Dynamic energy-saving adjusting method and system for tunnel water pump

Abstract

The invention provides a tunnel water pump dynamic energy-saving adjusting method and system, and relates to the technical field of tunnel water pump energy-saving control, and the method comprises the steps: collecting multi-dimensional working condition data in a tunnel drainage system through a collection unit; based on the multi-dimensional working condition data, combining the water level change rate, the rainfall capacity and the total drainage demand, classifying tunnel drainage working conditions to obtain a working condition classification result; according to the working condition classification result, the water pump rotating speed is adjusted through a fusion control algorithm, and multi-pump cooperative operation is completed based on water pump efficiency sorting and load distribution logic; a fault judgment threshold value is set based on rated parameters of water pumps, historical operation data and real-time working condition data, potential faults are warned in advance by analyzing the parameter change trend, and therefore the working condition of the water pumps can be accurately judged through multi-dimensional working condition data collection, intelligent algorithm dynamic adjustment, multi-pump collaborative optimization and accurate fault warning. And on-demand energy supply of the tunnel water pump is achieved, energy consumption is reduced, operation is stable, and maintenance is efficient.

Description

Technical Field

[0001] This invention relates to the field of energy-saving control technology for tunnel water pumps, and in particular to a dynamic energy-saving adjustment method and system for tunnel water pumps. Background Technology

[0002] Currently, the core of the tunnel water pump control system consists of a water level acquisition module, a fixed logic control module, a simple start / stop module, and a basic alarm module. The water level acquisition module connects only to a liquid level sensor via wired connection, collecting water level data from the collection well once every 30 seconds, without any other operational parameter acquisition functions. The fixed logic control module presets a fixed water level threshold, triggering pump start / stop or fixed-frequency operation solely based on whether the water level meets the threshold; the control logic lacks dynamic adjustment capabilities. The simple start / stop module, for multi-pump systems, only controls according to a "first-in, first-out" or "fixed sequence," lacking load distribution and operational optimization logic. The basic alarm module only pops up an alarm when the water level exceeds the warning value or the pump completely stops, lacking fault prediction and cause analysis functions.

[0003] The existing technology has significant shortcomings: First, the data acquisition for operating conditions is singular and infrequent, only collecting water level parameters, which cannot capture sudden situations such as short-term heavy rainfall and instantaneous load changes in water pumps, resulting in a one-sided control basis; second, the control method is rigid, relying on fixed thresholds and lacking speed regulation function, leading to serious energy waste under low-load conditions, and frequent start-stop operations shortening the lifespan of water pumps; third, it lacks multi-pump coordination capabilities, failing to consider individual differences among water pumps, resulting in system operating efficiency below 60%; fourth, its fault diagnosis capabilities are weak, only able to identify extreme faults, lacking the ability to predict potential problems, and fault handling takes as long as 2-4 hours; fifth, it has poor adaptability, failing to differentiate the drainage needs of different types of tunnels, and lacking energy consumption analysis and optimization suggestion functions.

[0004] Therefore, it is necessary to provide a dynamic energy-saving adjustment method and system for tunnel water pumps to solve the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a dynamic energy-saving adjustment method and system for tunnel water pumps, which solves the problems of single data acquisition, rigid control, poor energy-saving effect, insufficient multi-pump coordination, and weak fault diagnosis in existing tunnel water pump control systems.

[0006] The present invention provides a dynamic energy-saving adjustment method for tunnel water pumps, the method comprising: The data acquisition unit collects water level parameters, pump operating status parameters, environmental parameters, and pipeline operating parameters from the tunnel drainage system, and then summarizes and generates multi-dimensional operating condition data. Based on the multi-dimensional working condition data, combined with the water level change rate, rainfall and total drainage demand, the tunnel drainage working conditions are classified to obtain the working condition classification results. Based on the operating condition classification results, a fusion control algorithm is used to dynamically adjust the pump speed, and multi-pump collaborative operation is completed based on pump efficiency ranking and load distribution logic. Based on the pump's rated parameters, historical operating data, and real-time operating condition data, the fault judgment threshold is dynamically set. By analyzing the trend of parameter changes, potential faults are given early warning, and the cause of the fault and handling instructions are output.

[0007] Preferably, the step of collecting water level parameters, pump operating status parameters, environmental parameters, and pipeline operating parameters of the tunnel drainage system through the acquisition unit, and summarizing them to generate multi-dimensional operating condition data, specifically includes: The system deploys a water level acquisition unit, a water pump operation parameter acquisition unit, an environmental parameter acquisition unit, and a pipeline operation parameter acquisition unit. The water level acquisition unit deploys one distributed liquid level sensor every 500m within the tunnel. The water pump operation parameter acquisition unit is equipped with a frequency converter interface and a current transformer. The environmental parameter acquisition unit includes a rainfall sensor and a water turbidity sensor. The pipeline operation parameter acquisition unit includes a pipeline flow sensor. In the tunnel drainage system, the water level acquisition unit establishes communication with the distributed liquid level sensor via wired or wireless means and acquires the water level parameter, i.e., water level h; the water pump operation parameter acquisition unit accesses the water pump operation status parameters, including water pump motor current I, water pump motor power P, water pump speed n, and outlet pressure p, through the frequency converter interface and the current transformer; the environmental parameter acquisition unit acquires the rainfall R and the water turbidity T through the rainfall sensor and the water quality turbidity sensor; the pipeline operation parameter acquisition unit acquires the pipeline operation parameter, i.e., pipeline flow rate Q, through the pipeline flow sensor, and all acquisition units are connected to the edge gateway; Each data acquisition unit transmits the collected data, including water level h, pump motor current I, pump motor power P, pump speed n, outlet pressure p, rainfall R, water turbidity T, and pipeline flow rate Q, to the edge gateway for format standardization processing, and then summarizes and generates the multi-dimensional operating condition data.

[0008] Preferably, based on the multi-dimensional working condition data, combined with the water level change rate, rainfall, and total drainage demand, the tunnel drainage working conditions are classified to obtain the working condition classification results, specifically including: Outlier removal and smoothing are performed on the multi-dimensional working condition data, and the water level change rate v is obtained based on the water level h. Set the working condition classification thresholds, including the first water level threshold h1, the second water level threshold h2, the first water level change rate threshold v1, the second water level change rate threshold v2, the first rainfall threshold R1, the second rainfall threshold R2, the first drainage demand threshold Q1, and the second drainage demand threshold Q2; Classify the tunnel drainage working conditions based on the multi-dimensional working condition data after outlier rejection and smoothing processing and the working condition classification thresholds to obtain the working condition classification results; Among them, the classification rules for the tunnel drainage working conditions are as follows: when h < h1 and v < v1 and R < R1 and Q < Q1, it is determined as a low-load working condition; when h1 ≤ h ≤ h2 and v1 ≤ v ≤ v2 and R1 ≤ R ≤ R2 and Q1 ≤ Q ≤ Q2, it is determined as a medium-load working condition; when h > h2 or v > v2 or R > R1 or Q > Q2, it is determined as a high-load working condition.

[0009] Preferably, the dynamic adjustment process of the pump speed under the low-load working condition is as follows: Retrieve the water level h, the rated pump speed n0, the pump motor power P, and the theoretical pump motor power P0, where the theoretical pump motor power P0 is calculated based on the rated pump motor power and the load coefficient under the low-load working condition; Obtain the low-load pump speed coefficient and calculate the initial pump speed based on the water level h and the pump rated speed n0 as follows: ; Combine the deviation between the pump motor power P and the theoretical pump motor power P0 to correct the initial pump speed to obtain the corrected pump speed as follows: Obtain the low-load pump speed constraint range under the low-load working condition, compare the corrected pump speed with the low-load pump speed constraint range. If the corrected pump speed is within the low-load pump speed constraint range, then use the corrected pump speed as the low-load final pump speed . If the corrected pump speed exceeds the low-load pump speed constraint range, then use the lower boundary value of the low-load pump speed constraint range as the low-load final pump speed .

[0010] Preferably, the dynamic adjustment process of the pump speed under the medium-load working condition is as follows: Retrieve the target water level PID control parameters, including proportional gain Integral coefficient Differential coefficients Energy consumption constraint value W per unit of drainage volume; The pump speed regulation Δn is calculated based on the PID control algorithm as follows: In the formula, e(t) represents the water level deviation at the current time t; Based on the pump speed adjustment amount Δn, and combined with the pump speed n, the final pump speed under medium load is calculated. ; Obtain actual drainage volume Based on the power P of the water pump motor, calculate the energy consumption per unit of drainage. ; Obtain the medium-load water pump speed constraint range under the medium-load operating condition. And the final speed of the medium-load water pump As the final pump speed under medium load; if Then, the PID control parameters are readjusted to reduce the pump speed regulation amount Δn and the final pump speed under medium load. until .

[0011] Preferably, the dynamic adjustment process of the pump speed under high load conditions is as follows: Obtain total drainage requirements and maximum flow rate of a single pump Calculate the drainage demand gap ; like Based on the aforementioned drainage demand gap and the maximum flow rate of the single pump Determine the number of water pumps to start ,in, This represents the floor function; if In this case, there is no need to start an additional water pump; simply adjust the speed of the current water pump. Obtain the speed coefficient of high-load water pump Calculate the target speed of a single pump ,in, The rainfall adaptation coefficient; The corresponding pump speed is determined based on the efficiency of each pump until the overall efficiency of the tunnel drainage system is maximized.

[0012] Preferably, the formula for calculating the pump speed of each water pump is as follows: In the formula, This represents the pump speed of the i-th pump; Let be the efficiency of the i-th water pump; This indicates the actual water output power of the water pump motor; Indicates the input power of the water pump motor; The density of water is represented by g; the acceleration due to gravity is represented by H; and the actual head of the water pump is represented by H. The overall efficiency of the tunnel drainage system The calculation formula is as follows: In the formula, This represents the flow rate distributed by the i-th pump.

[0013] Preferably, the formula for calculating the actual head H of the water pump is: ,in, This indicates the loss of head in the pipeline; Real-time calculation of pump operating efficiency based on speed-efficiency curve In the formula, a, b, and c represent the fitting coefficients of the speed-efficiency curve; Calculate the optimal energy consumption of the water pump based on the energy consumption optimization formula. By comparing the power P of the water pump motor with the optimal energy consumption of the water pump If the energy consumption error is less than a preset energy consumption error threshold, the pump speed is adjusted again until the pump motor power P reaches the optimal energy consumption of the pump. .

[0014] A dynamic energy-saving adjustment system for tunnel water pumps, the system comprising: The data acquisition module is used to collect water level parameters, pump operating status parameters, environmental parameters and pipeline operating parameters in the tunnel drainage system through the acquisition unit, and to summarize and generate multi-dimensional operating condition data. The working condition classification module is used to classify the tunnel drainage working conditions based on the multi-dimensional working condition data, combined with the water level change rate, rainfall and total drainage demand, and obtain the working condition classification results. The speed regulation module is used to dynamically adjust the pump speed according to the working condition classification results, using a fusion control algorithm, and to complete the coordinated operation of multiple pumps based on pump efficiency ranking and load distribution logic. The fault early warning module is used to dynamically set fault judgment thresholds based on the pump's rated parameters, historical operating data, and real-time operating condition data. It provides early warnings of potential faults by analyzing parameter change trends and outputs fault causes and handling guidelines.

[0015] Compared with related technologies, the dynamic energy-saving adjustment method and system for tunnel water pumps provided by this invention have the following beneficial effects: This invention collects water level parameters, pump operating status parameters, environmental parameters, and pipeline operating parameters from the tunnel drainage system through a data acquisition unit, and aggregates them to generate multi-dimensional operating condition data. Based on this multi-dimensional operating condition data, combined with water level change rate, rainfall, and total drainage demand, the tunnel drainage operating conditions are classified to obtain operating condition classification results. According to the operating condition classification results, a fusion control algorithm is used to dynamically adjust the pump speed, and multi-pump collaborative operation is completed based on pump efficiency ranking and load distribution logic. Based on pump rated parameters, historical operating data, and real-time operating condition data, a fault judgment threshold is dynamically set, and potential faults are warned in advance by analyzing parameter change trends, and the cause of the fault and handling guidance are output. Thus, through multi-dimensional operating condition data acquisition, intelligent algorithm dynamic adjustment, multi-pump collaborative optimization, and accurate fault warning, the tunnel pumps achieve on-demand power supply, reduced energy consumption, stable operation, and efficient maintenance, solving the core needs of refined, energy-saving, and safe management that existing tunnel drainage systems cannot meet.

[0016] This invention reduces the overall energy consumption of the water pump system by 15%-30% through dynamic speed adjustment and multi-pump collaborative optimization, thereby saving significant electricity costs. Simultaneously, it reduces energy consumption per unit drainage volume by over 20%, significantly improving energy efficiency and effectively reducing energy waste compared to existing fixed control modes. This invention can control water level errors within 5cm, precisely maintaining water levels within a safe range and avoiding drainage safety hazards caused by excessively high or low water levels. The multi-pump collaborative operation strategy reduces frequent pump start-stops, extending pump lifespan by 15%-20%, correspondingly reducing equipment maintenance costs by 25%, significantly improving water level control accuracy and system operational stability, and ensuring long-term stable operation of the tunnel drainage system. This invention can provide 24-72 hours' advance warning of potential faults such as motor overheating, bearing wear, and pipe blockage, effectively preventing the risk of tunnel water accumulation due to escalating faults. It also ensures a fault diagnosis accuracy rate of over 90%, and with clear fault cause analysis and handling guidelines, shortens fault handling time from the original 2-4 hours to within 30 minutes, improving maintenance efficiency by 80% and reducing reactive maintenance. This invention supports personalized configurations for different scenarios such as highways, railways, and long and short tunnels. For example, long tunnels can achieve segmented drainage control, and rainy areas can enhance rainfall linkage regulation. It is also equipped with multi-terminal interaction functions on web and mobile terminals, supporting remote monitoring and operation without the need for on-site personnel, greatly improving the system's operational flexibility and management convenience. Attached Figure Description

[0017] Figure 1 This is a flowchart of the dynamic energy-saving adjustment method for tunnel water pumps according to the present invention; Figure 2 This is a system block diagram of the tunnel water pump dynamic energy-saving adjustment system of the present invention; Figure 3This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] 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. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 The diagram shown is a flowchart of the dynamic energy-saving adjustment method for tunnel water pumps provided in an embodiment of the present invention. Figure 1 The execution entity of the method shown can be a software and / or hardware device. The execution entity of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. User equipment can include, but is not limited to, computers, smartphones, personal digital assistants (PDAs), and the aforementioned electronic devices. Network equipment can include, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers. Cloud computing is a type of distributed computing, consisting of a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. Steps S1 to S4 are detailed as follows: S1 collects water level parameters, pump operating status parameters, environmental parameters, and pipeline operating parameters in the tunnel drainage system through the acquisition unit, and summarizes them to generate multi-dimensional operating condition data. S2. Based on the multi-dimensional working condition data, combined with the water level change rate, rainfall and total drainage demand, the tunnel drainage working conditions are classified to obtain the working condition classification results. S3, based on the working condition classification results, the pump speed is dynamically adjusted using a fusion control algorithm, and multi-pump collaborative operation is completed based on pump efficiency ranking and load distribution logic; S4 dynamically sets fault judgment thresholds based on pump rated parameters, historical operating data, and real-time operating condition data. It provides early warnings of potential faults by analyzing parameter change trends and outputs fault causes and handling guidelines.

[0020] The water level acquisition unit employs distributed liquid level sensors, supporting wired (RS485) and wireless dual-mode communication to accurately collect water level data from the collection well. The pump operation parameter acquisition unit connects to core operating parameters such as motor current, power, speed, and outlet pressure via a frequency converter interface and current transformer. The environmental parameter acquisition unit captures external environmental data using rainfall sensors and water turbidity sensors. The pipeline operation parameter acquisition unit obtains real-time flow information through pipeline flow sensors. All acquisition units are connected to an edge gateway, and after data format standardization processing, a multi-dimensional operating condition dataset containing water level, equipment operation, environmental conditions, and pipeline status is generated.

[0021] Based on the collected multi-dimensional operating condition data, outlier removal and smoothing preprocessing are first performed to eliminate data noise interference, while the water level change rate is calculated. An operating condition classification model is constructed by setting multi-dimensional classification thresholds for water level, water level change rate, rainfall, and drainage demand. Combining the dynamic characteristics of water level changes, real-time rainfall, and total drainage demand, a multi-parameter fusion judgment logic is adopted to accurately classify tunnel drainage conditions into three categories: low load, medium load, and high load. The classification process fully considers the synergistic effects of various parameters to ensure a high degree of match between the operating condition judgment and actual drainage demand.

[0022] Based on the classification results of different operating conditions, a core algorithm integrating PID and fuzzy control is adopted to implement differentiated dynamic speed adjustment. The pump operating status is precisely matched according to the load intensity to achieve on-demand energy supply. In multi-pump systems, the operating efficiency of each pump is calculated and ranked based on historical operating data and real-time parameters, prioritizing the activation of high-efficiency pumps. Through load balancing distribution logic combined with peak-shaving start-stop strategies, the multi-pump operation combination is optimized to avoid the superposition of inefficiencies and grid impact, improving the overall system operating efficiency while simultaneously meeting the dual objectives of water level stability and minimum energy consumption.

[0023] By integrating pump rated parameters, historical operating data, and real-time operating condition data, a dynamic fault threshold model is constructed, with the threshold adjusted in real time according to changes in operating conditions. By continuously monitoring the changing trends of key parameters such as motor current, power, efficiency, and temperature, and employing parameter trend analysis algorithms, potential faults such as motor overheating, bearing wear, and pipe blockage can be predicted 24-72 hours in advance, significantly improving early warning accuracy. When abnormal trends are detected, the system immediately outputs the fault type, characteristics of related parameter changes, fault cause analysis, and targeted handling guidelines, reducing fault handling time from 2-4 hours to within 30 minutes, significantly improving maintenance efficiency and ensuring the safe and stable operation of the tunnel drainage system.

[0024] In the specific implementation process, the data acquisition unit collects water level parameters, pump operating status parameters, environmental parameters, and pipeline operating parameters of the tunnel drainage system, and summarizes them to generate multi-dimensional operating condition data, specifically including: Deploy a water level acquisition unit, a water pump operation parameter acquisition unit, an environmental parameter acquisition unit, and a pipeline operation parameter acquisition unit. Among them, one distributed liquid level sensor is deployed every 500m in the tunnel for the water level acquisition unit; the water pump operation parameter acquisition unit is equipped with a frequency converter interface and a current transformer; the environmental parameter acquisition unit includes a rainfall sensor and a water quality turbidity sensor; the pipeline operation parameter acquisition unit includes a pipeline flow sensor. In the tunnel drainage system, the water level acquisition unit establishes communication with the distributed liquid level sensor by wired or wireless means and acquires the water level parameter, that is, the water level h; the water pump operation parameter acquisition unit accesses the water pump operation state parameters through the frequency converter interface and the current transformer, including the water pump motor current I, the water pump motor power P, the water pump speed n, and the outlet pressure p; the environmental parameter acquisition unit acquires the rainfall R and the water quality turbidity T through the rainfall sensor and the water quality turbidity sensor; the pipeline operation parameter acquisition unit acquires the pipeline operation parameter, that is, the pipeline flow Q, through the pipeline flow sensor, and all acquisition units are connected to the edge gateway. Each acquisition unit transmits the acquired water level h, water pump motor current I, water pump motor power P, water pump speed n, outlet pressure p, rainfall R, water quality turbidity T, and pipeline flow Q to the edge gateway and performs format standardization processing, and aggregates and generates the multi-dimensional working condition data.

[0025] Based on the multi-dimensional working condition data, combined with the water level change rate, rainfall, and total drainage demand, classify the tunnel drainage working conditions to obtain the working condition classification result, specifically including: Perform outlier removal and smoothing processing on the multi-dimensional working condition data, and obtain the water level change rate v based on the water level h. Set working condition classification thresholds, including the first water level threshold h1, the second water level threshold h2, the first water level change rate threshold v1, the second water level change rate threshold v2, the first rainfall threshold R1, the second rainfall threshold R2, the first drainage demand threshold Q1, and the second drainage demand threshold Q2. Classify the tunnel drainage working conditions based on the multi-dimensional working condition data after outlier removal and smoothing processing and the working condition classification thresholds to obtain the working condition classification result; Among them, the classification rule of the tunnel drainage working conditions is: when h < h1 and v < v1 and R < R1 and Q < Q1, it is determined as a low-load working condition; when h1 ≤ h ≤ h2 and v1 ≤ v ≤ v2 and R1 ≤ R ≤ R2 and Q1 ≤ Q ≤ Q2, it is determined as a medium-load working condition; when h > h2 or v > v2 or R > R1 or Q > Q2, it is determined as a high-load working condition.

[0026] First, for the collected multi-dimensional operational data such as water level, rainfall, and pipeline flow, an outlier removal algorithm is used to remove invalid data caused by sensor malfunctions and signal interference. Then, smoothing processing is used to eliminate data fluctuation noise, ensuring data reliability. Based on the preprocessed water level time-series data, the water level change rate is obtained through time-series difference calculation. This rate can intuitively reflect how fast the water level in the tunnel's sump rises or falls, and is a key indicator for judging the urgency of the operational situation.

[0027] Subsequently, based on the tunnel drainage system design parameters, pump operating characteristics, and historical operating data, multi-dimensional classification thresholds were determined. Among them, the first and second water level thresholds are used to define water level ranges under different loads; the first and second water level change rate thresholds distinguish between the gentleness and rapidity of water level changes; the first and second rainfall thresholds correspond to different rainfall intensity levels; and the first and second drainage demand thresholds match different load capacity ranges of the pump system, forming a comprehensive threshold system.

[0028] Next, the preprocessed multi-dimensional operating condition data is compared one by one with the set classification thresholds, and accurate determination of operating conditions is achieved through multi-parameter collaborative verification. During the classification process, the correlation between water level, water level change rate, rainfall, and drainage demand is fully considered to avoid misjudgment based on a single parameter and ensure that the classification results are highly consistent with the actual drainage scenario.

[0029] When the water level is below the first water level threshold, the rate of water level change is below the first rate of change threshold, the rainfall is below the first rainfall threshold, and the drainage demand is below the first drainage demand threshold, it is determined to be a low-load condition, in which case the drainage demand is moderate. When all parameters are between the corresponding first and second thresholds, it is determined to be a medium-load condition, and the system is in a stable drainage state. When the water level is above the second water level threshold, or the rate of water level change is above the second rate of change threshold, or the rainfall is above the first rainfall threshold, or the drainage demand is above the second drainage demand threshold, it is determined to be a high-load condition, corresponding to emergency scenarios such as sudden rainfall and large-scale drainage, providing a basis for subsequent differentiated control strategies.

[0030] The dynamic adjustment process of the water pump speed under low load conditions is as follows: The water level h, the rated pump speed n0, the pump motor power P, and the theoretical pump motor power P0 are retrieved, wherein the theoretical pump motor power P0 is calculated based on the rated pump motor power and the load factor under the low load condition. Obtain the speed coefficient of the low-load water pump The initial pump speed is calculated based on the water level h and the rated pump speed n0. as follows: ; The initial pump speed is determined by the deviation between the pump motor power P and the theoretical pump motor power P0. The corrected pump speed is obtained by making adjustments. as follows: Obtain the low-load water pump speed constraint range under the low-load operating condition, and adjust the corrected water pump speed. Compare the adjusted pump speed with the low-load pump speed constraint range. If the pump speed is within the low-load pump speed constraint range, then the corrected pump speed will be adjusted. As the final water pump speed under low load If the corrected water pump speed If the pump speed exceeds the low-load water pump speed constraint range, then the lower boundary value of the low-load water pump speed constraint range will be taken as the final low-load water pump speed. .

[0031] Understandably, priority is given to retrieving real-time water level h, preset rated pump speed n0, real-time pump power P, and theoretical pump motor power P0 from multi-dimensional operating condition data. The rated pump speed n0 is the system's preset standard operating speed, with a default frequency of 50Hz corresponding to 1480 r / min, suitable for the pump's rated operating conditions. The theoretical pump motor power P0 is calculated using the rated pump motor power and a low-load load factor. This load factor is calibrated based on factors such as drainage demand intensity and pipeline resistance characteristics under low load, reflecting the pump's theoretical optimal energy consumption level under this condition. The real-time power P is captured in real-time through the current transformer and inverter interface of the pump operating parameter acquisition unit, ensuring parameter timeliness.

[0032] Then, a preset low-load pump speed coefficient is obtained. This coefficient consists of a base speed coefficient and a water level adaptation coefficient. The base speed coefficient is set to 0.6 to adapt to the basic operating requirements under low-load conditions. The water level adaptation coefficient is positively correlated with the current water level h and increases as the water level rises, ensuring that the initial speed matches the actual water level requirement. Based on the rated pump speed n0, and combined with the correlation between the low-load speed coefficient and the water level h, the initial pump speed is obtained through collaborative calculation. This speed is the benchmark operating speed under low-load conditions, initially achieving a match with the water level requirement.

[0033] Next, by comparing the deviation between the real-time pump motor power P and the theoretical pump motor power P0, the difference between the actual operating energy consumption and the theoretical optimal energy consumption is determined: if the real-time power is higher than the theoretical power, it indicates that the current speed is too high, resulting in energy waste, and the speed needs to be corrected downward; if the real-time power is lower than the theoretical power, but meets the drainage requirements, the speed can be finely adjusted to ensure operational stability. Finally, the corrected pump speed is obtained through the deviation compensation algorithm, so that the speed is precisely matched with the energy consumption optimization target.

[0034] Under low-load conditions, the pump speed constraint range is preset to 30Hz-40Hz, corresponding to a mechanical speed of 888r / min-1184r / min. This range is determined based on the pump's operating characteristics to avoid pump cavitation caused by excessively low speeds and ensure equipment operation safety. The corrected pump speed is compared with this constraint range: if the corrected speed is within the constraint range, it is directly used as the final pump speed under low load and output to the pump control module for execution; if the corrected speed exceeds the constraint range, to avoid equipment damage risks, the system automatically takes the lower boundary value of the constraint range as the final speed, ensuring that the pump meets drainage requirements while achieving the dual goals of optimal energy consumption and safe operation under low-load conditions. The entire adjustment process dynamically responds to changes in operating conditions, fully demonstrating the system's refined control capabilities.

[0035] The dynamic adjustment process of the water pump speed under medium load conditions is as follows: Get target water level PID control parameters, including proportional gain Integral coefficient Differential coefficients Energy consumption constraint value W per unit of drainage volume; The pump speed regulation Δn is calculated based on the PID control algorithm as follows: In the formula, e(t) represents the water level deviation at the current time t; Based on the pump speed adjustment amount Δn, and combined with the pump speed n, the final pump speed under medium load is calculated. ; Obtain actual drainage volume Based on the power P of the water pump motor, calculate the energy consumption per unit of drainage. ; Obtain the medium-load water pump speed constraint range under the medium-load operating condition. And the final speed of the medium-load water pump As the final pump speed under medium load; if Then, the PID control parameters are readjusted to reduce the pump speed regulation amount Δn and the final pump speed under medium load. until .

[0036] In practical applications, the system accurately retrieves preset target water level, PID control parameters, energy consumption constraints per unit drainage volume, and current pump operating speed from a multi-dimensional operating condition database. The target water level is set at 3.5m, representing the optimal water level benchmark that balances drainage efficiency and energy consumption under medium-load conditions. The PID control parameters include proportional, integral, and derivative coefficients, whose values ​​are calibrated for operating conditions to ensure a balance between adjustment response speed and stability. The energy consumption constraint per unit drainage volume is 0.8 kW·h / m³, a key indicator for measuring the effectiveness of energy consumption optimization. The current pump operating speed is acquired in real-time through the pump operating parameter acquisition unit.

[0037] Secondly, a regulation model is constructed based on the PID control algorithm, using the current water level deviation as the core input. This deviation is the difference between the target water level and the actual water level, directly reflecting the degree to which the current water level deviates from the optimal range. The proportional coefficient is used to quickly respond to water level deviations and output the basic regulation amount in real time; the integral coefficient is used to eliminate steady-state error generated during long-term operation, ensuring that the water level remains stable within the target range without deviation; the derivative coefficient predicts the water level change trend and outputs the compensation regulation amount in advance, avoiding over-regulation or lag. Through the coordinated calculation of these three coefficients, a precise pump speed regulation amount is obtained, achieving a rapid and stable response to water level changes.

[0038] Next, the calculated speed adjustment is superimposed with the current pump operating speed to obtain the preliminary final speed under medium load conditions. During the adjustment process, the speed adjustment accuracy is strictly controlled to ±1Hz to ensure smooth speed changes and avoid pipeline pressure fluctuations or equipment shocks caused by sudden speed changes. At the same time, the continuity and stability of water level adjustment are ensured, so that the water level quickly approaches and stabilizes within the target range.

[0039] Then, the actual drainage volume is obtained through the pipeline operation parameter acquisition unit, and combined with the real-time motor power captured by the pump operation parameter acquisition unit, the energy consumption per unit drainage volume is calculated. This calculation process directly reflects the energy consumption optimization effect of the speed regulation scheme, ensuring that the regulation behavior meets both water level control requirements and energy-saving goals.

[0040] Finally, under medium load conditions, the preset speed constraint range is 40Hz-48Hz, corresponding to a mechanical speed of 1184r / min-1411r / min. This range is determined based on the pump's operating characteristics and energy consumption optimization objectives. The initial final speed is then double-checked against the speed constraint range and the energy consumption constraint value per unit discharge volume: if the speed is within the constraint range and the energy consumption is less than or equal to the constraint value, it is directly output as the final speed to the control module; if the energy consumption exceeds the constraint value or the speed exceeds the range, the PID control parameters are readjusted to reduce the speed adjustment, thereby correcting the final speed. This check process is repeated until both the speed and energy consumption meet the constraint requirements, achieving the dual objectives of stable water level and optimal energy consumption under medium load conditions. The entire adjustment process operates in a closed loop, fully demonstrating the system's refined control capabilities and energy-saving orientation.

[0041] The dynamic adjustment process of the water pump speed under high load conditions is as follows: Obtain total drainage requirements and maximum flow rate of a single pump Calculate the drainage demand gap ; like Based on the aforementioned drainage demand gap and the maximum flow rate of the single pump Determine the number of water pumps to start ,in, This represents the floor function; if In this case, there is no need to start an additional water pump; simply adjust the speed of the current water pump. Obtain the speed coefficient of high-load water pump Calculate the target speed of a single pump ,in, The rainfall adaptation coefficient; The corresponding pump speed is determined based on the efficiency of each pump until the overall efficiency of the tunnel drainage system is maximized.

[0042] First, based on multi-dimensional operating condition data, the total drainage demand is accurately obtained through a load prediction algorithm. This demand directly reflects the actual pressure on tunnel drainage under high load conditions. Simultaneously, the maximum flow rate parameter of a single pump is retrieved. This parameter represents the maximum drainage capacity of the pump under rated operating conditions and is calibrated by the pump model. By calculating the difference between the total drainage demand and the maximum flow rate of a single pump, the drainage demand gap is obtained. This gap directly reflects whether the currently operating pumps can independently meet the drainage demand, providing a basis for decision-making regarding subsequent pump start-up, shutdown, and speed adjustment.

[0043] Secondly, the drainage demand gap is determined: if the gap is greater than 0, it indicates that the operation of a single pump cannot meet the drainage demand, and an additional standby pump needs to be started. Based on the ratio of the gap value to the maximum flow rate of a single pump, the total number of pumps to be started is determined using rounding logic to ensure that the total drainage capacity of the pump set covers the demand gap and avoids water accumulation in the tunnel due to insufficient drainage; if the gap is less than or equal to 0, it means that the drainage capacity of the currently operating pumps can meet the demand, and there is no need to start an additional pump. The high-load condition can be adapted simply by adjusting the speed of the current pumps. When starting the pump set, high-efficiency pumps with high historical operating efficiency and stable status are given priority, which is in line with the system's energy-saving optimization goals.

[0044] Next, the preset high-load pump speed coefficient is retrieved. This coefficient is calibrated based on the pump's rated operating characteristics and high-load conditions to ensure the rationality of the speed adjustment. Simultaneously, a rainfall adaptation coefficient is introduced. This coefficient is positively correlated with real-time rainfall; the greater the rainfall, the higher the adaptation coefficient, allowing the speed adjustment to dynamically respond to changes in rainfall intensity. Using the pump's rated speed as a benchmark, and combining the high-load speed coefficient and the rainfall adaptation coefficient, the target speed for a single pump is calculated. This speed falls within the constraint range of 48Hz-50Hz, corresponding to a mechanical speed of 1411r / min-1480r / min, ensuring drainage efficiency while preventing pump overload operation.

[0045] Finally, the operating efficiency of each pump is calculated in real time. The efficiency calculation is based on the ratio of actual output power to motor input power, adjusted by parameters such as pump speed, outlet pressure, and actual drainage volume to ensure accurate efficiency assessment. Speeds are allocated according to efficiency ranking, with high-efficiency pumps undertaking a higher proportion of the load and low-efficiency pumps having their load appropriately reduced, achieving load balance and energy optimization. A staggered start-stop strategy is also adopted, with a 30-second start-stop interval to avoid impacting the power grid from simultaneous pump start-stop operations. By dynamically adjusting the speed of each pump, the overall efficiency of the tunnel drainage system is maximized, ensuring that the system meets the demand for rapid drainage under high load conditions while controlling the overall energy consumption to be less than or equal to 1.2 kW·h / m³, achieving a dual balance between drainage efficiency and energy saving targets. The entire adjustment process dynamically responds to changes in operating conditions, fully demonstrating the system's refined and coordinated control capabilities.

[0046] The formula for calculating the pump speed of each water pump is as follows: In the formula, This represents the pump speed of the i-th pump; Let be the efficiency of the i-th water pump; This indicates the actual water output power of the water pump motor; Indicates the input power of the water pump motor; The density of water is represented by g; the acceleration due to gravity is represented by H; and the actual head of the water pump is represented by H. The overall efficiency of the tunnel drainage system The calculation formula is as follows: In the formula, This represents the flow rate distributed by the i-th pump.

[0047] The formula for calculating the actual head H of the water pump is: ,in, This indicates the loss of head in the pipeline; Real-time calculation of pump operating efficiency based on speed-efficiency curve In the formula, a, b, and c represent the fitting coefficients of the speed-efficiency curve; Calculate the optimal energy consumption of the water pump based on the energy consumption optimization formula. By comparing the power P of the water pump motor with the optimal energy consumption of the water pump If the energy consumption error is less than a preset energy consumption error threshold, the pump speed is adjusted again until the pump motor power P reaches the optimal energy consumption of the pump. .

[0048] In practical applications, the determination of a single pump speed is based on pump efficiency to achieve differentiated load allocation. Pump efficiency is calculated as the ratio of actual output power to motor input power. Actual output power requires comprehensive calculation considering parameters such as water density, gravitational acceleration, actual drainage volume, and actual pump head, directly reflecting the pump's energy conversion efficiency. Speeds are allocated according to the efficiency ranking of each pump, with high-efficiency pumps receiving a higher load share and low-efficiency pumps correspondingly reducing their load. This ensures that each pump operates within its efficient range while avoiding the cumulative effect of inefficiencies from multiple pumps operating simultaneously, aligning with the goal of multi-pump collaborative energy saving.

[0049] The overall efficiency of the tunnel drainage system is calculated using a weighted average method, with the allocated flow rate of each pump as the weight, combined with the corresponding pump's operating efficiency, to arrive at the result through collaborative calculation. This calculation method considers both the operating efficiency of individual pumps and the rationality of the overall flow distribution of the pump group, comprehensively reflecting the overall energy efficiency level of multi-pump collaborative operation. By optimizing the load distribution and speed adjustment of each pump, the overall system efficiency can be increased to over 75%, significantly better than the existing technology's efficiency level of 60%, thus maximizing the overall energy efficiency of the pump group.

[0050] The actual head of the water pump consists of two parts: the real-time water level in the sump and the pipeline head loss. The pipeline head loss is preset to a fixed value of 0.5m based on pipeline characteristics, eliminating the need for real-time calculation and simplifying the calculation process while ensuring accuracy. The actual head, as a core operating parameter of the water pump, directly affects the output power and energy consumption level. Its accurate calculation provides crucial data support for subsequent speed adjustment and energy consumption optimization, ensuring precise matching between the control strategy and actual operating conditions.

[0051] The pump's operating efficiency is calculated in real time using a speed-efficiency curve. This curve is generated by fitting the pump's factory parameters with historical operating data. The fitting coefficients are calibrated using a large amount of operating data to ensure the accuracy of efficiency calculations at different speeds. Based on the actual head, discharge volume, and real-time efficiency, the optimal energy consumption under the current operating conditions can be calculated. By comparing the pump's real-time operating power with the optimal energy consumption, the energy consumption error is obtained. If the error exceeds a preset threshold, closed-loop regulation is initiated to dynamically correct the pump speed until the actual operating power approaches the optimal energy consumption, achieving a dynamic balance between energy consumption and operating conditions, and maximizing energy savings.

[0052] like Figure 2 The diagram shown is a system block diagram of a dynamic energy-saving adjustment system for tunnel water pumps provided in an embodiment of the present invention. The system includes: The data acquisition module is used to collect water level parameters, pump operating status parameters, environmental parameters and pipeline operating parameters in the tunnel drainage system through the acquisition unit, and to summarize and generate multi-dimensional operating condition data. The working condition classification module is used to classify the tunnel drainage working conditions based on the multi-dimensional working condition data, combined with the water level change rate, rainfall and total drainage demand, and obtain the working condition classification results. The speed regulation module is used to dynamically adjust the pump speed according to the working condition classification results, using a fusion control algorithm, and to complete the coordinated operation of multiple pumps based on pump efficiency ranking and load distribution logic. The fault early warning module is used to dynamically set fault judgment thresholds based on the pump's rated parameters, historical operating data, and real-time operating condition data. It provides early warnings of potential faults by analyzing parameter change trends and outputs fault causes and handling guidelines.

[0053] Figure 2 The apparatus of the illustrated embodiment can be used to perform corresponding actions. Figure 1 The steps in the method embodiments shown are implemented in a similar manner and have similar technical effects, and will not be repeated here.

[0054] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor performs the steps of the dynamic energy-saving adjustment method for tunnel water pumps as described in any of the above claims.

[0055] like Figure 3 The diagram shown is a hardware structure schematic of an electronic device according to an embodiment of the present invention. The electronic device 30 includes: a processor 31, a memory 32, and a computer program; wherein... The memory 32 is used to store the computer program, and the memory may also be flash memory. The computer program is, for example, an application program or functional module that implements the above method.

[0056] Processor 31 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0057] Alternatively, the memory 32 can be either standalone or integrated with the processor 31.

[0058] When the memory 32 is a device independent of the processor 31, the device may further include: Bus 33 is used to connect the memory 32 and the processor 31.

[0059] A readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the dynamic energy-saving adjustment method for tunnel water pumps as described in any of the above claims.

[0060] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0061] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0062] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0063] Through the above embodiments, this invention, through a dynamic energy-saving adjustment method and system for tunnel water pumps, collects water level parameters, pump operating status parameters, environmental parameters, and pipeline operating parameters in the tunnel drainage system via a data acquisition unit, and summarizes them to generate multi-dimensional operating condition data. Based on the multi-dimensional operating condition data, combined with the water level change rate, rainfall, and total drainage demand, the tunnel drainage operating conditions are classified to obtain operating condition classification results. According to the operating condition classification results, a fusion control algorithm is used to dynamically adjust the pump speed, and multi-pump collaborative operation is completed based on pump efficiency ranking and load distribution logic. Based on the pump rated parameters, historical operating data, and real-time operating condition data, a fault judgment threshold is dynamically set, and potential faults are warned in advance by analyzing parameter change trends, and the cause of the fault and handling guidance are output. Thus, through multi-dimensional operating condition data acquisition, intelligent algorithm dynamic adjustment, multi-pump collaborative optimization, and accurate fault warning, the tunnel water pumps achieve on-demand energy supply, reduced energy consumption, stable operation, and efficient maintenance, solving the core needs of refined, energy-saving, and safe management that existing tunnel drainage systems cannot meet.

[0064] This invention reduces the overall energy consumption of the water pump system by 15%-30% through dynamic speed adjustment and multi-pump collaborative optimization, thereby saving significant electricity costs. Simultaneously, it reduces energy consumption per unit drainage volume by over 20%, significantly improving energy efficiency and effectively reducing energy waste compared to existing fixed control modes. This invention can control water level errors within 5cm, precisely maintaining water levels within a safe range and avoiding drainage safety hazards caused by excessively high or low water levels. The multi-pump collaborative operation strategy reduces frequent pump start-stops, extending pump lifespan by 15%-20%, correspondingly reducing equipment maintenance costs by 25%, significantly improving water level control accuracy and system operational stability, and ensuring long-term stable operation of the tunnel drainage system. This invention can provide 24-72 hours' advance warning of potential faults such as motor overheating, bearing wear, and pipe blockage, effectively preventing the risk of tunnel water accumulation due to escalating faults. It also ensures a fault diagnosis accuracy rate of over 90%, and with clear fault cause analysis and handling guidelines, shortens fault handling time from the original 2-4 hours to within 30 minutes, improving maintenance efficiency by 80% and reducing reactive maintenance. This invention supports personalized configurations for different scenarios such as highways, railways, and long and short tunnels. For example, long tunnels can achieve segmented drainage control, and rainy areas can enhance rainfall linkage regulation. It is also equipped with multi-terminal interaction functions on web and mobile terminals, supporting remote monitoring and operation without the need for on-site personnel, greatly improving the system's operational flexibility and management convenience.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic energy saving regulation of a tunnel water pump, characterized in that, The method includes: The data acquisition unit collects water level parameters, pump operating status parameters, environmental parameters, and pipeline operating parameters from the tunnel drainage system, and then summarizes and generates multi-dimensional operating condition data. Based on the multi-dimensional working condition data, combined with the water level change rate, rainfall and total drainage demand, the tunnel drainage working conditions are classified to obtain the working condition classification results. Based on the operating condition classification results, a fusion control algorithm is used to dynamically adjust the pump speed, and multi-pump collaborative operation is completed based on pump efficiency ranking and load distribution logic. Based on the pump's rated parameters, historical operating data, and real-time operating condition data, the fault judgment threshold is dynamically set. By analyzing the trend of parameter changes, potential faults are given early warning, and the cause of the fault and handling instructions are output.

2. The method of claim 1, wherein, The data acquisition unit collects water level parameters, pump operating status parameters, environmental parameters, and pipeline operating parameters from the tunnel drainage system, and summarizes them to generate multi-dimensional operating condition data, specifically including: The system deploys a water level acquisition unit, a water pump operation parameter acquisition unit, an environmental parameter acquisition unit, and a pipeline operation parameter acquisition unit. The water level acquisition unit deploys one distributed liquid level sensor every 500m within the tunnel. The water pump operation parameter acquisition unit is equipped with a frequency converter interface and a current transformer. The environmental parameter acquisition unit includes a rainfall sensor and a water turbidity sensor. The pipeline operation parameter acquisition unit includes a pipeline flow sensor. In the tunnel drainage system, the water level acquisition unit establishes communication with the distributed liquid level sensor via wired or wireless means and acquires the water level parameter, i.e., water level h; the water pump operation parameter acquisition unit accesses the water pump operation status parameters, including water pump motor current I, water pump motor power P, water pump speed n, and outlet pressure p, through the frequency converter interface and the current transformer; the environmental parameter acquisition unit acquires the rainfall R and the water turbidity T through the rainfall sensor and the water quality turbidity sensor; the pipeline operation parameter acquisition unit acquires the pipeline operation parameter, i.e., pipeline flow rate Q, through the pipeline flow sensor, and all acquisition units are connected to the edge gateway; Each data acquisition unit transmits the collected data, including water level h, pump motor current I, pump motor power P, pump speed n, outlet pressure p, rainfall R, water turbidity T, and pipeline flow rate Q, to the edge gateway for format standardization processing, and then summarizes and generates the multi-dimensional operating condition data.

3. The method of claim 2, wherein, Based on the multi-dimensional operating condition data, combined with the water level change rate, rainfall, and total drainage demand, the tunnel drainage operating conditions are classified to obtain the operating condition classification results, specifically including: Outlier removal and smoothing are performed on the multi-dimensional working condition data, and the water level change rate v is obtained based on the water level h. Set working condition classification thresholds, including first water level threshold h1, second water level threshold h2, first water level change rate threshold v1, second water level change rate threshold v2, first rainfall threshold R1, second rainfall threshold R2, first drainage demand threshold Q1, and second drainage demand threshold Q2. Classify the tunnel drainage condition based on the multi-dimensional operating condition data and the operating condition classification threshold after outlier rejection and smoothing processing to obtain the operating condition classification result; Among them, the classification rule of the tunnel drainage condition is: when h < h1 and v < v1 and R < R1 and Q < Q1, it is determined as a low-load condition; when h1 ≤ h ≤ h2 and v1 ≤ v ≤ v2 and R1 ≤ R ≤ R2 and Q1 ≤ Q ≤ Q2, it is determined as a medium-load condition; when h > h2 or v > v2 or R > R1 or Q > Q2, it is determined as a high-load condition.

4. The method of claim 3, wherein, The dynamic adjustment process of the pump speed under the low-load condition is as follows: Retrieve the water level h, the rated pump speed n0, the pump motor power P, and the theoretical pump motor power P0, where the theoretical pump motor power P0 is calculated based on the rated pump motor power and the load factor under the low-load condition; Obtaining a low-load water pump speed coefficient and calculating an initial water pump speed based on the water level h and the water pump rated speed n0 as follows: ； The initial water pump rotational speed is corrected in accordance with the deviation of the water pump motor power P from the theoretical water pump motor power P0 to obtain a corrected water pump rotational speed as follows: obtaining a low-load water pump speed constraint range under the low-load working condition, comparing the modified water pump speed with the low-load water pump speed constraint range, if the modified water pump speed is located in the low-load water pump speed constraint range, taking the modified water pump speed as a low-load final water pump speed , if the modified water pump speed exceeds the low-load water pump speed constraint range, taking a lower boundary value of the low-load water pump speed constraint range as the low-load final water pump speed .​​​​ 5. The method of dynamic energy regulation of a tunnel water pump according to claim 4, characterized in that, The dynamic adjustment process of the pump speed under the medium-load condition is as follows: Call target water level , PID control parameters, including proportional coefficient , integral coefficient , differential coefficient , unit displacement energy consumption constraint value W Calculate the pump speed adjustment amount Δn based on the PID control algorithm as follows: where e(t) represents the water level deviation at the current time t; calculating the final water pump rotating speed n based on the water pump rotating speed adjusting amount Δn and the water pump rotating speed n ; acquiring the actual displacement , in combination with the water pump motor power P, to calculate the current displacement energy consumption per unit ; Obtain the medium-load water pump speed constraint range under the medium-load operating condition. And the final speed of the medium-load water pump As the final pump speed under medium load; if Then, the PID control parameters are readjusted to reduce the pump speed regulation amount Δn and the final pump speed under medium load. until .

6. The dynamic energy-saving adjustment method for tunnel water pumps according to claim 5, characterized in that, The dynamic adjustment process of the pump speed under the high-load condition is as follows: Obtain total drainage requirements and maximum flow rate of a single pump Calculate the drainage demand gap ; like Based on the aforementioned drainage demand gap and the maximum flow rate of the single pump Determine the number of water pumps to start ,in, This represents the floor function; if In this case, there is no need to start an additional water pump; simply adjust the speed of the current water pump. Obtain the speed coefficient of high-load water pump Calculate the target speed of a single pump ,in, The rainfall adaptation coefficient; Determine the corresponding pump speed according to the efficiency of each pump until the comprehensive efficiency of the tunnel drainage system is maximized.

7. The dynamic energy-saving adjustment method for tunnel water pumps according to claim 6, characterized in that, The calculation formula for the pump speed of each pump is as follows: In the formula, This represents the pump speed of the i-th pump; Let be the efficiency of the i-th water pump; This indicates the actual water output power of the water pump motor; Indicates the input power of the water pump motor; The density of water is represented by g; the acceleration due to gravity is represented by H; and the actual head of the water pump is represented by H. The overall efficiency of the tunnel drainage system The calculation formula is as follows: In the formula, This represents the flow rate distributed by the i-th pump.

8. The dynamic energy-saving adjustment method for tunnel water pumps according to claim 1, characterized in that, The formula for calculating the actual head H of the water pump is: ,in, This indicates the loss of head in the pipeline; Real-time calculation of pump operating efficiency based on speed-efficiency curve In the formula, a, b, and c represent the fitting coefficients of the speed-efficiency curve; Calculate the optimal energy consumption of the water pump based on the energy consumption optimization formula. By comparing the power P of the water pump motor with the optimal energy consumption of the water pump If the energy consumption error is less than a preset energy consumption error threshold, the pump speed is adjusted again until the pump motor power P reaches the optimal energy consumption of the pump. .

9. A dynamic energy-saving adjustment system for tunnel water pumps, applied to the dynamic energy-saving adjustment method for tunnel water pumps as described in any one of claims 1-8, characterized in that, The system includes: A data acquisition module for collecting water level parameters, pump operating state parameters, environmental parameters, and pipeline operating parameters in the tunnel drainage system through a collection unit and summarizing them into multi-dimensional operating condition data; An operating condition classification module for classifying the tunnel drainage condition based on the multi-dimensional operating condition data, combined with the water level change rate, rainfall, and total drainage demand, to obtain the operating condition classification result; A speed adjustment module for dynamically adjusting the pump speed using a fusion control algorithm according to the operating condition classification result and completing multi-pump coordinated operation based on pump efficiency sorting and load distribution logic; A fault warning module for dynamically setting a fault judgment threshold based on the rated parameters of the pump, historical operating data, and real-time operating condition data, giving an early warning of potential faults by analyzing the parameter change trend, and outputting the fault cause and treatment guidelines.

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