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

Abstract

The present 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 system collects static head, suction head and drainage head data in real time through a head monitoring module, generates a comprehensive first data group, and dynamically presents the head change. The pipeline resistance evaluation module combines liquid characteristic data to obtain fluid dynamic viscosity, flow rate and cavitation margin data in real time, and comprehensively calculates pipeline friction loss index, local resistance loss index and cavitation margin, which are used to accurately evaluate pipeline resistance and avoid increased energy consumption due to increased resistance. At the same time, the liquid characteristic data acquisition module monitors the cavitation margin, and identifies cavitation risks in advance in combination with the prediction analysis module.

Description

Technical Field

[0001] The present invention relates to the technical field of water pump operation data analysis, and in particular to a water pump control system based on an intelligent water pump data acquisition function. Background Art

[0002] Traditional water pump systems mostly use fixed sensors to monitor static head or head data under a single operating state, which cannot fully reflect the changes in suction head, discharge head and dynamic operating head. Especially under variable operating conditions, the lag and singleness of head data can easily lead to reduced pump operating efficiency. During the operation of the water pump, the flow of liquid in the pipeline is affected by both friction resistance and local resistance. Traditional water pump control systems lack accurate assessment and dynamic response to pipeline resistance, which can easily lead to increased energy consumption or pipeline wear. During the operation of the water pump, as the local pressure drops below the saturated vapor pressure of the liquid, the liquid quickly vaporizes to form bubbles, forming cavitation. Cavitation may cause serious damage to the impeller and other key components, thereby affecting the equipment life and system stability. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a water pump control system based on an intelligent water pump data acquisition function to solve the problems mentioned in the background technology.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a water pump control system based on intelligent water pump data acquisition function, including 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;

[0005] 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;

[0006] 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;

[0007] 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 ;

[0008] 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;

[0009] 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.

[0010] Preferably, 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 ;

[0011] The water suction head data include: the flow rate of the liquid sucked by the water pump ;

[0012] The drainage head data includes: the flow rate of the fluid at the pump outlet ;

[0013] 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.

[0014] Preferably, the liquid flow rate includes: flow rate Q;

[0015] The fluid dynamic viscosity includes: fluid dynamic viscosity ;

[0016] The cavitation margin data include: fluid temperature T and liquid vapor pressure ;

[0017] 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.

[0018] Preferably, the prediction and analysis module includes a model building unit, a lift analysis unit and a pipeline loss analysis unit;

[0019] 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;

[0020] 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 :

[0021] 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 .

[0022] Preferably, the dynamic lift coefficient The way to obtain is:

[0023] 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 :

[0024] ;

[0025] 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;

[0026] 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 :

[0027] ;

[0028] In the formula, and The unit is m / s;

[0029] S13. Real-time collection of the current fluid temperature T, and calculation of the temperature correction coefficient using the following formula :

[0030] ;

[0031] In the formula, Indicates the base temperature, set at 25°C. is the correction constant, determined by experimental data;

[0032] S14, real-time collection of fluid dynamic viscosity , the viscosity influence coefficient is calculated by the following formula :

[0033] ;

[0034] In the formula, It represents the Reynolds number, which reflects the flow state of the fluid;

[0035] 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 :

[0036] .

[0037] Preferably, S11 specifically includes:

[0038] 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 :

[0039] ;

[0040] in, Indicates liquid density in kg / m³. Indicates the acceleration due to gravity, set to 9.81m / s²;

[0041] S112. Calculate the dynamic pressure caused by flow velocity :

[0042] ;

[0043] In the formula, Indicates the flow rate of liquid sucked by the pump;

[0044] 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: :

[0045] ;

[0046] 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 :

[0047] ;

[0048] 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.

[0049] Preferably, the pipeline friction loss index The acquisition method is as follows:

[0050] Collect the data of the water pump connection pipeline and calculate the pipeline friction loss index using the Darcy-Weisbach formula :

[0051] ;

[0052] 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;

[0053] 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;

[0054] The local drag loss index The way to obtain is:

[0055] 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 :

[0056] ;

[0057] 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;

[0058] The cavitation margin The way to obtain is:

[0059] 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:

[0060] ;

[0061] 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 ;

[0062] 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: :

[0063] ;

[0064] In the formula, , and They represent pipeline friction loss index , local resistance loss index and cavitation margin The weight of .

[0065] Preferably, the dynamic lift optimization module includes a fitting unit, a first evaluation unit and a first optimization unit;

[0066] The fitting unit is used to utilize the dynamic head coefficient and pipe resistance coefficient , fitting calculation to obtain the head risk index ;

[0067] ;

[0068] In the formula, and is the weight value, and ;

[0069] 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:

[0070] 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;

[0071] 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°;

[0072] 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°.

[0073] Preferably, the pipeline resistance evaluation module includes a second evaluation unit and a second optimization unit;

[0074] 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:

[0075] 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;

[0076] 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;

[0077] 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.

[0078] Preferably, 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;

[0079] 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.

[0080] The present invention provides a water pump control system based on intelligent water pump data acquisition function. It has the following beneficial effects:

[0081] (1) The present invention collects static head, suction head and discharge head data in real time through the head monitoring module, generates a comprehensive first data set, dynamically presents the head changes under different operating conditions, provides data support for precise optimization, and overcomes the shortcomings of data lag and singleness in traditional systems.

[0082] (2) Frictional resistance and local resistance in the pipeline significantly affect the efficiency of the water pump, and the traditional system lacks accurate evaluation of these resistances. The present invention uses the pipeline resistance evaluation module, combined with the fluid dynamic viscosity, flow rate and cavitation margin data acquired in real time by the liquid characteristic data acquisition module, to comprehensively analyze and calculate the pipeline friction loss index. , local resistance loss index and cavitation margin , comprehensively obtain the pipeline resistance coefficient , compared with the preset resistance threshold X, accurate management of fluid resistance is achieved to avoid increased energy consumption due to increased resistance.

[0083] (3) Under complex and changeable working conditions, the operating efficiency of traditional water pump systems is often reduced due to fixed head parameters. The present invention uses a dynamic head optimization module to calculate the dynamic head coefficient in real time. and pipe resistance coefficient , fitting calculation to obtain the head risk index The system compares and analyzes the head risk threshold Y and generates real-time adjustment instructions. This function significantly improves the adaptability and stability of the pump system under changing conditions.

[0084] (4) The occurrence of cavitation is usually difficult to predict and can easily cause serious damage to key components such as the impeller. The present invention uses the liquid property data acquisition module to monitor the cavitation margin in real time, combined with the deep calculation of the prediction and analysis module, to accurately determine whether the local pressure is lower than the liquid saturated vapor pressure and identify the cavitation risk in advance. The system also effectively prevents cavitation by dynamically adjusting operating parameters (such as impeller speed and installation angle), avoiding frequent maintenance or replacement of equipment due to cavitation problems in traditional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 It is a schematic diagram of the flow chart of the water pump control system based on the intelligent water pump data acquisition function of the present invention. DETAILED DESCRIPTION

[0086] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0087] Example 1

[0088] See also Figure 1 , the present invention provides a water pump control system based on intelligent water pump data acquisition function, including 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;

[0089] 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;

[0090] 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;

[0091] 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 ;

[0092] 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;

[0093] 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.

[0094] In this embodiment, the traditional water pump control system only monitors the static head or the head data under a single operating state, which cannot fully reflect the actual working conditions. The present invention uses a head monitoring module to collect static head, suction head and discharge head data in real time, generate a comprehensive first data group, and dynamically present the head changes under different operating conditions, providing data support for precise optimization, and overcoming the deficiencies of data lag and singleness in traditional systems. The friction resistance and local resistance in the pipeline significantly affect the efficiency of the water pump, and the traditional system lacks accurate assessment of these resistances. The present invention uses a pipeline resistance assessment module, combined with the fluid dynamic viscosity, flow rate and cavitation margin data obtained in real time by the liquid property data acquisition module, to comprehensively analyze and calculate the pipeline friction loss index. , local resistance loss index and cavitation margin , comprehensively obtain the pipeline resistance coefficient , compared with the preset resistance threshold X, accurate management of fluid resistance is achieved to avoid increased energy consumption due to increased resistance.

[0095] Traditional water pump systems often have reduced operating efficiency due to fixed head parameters under complex and changeable working conditions. The present invention uses a dynamic head optimization module to calculate the dynamic head coefficient in real time. and pipe resistance coefficient , fitting calculation to obtain the head risk index The system compares and analyzes the head risk threshold Y and generates real-time adjustment instructions. This function significantly improves the adaptability and stability of the pump system under changing conditions.

[0096] The occurrence of cavitation is usually difficult to predict and can easily cause serious damage to key components such as impellers. The present invention uses a liquid property data acquisition module to monitor the cavitation margin in real time, combined with the in-depth calculation of the prediction and analysis module, to accurately determine whether the local pressure is lower than the liquid saturated vapor pressure and identify cavitation risks in advance. The system also effectively prevents cavitation by dynamically adjusting operating parameters (such as impeller speed and installation angle), avoiding frequent maintenance or replacement of equipment due to cavitation problems in traditional systems.

[0097] Example 2

[0098] This embodiment is explained and illustrated in Example 1. Please refer to Figure 1 Specifically, the static head data includes: the liquid level height at the suction end of the pump , Liquid level at the discharge end of the pump , Liquid Density and the acceleration due to gravity ;

[0099] The water suction head data include: the flow rate of the liquid sucked by the water pump ;

[0100] The drainage head data includes: the flow rate of the fluid at the pump outlet ;

[0101] 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.

[0102] The liquid flow rate includes: flow rate Q;

[0103] The fluid dynamic viscosity includes: fluid dynamic viscosity ;

[0104] The cavitation margin data include: fluid temperature T and liquid vapor pressure ;

[0105] After cleaning, calibrating and dimensionless processing of the liquid flow rate, fluid dynamic viscosity and cavitation margin data, the data are summarized to generate a second data set.

[0106] The first data group and the second data group are collected and acquired by the following sensors, including one or more sensors, specifically: liquid level sensor, density sensor, accelerometer, flow rate sensor, electromagnetic flowmeter, temperature sensor, liquid vapor pressure sensor and liquid viscosity sensor.

[0107] After the first and second data sets are collected by different sensors, they are cleaned, calibrated, and dimensionless processed. Cleaning includes removing outliers, calibration is to ensure the accuracy of the sensor, and dimensionless processing is to eliminate the differences caused by different dimensions. The collection of the first and second data sets ensures that the system can accurately and real-time monitor the operation status of the water pump and provide data support for subsequent predictive analysis, dynamic optimization, and pipeline resistance evaluation.

[0108] Example 3

[0109] This embodiment is explained and illustrated in Example 1. Please refer to Figure 1 ,Specifically, the prediction and analysis module includes a model building unit, a head analysis unit and a pipeline loss analysis unit;

[0110] 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;

[0111] 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 :

[0112] 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 .

[0113] Specifically, the dynamic lift coefficient The way to obtain is:

[0114] 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 :

[0115] ;

[0116] 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; by monitoring the total dynamic pressure at the pump outlet , Total dynamic pressure at pump inlet and the density of the liquid , the basic head index of the water pump can be accurately calculated. This calculation provides key preliminary parameters for subsequent dynamic head analysis, which helps to dynamically adjust the operation of the water pump. This step can provide real-time feedback on the liquid flow state and pump pressure changes, ensuring that pressure fluctuations during the operation of the water pump are captured in time, thereby optimizing system control and operation strategies.

[0117] S11 specifically includes:

[0118] 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 :

[0119] ;

[0120] in, Indicates liquid density in kg / m³. Indicates the acceleration due to gravity, set to 9.81m / s²; the liquid level sensor provides real-time feedback on the liquid level at the suction end, which helps to dynamically adjust the working state of the pump, ensuring that the liquid level at the suction end is always within a reasonable range to avoid water shortage or idling of the pump. Static pressure calculation is a key step in analyzing the flow and head of the pump, which can provide early warning of the pump performance and help determine whether there is potential loss or instability.

[0121] S112. Calculate the dynamic pressure caused by flow velocity :

[0122] ;

[0123] In the formula, Indicates the flow rate of the liquid sucked by the water pump; real-time calculation of the dynamic pressure caused by the flow rate can help to promptly detect abnormal flow rates. If the flow rate is too high or too low, the system can automatically adjust the speed or switch status of the water pump to ensure that the water pump operates in the best condition. The induced dynamic pressure data helps to discover efficiency losses in fluid dynamics, optimize the operating parameters of the pump and reduce unnecessary energy consumption.

[0124] 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: :

[0125] By adding the static pressure and the dynamic pressure, the total dynamic pressure at the inlet is obtained, so as to fully understand the working status of the pump inlet end. This provides the necessary pressure data support for subsequent head prediction and efficiency optimization.

[0126] 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 :

[0127] ;

[0128] 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.

[0129] 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 :

[0130] ;

[0131] In the formula, and The unit is m / s. By collecting the flow rate data at the suction and discharge ends of the water pump, the kinetic energy loss index can be accurately calculated, reflecting the energy loss caused by friction, turbulence and other factors during the flow of the fluid. Understanding the kinetic energy loss helps identify the operating bottleneck of the water pump, thereby adjusting the power and operating conditions of the pump and reducing energy consumption.

[0132] S13. Real-time collection of the current fluid temperature T, and calculation of the temperature correction coefficient using the following formula :

[0133] ;

[0134] In the formula, Indicates the base temperature, set at 25°C. It is a correction constant determined by experimental data; the pump head is greatly affected by the fluid temperature, and the physical properties of the fluid, such as viscosity and density, change with temperature. By collecting the fluid temperature and calculating the correction coefficient, the pump head can be accurately predicted under different temperature conditions to ensure the stability of the system under various environmental conditions. This step can make the pump control system more adaptable, automatically adjust the head in situations where the temperature changes greatly, and improve the overall adaptability of the system.

[0135] S14, real-time collection of fluid dynamic viscosity , the viscosity influence coefficient is calculated by the following formula :

[0136] ;

[0137] In the formula, Reynolds number reflects the flow state of the fluid. The viscosity of the fluid directly affects the flow resistance of the fluid during the operation of the pump. The change in viscosity will cause different resistances to the flow of the fluid, which in turn affects the head. By real-time monitoring of dynamic viscosity and calculating the viscosity influence coefficient , and can more accurately adjust the pump operating parameters to reduce energy waste caused by viscosity changes. This step calculation helps to further refine the control of the pump and optimize the pump operation status, especially in media with large viscosity changes (such as oil, aqueous solution, etc.), which can improve efficiency.

[0138] 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 :

[0139] .

[0140] In this embodiment, this step is achieved by combining multiple key factors (including the basic lift index , Kinetic Energy Loss Index , Temperature correction factor and viscosity influence coefficient ) to calculate the dynamic head coefficient , which can fully reflect the working status of the water pump, eliminate the limitations of single parameter evaluation, and ensure accurate adjustment of the head.

[0141] Example 4

[0142] This embodiment is explained and illustrated in Example 1. Please refer to Figure 1 Specifically, the pipeline friction loss index The acquisition method is as follows:

[0143] Collect the data of the water pump connection pipeline and calculate the pipeline friction loss index using the Darcy-Weisbach formula :

[0144] ;

[0145] 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;

[0146] 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;

[0147] The local drag loss index The way to obtain is:

[0148] 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 :

[0149] ;

[0150] 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;

[0151] The cavitation margin The way to obtain is:

[0152] 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:

[0153] ;

[0154] 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 ;

[0155] 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: :

[0156] ;

[0157] In the formula, , and They represent pipeline friction loss index , local resistance loss index and cavitation margin The weight of .

[0158] In this embodiment, the pipeline friction loss index It can accurately reflect the energy loss caused by friction in the pipeline, help optimize the matching of water pumps and pipeline systems, and reduce unnecessary energy waste. The local resistance loss index of the pipeline is calculated through the pipe fitting information (such as elbows, tees, valves, etc.) and their corresponding local resistance coefficients. . Local resistance loss index The calculation helps to reduce the pressure loss caused by the pipe connection parts (such as elbows, valves, etc.), optimize the fluid flow path, and improve the overall flow efficiency of the system. The calculation helps monitor whether there is cavitation risk at the suction end of the pump and identify potential problems that may cause equipment damage in advance. Effectively avoid the impact of cavitation on the performance and life of the pump. By effectively controlling the cavitation margin, it is possible to avoid cavitation causing damage to the pump or reduced efficiency, ensuring that the pump operates in a safe and stable condition.

[0159] By combining the pipeline friction loss index , local resistance loss index and cavitation margin , the calculated pipeline resistance coefficient The resistance characteristics of the entire pipeline system can be comprehensively evaluated, providing data support for optimizing pipeline design and pump adjustment. This calculation helps to comprehensively evaluate and reduce the resistance loss of the entire pipeline system, optimize fluid transmission efficiency, reduce energy consumption, and improve the overall operating efficiency of the system.

[0160] Example 5

[0161] This embodiment is explained and illustrated in Example 1. Please refer to Figure 1 ,Specifically, the dynamic lift optimization module includes a fitting unit, a first evaluation unit and a first optimization unit;

[0162] The fitting unit is used to utilize the dynamic head coefficient and pipe resistance coefficient , fitting calculation to obtain the head risk index ;

[0163] ;

[0164] In the formula, and is the weight value, and ;

[0165] 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:

[0166] 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;

[0167] 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°;

[0168] 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°.

[0169] The installation angle of the water pump affects the way the liquid flows through the pump body. By adjusting the installation angle, the water flow path can be optimized, the friction and eddy loss of the water flow can be reduced, and the water flow efficiency can be improved. By accurately adjusting the installation angle of the water pump, it is ensured that the water flow can enter and exit the water pump more smoothly, reducing unnecessary pressure loss, thereby reducing the burden on the motor and reducing energy consumption. Improper installation angles may cause unbalanced operation of the water pump, which in turn causes vibration and noise. By adjusting the installation angle, the water pump can be operated under more stable conditions, reduce vibration, and improve the comfort and stability of the system. If the inlet angle of the water pump is not appropriate, it may cause the inhaled water flow to be too fast or too slow, thereby increasing the risk of cavitation. Cavitation can damage the water pump impeller and other key components. By adjusting the installation angle, the occurrence of cavitation can be effectively reduced and the service life of the water pump can be extended.

[0170] In this embodiment, the dynamic lift coefficient and pipe resistance coefficient The fitting unit can accurately calculate the head risk index. , timely evaluate the head risk of the pump under different working conditions. This helps to identify potential problems in the operation of the pump, such as insufficient or excessive pumping pressure. An accurate head risk index provides a basis for subsequent adjustments, allowing the pump to operate under efficient conditions and avoid overload. The fitting process makes full use of the actual pipeline resistance characteristics and head coefficient, allowing the system to make intelligent decisions through data-driven, thereby improving the intelligence level of the system.

[0171] When the risk index When the head risk index deviates from the preset head risk threshold Y, the system will automatically generate an adjustment instruction. This automated adjustment mechanism reduces human intervention and improves the system's response speed and processing efficiency. By reducing the motor power, reducing the pump impeller speed and adjusting the pump installation angle, the first optimization unit can effectively reduce the energy consumption of the pump, optimize power consumption during operation, and improve the energy efficiency of the system. When the head risk index deviates from the normal range, the adjustment measures help restore the pump to a more stable operating state, thereby reducing equipment damage or performance degradation caused by excessive deviation. Adjusting the range and proportion of pump parameters (such as adjusting motor power, impeller speed and installation angle) enables the system to adapt to different working conditions and environmental changes, enhancing the adaptability and flexibility of the pump.

[0172] Example 6

[0173] This embodiment is explained and illustrated in Example 5. Please refer to Figure 1 ,Specifically, the pipeline resistance evaluation module includes a second evaluation unit and a second optimization unit;

[0174] 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:

[0175] 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;

[0176] 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;

[0177] 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.

[0178] 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;

[0179] 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.

[0180] By regularly cleaning and replacing pipe fittings, impurities, sediments and losses in the pipeline can be significantly reduced, thereby ensuring the stable operation of the pipeline and water pump system and avoiding energy efficiency degradation and failures caused by accumulated pollutants. Replacing streamlined pipe fittings, ball valves or butterfly valves, and adding shock absorbers and compensators can effectively reduce pressure fluctuations and vibrations of water flow, improve the stability of water pumps and pipeline systems, reduce mechanical wear and extend equipment life. By optimizing the pipeline layout and reducing friction and local resistance, the efficiency of fluid transportation can be effectively improved and energy consumption can be reduced. This not only helps to reduce electricity consumption, but also reduces operating costs. Regular pipeline cleaning, pipe fitting replacement and other optimization measures can help extend the service life of pipeline systems and water pumps and reduce the occurrence of failures. By evaluating the "first" and "second" resistance levels, appropriate optimization strategies can be selected for different situations, avoiding unnecessary frequent large-scale repairs and reducing maintenance costs.

[0181] In this embodiment, by comparing and analyzing the pipeline resistance coefficient with the preset resistance threshold, the rationality of the current pipeline layout can be effectively evaluated. If the pipeline resistance coefficient is lower than the resistance threshold X, it means that the pipeline layout is reasonable and no change is required. The existing design can be maintained. This evaluation helps to confirm that the current pipeline system is in the best operating state, thereby saving unnecessary maintenance costs and time. When the pipe resistance coefficient exceeds the resistance threshold, the system will mark it as "the first resistance level", indicating that there is a certain resistance problem. At this time, the evaluation module recommends optimizing the pipeline layout, such as regularly cleaning and replacing some pipes, taking streamlined design and other measures to effectively reduce friction resistance and maintain efficient operation of the water pump. , the system will mark it as "second resistance level" and recommend a more thorough pipe optimization. This precise identification can help take timely measures in the case of excessive resistance, prevent the pump from being overloaded, and extend the service life of the equipment.

[0182] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0183] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, replace or change them, should be covered within the protection scope of the present invention.

Claims

1. A water pump control system based on intelligent water pump data acquisition function, characterized in that: It includes head monitoring module, liquid characteristic data acquisition module, prediction analysis module, dynamic head optimization module and pipeline resistance evaluation module; 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; 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; 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 ; 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, and set 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; 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. The water pump control system based on the intelligent water pump data acquisition function according to claim 1 is characterized in that: The static head data include: the liquid level height at the suction end of the pump , Liquid level at the discharge end of the pump , Liquid Density and the acceleration due to gravity ; The water suction head data include: the flow rate of the liquid sucked by the water pump ; The drainage head data includes: the flow rate of the fluid at the pump outlet ; 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. The water pump control system based on the intelligent water pump data acquisition function according to claim 2 is characterized in that: The liquid flow rate includes: flow rate Q; The fluid dynamic viscosity includes: fluid dynamic viscosity ; The cavitation margin data include: fluid temperature T and liquid vapor pressure ; 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. The water pump control system based on the intelligent water pump data acquisition function according to claim 3 is characterized in that: The prediction and analysis module includes a model building unit, a lift analysis unit and a pipeline loss analysis unit; 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; 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 : 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. The water pump control system based on the intelligent water pump data acquisition function according to claim 4 is characterized in that: The dynamic head coefficient The way to obtain is: 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 : ； 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, 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 : ； In the formula, and The unit is m / s; S13. Real-time collection of the current fluid temperature T, and calculation of the temperature correction coefficient using the following formula : ； In the formula, Indicates the base temperature, set at 25°C. is the correction constant, determined by experimental data; S14, real-time collection of fluid dynamic viscosity , the viscosity influence coefficient is calculated by the following formula : ； In the formula, It represents the Reynolds number, which reflects the flow state of the fluid; 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. The water pump control system based on the intelligent water pump data acquisition function according to claim 5 is characterized in that: S11 specifically includes: 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 : ； in, Indicates liquid density in kg / m³. Indicates the acceleration due to gravity, set to 9.81m / s²; S112. Calculate the dynamic pressure caused by flow velocity : ； In the formula, Indicates the flow rate of liquid sucked by the pump; 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. 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 : ； 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. The water pump control system based on the intelligent water pump data acquisition function according to claim 4 is characterized in that: The pipeline friction loss index The acquisition method is as follows: Collect the data of the water pump connection pipeline and calculate the pipeline friction loss index using the Darcy-Weisbach formula : ； 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; 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; The local drag loss index The way to obtain is: 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 : ； 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; The cavitation margin The way to obtain is: 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: ； 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 ; 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: : ； In the formula, , and They represent pipeline friction loss index , local resistance loss index and cavitation margin The weight of .

8. The water pump control system based on intelligent water pump data acquisition function according to claim 1 is characterized in that: The dynamic lift optimization module includes a fitting unit, a first evaluation unit and a first optimization unit; The fitting unit is used to utilize the dynamic head coefficient and pipe resistance coefficient , fitting calculation to obtain the head risk index ; ； In the formula, and is the weight value, and ; 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: 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; 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°; 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. The water pump control system based on intelligent water pump data acquisition function according to claim 1 is characterized in that: The pipeline resistance evaluation module includes a second evaluation unit and a second optimization unit; 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: 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; 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; 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. 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; 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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