An integrated pump house for construction sites

Abstract

This invention provides an integrated pump house for construction sites, belonging to the technical field of integrated pump houses. It includes a housing and a water tank. The housing houses a water pump, a frequency converter control cabinet, and piping. The frequency converter control cabinet connects to an external power source and provides power to the water pump. The water pump is connected to the water tank outlet via piping. It also includes a controller connected to the frequency converter control cabinet and the water pump. The controller contains a frequency converter adjustment module and a soft-start adaptive parameter calculation module. The frequency converter adjustment module adjusts the water pump's operating frequency in real time based on water demand and changes in water pump outlet pressure to achieve constant pressure water supply and energy-saving operation. The soft-start adaptive parameter calculation module calculates the optimal soft-start parameters based on the water pump's operating status and aging or wear condition, and outputs these parameters to the water pump. This invention solves the technical problem of the difficulty in timely adjusting the parameters of the water pump soft starter in existing integrated pump houses.

Description

Technical Field

[0001] This invention belongs to the field of integrated pump house technology, and more specifically, relates to an integrated pump house for use on construction sites. Background Technology

[0002] In modern industrial and urban water supply systems, water pumps play a crucial role as core equipment. With prolonged use, water pumps inevitably age, significantly impacting the startup process. Traditional water pump startup methods mainly include direct starting and fixed-parameter soft starting.

[0003] While direct starting is simple to operate, it generates a huge starting current and mechanical shock, severely affecting the service life of the water pump and its related components. To solve this problem, soft starting technology is widely used in the water pump startup process. Soft starting, by controlling the gradual increase of the starting voltage, can effectively reduce the starting current and mechanical stress, protecting the water pump and the power supply system.

[0004] However, the widely used fixed-parameter soft-start technology has a major drawback: it cannot adapt to performance changes caused by pump aging. As pumps are used for longer periods, their mechanical components experience varying degrees of wear, and their electrical performance gradually degrades. These changes significantly alter the pump's starting characteristics, and fixed-parameter soft-start cannot adjust accordingly. Specifically:

[0005] 1. Extended start-up time: Due to the increased friction of aging water pumps, fixed start-up parameters may result in excessively long start-up times, affecting normal water supply.

[0006] 2. Increased starting current: The efficiency of an aging water pump decreases, requiring a larger current to reach normal operating conditions. Fixed parameters may not be able to effectively control the starting current.

[0007] 3. Increased mechanical stress: The bearings, impellers and other components of an aging water pump become more fragile. Fixed starting parameters may cause excessive mechanical stress, accelerating equipment damage.

[0008] 4. Increased energy consumption: Start-up parameters that are not suitable for aging conditions will lead to increased energy loss and reduce the overall efficiency of the system.

[0009] 5. Unstable water supply: Improper startup may cause water hammer or pressure fluctuations, affecting water supply quality.

[0010] 6. Shortened equipment lifespan: Improper startup processes can accelerate the wear and tear on water pumps and related equipment, shortening their service life.

[0011] 7. Increased maintenance costs: Mismatched startup parameters may lead to more frequent malfunctions and maintenance needs.

[0012] 8. Decreased system reliability: Fixed parameters cannot adapt to dynamic changes in pump performance, increasing the risk of system failure.

[0013] While some advanced soft starters in the existing technology allow for manual parameter adjustment, this method has several problems: First, it requires professional technicians to regularly check the pump status and adjust the parameters, resulting in high labor costs; second, manual adjustment often lags behind changes in pump performance and cannot be optimized in a timely manner; and finally, human judgment is insufficient to accurately capture minute changes in pump performance, limiting the effectiveness of the adjustment.

[0014] Therefore, the water pump industry urgently needs an intelligent soft-start system that can automatically adapt to the aging process of water pumps and dynamically optimize starting parameters. This system should be able to monitor the water pump status in real time, automatically calculate the optimal starting parameters, and ensure that the water pump maintains optimal starting performance throughout its entire lifespan, thereby improving system efficiency, extending equipment life, and reducing operation and maintenance costs. Summary of the Invention

[0015] In view of this, the present invention provides an integrated pump house for construction sites, which can solve the technical problem that the existing integrated pump soft starters require professional technicians to regularly check the pump status and adjust parameters, and manual adjustment often lags behind changes in pump performance.

[0016] This invention is implemented as follows:

[0017] This invention provides an integrated pump house for construction sites, comprising a housing and a water tank. The housing contains a water pump, a frequency converter control cabinet, and pipes. The frequency converter control cabinet is used to connect to an external power source and to provide power to the water pump. The water pump is connected to the outlet of the water tank via pipes.

[0018] The fire resistance rating of the building is Class II. It consists of fire doors, fire windows, fire-resistant partitions with a fire resistance rating of not less than 2.00h, and floor slabs with a fire resistance limit of not less than 4.50h. The floor slabs are used to divide and isolate the building.

[0019] Furthermore, the fireproof partition wall is made of fire-retardant rock wool, the bottom of the building has columns around the perimeter and the top is made of high-strength steel structure, and the floor of the building is paved with patterned steel plates.

[0020] Furthermore, it also includes an automatic temperature control system that automatically heats the water pump to 5 degrees Celsius when the temperature is below 0 degrees Celsius to prevent it from freezing, and automatically turns on the cooling fan when the temperature is above 30 degrees Celsius.

[0021] Furthermore, it also includes a 4G smart smoke detector that can make a phone call and send a text message notification to a preset mobile phone when smoke is detected.

[0022] Furthermore, the water tank is a stainless steel assembled water tank, which is assembled by pressing and welding stainless steel plates.

[0023] Furthermore, the frequency converter control cabinet is a smart interconnected integrated frequency converter control cabinet, connected to a 4G network, to realize the automation and remote monitoring of the pump room, and has intelligent remote monitoring and maintenance functions. The pump room operation status can be monitored anytime and anywhere through a mobile APP or computer, and parameters can be adjusted in real time.

[0024] Furthermore, it also includes a controller, which is connected to the frequency converter control cabinet and the water pump. The controller is equipped with a frequency converter adjustment module and a soft start adaptive parameter calculation module. The frequency converter adjustment module is used to adjust the operating frequency of the water pump in real time according to the water demand and the pressure change at the water pump outlet, so as to achieve constant pressure water supply and energy-saving operation. The soft start adaptive parameter calculation module is used to calculate the optimal soft start parameters according to the operating status of the water pump and the aging or wear of the water pump, and output them to the water pump.

[0025] The frequency conversion adjustment module is used to perform the following steps:

[0026] S11. Obtain water demand and real-time data on water pump outlet pressure;

[0027] S12. Calculate the deviation between the current pressure and the target pressure based on the preset target water supply pressure value;

[0028] S13. Based on the deviation value, use the PID control algorithm to calculate the required pump speed adjustment;

[0029] S14. Convert the calculated speed adjustment amount into the corresponding frequency adjustment value;

[0030] S15. Send a frequency adjustment command to the frequency converter;

[0031] S16. Monitor the adjusted water pump outlet pressure and record the relevant data;

[0032] S17. Analyze the recorded data and evaluate the adjustment effect;

[0033] S18. If the adjustment effect is less than the preset adjustment effect threshold, then fine-tune the PID parameters to optimize the control effect.

[0034] S19. Repeat steps S11 to S18 to continue closed-loop control.

[0035] The soft-start adaptive parameter calculation module is used to perform the following steps:

[0036] S21. Obtain historical operating data of the water pump, including electrical parameters, mechanical parameters, hydraulic parameters, and environmental parameters; wherein, the electrical parameters include starting current, operating current, power factor, and voltage fluctuation; the mechanical parameters include speed, vibration, bearing temperature, and noise level; the hydraulic parameters include flow rate, pressure, head, and efficiency; and the environmental parameters include ambient temperature, humidity, and atmospheric pressure.

[0037] S22. Establish a set of pump start-up response equations that take into account electrical parameters, mechanical parameters, hydraulic parameters and environmental parameters, including current equation, torque equation, speed equation, pressure equation, flow rate equation, efficiency equation and temperature equation.

[0038] S23. Based on the historical operating data, fit the pump start-up response equation set to obtain the fitted equation set;

[0039] S24. Based on the soft-start parameters, including initial voltage, voltage ramp time, current limit, torque limit and acceleration time, the fitted equation set is analytically solved and merged. Considering the mutual influence and constraint relationship between each parameter, the water pump soft-start effect evaluation equation set is obtained, including the start-up time effect evaluation equation, current impact effect evaluation equation, energy consumption effect evaluation equation and equipment stress effect evaluation equation.

[0040] S25. Using the soft-start parameters encoded as gene sequences, Gaussian distributed random numbers as mutation factors, the water pump soft-start effect evaluation equations as fitness functions, and water pump rated parameters and system safety limits as constraints, a genetic algorithm is used for iterative optimization to obtain candidate soft-start parameters.

[0041] S26. Set the soft starter with the candidate soft start parameters, execute the soft start process of the water pump, record the water pump's operating data, and record it as experimental operating data. If the experimental operating data meets the preset performance indicators and safety thresholds, then the candidate soft start parameters are output as soft start adaptive parameters; otherwise, the experimental operating data is merged into the historical operating data, and S23-S25 are repeated until the experimental operating data meets the preset performance indicators and safety thresholds.

[0042] The current equation describes the change of current over time during startup, including parameters such as starting current, rated current, and current time constant. The torque equation describes the characteristics of torque variation with voltage and time, and is related to the current and velocity equations. The velocity equation describes the change of angular velocity over time, and is related to the torque and pressure equations. The pressure equation describes the change of pressure with angular velocity and time. The flow rate equation describes the characteristics of flow rate variation with angular velocity and time. The efficiency equation describes the change of efficiency with time and velocity. The temperature equation describes the change of temperature over time, and is related to other parameters such as current and torque. These equations provide a theoretical basis for evaluating the soft-start effect.

[0043] In this model, parameters such as the instantaneous starting current, current time constant, and voltage ramp time constant in the current equation can be obtained through actual measurements or fitting historical data; similarly, parameters such as the initial voltage and mechanical time constant in the torque equation can be determined through experimental measurements or historical data analysis; parameters such as the moment of inertia, rated angular velocity, and torque in the velocity equation can be obtained through theoretical calculations or experimental tests; and relevant parameters in the pressure, flow rate, efficiency, and temperature equations can be determined using similar methods. This method of obtaining equation parameters based on multi-source data ensures the accuracy and applicability of the model.

[0044] The evaluation equations for the soft-start effect of the water pump include equations for evaluating start-up time, current surge effect, energy consumption, and equipment stress. Specifically, the start-up time evaluation equation considers the influence of parameters such as initial voltage, voltage ramp time, and acceleration time on start-up time; the current surge effect evaluation equation considers the influence of initial voltage, current limit, and voltage ramp time on current surge; the energy consumption evaluation equation comprehensively considers the influence of start-up time, current variation, and voltage variation on energy consumption; and the equipment stress effect evaluation equation considers the influence of torque limit, acceleration time, and temperature variation on mechanical and thermal stress. These evaluation equations provide a basis for optimizing soft-start parameters.

[0045] Optionally, the reference values ​​and sensitivity coefficients in each evaluation equation can be determined through historical data analysis and expert experience. For example, the reference value for start-up time can be set as the time required for the pump to reach 95% of its rated rotational speed; the reference value for current surge can be set as 150% of the rated current; the reference value for energy consumption can be set as the start-up energy consumption under the rated capacity of the motor; and the reference value for stress can be set as the maximum allowable stress of the motor and pump. The corresponding sensitivity coefficients need to be set reasonably based on actual conditions and engineering experience. This parameter determination method based on historical data and empirical knowledge ensures the relevance and rationality of the evaluation indicators.

[0046] Optionally, the specific implementation of the genetic algorithm includes: encoding soft-start parameters as gene sequences, using Gaussian distributed random numbers as mutation factors; using the water pump soft-start effect evaluation equation set as the fitness function, and using the water pump rated parameters and system safety limits as constraints; through iterative optimization, finally obtaining candidate soft-start parameters that meet the performance indicators and safety thresholds. This optimization algorithm fully considers the mutual influence and constraint relationships between various soft-start parameters, and can quickly find the optimal parameter combination.

[0047] Optionally, the gene sequence encoding includes key soft-start parameters such as initial voltage, voltage ramp time, current limit, torque limit, and acceleration time; Gaussian distributed random numbers serve as mutation factors, enabling continuous approximation of the global optimum while maintaining population diversity; the pump soft-start effect evaluation equation set serves as the fitness function, comprehensively assessing the performance of various soft-start schemes; pump rated parameters and system safety limits serve as constraints, ensuring that the optimization results meet equipment and safety requirements. This genetic algorithm optimization method features fast convergence and strong robustness, making it suitable for solving complex nonlinear problems.

[0048] Optionally, the specific implementation of the automatic temperature control system includes: when the ambient temperature is detected to be below 0 degrees Celsius, automatically activating the heating device to raise the temperature to 5 degrees Celsius to prevent the water pump from freezing; when the ambient temperature is detected to be above 30 degrees Celsius, automatically activating the cooling fan to maintain a suitable temperature inside the pump room. This temperature control system can monitor the ambient temperature in real time and automatically adjust heating or cooling according to temperature changes, ensuring that the water pump equipment operates in the optimal temperature environment, thus improving the reliability and service life of the equipment.

[0049] Optional implementations of the intelligent alarm system include: a 4G intelligent smoke detector capable of real-time monitoring of fire hazards inside the pump room; once smoke is detected, the detector immediately calls a preset mobile phone number and sends an SMS notification, promptly issuing an alarm and triggering an emergency response. This intelligent alarm system can quickly detect and alert, minimizing damage to the pump equipment from fire accidents and improving the overall safety of the pump room.

[0050] Optionally, the specific structure of the stainless steel assembled water tank includes: it is made of stainless steel plates pressed and welded, possessing high strength and corrosion resistance; it adopts a modular assembly design, facilitating transportation and on-site installation; its internal structure is simple, making maintenance and cleaning convenient; and it can effectively store and supply water resources required at the construction site. This water tank design fully considers the usage environment and needs, exhibiting excellent performance and reliability.

[0051] Optionally, the specific implementation of the intelligent interconnected integrated frequency converter control cabinet includes: adopting advanced 4G communication technology to achieve remote monitoring and automated control of the pump room; monitoring the operating status of the pump room anytime and anywhere via a mobile APP or computer, and adjusting relevant parameters in real time; internally integrating a frequency converter adjustment module and a soft-start adaptive parameter calculation module, which can dynamically optimize the pump's operating frequency and soft-start process, significantly improving energy utilization efficiency and equipment reliability. This intelligent control cabinet fully leverages the advantages of information technology, greatly improving the ease of use and management efficiency of the pump room.

[0052] Optionally, the building structure adopts a Class II fire resistance design, consisting of fire doors, fire-resistant windows, fire-resistant partitions with a fire resistance rating of not less than 2.00 hours, and floor slabs with a fire resistance limit of not less than 4.50 hours. The fire-resistant partition walls are made of fire-retardant rock wool, the bottom perimeter columns and top are made of high-strength steel structure, and the floor is paved with patterned steel plates. This fire-resistant structure can effectively prevent the spread of fire, protect the pump room from damage in the event of a fire, and significantly improve the safety of the entire pump room system.

[0053] Furthermore, the pump start-up response equations are expressed as follows:

[0054] 1. Current equation:

[0055]

[0056] Where I(t) is the current value (A) at time t; n Rated current (A); I s The starting instantaneous current (A); τ i τ is the current time constant (s); v t is the voltage ramp time constant (s); t is time (s).

[0057] I s τ was obtained through experimental measurement. i and τ v Obtained by fitting historical data.

[0058] 2. Torque equation:

[0059]

[0060] Where T(t) is the torque (N·m) at time t; T n V is the rated torque (N·m); V(t) is the voltage at time t (V); V n Rated voltage (V); τ m Let be the mechanical time constant (s).

[0061]

[0062] Among them, V i The initial voltage is (V).

[0063] 3. Velocity equation:

[0064]

[0065] Where ω(t) is the angular velocity at time t (rad / s); ω n τ is the rated angular velocity (rad / s); ω Let be the velocity time constant (s).

[0066]

[0067] Where J is the rotor moment of inertia (kg·m) 2 ).

[0068] 4. Pressure equation:

[0069]

[0070] Where P(t) is the pressure (Pa) at time t; P n Rated pressure (Pa); τ p is the pressure time constant (s).

[0071] 5. Flow equation:

[0072]

[0073] Where Q(t) is the flow rate (m³) at time t. 3 / s); Q n Rated flow rate (m³) 3 / s); τ q Let be the flow rate time constant (s).

[0074] 6. Efficiency equation:

[0075]

[0076] Where η(t) is the efficiency at time t; η n Rated efficiency; τ η Let be the efficiency time constant (s).

[0077] 7. Temperature equation:

[0078]

[0079] Among them, T emp (t) represents the temperature (°C) at time t; T amb Ambient temperature (°C); T maxThe highest temperature (°C); τ T is the temperature-time constant (s).

[0080] The further evaluation equations for the soft-start effect of the water pump are expressed as follows:

[0081] 1. Startup time performance evaluation equation:

[0082]

[0083] Among them, E t k is the evaluation value for startup time performance. t t is the time sensitivity coefficient; start The actual startup time (s); t ref For reference startup time (s).

[0084]

[0085] 2. Evaluation equation for current impact effect:

[0086]

[0087] Among them, E i k is the evaluation value for the effect of current impact. i I is the current sensitivity coefficient; max Maximum starting current (A); I ref For reference maximum current (A); k di This is the current change rate sensitivity coefficient; The maximum rate of change of current (A / s); The reference current change rate (A / s).

[0088]

[0089] 3. Energy efficiency evaluation equation:

[0090]

[0091] Among them, E e k is the energy efficiency evaluation value. e E is the energy sensitivity coefficient. start Energy consumption during startup (J); E ref For reference energy consumption (J).

[0092]

[0093] 4. Equipment stress effect evaluation equation:

[0094]

[0095] Among them, Es k is the evaluation value for the stress effect of the equipment. s S is the stress sensitivity coefficient; max Maximum mechanical stress (Pa); S ref Reference mechanical stress (Pa); k T T is the temperature sensitivity coefficient. max The highest temperature (°C); T ref Reference temperature (°C).

[0096]

[0097] All reference values ​​(t) ref ,I ref , E ref ,S ref ,T ref ) and sensitivity coefficient (k t ,k i ,k di ,k e ,k s ,k T This was determined through historical data analysis and expert experience.

[0098] These equations take into account various physical phenomena and interrelationships during the pump startup process, including electrical, mechanical, and thermodynamic factors. The parameters in the equations can be obtained through experimental measurements, historical data fitting, or theoretical calculations. The evaluation equations are based on the output of the startup response equations, comprehensively assessing the effectiveness of the soft start.

[0099] The transformation process from the fitted pump start-up response equations to the pump soft-start performance evaluation equations is as follows:

[0100] 1. Derivation of the start-up time effect evaluation equation:

[0101] First, define the startup completion time t. start Time required for the water pump to reach 95% of its rated speed:

[0102] ω(t start )=0.95·ω n ;

[0103] Substituting the velocity equation into:

[0104]

[0105] Solving for:

[0106] t start =-τ ω ·ln(0.05);

[0107] Considering τ ω With initial voltage V i and voltage ramp time τ v The following relationship can be established:

[0108] t start =f t (V i ,τ v )=-k t ·τ ω (V i ,τ v )·ln(0.05); where k t τ is the correction factor. ω (V i ,τ v ) represents τ ω It is V i and τ v The function.

[0109] Then, define the startup time performance evaluation equation:

[0110]

[0111] In the formula, E t α is the evaluation value for startup time performance. t t is the time sensitivity coefficient; ref For reference startup time (s).

[0112] 2. Derivation of the equation for evaluating the effect of current impact:

[0113] Maximum starting current I max It can be obtained from the current equation:

[0114]

[0115] Maximum current change rate This can be obtained by differentiating the current equation and finding its maximum value:

[0116]

[0117] Among them, f i It is a complex function that depends on the initial voltage, voltage ramp time, and other current parameters.

[0118] The equation for evaluating the effect of current surge is defined as follows:

[0119]

[0120] In the formula, E i α is the evaluation value for the effect of current impact. i and βi These are the sensitivity coefficients for current and the rate of change of current, respectively; I ref Reference maximum current (A); The reference current change rate (A / s) is used; w1 and w2 are weighting coefficients, and w1+w2=1.

[0121] 3. Derivation of the energy consumption performance evaluation equation:

[0122] Startup process energy consumption E start This can be obtained by integrating the power:

[0123]

[0124] Among them, f e It is a complex integral function that depends on multiple parameters.

[0125] The energy consumption performance evaluation equation is defined as follows:

[0126]

[0127] In the formula, E e This is the energy consumption performance evaluation value; α e E is the energy sensitivity coefficient. ref For reference energy consumption (J).

[0128] 4. Derivation of the equipment stress effect evaluation equation:

[0129] Maximum mechanical stress S max It can be derived from the torque equation and the velocity equation:

[0130]

[0131] Among them, f s It is a complex function that depends on multiple parameters.

[0132] Maximum temperature T max This can be obtained from the temperature equation:

[0133]

[0134] The equipment stress effect evaluation equation is defined as follows:

[0135]

[0136] In the formula, E s α is the evaluation value for equipment stress effect; s and β s These are the sensitivity coefficients for mechanical stress and temperature, respectively; S ref Reference mechanical stress (Pa); T refThe reference temperature is ℃; w3 and w4 are weighting coefficients, and w3+w4=1.

[0137] Finally, the comprehensive evaluation equation can be obtained:

[0138] E total =k1·E t +k2·E i +k3·E e +k4·E s

[0139] In the formula, E total The value is the overall evaluation value; k1, k2, k3 and k4 are the weight coefficients of each evaluation item, and k1+k2+k3+k4=1.

[0140] These weighting and sensitivity coefficients can be determined through expert experience or historical data analysis. In this way, the pump start-up response equations are transformed into a set of equations for evaluating the soft-start effect of the pump, taking into account the mutual influence and constraint relationships between the parameters.

[0141] Compared with existing technologies, the advantages of the integrated pump house for construction sites provided by this invention are:

[0142] 1. Dynamically optimized startup parameters: The system can automatically adjust soft-start parameters, such as initial voltage, voltage ramp time, and current limit, according to the real-time status of the water pump, ensuring that the optimal parameters are used for each startup, effectively solving the problem that fixed parameters cannot adapt to the aging of the water pump.

[0143] 2. Reduced start-up time: Through precise calculation and control, the system can minimize the start-up time of the water pump while ensuring safety, thereby improving the response speed and efficiency of the water supply system.

[0144] 3. Effective control of starting current: The adaptive parameter calculation module can accurately predict the starting characteristics of the aging water pump and precisely control the starting current, avoiding the impact of excessive current on the power system and the water pump itself.

[0145] 4. Reduced mechanical stress: By optimizing the torque curve during startup, the system significantly reduces the mechanical stress on components such as pump bearings and impellers, extending the service life of these critical components.

[0146] 5. Achieve intelligent management: Through continuous learning and optimization, the system has achieved intelligent management of the water pump startup process, reducing the need for manual intervention.

[0147] In summary, the solution of the present invention solves the technical problem that existing integrated pump soft starters in pump rooms require professional technicians to periodically check the pump status and adjust parameters, and manual adjustments often lag behind changes in pump performance. Attached Figure Description

[0148] Figure 1 This invention provides a structural schematic diagram of an integrated pump house for use on construction sites.

[0149] Figure 2 A flowchart of the steps performed by the frequency converter control module;

[0150] Figure 3 This is a flowchart of the steps performed by the soft-start adaptive parameter calculation module. Detailed Implementation

[0151] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0152] like Figure 1 The diagram shows a structural schematic of an integrated pump house for construction sites provided by this invention. It includes a housing and a water tank. The housing houses a water pump, a frequency converter control cabinet, and piping. The frequency converter control cabinet connects to an external power source and provides power to the water pump. The water pump is connected to the outlet of the water tank via piping. The system also includes a controller connected to the frequency converter control cabinet and the water pump. The controller contains a frequency converter adjustment module and a soft-start adaptive parameter calculation module. The frequency converter adjustment module adjusts the operating frequency of the water pump in real time based on water demand and changes in the pump outlet pressure to achieve constant pressure water supply and energy-saving operation. The soft-start adaptive parameter calculation module calculates the optimal soft-start parameters based on the pump's operating status and its aging or wear condition, and outputs these parameters to the pump.

[0153] like Figure 2 As shown, the frequency converter module is used to perform the following steps:

[0154] S11. Obtain water demand and real-time data on water pump outlet pressure;

[0155] S12. Calculate the deviation between the current pressure and the target pressure based on the preset target water supply pressure value;

[0156] S13. Based on the deviation value, use the PID control algorithm to calculate the required pump speed adjustment;

[0157] S14. Convert the calculated speed adjustment amount into the corresponding frequency adjustment value;

[0158] S15. Send a frequency adjustment command to the frequency converter;

[0159] S16. Monitor the adjusted water pump outlet pressure and record the relevant data;

[0160] S17. Analyze the recorded data and evaluate the adjustment effect;

[0161] S18. If the adjustment effect is less than the preset adjustment effect threshold, then fine-tune the PID parameters to optimize the control effect.

[0162] S19. Repeat steps S11 to S18 to continue closed-loop control.

[0163] like Figure 3 As shown, the soft-start adaptive parameter calculation module is used to perform the following steps:

[0164] S21. Obtain historical operating data of the water pump, including electrical parameters, mechanical parameters, hydraulic parameters, and environmental parameters; wherein, the electrical parameters include starting current, operating current, power factor, and voltage fluctuation; the mechanical parameters include speed, vibration, bearing temperature, and noise level; the hydraulic parameters include flow rate, pressure, head, and efficiency; and the environmental parameters include ambient temperature, humidity, and atmospheric pressure.

[0165] S22. Establish a set of pump start-up response equations that take into account electrical parameters, mechanical parameters, hydraulic parameters and environmental parameters, including current equation, torque equation, speed equation, pressure equation, flow rate equation, efficiency equation and temperature equation.

[0166] S23. Based on the historical operating data, fit the pump start-up response equation set to obtain the fitted equation set;

[0167] S24. Based on the soft-start parameters, including initial voltage, voltage ramp time, current limit, torque limit and acceleration time, the fitted equation set is analytically solved and merged. Considering the mutual influence and constraint relationship between each parameter, the water pump soft-start effect evaluation equation set is obtained, including the start-up time effect evaluation equation, current impact effect evaluation equation, energy consumption effect evaluation equation and equipment stress effect evaluation equation.

[0168] S25. Using the soft-start parameters encoded as gene sequences, Gaussian distributed random numbers as mutation factors, the water pump soft-start effect evaluation equations as fitness functions, and water pump rated parameters and system safety limits as constraints, a genetic algorithm is used for iterative optimization to obtain candidate soft-start parameters.

[0169] S26. Set the soft starter with the candidate soft start parameters, execute the soft start process of the water pump, record the water pump's operating data, and record it as experimental operating data. If the experimental operating data meets the preset performance indicators and safety thresholds, then the candidate soft start parameters are output as soft start adaptive parameters; otherwise, the experimental operating data is merged into the historical operating data, and S23-S25 are repeated until the experimental operating data meets the preset performance indicators and safety thresholds.

[0170] Specifically, the structure of the integrated pump house for the construction site includes the following:

[0171] 1. Building structure

[0172] The building is designed with a Class II fire resistance rating, consisting of fire doors, fire-resistant windows, fire-resistant partitions with a fire resistance rating of not less than 2.00 hours, and floors with a fire resistance limit of not less than 4.50 hours. The fire-resistant partitions are constructed with fire-retardant rock wool panels, and the bottom columns and top are made of high-strength steel. The floor is paved with patterned steel plates. This design effectively prevents the spread of fire and protects the pump room from fire damage.

[0173] 2. Automatic temperature control system

[0174] The integrated pump house is also equipped with an automatic temperature control system. When the ambient temperature is below 0 degrees Celsius, the system automatically heats up to 5 degrees Celsius to prevent the pumps from freezing. When the ambient temperature is above 30 degrees Celsius, the system automatically turns on the cooling fans to maintain a suitable temperature inside the pump house. This temperature regulation mechanism ensures the normal operation of the pumps and improves the reliability of the equipment.

[0175] 3. Intelligent alarm system

[0176] The pump room is also equipped with a 4G smart smoke detector. Once smoke is detected, the detector will call and send an SMS notification to a pre-set mobile phone number, quickly alerting the authorities and triggering an emergency response. This intelligent monitoring and alarm function can effectively prevent fire accidents.

[0177] 4. Water tank design

[0178] The water tank adopts a stainless steel assembly structure, which is made of stainless steel plates pressed and welded together. This design is not only corrosion-resistant but also highly reliable, and can effectively store and supply the water resources required at the construction site.

[0179] 5. Intelligent frequency conversion control

[0180] The pump house is equipped with a smart, interconnected, integrated frequency converter control cabinet, which can connect to a 4G network for remote monitoring and automated control. The operating status of the pump house can be monitored anytime, anywhere via a mobile app or computer, and relevant parameters can be adjusted in real time. The control system includes the following key modules:

[0181] (1) Variable frequency control module

[0182] The variable frequency drive (VFD) module dynamically adjusts the pump's operating frequency based on real-time changes in water demand and pump outlet pressure, achieving constant pressure water supply and energy-saving operation. The specific implementation steps are as follows:

[0183] Step S11: Obtain real-time data on water demand and water pump outlet pressure.

[0184] Step S12: Calculate the deviation between the current pressure and the target pressure based on the preset target water supply pressure value.

[0185] Step S13: Based on the deviation value, calculate the required pump speed adjustment using a PID control algorithm. The PID control algorithm, through the coordinated operation of proportional, integral, and derivative components, can quickly and accurately adjust the system output to approximate the target value.

[0186] Step S14: Convert the calculated speed adjustment amount into the corresponding frequency adjustment value and send it to the frequency converter.

[0187] Step S15: Send a frequency adjustment command to the frequency converter so that the water pump runs at the new frequency.

[0188] Step S16: Monitor the adjusted water pump outlet pressure and record the relevant data.

[0189] Step S17: Analyze the recorded data and evaluate the adjustment effect. If the adjustment effect does not meet expectations, proceed to step S18.

[0190] Step S18: Fine-tune the PID control parameters to optimize the control effect. Parameter adjustment can be done using a trial-and-error method or an optimization algorithm until the adjustment effect meets the requirements.

[0191] Step S19: Repeat steps S11 to S18 to continue closed-loop control.

[0192] Through this adaptive variable frequency control strategy, the pumping station can achieve constant pressure water supply and dynamically adjust the operating frequency according to actual water demand, which greatly improves energy efficiency.

[0193] (2) Soft-start adaptive parameter calculation module

[0194] The soft-start adaptive parameter calculation module is used to calculate the optimal soft-start parameters based on the historical operating data of the water pump and automatically set them into the soft starter. The specific implementation steps are as follows:

[0195] Step S21: Obtain historical operating data of the water pump, including electrical parameters, mechanical parameters, hydraulic parameters, and environmental parameters. Electrical parameters include starting current, operating current, power factor, and voltage fluctuation; mechanical parameters include speed, vibration, bearing temperature, and noise level; hydraulic parameters include flow rate, pressure, head, and efficiency; environmental parameters include temperature, humidity, and atmospheric pressure.

[0196] Step S22: Establish a set of pump start-up response equations that consider electrical parameters, mechanical parameters, hydraulic parameters and environmental parameters, including current equation, torque equation, speed equation, pressure equation, flow rate equation, efficiency equation and temperature equation.

[0197] Step S23: Fit the pump start-up response equations based on historical operating data to obtain the fitted equations.

[0198] Step S24: Based on the soft-start parameters, including initial voltage, voltage ramp time, current limit, torque limit, and acceleration time, the fitted equations are analytically solved and merged. Considering the mutual influence and constraint relationships between the parameters, the pump soft-start effect evaluation equations are obtained, including the start-up time effect evaluation equation, the current impact effect evaluation equation, the energy consumption effect evaluation equation, and the equipment stress effect evaluation equation.

[0199] Step S25: Using the soft-start parameters encoded as gene sequences, Gaussian distributed random numbers as mutation factors, the water pump soft-start effect evaluation equations as fitness functions, and the water pump rated parameters and system safety limits as constraints, a genetic algorithm is used for iterative optimization to obtain candidate soft-start parameters.

[0200] Step S26: Set the soft starter with the candidate soft start parameters, execute the soft start process of the water pump, and record the operating data. If the experimental data meets the preset performance indicators and safety thresholds, output the candidate soft start parameters as adaptive parameters; otherwise, merge the experimental data into the historical data and repeat steps S23-S25 until the results meet the requirements.

[0201] The start-up time effect evaluation equation is used to assess the time required to reach stable operation from a standstill, considering the influence of parameters such as initial voltage, voltage ramp time, and acceleration time. The current surge effect evaluation equation is used to assess the maximum current value and current change rate, considering the influence of initial voltage, current limits, and voltage ramp time. The energy consumption effect evaluation equation is used to assess the energy consumption during the start-up process, comprehensively considering the influence of start-up time, current changes, and voltage changes. The equipment stress effect evaluation equation is used to assess mechanical and thermal stress, considering the influence of torque limits, acceleration time, and temperature changes.

[0202] The soft-start adaptive parameter calculation module can dynamically adjust the optimal soft-start parameters according to the actual operating status and environmental conditions of the water pump, thereby minimizing current surges, energy consumption, and equipment stress during the startup process, protecting the water pump equipment, and improving operational reliability.

[0203] In summary, this integrated pump house, through fireproof building design, automatic temperature control, intelligent alarm, high-quality water tank, and intelligent frequency conversion control, fully considers the usage needs and safety hazards of the construction site, integrates multiple innovative functions, and significantly improves operational reliability and ease of use.

[0204] To better understand and implement this invention, more specific implementation steps of the variable frequency control module in an integrated pump house are provided below. The variable frequency control module dynamically adjusts the operating frequency of the pump based on real-time changes in water demand and pump outlet pressure, achieving constant pressure water supply and energy-saving operation. The specific implementation steps are as follows:

[0205] Step S11: Obtain real-time data on water demand and water pump outlet pressure.

[0206] Record the water demand at the current time t as Q(t) and the water pump outlet pressure as P(t). The water demand Q(t) reflects the actual water consumption at the construction site, while the water pump outlet pressure P(t) represents the system's water supply pressure status. These two parameters' real-time data serve as input information for subsequent adjustment and control.

[0207] Step S12: Calculate the deviation between the current pressure and the target pressure based on the preset target water supply pressure value. Let the preset target water supply pressure value be P. target The deviation between the current pressure and the target pressure is:

[0208] ΔP(t)=P(t)-P target

[0209] Here, ΔP(t) represents the pressure deviation at the current moment. This deviation value reflects the difference between the actual water supply pressure and the expected target pressure, and is the error signal for regulation and control.

[0210] Step S13: Based on the deviation value, calculate the required pump speed adjustment using a PID control algorithm.

[0211] PID control algorithms can quickly and accurately adjust the system output to approximate the target value. Its mathematical expression is:

[0212]

[0213] Where, ω adjust (t) represents the required pump speed adjustment, K p Ki and K d These are the control parameters for the proportional, integral, and derivative components, respectively. By weighting and synthesizing the deviation ΔP(t), the PID algorithm can quickly calculate the required speed adjustment to make the system output (i.e., water supply pressure) approach the target value as quickly as possible.

[0214] Step S14: Convert the calculated speed adjustment amount into the corresponding frequency adjustment value.

[0215] The pump speed ω is linearly proportional to the driving frequency f.

[0216]

[0217] Where p is the number of pole pairs of the motor. Therefore, the speed adjustment ω calculated in step S13 can be used to adjust the speed. adjust , converted into the corresponding frequency adjustment amount f adjust :

[0218]

[0219] This gives us control commands for adjusting the operating frequency of the water pump.

[0220] Step S15: Send a frequency adjustment command to the frequency converter

[0221] The frequency adjustment amount f calculated in step S14 adjust (t) is sent to the frequency converter, driving the water pump to operate at the new frequency. The frequency converter will dynamically control the motor's output frequency according to this adjustment command, thereby regulating the water pump speed.

[0222] Step S16: Monitor the adjusted water pump outlet pressure and record the relevant data.

[0223] After executing the frequency adjustment command, it is necessary to monitor the pressure P(t) at the water pump outlet in real time and record the pressure data. This data will provide a basis for the next step of evaluating the adjustment effect.

[0224] Step S17: Analyze the recorded data and evaluate the adjustment effect.

[0225] Based on the water pump outlet pressure data P(t) recorded in step S16, calculate the adjusted pressure deviation ΔP(t) and compare it with the preset adjustment effect threshold ΔP. threshold Compare and determine whether the adjustment effect meets the standard:

[0226]

[0227] Where T is the adjustment time. If the adjustment effect is not satisfactory, proceed to step S18 for parameter optimization.

[0228] Step S18: Fine-tune the PID control parameters to optimize the control effect.

[0229] If the pressure deviation ΔP(t) in step S17 exceeds the preset adjustment effect threshold ΔP threshold This indicates the current PID control parameter K. p K i and K d The control requirements cannot be met. In this case, these parameters need to be fine-tuned to improve control accuracy. Specific optimization methods can employ heuristic algorithms, such as genetic algorithms and particle swarm optimization, to obtain new combinations of PID parameters through iterative optimization, ensuring the adjustment effect meets the requirements.

[0230] Step S19: Repeat steps S11 to S18 to continue closed-loop control.

[0231] After evaluating the adjustment effect and optimizing the PID parameters, steps S11 to S18 need to be repeated to continuously perform closed-loop control. This allows for real-time monitoring of changes in water demand and supply pressure, dynamically adjusting the pump's operating frequency to keep the system in optimal operating condition at all times.

[0232] Through this adaptive frequency conversion regulation strategy, the pumping station can achieve constant pressure water supply and dynamically adjust the operating frequency according to actual water demand, significantly improving energy efficiency. The specific implementation methods of the key algorithms and control parameters are as follows:

[0233] 1. PID control algorithm

[0234] The PID control algorithm, through the coordinated operation of its proportional, integral, and derivative components, can quickly and accurately adjust the system output to approximate the target value. Its mathematical expression is:

[0235]

[0236] Where, ω adjust (t) represents the required pump speed adjustment, K p K i and K d These are the control parameters for the proportional, integral, and derivative components, respectively. By weighting and synthesizing the pressure deviation ΔP(t), the PID algorithm can quickly calculate the required speed adjustment to make the system output (i.e., the water supply pressure) approach the target value as quickly as possible.

[0237] 2. Rotational speed to frequency conversion

[0238] The pump speed ω is linearly proportional to the driving frequency f.

[0239]

[0240] Where p is the number of pole pairs of the motor. Therefore, the speed adjustment ω calculated by the PID algorithm can be used to... adjust , converted into the corresponding frequency adjustment amount f adjust :

[0241]

[0242] This gives us control commands for adjusting the operating frequency of the water pump.

[0243] 3. Evaluation of the adjustment effect

[0244] Based on the water pump outlet pressure data P(t) recorded in step S16, calculate the adjusted pressure deviation ΔP(t) and compare it with the preset adjustment effect threshold ΔP. threshold Compare and determine whether the adjustment effect meets the standard:

[0245]

[0246] Where T is the settling time. If the settling effect is not satisfactory, the PID parameters need to be optimized.

[0247] 4. PID Parameter Optimization

[0248] If the pressure deviation ΔP(t) in step S17 exceeds the preset adjustment effect threshold ΔP threshold This indicates the current PID control parameter K. p K i and K d The control requirements cannot be met. In this case, these parameters need to be optimized to improve control accuracy. Heuristic algorithms, such as genetic algorithms and particle swarm optimization, can be used to iteratively optimize and obtain new combinations of PID parameters, ensuring the desired adjustment effect.

[0249] The above describes the specific implementation scheme of the variable frequency control module. By monitoring water demand and supply pressure in real time, it dynamically adjusts the pump operating frequency and, combined with advanced PID control and parameter optimization technology, achieves the goals of constant pressure water supply and energy consumption optimization. This adaptive adjustment strategy is one of the core innovations of integrated pump houses.

[0250] To better understand and implement this invention, more specific implementation steps of the soft-start adaptive parameter calculation module of this invention are provided below. The soft-start adaptive parameter calculation module is used to calculate the optimal soft-start parameters based on the historical operating data of the water pump and automatically set them into the soft starter. The specific implementation steps are as follows:

[0251] Step S21: Obtain historical operating data of the water pump

[0252] Obtain historical operating data of the water pump, including electrical parameters, mechanical parameters, hydraulic parameters, and environmental parameters. Among these:

[0253] Electrical parameters include starting current I s Operating current I n Power factor and voltage fluctuation ΔV;

[0254] Mechanical parameters include rotational speed ω n Vibration a, bearing temperature T b and noise level L n ;

[0255] Hydraulic parameters include flow rate Q n Pressure P n , head H n and efficiency η n ;

[0256] Environmental parameters include ambient temperature T a Humidity φ and atmospheric pressure p a .

[0257] These historical data provide a foundation for the subsequent development of a pump start-up response model.

[0258] Step S22: Establish the pump start-up response equation set

[0259] Based on the various parameters obtained in step S21, a set of pump start-up response equations considering electrical parameters, mechanical parameters, hydraulic parameters, and environmental parameters is established, including:

[0260] Current equation:

[0261] Torque equation:

[0262] Velocity equation:

[0263] Pressure equation:

[0264] Flow equation:

[0265] Efficiency equation:

[0266] Temperature equation:

[0267] The specific meanings of each parameter are as follows:

[0268] The current value (A) at time I(t)-t, I n -Rated current (A), I s-Initial starting current (A), τ i - Current time constant (s), τ v - Voltage ramp time constant (s);

[0269] Torque (N·m) at time T(t) - t, T n - Rated torque (N·m), V(t) - Voltage at time t (V), V n - Rated voltage (V), τ m -Mechanical time constant (s);

[0270] ω(t) - angular velocity at time t (rad / s), ω n - Rated angular velocity (rad / s), J - Rotor moment of inertia (kg·m) 2 );

[0271] Pressure (Pa) at time P(t) - t, P n - Rated pressure (Pa), τ p - Pressure time constant (s);

[0272] The flow rate (m³) at time Q(t)-t 3 / s),Q n -Rated flow rate (m³) 3 / s),τ q - Flow rate time constant (s);

[0273] Efficiency at time η(t) - t, η n - Rated efficiency, τ η - Efficiency time constant (s);

[0274] T emp Temperature (°C) at time (t) - t, T a -Ambient temperature (°C), T max - Maximum temperature (°C), τ T -Temperature time constant (s).

[0275] These equations take into account the physical characteristics and interrelationships of electrical, mechanical, and thermodynamic aspects during the pump startup process, providing a theoretical basis for subsequent evaluation of soft-start performance.

[0276] Step S23: Fit the pump start-up response equations based on historical data.

[0277] Using the historical operating data of the water pump obtained in step S21, the parameters of the water pump start-up response equation set established in step S22 are fitted to obtain the fitted equation set. In this way, the start-up characteristics of the water pump can be accurately characterized according to the actual operating conditions.

[0278] Step S24: Establish a set of equations to evaluate the soft start effect of the water pump.

[0279] Based on soft-start parameters, including the initial voltage V i Voltage ramp time τ v Current Limit I max Torque Limit T max and acceleration time t a The fitted equations obtained in step S23 are solved analytically and combined. Considering the mutual influence and constraint relationships between the parameters, the following four evaluation equations for the soft start effect of the water pump are established:

[0280] 1. Startup time performance evaluation equation:

[0281]

[0282] Among them, E t k is the evaluation value for startup time performance. t The time sensitivity coefficient, t start t represents the actual startup time (s). ref Reference startup time (s). Actual startup time t start It can be calculated by integration:

[0283]

[0284] 2. Evaluation equation for current impact effect:

[0285]

[0286] Among them, E i k is the evaluation value for the effect of current impact. i I is the current sensitivity coefficient. max For the maximum starting current (A), I ref For the reference maximum current (A), k di The current change rate sensitivity coefficient, Maximum current change rate (A / s) Reference current change rate (A / s). Maximum current I max and maximum current change rate It can be calculated from the current equation.

[0287] 3. Energy efficiency evaluation equation:

[0288]

[0289] Among them, E e k is the energy efficiency evaluation value. e E is the energy sensitivity coefficient. start Energy consumption during startup (J), E refEnergy consumption for reference (J). Energy consumption during startup (E). start It can be calculated by integration:

[0290]

[0291] 4. Equipment stress effect evaluation equation:

[0292]

[0293] Among them, E s k is the evaluation value for the stress effect of the equipment. s S is the stress sensitivity coefficient. max For the maximum mechanical stress (Pa), S ref For reference mechanical stress (Pa), k T T is the temperature sensitivity coefficient. max The highest temperature (°C), T ref Reference temperature (°C). Maximum mechanical stress S max It can be calculated from the torque equation and the velocity equation:

[0294]

[0295] These four evaluation equations comprehensively assess the performance of the soft-start scheme from multiple perspectives, including start-up time, current surge, energy consumption, and equipment stress. Reference values ​​and sensitivity coefficients for each evaluation indicator can be determined through historical data analysis and expert experience.

[0296] Step S25: Optimize soft-start parameters using a genetic algorithm.

[0297] With soft start parameter V i τ v I max T max and t a The code is encoded as a gene sequence, with Gaussian distributed random numbers as the mutation factor, the water pump soft start effect evaluation equation set established in step S24 as the fitness function, and the water pump rated parameters and system safety limits as constraints. The genetic algorithm is used for iterative optimization to finally obtain candidate soft start parameters that meet the performance indicators and safety thresholds.

[0298] Genetic algorithms are stochastic search algorithms that simulate the biological evolution process in nature. They have advantages such as fast convergence speed and strong robustness, making them very suitable for handling complex nonlinear optimization problems. The algorithm iterates continuously, selecting, crossovering, and mutating individuals according to the fitness function (i.e., the equations for evaluating the soft-start effect of the water pump), ultimately approximating the global optimum. Constraints ensure that the optimization results meet the safety requirements of the water pump equipment and system.

[0299] Step S26: Verify the optimization results and update historical data.

[0300] Actual tests are conducted using the candidate soft-start parameters obtained in step S25, and the pump's operating data is recorded. If the test data meets the preset performance indicators and safety thresholds, these parameters are output as soft-start adaptive parameters; otherwise, the test data is merged into historical operating data, and steps S23-S25 are repeated until the optimal parameter combination that meets the requirements is obtained.

[0301] This adaptive optimization method for soft-start parameters based on historical pump operating data can fully consider the impact of various operating conditions on soft-start performance and obtain the optimal soft-start parameter configuration. Simultaneously, by continuously feeding experimental data back into historical data, the model and optimization algorithm can continuously learn and improve, thereby enhancing the overall system's intelligence level.

[0302] In summary, the soft-start adaptive parameter calculation module, through steps such as establishing a set of pump start-up response equations, constructing soft-start effect evaluation equations, and using genetic algorithms for parameter optimization, can dynamically adjust the optimal soft-start parameters according to the actual operating conditions of the pump, thereby minimizing current surges, energy consumption, and equipment stress during the start-up process and significantly improving the reliability and service life of the pump equipment.

[0303] Specifically, the principle of this invention is:

[0304] 1. Comprehensive Data Acquisition: The system comprehensively collects pump operating data through a sensor network, including electrical parameters (such as starting current, operating current, power factor, and voltage fluctuation), mechanical parameters (such as speed, vibration, bearing temperature, and noise level), hydraulic parameters (such as flow rate, pressure, head, and efficiency), and environmental parameters (such as ambient temperature, humidity, and atmospheric pressure). This multi-dimensional data provides a comprehensive foundation for subsequent analysis and optimization.

[0305] 2. Establishing a Start-up Response Model: Based on collected historical data, the system established a complex set of equations for the pump's start-up response. This set of equations includes current equations, torque equations, velocity equations, pressure equations, flow rate equations, efficiency equations, and temperature equations. These equations comprehensively consider the electrical, mechanical, hydraulic, and thermodynamic characteristics of the pump during startup, accurately describing the pump's startup behavior under different conditions.

[0306] 3. Data-driven model fitting: The system uses accumulated historical operating data to fit the startup response equations, obtaining a set of fitted equations that accurately describe the startup characteristics of a specific water pump. This step enables the model to accurately reflect the individual differences and aging characteristics of the water pump.

[0307] 4. Start-up Performance Evaluation Equations: The system substitutes soft-start parameters (such as initial voltage, voltage ramp time, current limit, torque limit, and acceleration time) into a set of fitted equations. Through analytical solution and merging, a series of start-up performance evaluation equations are obtained. These equations include start-up time performance evaluation equations, current surge performance evaluation equations, energy consumption performance evaluation equations, and equipment stress performance evaluation equations. These evaluation equations can quantitatively assess start-up performance under different soft-start parameters.

[0308] 5. Genetic Algorithm Optimization: The system uses a genetic algorithm to find the optimal soft-start parameters. The specific steps are as follows:

[0309] a) Encode the soft-start parameters as gene sequences.

[0310] b) Use Gaussian distributed random numbers as a variation factor to increase population diversity.

[0311] c) Use the set of equations for evaluating the startup effect as the fitness function.

[0312] d) Use the pump's rated parameters and system safety limits as constraints.

[0313] e) Through multiple generations of iterative optimization, candidate soft-start parameters are finally obtained.

[0314] The advantage of genetic algorithms lies in their ability to quickly find the global optimal solution in a complex multidimensional parameter space, making them particularly suitable for handling nonlinear, multi-objective optimization problems such as water pump startup.

[0315] 6. Experimental Verification and Adaptive Adjustment: The system not only relies on theoretical calculations but also conducts actual startup experiments. A soft starter is configured using candidate soft-start parameters, the actual pump startup process is executed, and detailed operational data is recorded. If the experimental data meets the preset performance indicators and safety thresholds, the set of parameters is adopted; otherwise, the system merges the new experimental data into the historical dataset and re-executes the model fitting and parameter optimization process. This closed-loop feedback mechanism ensures that the system can continuously learn and improve, adapting to dynamic changes in pump performance.

[0316] 7. Continuous Learning and Optimization: The system continuously collects operational data throughout the pump's entire lifespan and periodically updates the startup response model and optimization parameters. This continuous learning mechanism enables the system to adapt promptly to slow changes in pump performance, such as efficiency decline due to aging and mechanical wear.

[0317] 8. Comprehensive Consideration of Multiple Factors: During parameter optimization, the system comprehensively considers multiple key factors, including startup time, current surge, energy consumption, and equipment stress. Through a multi-objective optimization algorithm, the system can find the optimal balance between these potentially conflicting objectives, ensuring the best overall startup performance.

[0318] 9. Predictive Analytics: Based on the accumulation of historical data and continuous model optimization, the system can predict the changing trends of pump performance. This predictive analytics capability is not only used to optimize startup parameters, but also provides important information for equipment maintenance and replacement decisions.

[0319] To better understand and implement this invention, an embodiment of a specific application scenario is provided below: I. Overall structure of a pump house

[0320] At a large bridge construction site, concrete pouring for the main tower foundation is required, with a daily water consumption estimated at 500 cubic meters. To meet this water demand, the construction team decided to adopt the integrated pump house proposed in this invention.

[0321] This integrated pump house consists of two parts: the main building and the water tank. The main building is designed for Class II fire resistance and comprises fire doors, fire-resistant windows, fire-resistant walls with a fire resistance rating of at least 2.00 hours, and reinforced concrete floors with a fire resistance limit of at least 4.50 hours. The fire-resistant walls use 100mm thick fire-retardant rock wool composite wall panels, with 150mm x 150mm high-strength steel pipes for the bottom columns and top, and a 6mm thick checkered steel plate floor. The main building houses a 100kW water pump, a frequency converter control cabinet, and the corresponding piping system. The water tank is a stainless steel assembly structure, constructed from 5mm thick stainless steel plates, with a capacity of 1000 cubic meters.

[0322] This integrated pump room is also equipped with an automatic temperature control system, including temperature sensors, heating and cooling equipment. When the ambient temperature is below 0 degrees Celsius, the system automatically activates the electric heating device to raise the temperature to 5 degrees Celsius to prevent the pumps from freezing. When the ambient temperature is above 30 degrees Celsius, the system automatically activates the fan to lower the temperature inside the pump room. Additionally, a 4G smart smoke detector is installed inside the pump room; once smoke is detected, it immediately sends an alarm message to a pre-set mobile phone number.

[0323] II. Variable Frequency Control Module

[0324] This integrated pump station employs advanced variable frequency drive (VFD) technology, which automatically adjusts the pump's operating frequency based on real-time changes in water demand and supply pressure, achieving constant pressure water supply and optimized energy consumption. The specific execution steps of the VFD module are as follows:

[0325] Step S11: Obtain water demand and water supply pressure data

[0326] A flow meter installed at the water tank outlet monitors the current water demand Q(t) in real time, measured in cubic meters per second. Simultaneously, a pressure sensor installed on the water pump outlet pipe acquires real-time data on the water supply pressure P(t), measured in Pascals. These two real-time parameters serve as input information for subsequent regulation and control.

[0327] Step S12: Calculate the deviation between the current pressure and the target pressure.

[0328] Set the target value P for water supply pressure target The pressure is 0.8 MPa. Therefore, the deviation ΔP(t) between the current pressure and the target pressure is: ΔP(t) = P(t) - P target =P(t) - 0.8 × 10 6 Pa;

[0329] This pressure deviation reflects the difference between the actual water supply pressure and the expected target pressure, and is an error signal for regulation and control.

[0330] Step S13: Calculate the speed adjustment amount using the PID control algorithm.

[0331] The required pump speed adjustment ω is calculated using a PID control algorithm. adjust (t):

[0332]

[0333] Where, K p =0.5,K i =0.2,K d =0.1 is the PID control parameter. Through the coordinated operation of the proportional, integral, and derivative components, the PID algorithm can quickly and accurately adjust the system output to approach the target value.

[0334] Step S14: Convert the speed adjustment amount into a frequency adjustment value.

[0335] The pump speed ω is linearly proportional to the driving frequency f.

[0336]

[0337] Where p = 2 is the number of pole pairs of the motor. Therefore, the speed adjustment ω calculated in step S13 can be used to adjust the speed. adjust Converted to the corresponding frequency adjustment value f adjust :

[0338]

[0339] This gives us control commands for adjusting the operating frequency of the water pump.

[0340] Step S15: Send a frequency adjustment command to the frequency converter

[0341] The frequency adjustment amount f calculated in step S14 adjust (t) is sent to the frequency converter, driving the water pump to operate at the new frequency. The frequency converter will dynamically control the motor's output frequency according to this adjustment command, thereby regulating the water pump speed.

[0342] Step S16: Monitor the adjusted water supply pressure

[0343] After executing the frequency adjustment command, it is necessary to monitor the water supply pressure P(t) at the pump outlet pipe in real time and record the pressure data. This data will provide a basis for the next step of evaluating the adjustment effect.

[0344] Step S17: Evaluate the adjustment effect

[0345] Based on the water supply pressure data P(t) recorded in step S16, calculate the adjusted pressure deviation ΔP(t) and compare it with the preset adjustment effect threshold ΔP. threshold =0.02×10 6 Compare the pressure (Pa) to determine if the adjustment effect meets the standard.

[0346]

[0347] Where T = 300s is the settling time. If the settling effect is not satisfactory, the PID parameters need to be optimized.

[0348] Step S18: Optimize PID control parameters

[0349] If the pressure deviation ΔP(t) in step S17 exceeds the preset adjustment effect threshold ΔP threshold This indicates the current PID control parameter K. p =0.5, K i =0.2 and K d A value of 0.1 does not meet the control requirements. Therefore, these parameters need to be optimized to improve control accuracy. A genetic algorithm can be used to iteratively search for new PID parameter combinations that achieve the desired adjustment effect. The optimized PID parameters are: K p =0.6, K i =0.25 and K d =0.15.

[0350] Step S19: Continuously perform closed-loop control

[0351] After evaluating the adjustment effect and optimizing the PID parameters, steps S11 to S18 need to be repeated to continuously perform closed-loop control. This allows for real-time monitoring of changes in water demand and supply pressure, dynamically adjusting the pump's operating frequency to keep the system in optimal operating condition at all times.

[0352] Through this adaptive variable frequency control strategy, the integrated pump house can achieve constant pressure water supply and dynamically adjust the operating frequency according to actual water demand, which greatly improves energy efficiency.

[0353] III. Soft Start Adaptive Parameter Calculation Module

[0354] To further improve the reliability of this integrated pump house, a soft-start adaptive parameter calculation module is integrated within it. This module dynamically calculates the optimal soft-start parameter configuration based on the pump's historical operating data and automatically sets it into the soft starter. The specific execution steps are as follows:

[0355] Step S21: Obtain historical operating data of the water pump

[0356] Historical operating data of the water pump is acquired through various sensors installed on the water pump system, including:

[0357] Electrical parameters: Starting current I s =420A, operating current I n =280A, power factor And voltage fluctuation ΔV = 5%;

[0358] Mechanical parameters: Rated speed ω n =1450rpm, vibration acceleration a=0.12m / s² 2 Bearing temperature T b =65℃ and noise level L n =75dB;

[0359] Hydraulic parameters: Rated flow rate Q n =0.12m 3 / s, rated pressure P n =0.8×10 6 Pa, rated head H n =60m and rated efficiency η n =0.82;

[0360] Environmental parameters: Ambient temperature T a =25℃, relative humidity φ=60% and atmospheric pressure p a =1.01×10 5 Pa.

[0361] Step S22: Establish the pump start-up response equation set

[0362] Based on the various parameters obtained in step S21, a set of pump start-up response equations considering electrical parameters, mechanical parameters, hydraulic parameters, and environmental parameters is established, including:

[0363] Current equation:

[0364]

[0365] Where, τ i =0.12s is the current time constant, τ v =0.24s is the voltage ramp time constant.

[0366] Torque equation:

[0367]

[0368] Where, τ m =0.36s is the mechanical time constant. This represents voltage changes.

[0369] Velocity equation:

[0370]

[0371] Where, τ ω =0.42s is the velocity time constant, J = 0.8 kg·m 2 This represents the rotor's moment of inertia.

[0372] Pressure equation:

[0373]

[0374] Where, τ p =0.48s is the pressure time constant.

[0375] Flow equation:

[0376]

[0377] Where, τ q =0.30s is the flow rate time constant.

[0378] Efficiency equation:

[0379]

[0380] Where, τ η =0.54s is the efficiency time constant.

[0381] Temperature equation:

[0382]

[0383] Among them, T max =90℃ is the highest temperature, τ T =1.2s is the temperature time constant.

[0384] These equations take into account the physical characteristics of the water pump during startup, including electrical, mechanical, and thermodynamic aspects, providing a theoretical basis for subsequent evaluation of soft-start performance.

[0385] Step S23: Fit the pump start-up response equations

[0386] Using the historical operating data of the water pump obtained in step S21, parameter fitting is performed on the above-mentioned water pump start-up response equation set to determine the specific values ​​of each time constant and nonlinear coefficient. This allows for an accurate characterization of the water pump's start-up characteristics based on actual operating conditions.

[0387] Step S24: Establish a set of equations to evaluate the soft-start effect.

[0388] Based on soft-start parameters, including the initial voltage V i =380V, voltage ramp time τ v =0.24s, current limit I max =420A, Torque Limit T max =1.2×T n and acceleration time t a =2.5s, the fitted pump start-up response equations are analytically solved and combined. Considering the mutual influence and constraint relationships between the parameters, the following four evaluation equations for the soft start effect of the pump are established:

[0389] 1. Startup time performance evaluation equation:

[0390]

[0391] Where, t ref =3.5s is the reference startup time.

[0392] 2. Evaluation equation for current impact effect:

[0393]

[0394] Among them, I ref =1.5×I n =420A is the reference maximum current. This is the reference current change rate.

[0395] 3. Energy efficiency evaluation equation:

[0396]

[0397] Among them, E ref=1.2×Q n ·P n ·t a =3.6×10 5 J represents the reference energy consumption.

[0398] 4. Equipment stress effect evaluation equation:

[0399]

[0400] Where, S ref =2.4×10 6 Pa is the reference mechanical stress, T ref =85℃ is the reference temperature.

[0401] These four evaluation equations comprehensively assess the performance of the soft-start scheme from multiple perspectives, including start-up time, current surge, energy consumption, and equipment stress. The reference values ​​and sensitivity coefficients for each evaluation indicator are determined based on historical data analysis and expert experience.

[0402] Step S25: Optimize soft-start parameters using a genetic algorithm.

[0403] With soft start parameter V i τ v I max T max and t a The code is encoded as a gene sequence, with Gaussian distributed random numbers as the mutation factor, the soft-start effect evaluation equation set established in step S24 as the fitness function, and the pump rated parameters and system safety limits as constraints. The genetic algorithm is used for iterative optimization to finally obtain candidate soft-start parameter combinations that meet the performance indicators and safety thresholds.

[0404] Genetic algorithms iterate continuously, selecting, crossovering, and mutating individuals based on the fitness function (i.e., the set of equations for evaluating soft-start performance), ultimately approximating the global optimum. In this case, after 50 generations of iterative optimization, the optimal soft-start parameters obtained are: V i =400V, τ v =0.20s, I max =380A,T max =1.1×T n and t a =2.0s.

[0405] Step S26: Verify the optimization results and update historical data.

[0406] Using the candidate soft-start parameter combinations obtained in step S25, a test run was conducted on an actual water pump. By monitoring parameters such as current, torque, and temperature during the startup process, various soft-start performance evaluation indicators were calculated.

[0407] Startup time t start = 3.2s, satisfying t start ≤t ref = 3.5s requirement;

[0408] Maximum current I max =380A, satisfying I max ≤I ref =420A requirements;

[0409] Maximum current change rate satisfy Requirements;

[0410] Start-up energy consumption E start =3.4×10 5 J satisfies E start ≤E ref =3.6×10 5 J's requirements;

[0411] Maximum mechanical stress S max =2.1×10 6 Pa, satisfying S max ≤S ref =2.4×10 6 Pa's requirements;

[0412] Maximum temperature T max =82℃, satisfying T max ≤T ref =85℃ requirement.

[0413] Based on the comprehensive evaluation indicators, the test results meet the expected performance requirements and safety thresholds. Therefore, this set of soft-start parameters is set as the final adaptive parameters, and the test data is updated to the historical operation database.

[0414] Through continuous optimization and learning, this soft-start adaptive parameter calculation module can fully consider the actual operating conditions of the water pump and dynamically adjust to the best soft-start scheme, minimizing current surges, energy consumption, and equipment stress during the startup process, and significantly improving the reliability and service life of the entire integrated pump house.

[0415] In summary, the integrated pump house in this embodiment fully utilizes advanced fire prevention, temperature control, monitoring, control, and soft-start optimization technologies, solving the problems of existing pump houses in terms of safety, reliability, and management efficiency. It is a more intelligent, efficient, and safer water supply solution for construction sites.

[0416] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An integrated pump house for use on construction sites, characterized in that, The system includes a housing and a water tank. The housing houses a water pump, a frequency converter control cabinet, and piping. The frequency converter control cabinet connects to an external power source and provides power to the water pump. The water pump is connected to the outlet of the water tank via piping. The system also includes a controller connected to the frequency converter control cabinet and the water pump. The controller contains a frequency converter adjustment module and a soft-start adaptive parameter calculation module. The frequency converter adjustment module adjusts the water pump's operating frequency in real time based on water demand and changes in the water pump's outlet pressure to achieve constant pressure water supply and energy-saving operation. The soft-start adaptive parameter calculation module calculates the optimal soft-start parameters based on the water pump's operating status and its aging or wear condition, and outputs these parameters to the water pump. The soft-start adaptive parameter calculation module performs the following steps: S21. Obtain historical operating data of the water pump, including electrical parameters, mechanical parameters, hydraulic parameters, and environmental parameters; wherein, the electrical parameters include starting current, operating current, power factor, and voltage fluctuation; the mechanical parameters include speed, vibration, bearing temperature, and noise level; the hydraulic parameters include flow rate, pressure, head, and efficiency; and the environmental parameters include ambient temperature, humidity, and atmospheric pressure. S22. Establish a set of pump start-up response equations that take into account electrical parameters, mechanical parameters, hydraulic parameters and environmental parameters, including current equation, torque equation, speed equation, pressure equation, flow rate equation, efficiency equation and temperature equation. S23. Based on the historical operating data, fit the pump start-up response equation set to obtain the fitted equation set; S24. Based on the soft-start parameters, including initial voltage, voltage ramp time, current limit, torque limit and acceleration time, the fitted equation set is analytically solved and merged. Considering the mutual influence and constraint relationship between each parameter, the water pump soft-start effect evaluation equation set is obtained, including the start-up time effect evaluation equation, current impact effect evaluation equation, energy consumption effect evaluation equation and equipment stress effect evaluation equation. S25. Using the soft-start parameters encoded as gene sequences, Gaussian distributed random numbers as mutation factors, the water pump soft-start effect evaluation equations as fitness functions, and water pump rated parameters and system safety limits as constraints, a genetic algorithm is used for iterative optimization to obtain candidate soft-start parameters. S26. Set the soft starter with the candidate soft start parameters, execute the soft start process of the water pump, record the water pump's operating data, and record it as experimental operating data. If the experimental operating data meets the preset performance indicators and safety thresholds, then the candidate soft start parameters are output as soft start adaptive parameters; otherwise, the experimental operating data is merged into the historical operating data, and S23-S25 are repeated until the experimental operating data meets the preset performance indicators and safety thresholds.

2. An integrated pump house for construction sites according to claim 1, characterized in that, The fire resistance rating of the building is Class II. It consists of fire doors, fire windows, fire-resistant partitions with a fire resistance rating of not less than 2.00h, and floor slabs with a fire resistance limit of not less than 4.50h. The floor slabs are used to divide and isolate the building.

3. An integrated pump house for construction sites according to claim 2, characterized in that, The fireproof partition wall is made of fireproof and flame-retardant rock wool, the bottom of the building has columns around the perimeter and the top is made of high-strength steel structure, and the floor of the building is paved with patterned steel plates.

4. An integrated pump house for construction sites according to claim 3, characterized in that, It also includes an automatic temperature control system, which automatically heats the water pump to 5 degrees when the temperature is below 0 degrees to prevent it from freezing, and automatically turns on the cooling fan when the temperature is above 30 degrees.

5. An integrated pump house for construction sites according to claim 4, characterized in that, It also includes a 4G smart smoke detector that can make a phone call and send a text message notification to a preset mobile phone when smoke is detected.

6. An integrated pump house for construction sites according to claim 5, characterized in that, The water tank is a stainless steel assembled water tank, which is assembled by pressing and welding stainless steel plates.

7. An integrated pump house for construction sites according to claim 6, characterized in that, The frequency converter control cabinet is a smart interconnected integrated frequency converter control cabinet that connects to a 4G network to realize the automation and remote monitoring of the pump room. It has intelligent remote monitoring and maintenance functions, and can monitor the pump room operation status anytime and anywhere and adjust parameters in real time via a mobile APP or computer.

8. An integrated pump house for construction sites according to claim 1, characterized in that, The frequency converter module is used to perform the following steps: S11. Obtain water demand and real-time data on water pump outlet pressure; S12. Calculate the deviation between the current pressure and the target pressure based on the preset target water supply pressure value; S13. Based on the deviation value, use the PID control algorithm to calculate the required pump speed adjustment; S14. Convert the calculated speed adjustment amount into the corresponding frequency adjustment value; S15. Send a frequency adjustment command to the frequency converter; S16. Monitor the adjusted water pump outlet pressure and record the relevant data; S17. Analyze the recorded data and evaluate the adjustment effect; S18. If the adjustment effect is less than the preset adjustment effect threshold, then fine-tune the PID parameters to optimize the control effect. S19. Repeat steps S11 to S18 to continue closed-loop control.

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