A self-adaptive pumping system and method based on drainage piles

Abstract

The application relates to the technical field of water pumping by drainage piles, and discloses a self-adaptive water pumping system and method based on drainage piles. The method acquires real-time water level data in a water storage pipe of a drainage pile and a historical water pumping record data set; a dynamic water level analysis model is called to perform trend prediction processing on the real-time water level data, so as to generate a time window marker sequence containing future water level extreme points and fluctuation periods; based on a matching result of the time window marker sequence and the historical water pumping record data set, a water pumping equipment start-stop strategy set containing pump power grading parameters and running time length combinations is generated; photovoltaic panel power generation efficiency curves and remaining capacity data of storage batteries are combined with the start-stop strategy set to perform energy adaptability correction processing, so as to generate an optimized dynamic water pumping instruction set; when the instruction set is executed, pump vibration frequency spectrum and pipeline pressure fluctuation data are synchronously collected, and a device running state feedback log is generated.

Description

Technical Field

[0001] This invention relates to the field of drainage pile pumping technology, specifically to an adaptive pumping system and method based on drainage piles. Background Technology

[0002] Drainage piles are an important drainage facility widely used in foundation pit engineering, slope stabilization, and geological disaster prevention. Their core function is to promptly drain seepage water that has seeped into the internal water collection pipes of the pile through built-in pumping equipment, thereby controlling the groundwater level and ensuring project safety. Traditional drainage pile pumping operations often rely on simple level switch control: the pump starts when the water level rises to a preset high point and stops when the water level drops to a preset low point. While this switch control method is simple and reliable, it has significant limitations. It cannot detect trends in water level changes and is slow to respond to dynamic changes in seepage volume due to seasonal and weather conditions. When seepage volume is small, frequent pump starts and stops not only increase equipment wear and energy consumption but may also fail to effectively remove sediment due to insufficient pumping time per cycle. In cases of sudden increases in seepage volume caused by heavy rain, fixed start / stop thresholds may lead to untimely drainage and the risk of water level exceeding limits.

[0003] Many drainage pumping stations are located in remote areas or regions with limited access to municipal power, often relying on renewable energy sources such as solar power. However, the output of these renewable energy sources is intermittent and unstable, significantly affected by sunlight and weather conditions. Traditional pumping control strategies rarely consider real-time energy supply, easily leading to system power outages when high-power pumps are forced to start in situations with insufficient sunlight or low battery capacity, or failing to fully utilize energy for efficient drainage when it is readily available. Furthermore, traditional methods lack effective monitoring of the pumping equipment's own operational status, such as abnormal pump vibration or pipe blockages, making it difficult to detect potential equipment malfunctions in a timely manner, potentially affecting drainage reliability. Therefore, an intelligent pumping method is needed that integrates water level changes, renewable energy supply, and the equipment's own operational status to achieve efficient, reliable, and energy-saving drainage operations. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive pumping system and method based on drainage piles to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an adaptive pumping method based on drainage piles, the method comprising: Acquire real-time water level data and historical pumping record data set in the drainage pile water accumulation pipe, wherein the real-time water level data includes water level height change curve and sensor trigger time series; The dynamic water level analysis model is invoked to perform trend prediction processing on the real-time water level data, generating a time window marker sequence containing future water level extreme points and fluctuation cycles. Based on the matching results between the time window marker sequence and the historical pumping record data set, a set of pumping equipment start-up and shutdown strategies is generated, which includes a combination of pump power classification parameters and running time. The photovoltaic panel power generation efficiency curve and battery remaining capacity data are obtained by the new energy power supply monitoring module. Combined with the pumping equipment start-stop strategy set, energy adaptability correction is performed to generate an optimized dynamic pumping instruction set. When executing the dynamic pumping command set, pump vibration spectrum and pipeline pressure fluctuation data are collected synchronously to generate equipment operation status feedback log; The parameters of the dynamic water level analysis model are iteratively updated based on the equipment operation status feedback log, forming a closed-loop optimization link between water level prediction accuracy and pumping energy consumption.

[0006] Preferably, the step of calling the dynamic water level analysis model to perform trend prediction processing on the real-time water level data includes: The water level change curve is subjected to seasonal decomposition to separate the trend component, periodic component and random noise component. A temporal convolutional network is used to extract multi-scale features from the trend component, generating feature vectors for water level rise rate and fall slope. An autoregressive moving average model of the periodic component is constructed to predict the water level phase shift over the next three periods. The random noise component is input into the Generative Adversarial Network to simulate abnormal fluctuations and generate the boundary of the water level fluctuation range after noise enhancement. The time window marker sequence is generated by fusing the water level rise rate feature vector, the fall slope feature vector, the water level phase offset, and the water level fluctuation interval boundary.

[0007] Preferably, the step of generating a set of pumping equipment start-up and shutdown strategies based on the matching results between the time window marker sequence and the historical pumping record data set includes: Extract all historical pumping record segments from the historical pumping record dataset whose similarity to the current time window marked sequence exceeds a threshold; Calculate the ratio of water level recovery delay time to cumulative pump energy consumption for each historical operating condition segment, and generate an energy efficiency priority score; The optimal power-duration combination mode is selected from historical operating condition segments based on the energy efficiency priority score. The optimal power-duration combination mode is compensated and corrected for the current ambient temperature and humidity parameters to generate the pump power classification parameters and running time combination.

[0008] Preferably, the step of acquiring the photovoltaic panel power generation efficiency curve and the remaining battery capacity data through the new energy power supply monitoring module, and performing energy adaptability correction processing in conjunction with the pumping equipment start-stop strategy set, includes: Identify the points of sudden changes in light intensity and the range of power generation decay in the photovoltaic panel power generation efficiency curve; Within the power generation decay range, the remaining capacity data of the storage battery is subjected to a discharge depth safety verification to generate an allowable discharge capacity threshold. When the total energy consumption demand in the pumping equipment start-up and shutdown strategy set exceeds the allowable discharge capacity threshold, the running time of each power level parameter is compressed proportionally. The compressed runtime and pump power classification parameters are recombined to generate the dynamic pumping instruction set.

[0009] Preferably, the simultaneous acquisition of pump vibration spectrum and pipeline pressure fluctuation data during the execution of the dynamic pumping command set includes: Harmonic components of motor winding current are collected during the pump startup phase to generate a startup impact characteristic spectrum. During the stable operation phase, the low-frequency resonant band of pipeline pressure fluctuation data is captured to generate fluid pulsation feature codes. When a preset abnormal frequency band appears in the starting impact feature spectrum, the pump power grading parameter downgrade mechanism is triggered. The predicted fluctuation cycle value in the subsequent time window marker sequence is adjusted based on the fluid pulsation feature encoding.

[0010] Preferably, the step of iteratively updating the parameters of the dynamic water level analysis model based on the equipment operation status feedback log includes: Extract the deviation data between the fluid pulsation feature encoding and the actual measured value of the water level height change curve, and generate a model correction matrix; The model calibration matrix is ​​decomposed into the kernel weight update amount of the temporal convolutional network and the coefficient adjustment amount of the autoregressive moving average model through the backpropagation algorithm. A seasonal rainfall intensity correction factor is injected into the boundary of the noise-enhanced water level fluctuation range to generate a seasonally adaptive prediction range. The updated dynamic water level analysis model is used to regenerate the time window marker sequence for the next detection cycle.

[0011] Preferably, the method further includes: A multispectral turbidity sensor array was installed inside the drainage pile's water collection pipe to collect data on the concentration distribution of suspended particulate matter at different water depths. When a sudden change in the vertical gradient occurs in the suspended particulate matter concentration distribution data, a backwashing mode switching command is triggered for the water pump. The upper limit of torque output of the pump power classification parameters is dynamically adjusted based on pipeline pressure fluctuation data under backflushing mode.

[0012] Preferably, the method further includes: Establish a real-time connection channel between the pumping controller and the meteorological data API to obtain precipitation probability and intensity forecast data for the next six hours; When the probability of precipitation exceeds the critical value, a pre-drainage command is inserted into the dynamic pumping command set to expand the buffer capacity of the water collection pipe. The rainfall response coefficient of the dynamic water level analysis model is calibrated based on the comparison between the actual rainfall intensity and the water level drop rate after the pre-drainage command is executed.

[0013] Preferably, the method further includes: A wind speed sensor is installed on top of the integrated new energy platform to monitor the intensity of airflow disturbance on the surface of the photovoltaic panel in real time; When the intensity of airflow disturbance causes the vibration frequency of the photovoltaic panel support to exceed the safety threshold, a support angle fine-tuning instruction is added to the dynamic pumping instruction set. The fine-tuned photovoltaic panel tilt angle data is fed back to the power generation efficiency curve prediction module to update the detection sensitivity of sudden changes in light intensity.

[0014] Preferably, the present invention also includes an adaptive pumping system based on drainage piles, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the aforementioned adaptive pumping method based on drainage piles.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention integrates water level prediction, energy adaptation, and equipment status feedback to construct a highly adaptive and energy-efficient intelligent pumping system. The dynamic water level analysis model, through analysis of real-time and historical water level data, can predict future water level trends and critical time windows, transforming pumping strategy formulation from reactive response to proactive prediction. The set of start-stop strategies generated based on the prediction results considers a combination of power grading and runtime, allowing for flexible adjustment of drainage intensity according to the estimated seepage volume. This avoids ineffective and frequent pump start-stops, helping to extend equipment lifespan and reduce operating energy consumption.

[0016] A significant feature of this method is the incorporation of renewable energy power supply monitoring data for energy adaptability correction. This method dynamically links the energy demand of pumping operations with solar power generation efficiency and battery storage status, prioritizing pumping tasks during periods of sufficient energy and adjusting strategies during energy shortages to maintain basic system operation. This improves the efficiency of renewable energy utilization and adaptability to intermittent energy sources, enhancing the independent operational reliability of the drainage system in scenarios without a stable mains power supply. Simultaneous monitoring of pump vibration spectrum and pipeline pressure during execution provides direct evidence for assessing equipment health, helping to promptly detect abnormal signs. Feedback of equipment operating status to the water level analysis model for iterative parameter updates forms a closed-loop optimization chain. This chain allows the system to continuously learn from actual operating results, continuously optimizing its prediction accuracy and control strategies, gradually adapting to local seepage patterns and equipment characteristics, demonstrating self-adjustment and continuous improvement capabilities. This achieves a transformation in drainage operations from automation based on fixed rules to intelligent operations based on real-time data and model optimization, improving the precision of drainage management. Attached Figure Description

[0017] Figure 1 The result of model iteration and optimization is shown in the figure; Figure 2 This is a flowchart of a dynamic water level analysis model for trend prediction of real-time water level data. Figure 3 A flowchart for generating a set of pumping equipment start-up and shutdown strategies based on the matching results of time window marked sequences and historical pumping record data sets; Figure 4 This is a time-series monitoring and analysis chart of pump power and energy consumption. Detailed Implementation

[0018] 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] Please see Figure 1This invention provides an adaptive pumping system and method based on drainage piles. The method includes: acquiring real-time water level data and a historical pumping record data set within the drainage pile's water collection pipe; the real-time water level data is continuously monitored and recorded by a water level sensor, including water level height change curves and sensor trigger time sequences; the historical pumping record data set stores parameters and results of past pumping operations; processing the real-time water level data using a dynamic water level analysis model, which employs machine learning algorithms to analyze water level trends and outputs a time window marker sequence to mark future water level extremes and fluctuation cycles; matching the time window marker sequence with the historical pumping record data set, calculating a similarity threshold and filtering historical data to generate a pumping equipment start-stop strategy set defining pump power grading parameters and runtime combinations; a new energy power supply monitoring module collects real-time photovoltaic panel power generation efficiency curves and battery remaining capacity data, with the power generation efficiency curve reflecting changes in light intensity and the battery remaining capacity data indicating available energy reserves; and an energy adaptability correction process integrates the pumping equipment start-stop strategy set with the new energy data, adjusting power parameters to generate a dynamic pumping instruction set. When the dynamic pumping command set is executed, the pump controller is activated, and the pump vibration spectrum and pipeline pressure fluctuation data are collected synchronously. The vibration spectrum is captured by an accelerometer, and the pressure fluctuation data is recorded by a pressure transmitter. The data are combined to generate an equipment operation status feedback log. The equipment operation status feedback log is input into the update module of the dynamic water level analysis model. Through deviation calculation and parameter adjustment, the model is iterated, forming a closed-loop optimization link to continuously improve the accuracy of water level prediction and energy efficiency.

[0020] Example 1: See Figure 2After acquiring real-time water level data from the drainage pile's water collection pipe, the water level height change curve continuously recorded by the water level sensor and the sensor trigger time series are input into the dynamic water level analysis model. The dynamic water level analysis model is deployed on the edge computing device of the pumping control system, employing a multi-threaded processing architecture to execute data analysis tasks in parallel. The real-time water level data undergoes a preprocessing module, which filters and denoises the raw water level signal to eliminate measurement errors caused by water surface fluctuations or sensor jitter. The filtering algorithm uses a threshold denoising method based on wavelet transform to preserve the true trend characteristics of water level changes. The preprocessed water level height change curve is timestamped, forming a regular time series data format, ready for in-depth decomposition analysis. Seasonal decomposition of the water level height change curve constitutes the foundation for trend prediction. Seasonal decomposition uses a classic time series decomposition model to break down the water level data into three independent components. The trend component is extracted using the moving average method. The size of the moving average window is dynamically adjusted according to the periodic characteristics of historical water level changes. The trend component reflects the rising or falling trend of the water level on a long-term scale. The periodic component was separated using Fourier series fitting to identify regular fluctuation patterns such as daily and weekly cycles in water level changes. The random noise component was obtained by subtracting the trend and periodic components from the original data; the random noise component contains residual fluctuation information that cannot be explained by the regularity model. The parameter settings for seasonal decomposition took into account the specific geographical characteristics of the drainage piles.

[0021] A temporal convolutional network (TCNN) is employed for multi-scale feature extraction of the trend component. The TCNN uses a hierarchical convolutional structure with multiple convolutional and pooling layers. The input layer of the TCNN receives standardized data from the trend component. The first convolutional layer uses a wide kernel to capture long-term trend features, while the second convolutional layer uses a narrower kernel to capture short-term variation details. During multi-scale feature extraction, batch normalization is applied after each convolutional layer to accelerate network training convergence and improve feature stability. Through forward propagation computation of the TCNN, feature vectors representing the rate of water level rise and the slope of water level fall are extracted from the trend component. The rate of water level rise quantifies the magnitude of water level increase per unit time, while the slope of water level fall describes the steepness of the water level decrease. The TCNN is trained using a historical water level dataset for supervised learning. The loss function employs the mean squared error criterion, and the optimization algorithm uses an adaptive moment estimation method. An autoregressive moving average (ARM) model with periodic components is constructed to handle the cyclical characteristics of water level changes. The order of the ARM model is determined based on information criteria to balance model complexity and fitting accuracy. The autoregressive part captures the linear dependence between the current and historical values ​​of the periodic components, while the moving average part models the impact of random disturbances on periodic fluctuations. Model parameters are solved using maximum likelihood estimation, and optimal coefficients are obtained through training with historical data of the periodic components. The trained ARM model extrapolates three period lengths forward to predict the water level phase shift in future time periods. The water level phase shift represents the temporal difference between the actual water level cycle and the ideal cycle model, reflecting the degree of interference of external factors on the cyclical pattern. The prediction results of the ARM model include confidence interval estimates, providing a measure of the uncertainty of water level phase prediction.

[0022] Random noise components are input into a Generative Adversarial Network (GAN) to simulate abnormal fluctuations. The GAN consists of a competitive learning framework composed of two neural networks: a generator and a discriminator. The generator receives random noise input and synthesizes simulated noise sequences, while the discriminator distinguishes between real random noise components and the spurious noise synthesized by the generator. During adversarial training, the generator learns the statistical distribution characteristics of real random noise components, producing data with statistical characteristics similar to real noise. A fully trained GAN can generate synthetic noise sequences that conform to historical noise characteristics; this synthetic data is used to enhance the diversity of the original random noise components. The boundary of the water level fluctuation interval after noise enhancement is determined by calculating the extreme value distribution of the synthetic noise, establishing a probabilistic prediction interval for water level fluctuations. The training of the GAN employs a gradient penalty strategy to improve training stability and generation quality. Integrating the water level rise rate feature vector, fall slope feature vector, water level phase offset, and water level fluctuation interval boundary requires establishing a multi-source information integration framework. The fusion process uses an attention mechanism to weightedly integrate the contributions of different feature vectors; the attention weights are dynamically adjusted based on the accuracy of each feature component in historical predictions. The feature vectors of water level rise rate and fall slope primarily affect the accuracy of short-term water level prediction, while water level phase offset is more important for medium-term prediction. The boundaries of water level fluctuation intervals provide the range of uncertainty in the prediction results. The feature fusion module combines prediction information from different time scales into a unified time window marker sequence. The time window marker sequence uses a structured data format to record the time location of future water level extreme points and the start and end times of rise and fall cycles, with each marker point accompanied by a confidence index. The output interface of the time window marker sequence seamlessly interfaces with the downstream pumping strategy generation module, and the lightweight JSON format is used as the transmission protocol to ensure efficient data exchange.

[0023] The dynamic water level analysis model operates on a high-performance computing environment with optimized memory usage during model inference. GPU acceleration is used for convolution operations in the temporal convolutional network to improve the processing speed of large-scale water level data. The parameter estimation of the autoregressive moving average model employs a recursive algorithm to adapt to the real-time update requirements of online learning scenarios. Training of the generative adversarial network is completed offline, and pre-trained model parameters are directly loaded during online prediction. The computational complexity of seasonal decomposition is controlled through block processing technology, dividing long-term water level data into appropriately long segments for parallel processing. An anomaly handling mechanism is designed throughout the trend prediction process, automatically activating backup algorithm modules when a failure occurs in any analysis step. The dynamic water level analysis model provides an application programming interface (API) to support remote monitoring platforms calling the prediction service; the interface design follows a RESTful architecture style. Model version management adopts a containerized deployment scheme, facilitating migration and expansion on different hardware devices. A caching mechanism for prediction results stores recent time window marker sequences, reducing resource overhead from redundant calculations.

[0024] Example 2: See Figure 3 After obtaining the time window marker sequence output by the dynamic water level analysis model, the future water level extreme points and fluctuation cycle information contained in the sequence are sent to the strategy generation engine. The strategy generation engine accesses a historical pumping record dataset, which is stored in a time-series database. Each record contains historical water level curves, pump power parameters used, operating duration, and final water level change data. The matching process uses a dynamic time warping algorithm to calculate the similarity between the time window marker sequence and each operating condition segment in the historical records. The dynamic time warping algorithm can align time series of different lengths. The similarity threshold is set to 0.85, and all historical operating condition segments exceeding this value are filtered out to form a candidate set. Each historical operating condition segment in the candidate set has complete metadata tags, including the timestamp of the pumping operation, environmental condition parameters, and equipment operating status records.

[0025] After extracting all historical operating condition segments from the historical pumping record dataset whose similarity to the current time window's marked sequence exceeds a threshold, energy efficiency features are extracted for each segment. The ratio of water level recovery delay time to pump cumulative energy consumption is calculated for each historical operating condition segment. The water level recovery delay time is calculated from the end time of pumping in the historical record until the water level sensor detects that the water level has recovered to a preset normal level. Pump cumulative energy consumption is obtained from historical data recorded by the electricity metering module, including the total electrical energy consumed by the pump during this pumping operation. The ratio calculation uses a normalization method to map the water level recovery delay time and pump cumulative energy consumption to the same order of magnitude, generating an energy efficiency priority score. The energy efficiency priority score is a dimensionless numerical value; a higher value indicates a better water level recovery effect per unit of energy consumption in that historical operating condition segment. The energy efficiency priority score is attached to the corresponding historical operating condition segment as a selection weight. The optimal power-duration combination pattern is selected from historical operating condition segments based on energy efficiency priority scores. The selection process ranks the historical operating condition segments from highest to lowest energy efficiency priority score, and the top 10% of segments are selected as the candidate pattern library. The optimal power-duration combination pattern is determined from the candidate pattern library through cluster analysis. The clustering algorithm identifies frequently occurring combinations of power parameters and operating time, and selects the power-duration combination corresponding to the cluster center as the optimal pattern. The optimal power-duration combination pattern includes pump power level settings, typically divided into high, medium, and low levels, each corresponding to motor speed and flow output. The operating time combination defines the length of time the pump needs to operate continuously at different power levels, matching the rate of water level decline. The optimal power-duration combination pattern also includes power switching time information to guide the pump in adjusting its output power according to water level changes during pumping.

[0026] The optimal power-duration combination mode is compensated and corrected for the current ambient temperature and humidity parameters. Ambient temperature and humidity data are acquired in real-time from IoT sensors installed around the drainage piles. The temperature sensor uses a PT100 platinum resistance element, and the humidity sensor uses a capacitive polymer thin-film element. The compensation and correction algorithm establishes a correlation model between temperature, humidity, and pump efficiency, based on fluid dynamics principles and motor thermodynamic characteristics. When the ambient temperature rises, the pump motor winding resistance increases, leading to a decrease in efficiency. The compensation and correction algorithm appropriately increases the power parameter to maintain rated output. When the ambient humidity is high, the moisture content in the air affects the motor's heat dissipation. The compensation and correction algorithm adjusts the runtime allocation strategy to prevent motor overheating. The corrected parameters generate pump power level parameters and runtime combinations. The pump power level parameters are encapsulated in JSON format, including power level values, switching conditions, and safety boundaries. The runtime combinations are stored in time series format, with each time point corresponding to a power level setting.

[0027] The new energy power supply monitoring module acquires the photovoltaic panel power generation efficiency curve and the remaining battery capacity data. The photovoltaic panel power generation efficiency curve is monitored in real time by a smart inverter, recording the output voltage, current, and power data of the photovoltaic panel every five minutes. The power generation efficiency curve reflects the impact of changes in solar irradiance on power generation capacity; the peak value in the curve corresponds to the maximum power generation at noon, and the valley value corresponds to the lower power generation in the morning and evening. The remaining battery capacity data is obtained from the state estimation algorithm of the battery management system, which is based on the fusion calculation of the ampere-hour integral method and the open-circuit voltage method, and the remaining capacity is expressed as a percentage. The data from the new energy power supply monitoring module is transmitted to the pump controller via the Modbus communication protocol, and interacts with the control command generation module. The module identifies abrupt changes in irradiance and power generation decay intervals in the photovoltaic panel power generation efficiency curve. The detection of abrupt changes in irradiance uses the sliding window derivative analysis method to calculate the first derivative of the power generation efficiency curve. When the derivative value exceeds a set threshold, it is marked as an abrupt change. Abrupt changes typically correspond to rapid fluctuations in power generation caused by cloud cover or sudden weather changes. The power generation decay range is determined by identifying the falling edge of the power generation efficiency curve. The start of the range is when power generation begins to decline continuously, and the end of the range is when power generation stabilizes at a low level. The power generation decay range typically occurs during the evening when sunlight weakens, and also includes periods of reduced power generation capacity due to temporary weather changes. Within the power generation decay range, the remaining battery capacity data is used for depth-of-discharge safety verification. This verification references the technical parameters provided by the battery manufacturer, setting the maximum permissible depth of discharge for lithium-ion batteries at 80%. The permissible discharge capacity threshold is calculated by multiplying the remaining battery capacity data by the maximum permissible depth of discharge percentage. For example, when the remaining capacity is 90%, the permissible discharge capacity threshold is 72% of the total capacity. The safety verification module continuously compares the current remaining capacity with the preset threshold to ensure the battery operates within a safe range. The permissible discharge capacity threshold serves as a hard constraint on energy supply and is used in subsequent pumping strategy adjustment calculations.

[0028] When the total energy demand in the pumping equipment start / stop strategy set exceeds the allowable discharge capacity threshold, the energy allocation module initiates a power compression program. The total energy demand is calculated by integrating the pump power level parameters with the runtime, representing the total electrical energy expected to be consumed to complete the current pumping operation. The compression algorithm proportionally reduces the runtime of each power level parameter, with the proportionality coefficient determined by the ratio of the allowable discharge capacity threshold to the total energy demand. During compression, the relative relationship between power levels remains unchanged; only the duration of each level is adjusted. The runtime compression ratio for high-power levels is slightly lower than that for low-power levels, prioritizing critical stages that ensure pumping efficiency. The compressed runtime is then recombined with the pump power level parameters to generate a dynamic pumping instruction set. This dynamic pumping instruction set uses a structured command format, containing three basic fields: timestamp, power level, and duration. The instruction set is sent to the pump inverter via industrial Ethernet for execution. The pump inverter adjusts the motor speed according to the instructions, achieving precise power control and energy management. The historical pumping record dataset is updated after each operation, with new pumping operation parameters and results added to the database, enriching the case library and providing more samples for subsequent matching. The environmental temperature and humidity parameter compensation and correction module establishes an adaptive learning mechanism, optimizing and correcting model parameters based on long-term operational data. The analysis of the photovoltaic panel power generation efficiency curve includes cleanliness influencing factors, considering the attenuation effect of dust accumulation on power generation efficiency. The verification of the remaining battery capacity data introduces a temperature compensation coefficient to correct the impact of environmental temperature on the actual battery capacity. Energy adaptability correction processing forms a closed-loop optimization, comparing the actual energy consumption of each pumping operation with the predicted value to correct the parameter settings of the new energy power supply model.

[0029] See Figure 4 In the time-series monitoring and analysis of pump power and energy consumption, the charts employ a dual-axis vertical layout to simultaneously visualize power status and energy consumption data, revealing the correlation between equipment operation behavior and energy efficiency under dynamic pumping strategies. Pump power is categorized using discrete bar charts to encode power level changes. The vertical axis is labeled with four states: "High Power," "Medium Power," "Low Power," and "Stop." The bar height maps the duration of each power range on the time axis, while the horizontal axis aligns with the time series from January 1st to 10th, 2025, using a date scale. Hourly energy consumption is quantified using continuous bar charts, with the vertical axis using 0.5 kWh intervals. The bar density distribution reflects the coupling relationship between energy consumption and power level. The power level vertical axis uses qualitative labels for discrete partitioning, while the energy consumption vertical axis has a fixed scale range of 0.0–3.0 kWh. The date labeling on the horizontal axis is done on a daily basis to ensure time-series consistency.

[0030] Example 3: After the dynamic pumping command set is sent to the pump inverter, the pump starts working according to the preset power level parameters and operating time combination. The vibration sensor array installed on the pump unit synchronously starts data acquisition. The vibration sensor uses a piezoelectric accelerometer, with a measurement range covering a frequency bandwidth of 0 Hz to 5000 Hz. The sampling frequency is set to 10240 Hz to satisfy the Nyquist sampling theorem. The sensor array contains three orthogonal measurement units, capturing the axial, radial, and tangential vibration signals of the pump, respectively. After analog filtering and digital amplification, the vibration signal is transmitted to the signal processing unit for spectrum analysis via industrial Ethernet. The spectrum analysis uses a fast Fourier transform algorithm to convert the time-domain vibration signal into a frequency-domain vibration spectrum, which displays the amplitude distribution characteristics of each frequency component. During the pump startup phase, the harmonic components of the motor winding current are acquired. These harmonic components are measured using a Rogowski coil current sensor with an accuracy of 0.5, capable of capturing minute current distortion characteristics. The startup phase is defined as the transition process from pump power-on to reaching rated speed, typically lasting 3 to 5 seconds. The current harmonic components are decomposed into frequency band signals of different scales through discrete wavelet transform, generating a startup impact characteristic spectrum. This spectrum is presented in three dimensions, with coordinate axes representing time, frequency, and amplitude, highlighting the intensity variation patterns of each harmonic. The feature extraction module of the startup impact characteristic spectrum identifies the amplitude trajectories of characteristic frequency points, including the fundamental frequency, second harmonic frequency, and high-frequency resonant points. The amplitude change rate of the characteristic frequencies is calculated using the finite difference method to quantify the severity of the current impact during startup.

[0031] During the stable operation phase, the low-frequency resonant band of pipeline pressure fluctuation data is extracted. The stable operation phase refers to the period of continuous operation after the pump speed has stabilized. Pipeline pressure fluctuation data is acquired using a diffused silicon pressure transmitter with a response time of less than 10 milliseconds. Preprocessing of the pressure fluctuation data includes detrending to eliminate the influence of static operating pressure on fluctuation analysis. The low-frequency resonant band is defined as the frequency range from 0 Hz to 200 Hz, which includes the pump impeller passing frequency and its harmonics. Bandwidth extraction is achieved using a digital bandpass filter, designed as a Chebyshev II type, with passband ripple controlled within 0.1 dB. The extracted low-frequency resonant signal undergoes Hilbert transform to extract the envelope, generating a fluid pulsation feature code. The fluid pulsation feature code includes the pulsation dominant frequency, amplitude modulation depth, and phase jitter parameters, stored in a binary array format, with each parameter allocated 16 bits of data. When a preset abnormal frequency band appears in the impact characteristic spectrum, the abnormal frequency band is determined based on historical pump fault data. Typical abnormal frequency bands include the bearing fault characteristic frequency range and the rotor imbalance frequency range. The anomaly detection algorithm calculates the correlation coefficient between the real-time spectrum and the reference spectrum. When the correlation coefficient is lower than 0.7, an alarm is triggered. The alarm signal is transmitted to the pump power grading parameter's step-down mechanism, which gradually reduces the pump power output according to a preset downgrading sequence. The downgrading sequence is set to three gradients, each reducing the rated power by 25%, with a downgrading interval of 30 seconds to avoid frequent switching. During the downgrading process, the changing trend of the vibration spectrum is monitored. If the amplitude of the abnormal frequency band significantly decreases, the downgrading stops; otherwise, the downgrading sequence continues until the lowest power level is reached.

[0032] The predicted fluctuation period values ​​in the subsequent time window marker sequence are adjusted based on the fluid pulsation feature encoding. The adjustment algorithm establishes a mapping relationship between the dominant fluid pulsation frequency and the water level fluctuation frequency. This mapping relationship is obtained through training on historical data, with training samples containing pulsation frequency and measured water level period data under different operating conditions. The adjustment coefficients are calculated using least squares fitting, yielding a linear relationship between pulsation frequency and water level period: ,in: This represents the adjusted predicted water level fluctuation cycle. This represents the dominant frequency value in the fluid pulsation feature encoding. and These coefficients are obtained by fitting historical data. The adjusted predicted values ​​of the fluctuation cycles replace the original predicted values, updating the time window marker sequence output by the dynamic water level analysis model.

[0033] Deviation data between the fluid pulsation feature encoding and the actual measured values ​​of the water level change curve are extracted. The deviation data is calculated using a sliding window comparison method, with the window length set to one complete water level fluctuation cycle. Actual measurements are obtained from real-time readings of the water level sensor, with the sampling interval synchronized with the fluid pulsation feature encoding. The deviation data includes three dimensions: amplitude deviation, phase deviation, and waveform distortion, each quantized as a standard score. The deviation data forms a model calibration matrix, which is 3 rows and N columns, where N represents the number of sampling points within the comparison window. Each row of the matrix corresponds to a deviation type, and each column corresponds to the deviation value at a given sampling time. The model calibration matrix is ​​decomposed into the kernel weight update of the temporal convolutional network and the coefficient adjustment of the autoregressive moving average model using a backpropagation algorithm. The backpropagation algorithm calculates the gradient along the network structure of the dynamic water level analysis model. The kernel weight update of the temporal convolutional network is derived using the chain rule, and the gradient calculation incorporates the local connectivity characteristics of the convolutional layers. The coefficient adjustment of the autoregressive moving average model is solved using the gradient descent method, with the learning rate set to an adaptive adjustment mode. The weight update process employs stochastic gradient descent optimization, with a momentum parameter set to 0.9 to accelerate convergence. The updated parameters are then checked by the model validation module to ensure that prediction accuracy is improved while model stability remains unaffected.

[0034] A rainfall intensity correction factor for the current season is injected into the boundary of the noise-enhanced water level fluctuation interval. This correction factor is obtained from meteorological monitoring stations, with data updated hourly. The correction factor is calculated based on historical rainfall statistics for the current season, including the quantile parameters of the rainfall intensity distribution function. The injection operation is achieved by modifying the input noise distribution of the generative adversarial network, introducing rainfall correlation during noise generation. The generation of the seasonally adaptive prediction interval uses a quantile regression method to estimate the water level fluctuation boundary at different confidence levels. The width of the prediction interval is dynamically adjusted according to seasonal characteristics, appropriately expanding the interval range during the rainy season and shrinking it during the dry season. The updated dynamic water level analysis model is used to regenerate the time window marker sequence for the next detection period, which is set to 6 hours to match the meteorological data update cycle. The regeneration process employs a rolling prediction mechanism, predicting the water level change trend for the next 24 hours each time. The format optimization of the time window marker sequence includes improved timestamp accuracy, increasing it from minutes to seconds. A confidence index is added to the marker sequence, calculated based on historical statistics of the model's prediction error. The new time window marker sequence is published to the pumping strategy generation module via a message queue. The message format uses the Protocol Buffers serialization protocol to ensure transmission efficiency. The model update log records detailed information about each parameter adjustment, including the adjustment time, adjustment magnitude, and performance metrics after the adjustment. The log data is used for model version management and rollback operations.

[0035] The pump vibration spectrum acquisition system includes an online calibration function. The calibration signal is generated by a built-in standard vibration source, which produces a sinusoidal vibration signal with a frequency of 159.2 Hz and an amplitude of 10 m / s². The calibration process is automatically performed once a day to ensure that the sensor measurement accuracy does not drift over time. The pipeline for acquiring pipeline pressure fluctuation data is designed with a damping buffer structure to reduce the impact of pressure pulsations on the sensor. The storage of the initial impact feature spectrum uses a lossy compression algorithm, which preserves detailed information in the characteristic frequency region, with a compression ratio controlled within 5:1. The transmission protocol for fluid pulsation feature encoding is designed with an error checking mechanism, and the check code uses a cyclic redundancy check method to ensure data integrity. The backpropagation algorithm is implemented using a distributed computing framework, distributing gradient calculation tasks to multiple processing cores for parallel execution. The visualization interface for the seasonal adaptive forecast interval provides interactive adjustment functions, allowing operators to fine-tune the interval parameters based on experience.

[0036] Example 4: The multispectral turbidity sensor array adopts a distributed installation structure, with eight measurement nodes arranged vertically along the inner wall of the drainage pile's water collection pipe. The node spacing is uniformly distributed according to the height of the water collection pipe. Each measurement node includes three sets of light source emitters with different wavelengths and corresponding photosensitive receivers. The wavelengths are selected as 850 nm in the near-infrared band, 550 nm in the visible light band, and 380 nm in the ultraviolet band. The light source emitters use high-stability LED devices, and the photosensitive receivers use photodiodes in conjunction with amplifier circuits, achieving a measurement accuracy of ±3%. The multispectral turbidity sensor array is powered by a waterproof bus structure, and data transmission is achieved through the RS-485 industrial bus protocol, with a communication rate set to 115,200 bits per second. The sensor array's sampling frequency is set to once per minute, with each sampling including transmittance measurements of three wavelengths. Collecting suspended particulate matter concentration distribution data at different water depths requires converting the raw optical signal from the multispectral turbidity sensor array into concentration values. The conversion algorithm establishes a relationship model between optical absorption and particulate matter concentration based on the Lambert-Beer law. The transmittance data for each wavelength are used to calculate the corresponding turbidity value. The near-infrared band mainly reflects the concentration of larger particles, the visible band is sensitive to medium-sized particles, and the ultraviolet band detects small colloidal particles. The measurements from the three wavelengths are weighted and fused using a weighted fusion algorithm to generate a comprehensive suspended particulate matter concentration value. The weighting coefficients are pre-calibrated based on water quality characteristics. Measurement data from different water depths form a vertical concentration distribution curve. The curve data is stored in a two-dimensional array format, with the array row number corresponding to the water depth position and the array value recording the concentration value. The update cycle of the suspended particulate matter concentration distribution data is synchronized with the sensor sampling, and a distribution characteristic analysis program is triggered after each update.

[0037] When a vertical gradient abrupt change occurs in the suspended particulate matter concentration distribution data, the abrupt change is identified by calculating the concentration difference between adjacent measurement nodes. A change point is marked when the difference exceeds a set threshold of 50 mg / L. The abrupt change detection algorithm uses a sliding window comparison method, with the window size set to three consecutive measurement nodes. The spatial location of the abrupt change point is recorded, including its absolute height from the bottom of the pipe and its percentage position relative to the pipe height. The type of vertical gradient abrupt change is determined based on the direction of concentration change: a sharp increase in concentration from bottom to top indicates bottom sediment accumulation, while an increase in concentration from top to bottom indicates surface floating matter aggregation. The abrupt change detection result triggers a backwash mode switching command for the water pump, which is sent to the pump valve control system via a digital output module. After triggering the backwash mode switching command, the pump valve control system executes a series of sequential operations, closing the main valve of the outlet pipe and opening the backwash bypass valve. The backwash mode switching command includes a backwash intensity parameter, which is divided into three levels based on the degree of the vertical gradient abrupt change, with the level classification based on the concentration difference and the size of the abrupt change range. The backwash duration is preset to a base value plus an adjustment value calculated based on the concentration gradient. The base value is set to 120 seconds, and the adjustment value is calculated by extending the duration by 5 seconds for every 10 mg / L increase in concentration gradient. During backwashing, the pump rotates in the opposite direction, and the impeller reverses direction to generate a counterflow that impacts the sediment at the bottom of the pipeline. The upper limit of the pump's torque output is dynamically adjusted based on pipeline pressure fluctuation data during backwashing. Pipeline pressure fluctuation data is collected at a frequency of 100 Hz during backwashing, and the pressure sensor is installed at the pump's outlet flange. Feature extraction of the pressure fluctuation data focuses on amplitude and frequency characteristics. Amplitude characteristics reflect the impact intensity of the backwash flow, while frequency characteristics indicate the flow stability within the pipeline. The algorithm for adjusting the upper limit of torque output establishes a correspondence between pressure amplitude and allowable torque. When the pressure amplitude exceeds the safety threshold, the upper limit of torque output is gradually reduced. The dynamic adjustment process uses an incremental PID control algorithm with a proportional coefficient set to 0.8, an integral time set to 30 seconds, and a derivative time set to 5 seconds. The adjusted upper limit of torque output takes effect immediately, ensuring that the backwashing operation operates within the mechanical safety range.

[0038] A secure communication mechanism with two-way authentication is used to establish a real-time connection between the pump controller and the meteorological data API. The pump controller has an embedded security chip storing a digital certificate, and the meteorological data API server verifies the certificate's validity before establishing a TLS encrypted connection. The heartbeat detection interval for the real-time connection is set to 60 seconds, and a timeout reconnection mechanism automatically attempts to resume the connection after an interruption, with a maximum of five retries. The communication protocol uses a lightweight MQTT message queue, and the topic subscription mode supports parallel reception of multiple data streams. The meteorological data API provides two main service interfaces: precipitation probability query and intensity forecast, with data updated hourly. After obtaining the precipitation probability and intensity forecast data for the next six hours, the data parsing module extracts key parameters and converts them into an internal data format. Precipitation probability data is expressed as a percentage, and intensity forecast data is divided into four levels: light rain, moderate rain, heavy rain, and torrential rain, each corresponding to a specific rainfall intensity range. The data verification module checks the completeness and rationality of the received data, removing obviously abnormal values. The precipitation probability and intensity forecast data for the next six hours are cross-validated with readings from local barometric pressure and humidity sensors to improve the reliability of the forecast data. The validated data is input into the precipitation impact assessment model, which calculates the expected rainfall load on the drainage pile water inlet pipe.

[0039] When the probability of precipitation exceeds a critical value, which is dynamically calculated based on the design capacity of the drainage piles and the current water level, a typical value is 70% probability of precipitation. The judgment logic employs fuzzy reasoning, comprehensively considering both the probability of precipitation and the intensity level. A pre-drainage command is inserted into the dynamic pumping command set to expand the buffer capacity of the water collection pipes. The activation time of the pre-drainage command is scheduled in advance based on the precipitation forecast time, with the lead time set to one water level fluctuation cycle before the start of rainfall. The pumping intensity of the pre-drainage command is determined based on the expected rainfall intensity, employing a gradual enhancement strategy with a low initial power, gradually increasing the power level as rainfall approaches. The execution duration of the pre-drainage command covers the entire precipitation impact period, ensuring that the water collection pipes always maintain sufficient buffer space. The actual precipitation intensity is compared with the water level drop rate after the pre-drainage command is executed. The actual precipitation intensity is measured in real-time using a rain gauge installed in an open location at the top of the drainage piles, with a measurement accuracy of 0.1 mm. The water level drop rate is calculated differentially from the readings of the water level sensor, with a time window set to 10 minutes. The deviation between the expected and actual water level drops is calculated through comparative analysis; the deviation reflects the accuracy of the pre-drainage command. The rainfall response coefficients of the dynamic water level analysis model are calibrated using recursive least squares, with the deviation used as a correction factor to update the model parameters. The calibration process for the rainfall response coefficients includes stability checks to prevent over-adjustment of the model due to a single rainfall event.

[0040] Referring to Table 1, the maintenance cycle for the multispectral turbidity sensor array is set to three months, and maintenance work includes cleaning the optical window and calibrating the light source intensity. The vertical gradient abrupt change detection algorithm has an adaptive threshold adjustment function, with the threshold dynamically optimized based on seasonal changes and water quality characteristics. The backwash mode switching command has higher priority than the ordinary pumping command, but lower than the system safety protection command. Analysis of pipeline pressure fluctuation data includes pulsation frequency identification; abnormal frequencies indicate pipeline blockage or leakage. The connection status of the meteorological data API is monitored in real time; in case of connection failure, local meteorological trend forecasts are used as a backup. The trigger conditions for the pre-drainage command are set with a lag interval to prevent frequent command status switching near critical values. The calibration of the rainfall response coefficient records historical change trajectories, providing a data foundation for long-term model evolution analysis.

[0041] Table 1: Correspondence between backwash mode intensity level and parameters

[0042] The multispectral turbidity sensor array uses a circular buffer structure for data storage, with the buffer size set to 24 hours of continuous data. The visualization interface for suspended particulate matter concentration distribution data provides a 3D dynamic display, allowing operators to intuitively observe the distribution and movement of particles in the drainage pipes. Historical statistical analysis of vertical gradient abrupt changes helps identify pipe sections where frequent deposition occurs. The effectiveness of the backwashing mode is evaluated by comparing the improvement in concentration distribution before and after backwashing; the improvement is quantified as the reduction in concentration gradient. The frequency of meteorological data API requests adheres to the service provider's usage limits, ensuring data freshness while avoiding excessive requests. The dynamic calculation model for the precipitation probability critical value considers the current water level and trend prediction in the drainage pipes; when the water level is high, the critical value is appropriately lowered to allow for early pre-drainage measures. The optimized algorithm for pre-drainage commands balances energy efficiency with buffer capacity requirements, minimizing unnecessary pumping operations while ensuring safety. Correlation analysis of rainfall response coefficient calibration data with seasonal characteristics establishes a typical parameter library for different seasons, improving model adaptability.

[0043] Example 5: The integrated new energy platform adopts a steel structure frame design. The top of the platform is 6 meters above the ground. The wind speed sensor is installed in an open area at the highest point of the platform to avoid interference from surrounding obstacles in airflow measurement. The wind speed sensor uses the ultrasonic measurement principle and includes four sets of ultrasonic transducer arrays. It calculates wind speed and direction by measuring the time difference of ultrasonic wave propagation in the downwind and upwind directions. The wind speed sensor's measurement range covers 0 m / s to 60 m / s, with a resolution of 0.1 m / s and a wind direction measurement accuracy of ±3 degrees. Sensor data is transmitted via an RS-485 digital interface with a sampling frequency set to 10 Hz, capable of capturing instantaneous airflow fluctuation characteristics. The wind speed sensor is equipped with an automatic heating function to prevent frost from affecting measurement accuracy in low-temperature environments. The heating power is automatically adjusted according to the ambient temperature. Real-time monitoring of the airflow disturbance intensity on the photovoltaic panel surface requires converting the raw wind speed data into airflow disturbance indices. The airflow disturbance intensity calculation is based on two parameters: the standard deviation of wind speed and turbulence intensity. The time window is set to 10 minutes. Within each window, the statistical characteristics of the wind speed sequence are calculated. The standard deviation reflects the amplitude of wind speed fluctuations, and the turbulence intensity is defined as the ratio of the standard deviation to the average wind speed. The airflow disturbance intensity index integrates the weighted values ​​of the standard deviation and turbulence intensity, with the weighting coefficients determined experimentally based on the structural characteristics of the photovoltaic panel. The airflow distribution model on the photovoltaic panel surface considers the boundary layer effect at the top of the platform, with the airflow disturbance intensity in the edge region typically higher than that in the central region. The airflow disturbance intensity data is stored in a two-dimensional matrix format, where the rows of the matrix correspond to the time series, and the columns represent different statistical characteristic values. The update frequency is synchronized with the wind speed data sampling.

[0044] When airflow disturbance causes the vibration frequency of the photovoltaic panel support to exceed the safety threshold, the vibration frequency is monitored by an accelerometer installed at the support connection. The sensor uses MEMS technology and has a frequency response range of 5 Hz to 2000 Hz. The safety threshold is determined based on the finite element analysis results of the support structure. The first-order natural frequency is 12.5 Hz, and the safety threshold is set to 80% of the natural frequency, i.e., 10 Hz. Vibration frequency analysis uses real-time spectrum analysis technology, calculating the dominant frequency component once per second. The judgment logic compares the peak vibration frequency with the safety threshold. When the peak frequency exceeds the threshold for three consecutive sampling periods, the protection mechanism is triggered. The threshold comparison algorithm introduces a hysteresis interval to prevent frequent switching; the hysteresis interval is set to ±0.5 Hz.

[0045] A bracket angle fine-tuning command is added to the dynamic pumping command set. This command includes the target tilt angle value and adjustment speed parameters. The target tilt angle is calculated based on vector analysis of wind speed and direction, with the goal of reducing wind load. The adjustment speed is set according to the intensity of the current airflow disturbance; a slower adjustment speed is used when the intensity is low to avoid frequent actions, while a faster adjustment to a safe position is used when the intensity is high. The bracket angle fine-tuning command is sent to the bracket control system via the CAN bus. After parsing the command, the control system drives the electric actuator. The electric actuator has a stroke accuracy of 0.1 degrees and a maximum thrust of 2000 Newtons. An angle feedback potentiometer is installed at the end of the actuator to form a closed-loop control. During the fine-tuning process, the stress distribution of the bracket is monitored in real time to ensure that the structural adjustment is within the elastic deformation range. The fine-tuned photovoltaic panel tilt angle data is fed back to the power generation efficiency curve prediction module. The photovoltaic panel tilt angle data includes a timestamp, tilt angle value, and adjustment reason code. The power generation efficiency curve prediction module establishes a mapping relationship between tilt angle and power generation efficiency, based on the photovoltaic characteristic curve of the photovoltaic panel and a solar position algorithm. The impact of tilt angle changes on power generation efficiency is quantified using differential sensitivity analysis, with the impact coefficients pre-calibrated for different seasons and time periods. Updating the detection sensitivity for abrupt changes in irradiance involves adjusting the threshold parameter for abrupt change discrimination; this threshold has a functional relationship with the photovoltaic panel tilt angle, and is adjusted accordingly when the tilt angle changes. The detection algorithm incorporates a tilt angle change compensation term to eliminate irradiance measurement deviations caused by adjustments to the support frame angle. The output of the prediction module is used to optimize the energy dispatch strategy of the pumping system, improving the efficiency of renewable energy utilization.

[0046] Taking a specific application scenario as an example, the operational data record of a drainage pile station during a windy spring weather event shows that at 2:30 PM on March 15th, the wind speed sensor detected an average wind speed of 15 m / s, with gusts reaching 22 m / s. The calculated standard deviation of the airflow disturbance intensity index was 4.2 m / s, and the turbulence intensity was 0.28, resulting in a comprehensive airflow disturbance intensity level of Level II. Vibration frequency monitoring data of the photovoltaic panel support showed that between 2:31 PM and 2:33 PM, the dominant vibration frequency gradually increased from 8.5 Hz to 10.8 Hz, exceeding the safety threshold of 10 Hz and persisting for three sampling cycles. The system automatically triggered a protection mechanism, generating a fine-tuning command for the support angle. The command parameters were a target tilt angle of 25 degrees and an adjustment speed of 2 degrees per second. The support control system completed the angle adjustment within 35 seconds, changing the photovoltaic panel tilt angle from 35 degrees to 25 degrees. During the adjustment process, the accelerometer detected that the vibration frequency gradually decreased to 8.2 Hz, returning to the safe range. After tilt angle adjustment, the power generation efficiency curve prediction module receives the new tilt angle data and recalculates the theoretical power generation efficiency for the current period. The detection sensitivity parameter for sudden changes in irradiance is adjusted from 5% to 8% to avoid false alarms caused by tilt angle changes. The entire adjustment process's operational data is recorded in the system log, including timestamps, wind speed data, vibration frequency, tilt angle change curves, and other complete parameters, providing data support for subsequent analysis. Regular calibration of the wind speed sensor uses standard wind tunnel equipment, with a calibration cycle of six months, and calibration records are stored in the equipment management database. The calculation model for airflow disturbance intensity incorporates a temperature correction factor to compensate for the impact of air density changes on the measurement results. Monitoring of the photovoltaic panel support vibration frequency includes a frequency trend prediction function, providing early warning of potential resonance risks based on the ARIMA model. The dynamic adjustment mechanism for safety thresholds considers material fatigue factors, appropriately reducing the threshold as the equipment's service life increases. The priority setting of the support angle fine-tuning command is linked to the wind speed level; higher wind speeds result in higher command priority. The lifespan monitoring of the electric actuator records the number of operating cycles, prompting maintenance and replacement before reaching a preset value. The power generation efficiency curve prediction module uses incremental learning to gradually optimize prediction accuracy. Historical data from detecting abrupt changes in sunlight intensity is used to optimize the threshold adjustment algorithm, reducing false alarms and missed alarms. The overall system operation status is displayed in real time via a web interface, supporting remote monitoring and parameter adjustment. A data backup mechanism ensures the secure storage of operational data, with daily off-site backups. A fault self-diagnosis function monitors the operating status of each sensor and actuator, automatically switching to standby mode when an anomaly is detected. The system maintenance log records all operations and maintenance activities, meeting the traceability requirements of equipment management.

[0047] The structural health monitoring of the integrated new energy platform includes stress-strain measurement, with strain gauges installed at key load-bearing locations and sampling frequency synchronized with wind speed monitoring. The wind speed sensor mounting base employs a vibration-damping design to reduce the impact of platform vibration on measurement accuracy. Spatiotemporal correlation analysis of airflow disturbance intensity identifies wind field characteristics at different platform locations, optimizing sensor placement. Modal analysis of the photovoltaic panel support vibration frequency determines the natural frequencies of each order, providing a theoretical basis for setting safety thresholds. The optimized algorithm for support angle fine-tuning commands considers multi-objective optimization, balancing power generation efficiency and structural safety. The control algorithm for the electric actuator incorporates anti-jitter filtering to avoid frequent angle adjustments due to wind speed fluctuations. The power generation efficiency curve prediction module's input parameters include real-time weather data, improving the adaptability of the prediction model. Correlation analysis of the detection results of sudden changes in irradiance intensity with actual power generation changes verifies the effectiveness of the detection algorithm. The system energy efficiency assessment module calculates the power generation changes and energy consumption comparisons resulting from angle adjustments, optimizing control strategies. The remote upgrade function supports online updates of the algorithm model, continuously improving system performance. Statistical analysis of operational data generates various reports, providing data support for management decisions.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive pumping method based on drainage piles, characterized in that, include: Acquire real-time water level data and historical pumping record data set in the drainage pile water accumulation pipe, wherein the real-time water level data includes water level height change curve and sensor trigger time series; The dynamic water level analysis model is invoked to perform trend prediction processing on the real-time water level data, generating a time window marker sequence containing future water level extreme points and fluctuation cycles. Based on the matching results between the time window marker sequence and the historical pumping record data set, a set of pumping equipment start-up and shutdown strategies is generated, which includes a combination of pump power classification parameters and running time. The photovoltaic panel power generation efficiency curve and battery remaining capacity data are obtained by the new energy power supply monitoring module. Combined with the pumping equipment start-stop strategy set, energy adaptability correction is performed to generate an optimized dynamic pumping instruction set. When executing the dynamic pumping command set, pump vibration spectrum and pipeline pressure fluctuation data are collected synchronously to generate equipment operation status feedback log; The parameters of the dynamic water level analysis model are iteratively updated based on the equipment operation status feedback log, forming a closed-loop optimization link between water level prediction accuracy and pumping energy consumption.

2. The adaptive pumping method based on drainage piles according to claim 1, characterized in that, The process of calling the dynamic water level analysis model to perform trend prediction processing on the real-time water level data includes: The water level change curve is subjected to seasonal decomposition to separate the trend component, periodic component and random noise component. A temporal convolutional network is used to extract multi-scale features from the trend component, generating feature vectors for water level rise rate and fall slope. An autoregressive moving average model of the periodic component is constructed to predict the water level phase shift over the next three periods. The random noise component is input into the Generative Adversarial Network to simulate abnormal fluctuations and generate the boundary of the water level fluctuation range after noise enhancement. The time window marker sequence is generated by fusing the water level rise rate feature vector, the fall slope feature vector, the water level phase offset, and the water level fluctuation interval boundary.

3. The adaptive pumping method based on drainage piles according to claim 2, characterized in that, The process of generating a set of pumping equipment start-up and shutdown strategies based on the matching results between the time window marker sequence and the historical pumping record data set includes: Extract all historical pumping record segments from the historical pumping record dataset whose similarity to the current time window marked sequence exceeds a threshold; Calculate the ratio of water level recovery delay time to cumulative pump energy consumption for each historical operating condition segment, and generate an energy efficiency priority score; The optimal power-duration combination mode is selected from historical operating condition segments based on the energy efficiency priority score. The optimal power-duration combination mode is compensated and corrected for the current ambient temperature and humidity parameters to generate the pump power classification parameters and running time combination.

4. The adaptive pumping method based on drainage piles according to claim 3, characterized in that, The process of acquiring photovoltaic panel power generation efficiency curves and battery remaining capacity data through a new energy power supply monitoring module, and then performing energy adaptability correction processing in conjunction with the pumping equipment start-stop strategy set, includes: Identify the points of sudden changes in light intensity and the range of power generation decay in the photovoltaic panel power generation efficiency curve; Within the power generation decay range, the remaining capacity data of the storage battery is subjected to a discharge depth safety verification to generate an allowable discharge capacity threshold. When the total energy consumption demand in the pumping equipment start-up and shutdown strategy set exceeds the allowable discharge capacity threshold, the running time of each power level parameter is compressed proportionally. The compressed runtime and pump power classification parameters are recombined to generate the dynamic pumping instruction set.

5. The adaptive pumping method based on drainage piles according to claim 4, characterized in that, The process of simultaneously acquiring pump vibration spectrum and pipeline pressure fluctuation data during the execution of the dynamic pumping command set includes: Harmonic components of motor winding current are collected during the pump startup phase to generate a startup impact characteristic spectrum. During the stable operation phase, the low-frequency resonant band of pipeline pressure fluctuation data is captured to generate fluid pulsation feature codes. When a preset abnormal frequency band appears in the starting impact feature spectrum, the pump power grading parameter downgrade mechanism is triggered. The predicted fluctuation cycle value in the subsequent time window marker sequence is adjusted based on the fluid pulsation feature encoding.

6. The adaptive pumping method based on drainage piles according to claim 5, characterized in that, The step of iteratively updating the parameters of the dynamic water level analysis model based on the equipment operation status feedback log includes: Extract the deviation data between the fluid pulsation feature encoding and the actual measured value of the water level height change curve, and generate a model correction matrix; The model calibration matrix is ​​decomposed into the kernel weight update amount of the temporal convolutional network and the coefficient adjustment amount of the autoregressive moving average model through the backpropagation algorithm. A seasonal rainfall intensity correction factor is injected into the boundary of the noise-enhanced water level fluctuation range to generate a seasonally adaptive prediction range. The updated dynamic water level analysis model is used to regenerate the time window marker sequence for the next detection cycle.

7. The adaptive pumping method based on drainage piles according to claim 6, characterized in that, Also includes: A multispectral turbidity sensor array was installed inside the drainage pile's water collection pipe to collect data on the concentration distribution of suspended particulate matter at different water depths. When a sudden change in the vertical gradient occurs in the suspended particulate matter concentration distribution data, a backwashing mode switching command is triggered for the water pump. The upper limit of torque output of the pump power classification parameters is dynamically adjusted based on pipeline pressure fluctuation data under backflushing mode.

8. The adaptive pumping method based on drainage piles according to claim 7, characterized in that, Also includes: Establish a real-time connection channel between the pumping controller and the meteorological data API to obtain precipitation probability and intensity forecast data for the next six hours; When the probability of precipitation exceeds the critical value, a pre-drainage command is inserted into the dynamic pumping command set to expand the buffer capacity of the water collection pipe. The rainfall response coefficient of the dynamic water level analysis model is calibrated based on the comparison between the actual rainfall intensity and the water level drop rate after the pre-drainage command is executed.

9. The adaptive pumping method based on drainage piles according to claim 8, characterized in that, Also includes: A wind speed sensor is installed on top of the integrated new energy platform to monitor the intensity of airflow disturbance on the surface of the photovoltaic panel in real time; When the intensity of airflow disturbance causes the vibration frequency of the photovoltaic panel support to exceed the safety threshold, a support angle fine-tuning instruction is added to the dynamic pumping instruction set. The fine-tuned photovoltaic panel tilt angle data is fed back to the power generation efficiency curve prediction module to update the detection sensitivity of sudden changes in light intensity.

10. An adaptive pumping system based on drainage piles, characterized in that, It includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the steps of the adaptive pumping method based on drainage piles as described in any one of claims 1 to 9.

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