that, the static pressure caused by the liquid level height is obtained by the following calculation:
;
Wherein, Represents the density of the liquid, the unit kg/m3,The liquid level sensor feeds back the liquid level height of the suction end in real time, is favorable for dynamically adjusting the working state of the water pump, ensures that the liquid level of the suction end is always in a reasonable range, and avoids the condition of water shortage or water pump idling. The static pressure calculation is a key step for analyzing the flow and the lift of the water pump, can provide early warning for the performance of the water pump, and helps to judge whether potential loss or instability exists.
S112, calculating dynamic pressure caused by flow velocity:
;
In the formula,The dynamic pressure caused by the flow rate is calculated in real time to help to find the abnormal flow rate in time if the flow rate of the liquid sucked by the water pumpThe system can automatically adjust the rotating speed or the switching state of the water pump to ensure that the water pump runs in the optimal state. Flow rate of liquid sucked by water pumpThe resulting dynamic pressure data helps to find efficiency losses in terms of fluid dynamics, optimize the operating parameters of the water pump, and reduce unnecessary energy consumption.
S113, static pressure caused by liquid levelAnd dynamic pressure caused by flow velocityAdding to obtain the total dynamic pressure of the inlet of the water pump as:
And the static pressure and the dynamic pressure are added to obtain the total dynamic pressure of the inlet, so that the working state of the inlet end of the water pump is comprehensively known. This provides the necessary pressure data support for subsequent head prediction, efficiency optimization.
S114, combining the liquid level height of the water pump discharge end and the fluid flow rate for analysis, and calculating to obtain the total dynamic pressure of the water pump outlet through the following formula:
;
In the formula,The liquid level at the discharge end of the water pump is indicated,Representing the flow rate of the fluid at the outlet of the water pump, and is measured and obtained by a flow rate sensor.
S12, collecting the flow rate of liquid sucked by the water pumpFluid flow rate at outlet of water pumpThe kinetic energy loss index is calculated by the following formula:
;
In the formula,AndThe unit is m/s, and the kinetic energy loss index can be accurately calculated by collecting flow velocity data of the suction end and the discharge end of the water pump, so that the energy loss of the water pump caused by friction, turbulence and other factors in the fluid flowing process can be reflected. Knowing the kinetic energy loss helps to identify the operating bottleneck of the water pump, thereby adjusting the power and operating conditions of the pump, reducing energy consumption.
S13, collecting the current fluid temperature T in real time, and calculating and obtaining a temperature correction coefficient through the following formula:
;
In the formula,Indicating a reference temperature, set at 25C,The water pump lift is greatly influenced by the temperature of the fluid, and the viscosity, the density and other physical properties of the fluid change along with the temperature change. By collecting the fluid temperature and calculating the correction coefficient, the pump lift can be accurately predicted under different temperature conditions, and the stability of the system under various environmental conditions is ensured. The step can enable the water pump control system to be more adaptive, and in the occasion with larger temperature change, the lift is automatically adjusted, so that the overall adaptability of the system is improved.
S14, collecting the dynamic viscosity of the fluid in real timeThe viscosity influence coefficient is obtained by calculation according to the following formula:
;
In the formula,The viscosity of the fluid directly influences the flow resistance of the fluid in the operation process of the water pump, and the change of the viscosity can cause different resistances of the fluid to flow so as to influence the lift. By monitoring dynamic viscosity in real time and calculating viscosity influence coefficientThe running parameters of the water pump can be accurately adjusted, so that energy waste caused by viscosity change is reduced. The calculation in the step is favorable for further refining the control of the water pump, optimizing the running state of the water pump, and particularly improving the efficiency in media with larger viscosity change (such as oil, aqueous solution and the like).
S15, combining the basic lift indexes in S11 to S14Index of kinetic energy lossCoefficient of temperature correctionAnd viscosity influence coefficientThe dynamic lift coefficient is obtained through calculation according to the following formula:
。
In this embodiment, this step is accomplished by combining a number of key factors (including the base head indexIndex of kinetic energy lossCoefficient of temperature correctionAnd viscosity influence coefficient) To calculate the dynamic lift coefficientThe working state of the water pump can be comprehensively reflected, the limitation of single parameter evaluation is eliminated, and the accurate adjustment of the lift is ensured.
Example 4
This example is explained and illustrated in example 1, referring to FIG. 1, specifically, the pipe friction loss indexThe acquisition mode is as follows:
collecting and acquiring data of a connecting pipeline of a water pump, and calculating and acquiring a friction loss index of the pipeline through a Darcy-Weisbach formula :
;
Wherein f represents a friction factor, which is obtained through a Kelbuck-white formula or a Mordi diagram;
Wherein, Representing the average flow velocity of the pipeline, arranging a plurality of points on the pipeline, measuring the flow velocity of the points through a flow velocity sensor, and monitoring to obtain the average flow velocity;The calculation is as follows: q represents flow, NJ represents the inner diameter of the pipeline;
the local resistance loss index The acquisition mode of (a) is as follows:
Collecting pipe fitting information communicated with a water pump, wherein the pipe fitting information comprises resistance loss data of an elbow, a tee joint and a valve, and calculating and obtaining local resistance loss index through the following formula :
;
Wherein n represents the total number of pipes; The local resistance coefficient of the ith pipe fitting is represented and obtained through inquiring a pipe fitting manual, and specifically comprises 0.2-0.3 of elbow, 0.1-0.2 of main flow tee joint, 1.0-5.0 of branch flow tee joint, 0.1-0.2 of full-open valve and 1.0-5.0 of half-open valve;
the cavitation margin The acquisition mode of (a) is as follows:
using clean suction head, cavitation margin Is the difference between the pressure and vapor pressure at the inlet of the water pump, and is calculated and obtained by the following formula:
;
In the formula, Static pressure caused by liquid level,In the event of a liquid vapor pressure,For total dynamic pressure of water pump inlet;
Index of friction loss of pipelineIndex of local resistance lossAnd cavitation marginAfter dimensionless treatment, the pipeline resistance coefficient is obtained through calculation according to the following related formulas:
;
In the formula,、AndRespectively represent friction loss index of pipelineIndex of local resistance lossAnd cavitation marginAnd (2) weight of。
In this embodiment, the pipe friction loss indexThe energy loss caused by friction of water flow in the pipeline can be accurately reflected, the matching of the water pump and the pipeline system is optimized, and unnecessary energy waste is reduced. Calculating local resistance loss index of pipeline through pipe fitting information (such as elbow, tee joint, valve and the like) and corresponding local resistance coefficient. Index of local resistance lossThe calculation of (a) is helpful for reducing the pressure loss caused by the connection part (such as an elbow, a valve and the like) of the pipeline, optimizing the fluid flow path and improving the overall flow efficiency of the system. Cavitation marginThe calculation of (2) is helpful for monitoring whether cavitation risk exists at the suction end of the water pump, and potential problems possibly causing equipment damage are identified in advance. And the influence of cavitation on the performance and the service life of the water pump is effectively avoided. Through the effective control cavitation allowance, can avoid cavitation phenomenon to lead to the damage of water pump or efficiency to reduce, ensure that the water pump operates under safe, stable operating mode.
By combining the friction loss index of the pipelineIndex of local resistance lossAnd cavitation marginCalculating the obtained pipeline resistance coefficientThe resistance characteristics of the whole pipeline system can be comprehensively evaluated, and data support is provided for optimizing pipeline design and water pump adjustment. The calculation helps to comprehensively evaluate and reduce the resistance loss of the whole pipeline system, optimize the fluid transmission efficiency, reduce the energy consumption and improve the overall operation efficiency of the system.
Example 5
The embodiment is explained and illustrated in embodiment 1, referring to fig. 1, specifically, the dynamic lift optimization module includes a fitting unit, a first evaluation unit, and a first optimization unit;
the fitting unit is used for utilizing the dynamic lift coefficient Coefficient of resistance to pipeFitting calculation to obtain a lift risk index;
;
In the formula,AndIs a weight value, and;
The first evaluation unit is used for presetting a lift risk threshold value Y and indexing the lift riskComparing with a lift risk threshold value Y, and obtaining a first evaluation result, wherein the first evaluation result comprises:
When the risk index of the lift < Lift risk threshold Y, representing the current lift risk index of the water pumpThe water pump is in a normal running state without deviation;
when the lift risk threshold value Y is less than or equal to the lift risk index Risk threshold of lift less than or equal toRepresenting the current lift risk index of the water pumpWhen the water pump is in an offset abnormal running state, generating a first adjusting instruction, wherein the first adjusting instruction comprises the steps of adjusting and reducing the current motor power by 3% -5%, reducing the rotating speed of the current water pump impeller by 5% -10% and adjusting the current mounting angle of the water pump by +/-2-5 degrees;
When the risk index of the lift Threshold of risk of headRepresenting the current lift risk index of the water pumpWhen the water pump is in an offset abnormal running state, a second adjusting instruction is generated, wherein the second adjusting instruction comprises the steps of adjusting and reducing the current motor power by 6% -10%, reducing the rotating speed of the current water pump impeller by 11% -15% and adjusting the current mounting angle of the water pump by +/-6-10 degrees.
The angle of installation of the water pump affects the manner in which liquid flows through the pump body. Through adjusting the installation angle, the water flow path can be optimized, friction and eddy current loss of water flow are reduced, and therefore water flow efficiency is improved. Through the installation angle of accurate adjustment water pump, ensure that rivers can get into water pump and discharge water pump more smoothly, reduce unnecessary pressure loss to reduce the burden of motor, reduce energy consumption. Improper mounting angles can lead to unbalanced operation of the water pump, thereby causing vibration and noise. Through adjusting installation angle, can make the water pump operate under more stable condition, reduce vibration, improve comfort level and the stability of system. If the inlet angle of the pump is not appropriate, this may result in the sucked water flowing faster or slower, thereby increasing the risk of cavitation. Cavitation can damage the water pump impeller and other key components, and through adjusting the installation angle, the occurrence of cavitation can be effectively reduced, and the service life of the water pump is prolonged.
In this embodiment, by adjusting the dynamic lift coefficientCoefficient of resistance to pipeThe fitting unit can accurately calculate the risk index of the liftAnd (5) timely evaluating the lift risk of the water pump under different working conditions. This helps identify potential problems in the operation of the water pump, such as insufficient or excessive pumping pressure. The accurate lift risk index provides a basis for subsequent adjustment, so that the water pump can operate under efficient working conditions, and the condition of overload operation is avoided. The fitting process fully utilizes the actual pipeline resistance characteristic and the lift coefficient, so that the system can make an intelligent decision through data driving, and the intelligent level of the system is improved.
When the risk index of the liftWhen the preset lift risk threshold value Y is deviated, the system automatically generates an adjustment instruction. The automatic adjustment mechanism reduces human intervention and improves the response speed and the processing efficiency of the system. Through reducing motor power, reducing water pump impeller rotational speed and adjustment water pump installation angle, the energy consumption of water pump can effectively be reduced to first optimizing unit, optimizes power consumption in the operation in-process, promotes the energy utilization efficiency of system. The adjustment helps to restore the pump to a more stable operating condition when the head risk index deviates from the normal range, thereby reducing equipment damage or performance degradation due to excessive excursions. The range and proportion of the parameters of the water pump (such as the power of the motor, the rotating speed of the impeller and the installation angle) are adjusted, so that the system can adapt to different working conditions and environmental changes, and the self-adaptation capability and flexibility of the water pump are enhanced.
Example 6
This embodiment is explained and illustrated in embodiment 5, referring to fig. 1, specifically, the pipeline resistance evaluation module includes a second evaluation unit and a second optimization unit;
the second evaluation unit is used for presetting a resistance threshold value X and converting the pipeline resistance coefficient Analyzing frictional resistance and local resistance risks generated in the process of fluid passing through the pipeline in comparison with the resistance threshold X to obtain a second evaluation result, wherein the second evaluation result comprises the following steps:
Coefficient of resistance of pipeline The resistance threshold value X indicates that the current pipeline connected with the water pump is reasonable in layout, and the existing pipeline design is maintained;
when the resistance threshold value X is less than or equal to the resistance coefficient of the pipeline Resistance threshold value is less than or equal toThe method comprises the steps of indicating that the current pipeline connected with a water pump is unreasonable in layout, and marking a first resistance level;
when the resistance threshold value X is less than or equal to the resistance coefficient of the pipeline Resistance threshold value is less than or equal toThe second resistance level is marked as worse than the first resistance level, indicating that the current piping arrangement to the water pump is not reasonable.
The second optimizing unit is used for generating a first layout optimizing instruction according to a first resistance level, and comprises the steps of cleaning an elbow, a tee joint and a valve communicated with a water pump at a frequency of 2-3 times per month, replacing 10-20% of pipe fittings with streamline elbow and tee joint pipe fittings, replacing a stop valve with a ball valve or a butterfly valve, and arranging 1 intermediate compensator or a shock absorber in a water pump pipeline;
Generating a second layout optimization instruction according to a second resistance level, wherein the second layout optimization instruction comprises the steps of cleaning an elbow, a tee joint and a valve communicated with a water pump at a frequency of 4-6 times per month, replacing 21-30% of pipe fittings with streamline elbow and tee joint pipe fittings, replacing a stop valve with a ball valve or a butterfly valve, arranging 1 intermediate compensator or a damper in a water pump pipeline, and adding 1 balance valve or a pressure regulating device.
Through periodic cleaning and replacement of the pipe fitting, impurities, sediments and losses in the pipeline can be obviously reduced, so that stable operation of the pipeline and the water pump system is ensured, and energy efficiency reduction and faults caused by accumulated pollutants are avoided. The streamline pipe fitting, the ball valve or the butterfly valve is replaced, and the shock absorber and the compensator are added, so that the pressure fluctuation and vibration of water flow can be effectively reduced, the stability of the water pump and a pipeline system is improved, the mechanical abrasion is reduced, and the service life of equipment is prolonged. By optimizing the pipeline layout, friction and local resistance are reduced, fluid conveying efficiency can be effectively improved, and energy consumption is reduced. This not only helps to reduce power consumption but also reduces operating costs. Periodic pipe cleaning, pipe fitting replacement and other optimization measures are helpful for prolonging the service cycle of a pipe system and a water pump and reducing the failure occurrence rate. Through the evaluation of the first resistance level and the second resistance level, proper optimization strategies can be selected according to different conditions, unnecessary frequent large-scale maintenance is avoided, and maintenance cost is reduced.
In this embodiment, the comparison analysis is performed between the pipeline resistance coefficient and the preset resistance threshold value, so that the rationality of the current pipeline layout can be effectively evaluated. If the resistance coefficient of the pipeline is lower than the resistance threshold value X, the pipeline layout is reasonable, no change is needed, and the existing design is maintained. This assessment helps to confirm that the current ductwork is in optimal operation, thereby saving unnecessary maintenance costs and time. When the pipeline resistance coefficient is between the resistance threshold value X and the resistance threshold valueIn between, the system will be marked as "first resistance level", indicating that there is a certain resistance problem. At this time, the evaluation module recommends optimizing the pipeline layout, such as cleaning and replacing part of the pipe fitting regularly, adopting measures such as streamline design, thereby effectively reducing friction resistance and keeping the efficient operation of the water pump. If the pipe resistance coefficient exceeds the resistance thresholdThe system will flag it as "second resistance level" and suggest a more thorough pipe optimization. The accurate identification can help to take measures in time under the condition of overlarge resistance, prevent the water pump from being overloaded and prolong the service life of the equipment.
The size of the threshold is set for convenience of comparison, and depends on the number of sample data and the number of cardinalities set for each group of sample data by a person skilled in the art, so long as the proportional relationship between the parameter and the quantized value is not affected.
The above formula is obtained by collecting a large amount of data to perform software simulation, and selecting a formula close to the true value, and coefficients in the formula are set by a person skilled in the art according to practical situations, and the above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art within the scope of the present invention should be covered by the scope of the present invention by substituting or changing the technical scheme and the inventive concept thereof.

Claims (10)

1.基于智能水泵数据采集功能的水泵控制系统，其特征在于，包括扬程监测模块、液体特性数据采集模块、预测分析模块、动态扬程优化模块以及管道阻力评估模块；1. A water pump control system based on intelligent water pump data acquisition function, characterized in that it includes a head monitoring module, a liquid characteristic data acquisition module, a prediction and analysis module, a dynamic head optimization module and a pipeline resistance evaluation module; 所述扬程监测模块用于实时监测水泵运行过程中的静扬程数据、吸水扬程数据和排水扬程数据，生成第一数据组；The head monitoring module is used to monitor the static head data, suction head data and discharge head data in real time during the operation of the water pump to generate a first data group; 所述液体特性数据采集模块用于实时采集液体流量、流体动态黏度及气蚀余量数据，生成第二数据组；The liquid characteristic data acquisition module is used to collect liquid flow rate, fluid dynamic viscosity and cavitation margin data in real time to generate a second data set; 所述预测分析模块用于依据所述第一数据组进行深度分析计算以获取：动态扬程系数，并依据第二数据组进行深度分析计算以获取，以获取：管道摩擦损失指数、局部阻力损失指数和气蚀余量，并将管道摩擦损失指数、局部阻力损失指数和气蚀余量相关联，获得管道阻力系数；The prediction analysis module is used to perform in-depth analysis and calculation based on the first data set to obtain: dynamic lift coefficient , and perform in-depth analysis and calculation based on the second data set to obtain: Pipeline friction loss index , local resistance loss index and cavitation margin , and the pipeline friction loss index , local resistance loss index and cavitation margin Correlate and obtain the pipeline resistance coefficient ; 所述动态扬程优化模块用于依据所述动态扬程系数与管道阻力系数，拟合计算获得扬程风险指数；并预设扬程风险阈值Y，将所述扬程风险指数与扬程风险阈值Y进行对比分析，生成相对应调整指令，并对水泵运行参数进行实时调控，调整指令包括调整电机功率、叶轮转速及水泵安装角度；The dynamic lift optimization module is used to optimize the dynamic lift coefficient according to the dynamic lift coefficient. and pipe resistance coefficient , fitting calculation to obtain the head risk index ; And preset the lift risk threshold Y, the lift risk index Compare and analyze with the head risk threshold Y, generate corresponding adjustment instructions, and adjust the pump operating parameters in real time. The adjustment instructions include adjusting the motor power, impeller speed and pump installation angle; 所述管道阻力评估模块用于预设阻力阈值X，并将所述管道阻力系数与阻力阈值X进行对比分析流体通过管道过程中产生的摩擦阻力和局部阻力风险，并生成相对应的布局优化指令。The pipeline resistance evaluation module is used to preset a resistance threshold value X and to calculate the pipeline resistance coefficient The friction resistance and local resistance risk generated by the fluid passing through the pipeline are compared with the resistance threshold X, and corresponding layout optimization instructions are generated. 2.根据权利要求1所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，所述静扬程数据包括：水泵吸入端液位高度、水泵排出端液位高度、液体密度和重力加速度；2. The water pump control system based on intelligent water pump data acquisition function according to claim 1 is characterized in that the static head data includes: the liquid level height at the suction end of the water pump , Liquid level at the discharge end of the pump , Liquid Density and the acceleration due to gravity ; 所述吸水扬程数据包括：水泵吸入液体流速；The water suction head data include: the flow rate of the liquid sucked by the water pump ; 所述排水扬程数据包括：水泵出口流体流速；The drainage head data includes: the flow rate of the fluid at the pump outlet ; 对所述静扬程数据、吸水扬程数据和排水扬程数据进行清洗、校准和无量纲处理后，汇总生成第一数据组。After the static head data, the suction head data and the discharge head data are cleaned, calibrated and dimensionless processed, they are aggregated to generate a first data group. 3.根据权利要求2所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，所述液体流量包括：流量Q；3. The water pump control system based on the intelligent water pump data acquisition function according to claim 2, characterized in that the liquid flow rate includes: flow rate Q; 所述流体动态黏度包括：流体动态黏度；The fluid dynamic viscosity includes: fluid dynamic viscosity ; 所述气蚀余量数据包括：流体温度T和液体蒸汽压力；The cavitation margin data include: fluid temperature T and liquid vapor pressure ; 对所述液体流量、流体动态黏度及气蚀余量数据进行清洗、校准和无量纲处理后，汇总生成第二数据组。After cleaning, calibrating and dimensionless processing, the liquid flow rate, fluid dynamic viscosity and cavitation margin data are summarized to generate a second data set. 4.根据权利要求3所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，4. The water pump control system based on the intelligent water pump data acquisition function according to claim 3 is characterized in that: 所述预测分析模块包括模型建立单元、扬程分析单元和管道损失分析单元；The prediction and analysis module includes a model building unit, a lift analysis unit and a pipeline loss analysis unit; 所述模型建立单元用于利用卷积神经网络，构建卷积神经网络初始模型，并以第一数据组和第二数据组对卷积神经网络初始模型进行训练与测试，并将训练后的卷积神经网络初始模型作为水泵扬程分析模型，同时使用水泵扬程分析模型的中间层输出作为特征向量，以识别第一数据组和第二数据组中的特征信息，并通过获取的特征信息对水泵扬程分析模型进行训练和测试，将训练后的水泵扬程分析模型作为数据运行预测；The model building unit is used to use a convolutional neural network to construct an initial model of the convolutional neural network, and train and test the initial model of the convolutional neural network with the first data group and the second data group, and use the trained initial model of the convolutional neural network as a water pump head analysis model, and use the intermediate layer output of the water pump head analysis model as a feature vector to identify feature information in the first data group and the second data group, and train and test the water pump head analysis model through the acquired feature information, and use the trained water pump head analysis model as data operation prediction; 所述扬程分析单元用于通过训练后的水泵扬程分析模型对第一数据组和第二数据组进行深度分析，以获取动态扬程系数：The head analysis unit is used to perform in-depth analysis on the first data group and the second data group through the trained water pump head analysis model to obtain the dynamic head coefficient : 所述管道损失分析单元用于采集获取水泵连接管道数据，通过训练后的水泵扬程分析模型结合第一数据组和第二数据组进行深度分析，得到管道摩擦损失指数、局部阻力损失指数和气蚀余量，并将管道摩擦损失指数、局部阻力损失指数和气蚀余量相关联，获得管道阻力系数。The pipeline loss analysis unit is used to collect and obtain the data of the water pump connection pipeline, and perform in-depth analysis by combining the trained water pump head analysis model with the first data group and the second data group to obtain the pipeline friction loss index. , local resistance loss index and cavitation margin , and the pipeline friction loss index , local resistance loss index and cavitation margin Correlate and obtain the pipeline resistance coefficient . 5.根据权利要求4所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，所述动态扬程系数的获取方式为：5. The water pump control system based on intelligent water pump data acquisition function according to claim 4, characterized in that the dynamic head coefficient The way to obtain is: S11、首先监测获取水泵出口总动态压力、水泵入口总动态压力以及液体密度，通过以下公式计算获得基础扬程指数：S11. First, monitor and obtain the total dynamic pressure at the pump outlet , Total dynamic pressure at pump inlet and the density of the liquid , the basic lift index is calculated by the following formula : ； ; 式中，表示水泵出口总动态压力，表示水泵入口总动态压力，表示液体密度，表示重力加速度，设置为9.81m/s²；In the formula, Indicates the total dynamic pressure at the pump outlet, Indicates the total dynamic pressure at the pump inlet, represents the density of the liquid, Indicates the acceleration due to gravity, set to 9.81m/s²; S12、采集水泵吸入液体流速以及水泵出口流体流速造成的动能变化，通过以下公式计算获得动能损失指数：S12, collect the flow rate of liquid sucked by the water pump And the fluid flow rate at the pump outlet The kinetic energy change caused by the kinetic energy loss index is calculated by the following formula : ； ; 式中，和单位为m/s；In the formula, and The unit is m/s; S13、实时采集当前流体温度T，通过以下公式计算获取温度修正系数：S13. Real-time collection of the current fluid temperature T, and calculation of the temperature correction coefficient using the following formula : ； ; 式中，表示基准温度，设置在25℃，为修正常数，通过实验数据确定；In the formula, Indicates the base temperature, set at 25°C. is the correction constant, determined by experimental data; S14、实时采集流体动态黏度，通过以下公式计算获取黏度影响系数：S14, real-time collection of fluid dynamic viscosity , the viscosity influence coefficient is calculated by the following formula : ； ; 式中，表示雷诺数，反映流体的流动状态；In the formula, It represents the Reynolds number, which reflects the flow state of the fluid; S15、结合S11至S14中基础扬程指数、动能损失指数、温度修正系数和黏度影响系数，通过以下公式计算获取获得动态扬程系数：S15, combined with the basic lift index from S11 to S14 , Kinetic Energy Loss Index , Temperature correction factor and viscosity influence coefficient , the dynamic lift coefficient is calculated by the following formula : 。 . 6.根据权利要求5所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，S11具体包括：6. The water pump control system based on the intelligent water pump data acquisition function according to claim 5, characterized in that S11 specifically includes: S111、通过液位传感器测量水泵吸入端液位高度后，通过以下计算获得液位高度引起的静压力：S111, measure the liquid level at the suction end of the pump through the liquid level sensor Finally, the static pressure caused by the liquid level is obtained by the following calculation : ； ; 其中，表示液体密度，单位kg/m³，表示重力加速度，设置为9.81m/s²；in, Indicates liquid density in kg/m³. Indicates the acceleration due to gravity, set to 9.81m/s²; S112、计算流速引起的动压力：S112. Calculate the dynamic pressure caused by flow velocity : ； ; 式中，表示水泵吸入液体流速；In the formula, Indicates the flow rate of liquid sucked by the pump; S113、将液位高度引起的静压力和流速引起的动压力进行相加，得到水泵入口总动态压力为：S113, the static pressure caused by the liquid level and the dynamic pressure caused by the flow velocity Add them together to get the total dynamic pressure at the pump inlet: : ； ; S114、结合水泵排出端液位高度及流体流速进行分析，通过以下公式计算获得水泵出口总动态压力：S114. Combined with the liquid level height and fluid flow rate at the discharge end of the water pump, the total dynamic pressure at the outlet of the water pump is calculated by the following formula : ； ; 式中，表示水泵排出端液位高度，表示水泵出口流体流速，通过流速传感器测量获取。In the formula, Indicates the liquid level at the discharge end of the pump. Indicates the fluid flow rate at the pump outlet, which is measured by a flow rate sensor. 7.根据权利要求4所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，所述管道摩擦损失指数获取方式如下：7. The water pump control system based on intelligent water pump data acquisition function according to claim 4, characterized in that the pipeline friction loss index The acquisition method is as follows: 采集获取水泵连接管道数据，通过达西-威斯巴赫公式计算获取管道摩擦损失指数：Collect the data of the water pump connection pipeline and calculate the pipeline friction loss index using the Darcy-Weisbach formula : ； ; 式中，f表示摩擦因子，通过科尔布鲁克-怀特公式或摩迪图获取；L表示管道总长度，D表示管道直径；Where, f represents the friction factor, which is obtained by the Colebrook-White formula or the Mody diagram; L represents the total length of the pipeline, and D represents the diameter of the pipeline; 其中，表示管道平均流速，在管道设置若干个点，通过流速传感器测量若干个点的流速，监测获得平均流速；计算为：Q表示流量，NJ表示管道内径；in, Indicates the average flow velocity in the pipeline. Several points are set in the pipeline, and the flow velocity at several points is measured by flow velocity sensors to monitor and obtain the average flow velocity. ; Calculated as: Q represents flow rate, NJ represents the inner diameter of the pipe; 所述局部阻力损失指数的获取方式为：The local drag loss index The way to obtain is: 采集与水泵连通的管件信息，管件信息包括弯头、三通和阀门的阻力损失数据，通过以下公式计算获取局部阻力损失指数：Collect information about pipes connected to the water pump, including resistance loss data of elbows, tees, and valves, and calculate the local resistance loss index using the following formula : ； ; 式中，n表示管件总数；表示第i个管件的局部阻力系数，通过管件手册查询获得，具体包括：弯头为0.2~0.3；主流三通为0.1~0.2；支流三通为1.0~5.0；全开阀门为0.1~0.2；半开阀门1.0~5.0；In the formula, n represents the total number of pipe fittings; It represents the local resistance coefficient of the i-th pipe fitting, which is obtained by querying the pipe fitting manual, including: elbow is 0.2~0.3; mainstream tee is 0.1~0.2; branch tee is 1.0~5.0; fully open valve is 0.1~0.2; half-open valve is 1.0~5.0; 所述气蚀余量的获取方式为：The cavitation margin The way to obtain is: 使用净正吸入头，气蚀余量是水泵入口处的压力和蒸汽压之间的差值，通过以下公式计算获取：Using net positive suction head, cavitation head is the difference between the pressure at the pump inlet and the vapor pressure, and is calculated using the following formula: ； ; 式中，为液位高度引起的静压力，为液体蒸气压力，为水泵入口总动态压力；In the formula, The static pressure caused by the liquid level , is the liquid vapor pressure, is the total dynamic pressure at the pump inlet ; 将管道摩擦损失指数、局部阻力损失指数和气蚀余量无量纲处理后，通过以下相关联公式计算获得管道阻力系数：Pipeline Friction Loss Index , local resistance loss index and cavitation margin After dimensionless processing, the pipeline resistance coefficient is calculated by the following related formula: : ； ; 式中，、和分别表示管道摩擦损失指数、局部阻力损失指数和气蚀余量的权重，且。In the formula, , and They represent pipeline friction loss index , local resistance loss index and cavitation margin The weight of . 8.根据权利要求1所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，所述动态扬程优化模块包括拟合单元、第一评估单元和第一优化单元；8. The water pump control system based on intelligent water pump data acquisition function according to claim 1, characterized in that the dynamic head optimization module comprises a fitting unit, a first evaluation unit and a first optimization unit; 所述拟合单元用于利用所述动态扬程系数与管道阻力系数，拟合计算获得扬程风险指数；The fitting unit is used to utilize the dynamic head coefficient and pipe resistance coefficient , fitting calculation to obtain the head risk index ; ； ; 式中，和为权重值，且；In the formula, and is the weight value, and ; 所述第一评估单元用于预设扬程风险阈值Y，并将扬程风险指数与扬程风险阈值Y进行对比，获得第一评估结果，包括：The first evaluation unit is used to preset a lift risk threshold Y and to calculate a lift risk index Compare with the lift risk threshold Y to obtain the first assessment result, including: 当扬程风险指数＜扬程风险阈值Y，表示水泵当前的扬程风险指数未偏移，水泵处于正常运行状态；When the risk index <Head risk threshold Y, indicating the current head risk index of the pump There is no deviation, and the water pump is in normal operation; 当扬程风险阈值Y≤扬程风险指数≤扬程风险阈值，表示水泵当前的扬程风险指数处于偏移异常运行状态，生成第一调整指令，包括：调整降低当前3%-5%电机功率、减少当前水泵叶轮转速5%-10%和调整水泵当前±2-5°安装角度；When the lift risk threshold Y ≤ lift risk index ≤Lift risk threshold , indicating the current head risk index of the pump In the abnormal deviation operation state, the first adjustment instruction is generated, including: adjusting to reduce the current motor power by 3%-5%, reducing the current water pump impeller speed by 5%-10%, and adjusting the current installation angle of the water pump by ±2-5°; 当扬程风险指数＞扬程风险阈值，表示水泵当前的扬程风险指数处于偏移异常运行状态，生成第二调整指令，包括：调整降低当前6%-10%电机功率、减少当前水泵叶轮转速11%-15%和调整水泵当前±6-10°安装角度。When the risk index ＞Lift risk threshold , indicating the current head risk index of the pump When the pump is in an abnormal deviation operation state, a second adjustment instruction is generated, including: reducing the current motor power by 6%-10%, reducing the current water pump impeller speed by 11%-15%, and adjusting the current installation angle of the water pump by ±6-10°. 9.根据权利要求1所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，所述管道阻力评估模块包括第二评估单元和第二优化单元；9. The water pump control system based on intelligent water pump data acquisition function according to claim 1, characterized in that the pipeline resistance evaluation module includes a second evaluation unit and a second optimization unit; 所述第二评估单元用于预设阻力阈值X，并将所述管道阻力系数与阻力阈值X进行对比分析流体通过管道过程中产生的摩擦阻力和局部阻力风险，获得第二评估结果，包括：The second evaluation unit is used to preset a resistance threshold value X and to calculate the pipeline resistance coefficient The friction resistance and local resistance risk generated by the fluid passing through the pipeline are compared with the resistance threshold X to obtain the second assessment result, including: 当管道阻力系数＜阻力阈值X，表示当前与水泵连接的管道布局合理，维持现有管道设计；When the pipe resistance coefficient < resistance threshold X, indicating that the current layout of the pipeline connected to the water pump is reasonable and the existing pipeline design is maintained; 当阻力阈值X≤管道阻力系数≤阻力阈值，表示当前与水泵连接的管道布局不合理，标记第一阻力等级；When the resistance threshold X ≤ pipeline resistance coefficient ≤ Resistance Threshold , indicating that the current layout of the pipes connected to the water pump is unreasonable, marking the first resistance level; 当阻力阈值X≤管道阻力系数≤阻力阈值，表示当前与水泵连接的管道布局不合理，标记第二阻力等级，比第一阻力等级严重。When the resistance threshold X ≤ pipeline resistance coefficient ≤ Resistance Threshold , indicating that the current layout of the pipeline connected to the water pump is unreasonable, marking the second resistance level, which is more serious than the first resistance level. 10.根据权利要求9所述的基于智能水泵数据采集功能的水泵控制系统，其特征在于，所述第二优化单元用于根据第一阻力等级生成第一布局优化指令，包括：每月2-3次的频率对与水泵连通的弯头、三通和阀门进行清洗、并更换10-20%的管件，更换成流线型的弯头和三通管件，并将截止阀更换为球阀或蝶阀，并在水泵管道中设置1台中间补偿器或减振器；10. The water pump control system based on the intelligent water pump data acquisition function according to claim 9 is characterized in that the second optimization unit is used to generate a first layout optimization instruction according to the first resistance level, including: cleaning the elbows, tees and valves connected to the water pump 2-3 times a month, replacing 10-20% of the pipe fittings with streamlined elbows and tees, replacing the stop valve with a ball valve or a butterfly valve, and setting an intermediate compensator or shock absorber in the water pump pipeline; 并依据第二阻力等级生成第二布局优化指令，包括：每月4-6次的频率对与水泵连通的弯头、三通和阀门进行清洗、并更换21-30%的管件，更换成流线型的弯头和三通管件，并将截止阀更换为球阀或蝶阀，并在水泵管道中设置1台中间补偿器或减振器，以及增加1台平衡阀门或压力调节装置。The second layout optimization instruction is generated according to the second resistance level, including: cleaning the elbows, tees and valves connected to the water pump 4-6 times a month, replacing 21-30% of the pipe fittings with streamlined elbows and tees, replacing the stop valve with a ball valve or butterfly valve, and installing an intermediate compensator or shock absorber in the water pump pipeline, and adding a balancing valve or pressure regulating device.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103185003A (en) * 2011-12-27 2013-07-03 Abb公司 Method and apparatus for optimizing energy efficiency of pumping system
JP6436408B1 (en) * 2018-02-15 2018-12-12 有限会社北沢技術事務所 Pump flow measurement device
CN113536710A (en) * 2021-07-26 2021-10-22 杭州哲达科技股份有限公司 Pump and pump set energy efficiency visual monitoring method
CN117662449A (en) * 2024-01-02 2024-03-08 上海凯士比泵有限公司 Method, device and medium for determining pipe losses of a water pump system
CN118190374A (en) * 2022-12-14 2024-06-14 施耐德东芝换流器欧洲公司 Method for identifying impeller wear and excessive wear ring clearance in centrifugal pump

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103185003A (en) * 2011-12-27 2013-07-03 Abb公司 Method and apparatus for optimizing energy efficiency of pumping system
JP6436408B1 (en) * 2018-02-15 2018-12-12 有限会社北沢技術事務所 Pump flow measurement device
CN113536710A (en) * 2021-07-26 2021-10-22 杭州哲达科技股份有限公司 Pump and pump set energy efficiency visual monitoring method
CN118190374A (en) * 2022-12-14 2024-06-14 施耐德东芝换流器欧洲公司 Method for identifying impeller wear and excessive wear ring clearance in centrifugal pump
CN117662449A (en) * 2024-01-02 2024-03-08 上海凯士比泵有限公司 Method, device and medium for determining pipe losses of a water pump system

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