Low-power consumption ground water monitoring station device and system based on modular design

Abstract

The low-power-consumption surface water monitoring station device and system based on modular design belong to the technical field of water quality monitoring, and the device integrates a shore cabinet, a water quality detection unit, an analysis unit, a sampling unit, a video monitoring unit, a power supply unit and a centralized control unit. Through modular design, the functions can be flexibly adjusted according to specific monitoring requirements, each unit can be independently operated, the fault positioning and repair process is greatly simplified, and the maintenance efficiency is significantly improved. The system covers multiple modules such as water quality detection, multi-modal sampling, data analysis, video monitoring, energy management, remote communication and fault self-diagnosis, and each module works cooperatively to ensure the stable operation of the system in complex environments. Through optimization of the charging and discharging strategy, efficient utilization of photovoltaic power generation is realized, and the continuous operation ability of the system under insufficient light conditions is ensured. Not only the resource utilization efficiency is improved, but also the operation cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water quality monitoring, in particular to a low-power surface water monitoring station device and system based on modular design. BACKGROUND

[0002] With the increasing demand for surface water monitoring, the traditional surface water monitoring station device has exposed many defects and shortcomings in practical application. In the prior art, the surface water monitoring station usually adopts an integrated design, integrating water sampling, analysis, monitoring and other functions in one overall device. The integrated design makes the device function fixed, difficult to flexibly adjust or expand according to specific needs, unable to quickly replace or add sensors according to changes in monitoring targets, the internal structure of the device is complex, and each function module is dependent on each other. Once a module fails, the maintenance and replacement process is tedious and costly, and the troubleshooting and repair efficiency is low. In addition, the traditional device usually adopts a high-power design, lacks energy-saving optimization, resulting in high operating costs, especially in remote areas or in the case of insufficient power supply, it is difficult to continuously and stably operate.

[0003] Therefore, it is necessary to design a low-power surface water monitoring station device and system based on modular design to solve the above technical problems. SUMMARY

[0004] The purpose of the present application is to provide a low-power surface water monitoring station device and system based on modular design to solve the above technical problems.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] The low-power surface water monitoring station device based on modular design comprises:

[0007] A shore cabinet is provided with a protective door hingedly installed on the surface of the shore cabinet, and an installation plate is installed in the interior of the shore cabinet.

[0008] A water quality detection unit is provided, which comprises a measuring pool fixedly installed on the installation plate, a plurality of water quality sensors electrically connected to the centralized control unit are arranged on the top of the measuring pool, the monitoring end of the water quality sensor is inserted into the water body inside the measuring pool, and the measuring pool is fixedly connected with a water inlet pipe and a drain pipe extending to the outside of the shore cabinet.

[0009] An analysis unit is provided, which is electrically connected to the centralized control unit and is installed in the shore cabinet through a U-shaped support frame. The input end of the analysis unit is connected with the data output end of the water quality sensor, and is used for real-time analysis of the water quality data collected by the water quality detection unit.

[0010] The sampling unit comprises a water sampling buoy arranged in the water area to be measured, a water pump arranged in the water sampling buoy, a water outlet and a water inlet arranged on the water pump, the water outlet being connected with the water inlet pipe, the water inlet being connected with a water sampling pipe, and the water sampling pipe extending into the water area to be measured to collect water samples;

[0011] The video monitoring unit is installed on the outer wall of the shore cabinet and is used for monitoring the surrounding environment of the water area in real time.

[0012] The power supply unit comprises a photovoltaic panel installed on the top of the shore cabinet, and the photovoltaic panel is connected with an energy storage battery installed in the interior of the shore cabinet through a converter.

[0013] The centralized control unit is used for receiving and processing the data of the water quality detection unit, the analysis unit and the video monitoring unit and making decisions.

[0014] As a preferred technical scheme of the present application, the U-shaped support frame is fixedly installed in the interior of the shore cabinet, sliding grooves are arranged on the inner wall of the U-shaped support frame, clamping strips are protrusively arranged on the two sides of the analysis unit, the clamping strips are slidingly installed in the sliding grooves, and protective doors are hingedly installed on the front, right side and top of the shore cabinet.

[0015] As a preferred technical scheme of the present application, the water sampling buoy is provided with a floating ball at the top and a fixed anchor at the bottom, the fixed anchor is arranged at the bottom of the water area to be measured, and the pipe opening of the water sampling pipe is provided with a screen for blocking larger volume impurities.

[0016] As a preferred technical scheme of the present application, the water sampling buoy is further provided with buckles, the buckles are connected with fixing ropes, and the fixing ropes are connected with shore fixing points.

[0017] As a preferred technical scheme of the present application, the video monitoring unit comprises a rotating assembly and a camera, the rotating assembly comprises a mounting sleeve installed on the outer wall of the shore cabinet, a rotating motor is fixedly installed in the mounting sleeve, a worm is installed at the output end of the rotating motor, a rotating rod is installed at the bottom of the camera, the rotating rod is rotatably installed in the mounting sleeve, a worm wheel is installed on the rotating rod, and the worm wheel is meshingly connected with the worm.

[0018] As a preferred technical scheme of the present application, electromagnetic valves electrically connected with the centralized control unit are installed between the measuring pool and the drain pipe and the water inlet pipe, a fire-retardant layer is installed on the inner side of the shore cabinet and the protective door, and recessed handles are arranged on the two sides of the shore cabinet.

[0019] The present invention provides a low-power surface water monitoring system based on modular design, used to implement a low-power surface water monitoring station device based on modular design. The system includes:

[0020] A water quality testing module, comprising a measuring pool and a water quality sensor, wherein the monitoring end of the water quality sensor is inserted into the measuring pool for real-time collection of water quality data.

[0021] The multimodal sampling module includes a water sampling buoy, a water pump, and a water sampling pipe. The water sampling buoy is suspended in the water area to be tested by a fixed anchor and a buoy ball. The water pump extracts water samples through the water sampling pipe and delivers them to the measurement pool. The start and stop of the water pump are dynamically controlled by a centralized control unit based on an adaptive sampling algorithm.

[0022] The data analysis module communicates with the water quality detection module and is used to analyze water quality data and generate abnormal alarm signals through a sliding window dynamic threshold algorithm.

[0023] The video surveillance module includes a rotatable camera and a motion detection unit. The camera is connected to a centralized control unit via a network communication module to capture images of the surrounding environment of the water area. The motion detection unit triggers the camera's recording function through a background difference algorithm.

[0024] The energy management module includes a photovoltaic panel, an energy storage battery, and a dynamic power consumption controller. The photovoltaic panel is installed on the top of the shore cabinet and connected to the energy storage battery through a converter. The dynamic power consumption controller dynamically adjusts the system power consumption mode according to the energy storage battery charge and the ambient light intensity.

[0025] The remote communication module is used to compress and transmit water quality data, abnormal alarm signals and video streams to the cloud platform, and to receive remote control commands.

[0026] The fault self-diagnosis module is used to monitor the noise level of the water quality sensor, verify the equipment operating parameters, and identify hardware abnormalities by fusing the data from the water quality sensor through the Kalman filtering algorithm.

[0027] The centralized control unit includes a microprocessor and an edge computing chip, which coordinate the operation of each module and execute an adaptive sleep algorithm to reduce overall power consumption. The centralized control unit activates the multimodal sampling module to perform sampling operations at a preset time according to the user-set sampling cycle. After sampling is completed, the water quality detection module introduces the water sample into the measurement pool to collect water quality data and feeds the output water quality data back to the data analysis module. After sampling and data reporting are completed, the device enters sleep mode until it receives a manual wake-up command or the next sampling cycle, at which time it will automatically wake up and start the device.

[0028] As a preferred embodiment of the present invention, the sliding window dynamic threshold algorithm includes: calculating the mean μ and standard deviation σ of the water quality parameters within the current window, wherein the window length W is dynamically adjusted according to the sampling frequency, satisfying the following relationship:

[0029] W = max(10, 0.2 × f)

[0030] In the formula, f is the current sampling frequency;

[0031] If three consecutive sampling data exceed the range of [μ-2σ, μ+2σ], an anomaly alarm is triggered and fed back to the centralized control unit. The centralized control unit combines historical data to fit a seasonal variation curve and dynamically adjusts the threshold boundary. The seasonal variation curve is fitted to historical data using an ARIMA model, and the threshold boundary is updated to [μ-2σ×α(t), μ+2σ×β(t)], where α(t) and β(t) are seasonal adjustment factors.

[0032] As a preferred technical solution of the present invention, the strategy of the dynamic power consumption controller to dynamically adjust the system power consumption mode is as follows: when the energy storage battery charge is <30%, the video monitoring module is turned off and the water quality sampling cycle is extended to twice the benchmark value; when the ambient light intensity is <200W / m 2 At that time, the energy storage battery is activated to supply power and the calculation frequency of the data analysis module is limited; the photovoltaic power generation in the next 24 hours is predicted through the LSTM neural network, and the charging and discharging strategy is optimized.

[0033] The formula for using an LSTM neural network to measure photovoltaic power generation over the next 24 hours is as follows:

[0034]

[0035] In the formula, P t This is the predicted power generation value for the next 24 hours; P t-i This represents historical power generation data, where n is the time window, i is the historical time step, and represents the i-th hour; M j,t The data includes meteorological data such as temperature, humidity, and cloud cover; m represents the total number of meteorological features; j represents different meteorological features; η represents the photovoltaic panel efficiency parameters, including the photovoltaic panel's attenuation coefficient and tilt angle data. i b j γ are the weight coefficients of the model;

[0036] Based on the prediction of photovoltaic power generation in the next 24 hours using an LSTM neural network, and combined with the state of the energy storage battery and load demand, a dynamic optimization model is designed. The optimization objective is to maximize the power generation utilization rate of the photovoltaic panels while minimizing the cycle loss of the energy storage battery and the peak-valley difference rate of the power grid. The dynamic optimization model formula is as follows:

[0037]

[0038] In the formula, T is the total number of time steps in the optimization cycle; C t Let P be the electricity price at time t; 1,t Let be the power purchased from the grid at time t; λ be the weighting coefficient for energy storage losses; η' be the charging efficiency of the energy storage battery; P 2,t Let t be the charging power of the energy storage battery at time t;

[0039] The constraints of the dynamic optimization model include: energy storage battery capacity constraints, photovoltaic panel power generation constraints, load demand constraints, and charging and discharging power constraints.

[0040] The energy storage battery capacity constraint is: E min ≤E t ≤E max E t E is the remaining charge of the energy storage battery at time t. min and E max These are the minimum and maximum capacities of the energy storage battery, respectively.

[0041] The constraint condition for the power generation of the photovoltaic panel is: P pv,t ≤P pv,max , where P pv,t P is the power generation of the photovoltaic panel at time t. pv,max This is the maximum output power of the photovoltaic panel;

[0042] The load demand constraint is: P 4,t ≤P pv,t +P 1,t +P 3,t , where P 4,t P is the load demand at time t. 3,t It is the discharge power of the energy storage battery at time t;

[0043] The charging and discharging power constraint condition is: P 2,t ≤P 2,max P 3,t ≤P 3,max , where P 2,max and P 3,max These are the maximum charging and discharging power of the energy storage battery, respectively.

[0044] As a preferred technical solution of the present invention, the adaptive sleep algorithm includes: the water quality sensor enters a deep sleep mode when the fluctuation rate of five consecutive samplings is <5%; the data analysis module adopts an event-driven wake-up mechanism and is activated only when the water quality is abnormal or a remote command is received; the remote communication module switches to eDRX power-saving mode when there is no data transmission.

[0045] The trigger condition for the water quality sensor to enter deep sleep mode is that the volatility δ of the water quality sensor in k consecutive samplings is less than 5%, and the volatility δ satisfies the following relationship:

[0046]

[0047] In the formula, x t-k:t For the most recent k sampled data, μ t-k:t This represents the mean of the sampled data.

[0048] In summary, compared with the prior art, the beneficial effects of the present invention are:

[0049] This invention adopts a modular design for the surface water monitoring station device, which can flexibly adjust the function of the device according to specific monitoring needs, such as adding or reducing the types of water quality sensors to adapt to different monitoring targets. Each unit can operate independently, which facilitates quick location and repair of faults. If a unit has a problem, only that unit needs to be replaced or repaired, without the need for a major overhaul of the entire device, making maintenance work simpler and more efficient. In addition, the modular design allows the device to be transported separately and installed independently, reducing installation time and transportation costs.

[0050] The modular design-based low-power surface water monitoring system of this invention encompasses multiple modules, including water quality detection, multimodal sampling, data analysis, video monitoring, energy management, remote communication, and fault self-diagnosis. These modules work collaboratively to ensure stable operation of the system in complex environments. By rationally adjusting the system's power consumption mode and optimizing charging and discharging strategies, the system ensures efficient utilization of photovoltaic power generation, adapts to different lighting conditions and environmental changes, and guarantees continuous operation under insufficient lighting conditions. This not only improves resource utilization efficiency but also reduces operating costs and extends the lifespan of energy storage batteries. Attached Figure Description

[0051] Fig. 1 This is a schematic diagram of the low-power surface water monitoring station device based on modular design according to the present invention;

[0052] Fig. 2 This is a schematic diagram showing the installation of the analysis unit and the U-shaped support frame of the present invention;

[0053] Fig. 3 This is a schematic diagram of the video monitoring unit of the present invention;

[0054] Among them, 1-shore cabinet, 11-protective door, 12-mounting plate, 2-water quality testing unit, 21-measuring pool, 22-water quality sensor, 23-inlet pipe, 24-drainage pipe, 25-solenoid valve, 3-analysis unit, 31-bar clip, 4-sampling unit, 41-water buoy, 42-buoy ball, 43-fixed anchor, 44-fixed rope, 5-video monitoring unit, 51-rotating component, 52-camera, 53-mounting sleeve, 54-rotating motor, 55-worm gear, 56-rotating rod, 57-worm wheel, 6-photovoltaic panel, 13-U-shaped support frame, 131-slide groove. Detailed Implementation

[0055] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments given herein are for illustration and explanation only and are not intended to limit the present invention.

[0056] like Figs. 1 to 3 As shown, the low-power surface water monitoring station device based on modular design includes:

[0057] The shore cabinet 1 has a protective door 11 hinged to its surface and an installation plate 12 installed inside.

[0058] Water quality testing unit 2 includes a measuring pool 21 fixedly installed on the mounting plate 12. The top of the measuring pool 21 is provided with several water quality sensors 22 that are electrically connected to the centralized control unit. The monitoring end of the water quality sensor 22 is inserted into the water inside the measuring pool 21. The measuring pool 21 is fixedly connected with an inlet pipe 23 and a drain pipe 24 extending to the outside of the shore cabinet 1.

[0059] Analysis unit 3 is electrically connected to the centralized control unit and installed in the shore cabinet 1 through a U-shaped support frame 13. The input end of analysis unit 3 is connected to the data output end of water quality sensor 22 and is used to perform real-time analysis of water quality data collected by water quality detection unit 2.

[0060] Sampling unit 4 includes a water sampling buoy 41 set in the water area to be tested. The water sampling buoy 41 is equipped with a water pump. The water pump is equipped with an outlet and an inlet. The outlet is connected to the inlet pipe 23 and the inlet is connected to the water sampling pipe. The water sampling pipe extends into the water area to be tested to collect water samples.

[0061] Video monitoring unit 5 is installed on the outer wall of shore cabinet 1 and is used to monitor the environmental conditions around the water area in real time. Video monitoring unit 5 is electrically connected to the centralized control unit.

[0062] The power supply unit includes a photovoltaic panel 6 installed on the top of the shore cabinet 1. The photovoltaic panel 6 is connected to the energy storage battery installed inside the shore cabinet 1 via a converter.

[0063] The centralized control unit is used to receive and process data from the water quality testing unit 2, the analysis unit 3, and the video monitoring unit 5, and to make decisions.

[0064] The outer surface of the shore cabinet 1 is hinged with a protective door 11, which can be opened for easy maintenance and operation of the equipment. The water quality detection unit 2 contains multiple water quality sensors 22 for real-time monitoring of various water quality parameters, including but not limited to pH, dissolved oxygen, turbidity, conductivity, and water temperature. It features rapid response and can quickly adjust to different water environments to provide accurate data. The analysis unit 3 is responsible for real-time analysis of the data collected by the water quality detection unit 2. It has a built-in low-power microprocessor that can efficiently process various water quality data and perform early warning analysis. By setting thresholds, when water quality parameters exceed safe ranges, the system automatically generates an alarm and promptly notifies management personnel. A sampling unit 4 and a video monitoring unit 5 are also included. The video monitoring unit 5 is used for real-time monitoring of the surrounding environment of the water area and can be equipped with a high-definition camera. 52. The image from camera 52 can be transmitted synchronously with water quality data to the remote monitoring center, helping managers obtain complete information about the aquatic environment. The power supply unit is responsible for providing a stable power supply to the entire monitoring station. The photovoltaic panel 6 provides power to the system, and the converter converts it into a form suitable for power supply. The energy storage battery stores energy for use at night or on cloudy days, ensuring that the system can still operate normally when the photovoltaic panel 6 cannot supply power. The capacity of the energy storage battery and the area of ​​the photovoltaic panel 6 can be flexibly configured according to the specific usage environment. The centralized control unit is used to receive and uniformly process the data from the water quality detection unit 2, the analysis unit 3, and the video monitoring unit 5. It is responsible for coordinating the work of each module. The data it processes includes water quality detection data, video monitoring data, etc., and can make decisions or issue alarms based on this data.

[0065] This invention adopts a modular design for the surface water monitoring station device, which can flexibly adjust the function of the device according to specific monitoring needs, such as adding or reducing the types of water quality sensors 22 to adapt to different monitoring targets. Each unit can operate independently, which facilitates quick location and repair of faults. If a unit has a problem, only that unit needs to be replaced or repaired, without the need for a major overhaul of the entire device, making maintenance work simpler and more efficient. In addition, the modular design allows the device to be transported separately and installed independently, reducing installation time and transportation costs.

[0066] In a preferred embodiment of the present invention, a U-shaped support frame 13 is fixedly installed inside the shore cabinet 1. Slide grooves 131 are provided on both sides of the inner wall of the U-shaped support frame 13. The analysis unit 3 is provided with clips 31 protruding on both sides. The clips 31 are slidably installed in the slide grooves 131. Protective doors 11 are hinged to the front, right side and top of the shore cabinet 1.

[0067] The sliding installation method of the slide groove 131 and the locking strip 31 facilitates the quick installation and disassembly of the analysis unit 3. The position of the analysis unit 3 can be adjusted as needed to adapt to different installation environments. When the analysis unit 3 malfunctions, it can be quickly disassembled for inspection and repair, reducing the time for troubleshooting and repair, minimizing system downtime, and improving equipment availability. In addition, the slide groove 131 and the locking strip 31 can be standardized to ensure compatibility between different modules and facilitate integration with other equipment or systems.

[0068] The front, right, and top of the shore cabinet 1 are hinged protective doors 11 for unilateral maintenance of each unit. The front protective door is mainly used to maintain the water quality testing unit 2 and the analysis unit 3; the right protective door is mainly used to maintain the sampling unit 4; and the top protective door is mainly used to maintain the centralized control unit and the power supply unit.

[0069] In a preferred embodiment of the present invention, the water sampling buoy 41 is provided with a float 42 at the top and a fixed anchor 43 at the bottom. The fixed anchor 43 is located at the bottom of the water area to be measured, and the opening of the water sampling pipe is provided with a mesh for blocking larger impurities.

[0070] In a preferred embodiment of the present invention, the water intake buoy 41 is further provided with a buckle, and the buckle is connected to a fixing rope 44, which is connected to a fixing point on the shore.

[0071] The buoy 42 provides buoyancy, enabling the buoy to float stably on the water surface, while the anchor 43 fixes the buoy to the bottom, reducing the buoy's swaying and rotation in the water and minimizing equipment swaying caused by factors such as water flow and waves. The two work together to ensure the buoy's stable position in the water, maintaining relative stillness even under complex hydrological conditions such as rapid currents and waves, providing a stable water intake platform for water quality monitoring and ensuring the buoy's stability under complex hydrological conditions.

[0072] In a preferred embodiment of the present invention, the video monitoring unit 5 includes a rotating assembly 51 and a camera 52. The rotating assembly 51 includes a mounting sleeve 53 installed on the outer wall of the shore cabinet 1. A rotating motor 54 is fixedly installed inside the mounting sleeve 53. A worm gear 55 is installed at the output end of the rotating motor 54. A rotating rod 56 is installed at the bottom of the camera 52. The rotating rod 56 is rotatably installed inside the mounting sleeve 53. A worm wheel 57 is installed on the rotating rod 56. The worm wheel 57 is meshed with the worm gear 55.

[0073] When the rotating motor 54 starts, its output end drives the worm gear 55 to rotate. The worm gear 55 drives the worm wheel 57 to rotate through gear meshing. The rotation of the worm wheel 57 further drives the rotating rod 56 to rotate inside the mounting sleeve 53, thereby realizing the angle adjustment of the camera 52. This allows the camera 52 to flexibly adjust its angle, facilitating real-time monitoring of the environment around the monitoring station, expanding the monitoring range, ensuring comprehensive coverage of the water area's surrounding environment, real-time monitoring of the water area's surrounding environment, capturing abnormal situations, providing complete environmental information, and adapting to different monitoring needs.

[0074] In a preferred embodiment of the present invention, a solenoid valve 25 electrically connected to the centralized control unit is installed between the measuring pool 21 and the drain pipe 24 and the inlet pipe 23. A flame-retardant layer is installed on the inner side of the shore cabinet 1 and the protective door 11. The shore cabinet 1 has grooved handles on both sides.

[0075] The present invention provides a low-power surface water monitoring system based on modular design, used to implement a low-power surface water monitoring station device based on modular design. The system includes:

[0076] The water quality testing module includes a measuring pool 21 and a water quality sensor 22. The monitoring end of the water quality sensor 22 is inserted into the measuring pool 21 to collect water quality data in real time.

[0077] The water quality sensor 22 can be configured according to different scenarios, including but not limited to pH sensor, dissolved oxygen sensor, turbidity sensor, conductivity sensor and temperature sensor and other types.

[0078] The multimodal sampling module includes a water sampling buoy 41, a water pump, and a water sampling pipe. The water sampling buoy 41 is suspended in the water area to be tested by a fixed anchor 43 and a float 42. The water pump draws water samples through the water sampling pipe and delivers them to the measurement pool 21. The start and stop of the water pump are dynamically controlled by the centralized control unit based on an adaptive sampling algorithm.

[0079] The data analysis module communicates with the water quality detection module and is used to analyze water quality data and generate abnormal alarm signals through a sliding window dynamic threshold algorithm.

[0080] The video surveillance module includes a rotatable camera 52 and a motion detection unit. The camera 52 is connected to the centralized control unit via a network communication module to capture images of the surrounding environment of the water area. The motion detection unit triggers the recording function of the camera 52 through a background difference algorithm.

[0081] The system builds an analysis model based on real-time footage captured by camera 52. When it detects any human-caused damage to the equipment, it immediately triggers an alarm and reports the relevant information to the upper-level platform. Simultaneously, when the equipment is deployed in scenarios such as tributaries, camera 52 can also assist in water environment analysis, such as identifying abnormal water conditions like muddy water, achieving dual monitoring of water safety and water quality.

[0082] The energy management module includes a photovoltaic panel 6, an energy storage battery, and a dynamic power consumption controller. The photovoltaic panel 6 is installed on the top of the shore cabinet 1 and connected to the energy storage battery through a converter. The dynamic power consumption controller dynamically adjusts the system power consumption mode according to the energy storage battery charge and the ambient light intensity.

[0083] The remote communication module is used to compress and transmit water quality data, abnormal alarm signals and video streams to the cloud platform, and to receive remote control commands.

[0084] The fault self-diagnosis module is used to monitor the noise level of the water quality sensor 22, verify the equipment operating parameters, and identify hardware abnormalities by fusing the data of the water quality sensor 22 through the Kalman filtering algorithm.

[0085] The centralized control unit includes a microprocessor and an edge computing chip, which coordinate the operation of each module and execute an adaptive sleep algorithm to reduce overall power consumption. The centralized control unit activates the multimodal sampling module to perform sampling operations at a preset time according to the user-defined sampling cycle. After sampling is completed, the water quality detection module introduces the water sample into the measurement pool 21 to collect water quality data and feeds the output water quality data back to the data analysis module. After sampling and data reporting are completed, the device enters sleep mode until it receives a manual wake-up command or the next sampling cycle, at which time it will automatically wake up and start the device.

[0086] Users can customize the sampling period according to the application scenario. The default is to use the hourly water sampling scheme. The device can not only automatically wake up and execute the sampling process when the next cycle arrives, but also supports the function of manual real-time wake-up. Users can wake up the device at any time to perform equipment maintenance or other operations as needed. It can effectively balance power consumption and real-time requirements and ensure that the device operates efficiently in a low-power state.

[0087] This low-power surface water monitoring system, based on a modular design, achieves real-time, accurate, and intelligent water quality monitoring through the synergistic effect of its modules. The modular design not only enhances the system's flexibility and scalability but also reduces maintenance costs and power consumption, ensuring stable operation in various environments. It provides comprehensive water quality information by monitoring multiple parameters in real time; dynamically controls sampling frequency and location to adapt to different hydrological conditions; quickly identifies anomalies and generates alarm signals through intelligent algorithms; optimizes energy utilization to ensure normal operation even in low-light conditions; enables remote management and operation, improving the system's intelligence level; automatically detects and diagnoses faults, reducing equipment downtime and maintenance costs; coordinates the operation of each module to ensure efficient system operation; and optimizes system power consumption through an event-driven wake-up mechanism and deep sleep mode. These features give the system significant advantages in the field of water quality monitoring, meeting the needs of different users and providing strong support for water resource protection and management.

[0088] In a preferred embodiment of the present invention, the sliding window dynamic threshold algorithm includes: calculating the mean μ and standard deviation σ of the water quality parameters within the current window, wherein the window length W is dynamically adjusted according to the sampling frequency, satisfying the following relationship:

[0089] W = max(10, 0.2 × f)

[0090] In the formula, f is the current sampling frequency;

[0091] If three consecutive sampling data exceed the range of [μ-2σ, μ+2σ], an anomaly alarm is triggered and fed back to the centralized control unit. The centralized control unit combines historical data to fit a seasonal variation curve and dynamically adjusts the threshold boundary. The seasonal variation curve is fitted to historical data using an ARIMA model, and the threshold boundary is updated to [μ-2σ×α(t), μ+2σ×β(t)], where α(t) and β(t) are seasonal adjustment factors.

[0092] The sliding window dynamic threshold algorithm can quickly respond to changes in water quality parameters, identify anomalies, and ensure the real-time performance and accuracy of monitoring data. By dynamically adjusting the threshold boundary, it adapts to seasonal changes in water quality parameters, reduces false alarms and missed alarms, and improves the reliability of anomaly detection. By fitting seasonal change curves with historical data, the algorithm can more accurately identify anomalies and provide more reliable decision support for water quality monitoring.

[0093] In a preferred embodiment of the present invention, the strategy of the dynamic power consumption controller to dynamically adjust the system power consumption mode is as follows: when the energy storage battery charge is <30%, the video monitoring module is turned off and the water quality sampling cycle is extended to twice the baseline value; when the ambient light intensity is <200W / m 2At that time, the energy storage battery is activated to supply power and the calculation frequency of the data analysis module is limited; the photovoltaic power generation for the next 24 hours is predicted through the LST M neural network, and the charging and discharging strategy is optimized.

[0094] The formula for using an LSTM neural network to measure photovoltaic power generation over the next 24 hours is as follows:

[0095]

[0096] In the formula, P t This is the predicted power generation value for the next 24 hours; P t-i This represents historical power generation data, where n is the time window, i is the historical time step, and represents the i-th hour; M j,t The data includes meteorological data such as temperature, humidity, and cloud cover; m represents the total number of meteorological features; j represents different meteorological features; η represents the efficiency parameters of photovoltaic panel 6, including the attenuation coefficient and tilt angle data of photovoltaic panel 6. i b j γ are the weight coefficients of the model;

[0097] By utilizing historical power generation data and meteorological data, high-precision predictions are made through an LSTM neural network. Based on the prediction results, charging and discharging strategies are optimized to ensure the efficient utilization of photovoltaic power generation. This method can adapt to different lighting conditions and environmental changes, and provides reliable power generation predictions.

[0098] Based on the prediction of photovoltaic power generation in the next 24 hours using an LSTM neural network, and combined with the state of the energy storage battery and load demand, a dynamic optimization model is designed. The optimization objective is to maximize the power generation utilization rate of photovoltaic panel 6, while minimizing the cycle loss of the energy storage battery and the peak-valley difference rate of the power grid. The dynamic optimization model formula is as follows:

[0099]

[0100] In the formula, T is the total number of time steps in the optimization cycle; C t Let P be the electricity price at time t; 1,t Let be the power purchased from the grid at time t; λ be the weighting coefficient for energy storage losses; η' be the charging efficiency of the energy storage battery; P 2,t Let t be the charging power of the energy storage battery at time t;

[0101] The constraints of the dynamic optimization model include: energy storage battery capacity constraints, photovoltaic panel power generation constraints, load demand constraints, and charging and discharging power constraints.

[0102] The capacity constraint for energy storage batteries is: E min ≤E t ≤E max E t E is the remaining charge of the energy storage battery at time t.min and E max These are the minimum and maximum capacities of the energy storage battery, respectively.

[0103] The constraint condition for the power generation of photovoltaic panel 6 is: P pv,t ≤P pv,max , where P pv,t P is the power generation of the photovoltaic panel at time t. pv,max This is the maximum output power of photovoltaic panel 6;

[0104] The load requirement constraint is: P 4,t ≤P pv,t +P 1,t +P 3,t , where P 4,t P is the load demand at time t. 3,t It is the discharge power of the energy storage battery at time t;

[0105] The charging and discharging power constraint is: P 2,t ≤P 2,max P 3,t ≤P 3,max , where P 2,max and P 3,max These are the maximum charging and discharging power of the energy storage battery, respectively.

[0106] P t This is the photovoltaic power generation forecast for the next 24 hours obtained through an LSTM neural network, reflecting the potential for photovoltaic power generation in different time periods, including peak and off-peak hours. The dynamic optimization model is based on P... t Based on the predicted values ​​and other parameters such as the status of energy storage batteries, load demand, and electricity prices, the charging and discharging times of energy storage batteries are rationally arranged to maximize the utilization rate of photovoltaic power generation. When the predicted photovoltaic power generation is high, photovoltaic power generation is used first to meet the load demand, and the excess electricity is stored in the energy storage batteries. When the predicted photovoltaic power generation is low, the energy storage batteries are used first to discharge to meet the load demand, reducing the purchase of electricity from the grid.

[0107] In optimizing the charging and discharging strategy of photovoltaic power generation, the constraints ensure that the dynamic optimization model is feasible in actual operation, and the energy storage battery capacity constraints ensure that the remaining power of the energy storage battery is within the allowable range and will not be overcharged or over-discharged.

[0108] The photovoltaic power generation constraint ensures that the photovoltaic power generation does not exceed the maximum output power of the photovoltaic panel 6; the load demand constraint ensures that the load demand is met; and the charging and discharging power constraint ensures that the charging and discharging power of the energy storage system does not exceed its maximum limit. Through these constraints, the optimization model can find the optimal charging and discharging strategy while meeting actual operating requirements, thereby achieving efficient utilization of photovoltaic power generation and optimized management of the energy storage system.

[0109] In a preferred embodiment of the present invention, the adaptive sleep algorithm includes: the water quality sensor 22 enters a deep sleep mode when the fluctuation rate of five consecutive samplings is <5%; the data analysis module adopts an event-driven wake-up mechanism and is activated only when the water quality is abnormal or when a remote command is received; the remote communication module switches to eDRX power-saving mode when there is no data transmission.

[0110] The trigger condition for the water quality sensor 22 to enter deep sleep mode is that the volatility δ of the water quality sensor 22 in k consecutive samplings is less than 5%, and the volatility δ satisfies the following relationship:

[0111]

[0112] In the formula, x t-k:t For the most recent k sampled data, μ t-k:t This represents the mean of the sampled data.

[0113] By employing an event-driven wake-up mechanism and a deep sleep mode, the system significantly reduces power consumption. It only activates when there is an abnormality in water quality or when a remote command is received, ensuring timely processing of critical data, reducing unnecessary energy consumption, and extending the equipment's operating time. This makes it particularly suitable for remote or resource-constrained areas.

[0114] It should be understood that the above embodiments are one or more embodiments of the present invention. There are many other embodiments and variations based on the present invention. Any variations and modifications made by those skilled in the art without making pioneering innovations are within the protection scope of the present invention.

Claims

1. A low power consumption surface water monitoring system based on modular design, characterized by, The application discloses a low-power consumption surface water monitoring station device based on a modular design. The surface of the shore cabinet is hingedly provided with a protective door, and the interior of the shore cabinet is provided with a mounting plate. The water quality detection unit comprises a measuring pool fixedly installed on the mounting plate, a plurality of water quality sensors are arranged on the top of the measuring pool and electrically connected with the centralized control unit, the monitoring end of the water quality sensor is inserted into the water body in the measuring pool, and the measuring pool is fixedly connected with a water inlet pipe and a drain pipe extending to the outside of the shore cabinet. The analysis unit is electrically connected with the centralized control unit and installed in the shore cabinet through a U-shaped support frame, the input end of the analysis unit is connected with the data output end of the water quality sensor, and the water quality data collected by the water quality detection unit is analyzed in real time. The sampling unit comprises a water sampling buoy arranged in the water area to be measured, a water pump is arranged in the water sampling buoy, a water outlet and a water inlet are arranged on the water pump, the water outlet is connected with the water inlet pipe, the water inlet is connected with a water sampling pipe, and the water sampling pipe extends into the water area to be measured to collect water samples. The video monitoring unit is installed on the outer wall of the shore cabinet and is used for monitoring the surrounding environment of the water area in real time. The power supply unit comprises a photovoltaic panel installed on the top of the shore cabinet and connected with an energy storage battery installed in the interior of the shore cabinet through a converter. The centralized control unit is used for receiving and processing the data of the water quality detection unit, the analysis unit and the video monitoring unit and making decisions. The low-power consumption surface water monitoring system based on the modular design comprises: The water quality detection module comprises a measuring pool and water quality sensors, the monitoring end of the water quality sensor is inserted into the measuring pool, and the water quality data of the water body is collected in real time. The multi-modal sampling module comprises a water sampling buoy, a water pump and a water sampling pipe, the water sampling buoy is suspended in the water area to be measured through a fixed anchor and a floating ball, the water pump draws water samples through the water sampling pipe and conveys the water samples to the measuring pool, and the start and stop of the water pump are dynamically controlled by the centralized control unit based on an adaptive sampling algorithm. The data analysis module is in communication connection with the water quality detection module and is used for analyzing the water quality data through a sliding window dynamic threshold algorithm and generating an abnormal alarm signal. The video monitoring module comprises a rotatable camera and a motion detection unit, the camera is connected with the centralized control unit through a network port communication module and is used for capturing the environmental image of the surrounding water area, and the motion detection unit triggers the recording function of the camera through a background difference algorithm. The energy management module comprises a photovoltaic panel, an energy storage battery and a dynamic power consumption controller, the photovoltaic panel is installed on the top of the shore cabinet and connected with the energy storage battery through a converter, and the dynamic power consumption controller dynamically adjusts the power consumption mode of the system according to the electric quantity of the energy storage battery and the environmental light intensity. A remote communication module is configured to transmit water quality data, abnormal alarm signals and image stream compression to a cloud platform, and receive remote control instructions; A fault self-diagnosis module is configured to monitor the noise level of the water quality sensor, check the operation parameters of the equipment, and identify hardware abnormalities by fusing the data of the water quality sensor through a Kalman filtering algorithm; The centralized control unit includes a microprocessor and an edge computing chip, and is configured to coordinate the operation of each module and execute an adaptive sleep algorithm to reduce overall power consumption. The centralized control unit activates the multi-modal sampling module at a preset time according to a user-set sampling period to perform sampling work. After sampling is completed, the water quality detection module guides the water sample into a measuring pool to collect water quality data, and feeds back the output water quality data to the data analysis module. After sampling and data reporting are completed, the equipment enters a sleep mode, and is automatically woken up to start the equipment when a manual wake-up instruction is received or the next sampling period arrives. The sliding window dynamic threshold algorithm includes: calculating the mean μ and standard deviation σ of the water quality parameters in the current window, and dynamically adjusting the window length W according to the sampling frequency, satisfying the following relationship: where f is the current sampling frequency; If the sampling data exceeds the range of [μ-2σ, μ+2σ] for three consecutive times, an abnormal alarm is triggered and fed back to the centralized control unit. The centralized control unit combines historical data to fit a seasonal change curve, dynamically adjusts the threshold boundary, and updates the threshold boundary to [μ-2σ×α(t), μ+2σ×β(t)] by fitting historical data through an ARIMA model, where α(t) and β(t) are seasonal adjustment factors. The dynamic power consumption controller dynamically adjusts the system power consumption mode as follows: when the energy storage battery power is less than 30%, the video monitoring module is turned off and the water sampling period is extended to twice the reference value; when the ambient light intensity is less than 200 W / m², the energy storage battery is powered on and the calculation frequency of the data analysis module is limited; the future 24-hour photovoltaic power generation is predicted through an LSTM neural network, and the charging and discharging strategy is optimized; The formula for predicting the future 24-hour photovoltaic power generation through the LSTM neural network is as follows: wherein, is the predicted value of the power generation in the next 24 hours; is the historical power generation data, where n is the time window, i is the historical time step, indicating the ith hour; is the weather data, including temperature, humidity, and cloud cover data, m is the total number of weather features, and j indicates different weather features; is the photovoltaic panel efficiency parameter, including the attenuation coefficient and the tilt angle data of the photovoltaic panel; is the weight coefficient of the model; Based on the LSTM neural network to predict the future 24-hour photovoltaic power generation, a dynamic optimization model is designed in combination with the state of the energy storage battery and the load demand, with the optimization goal being to maximize the utilization rate of photovoltaic panel power generation and minimize the cycle loss of the energy storage battery and the peak-valley difference rate of the power grid. The formula of the dynamic optimization model is as follows: T is the total number of time steps of the optimization period; is the electricity price at time t; is the power purchased from the grid at time t; is the weight coefficient of the energy storage loss; is the charging efficiency of the energy storage battery; is the charging power of the energy storage battery at time t; The constraint conditions of the dynamic optimization model include: energy storage battery capacity constraint condition, photovoltaic panel power generation constraint condition, load demand constraint condition, and charging and discharging power constraint condition; The energy storage battery capacity constraint condition is: E min ≤ E t ≤ E max , wherein E t is the remaining capacity of the energy storage battery at time t, E min and E max are the minimum and maximum capacities of the energy storage battery, respectively. The photovoltaic panel power generation amount constraint condition is: P pv,t ≤ P pv,max , wherein P pv,t is the photovoltaic panel power generation amount at time t, and P pv,max is the maximum output power of the photovoltaic panel. The load demand constraint is: P 4,t ≤ P pv,t + P 1,t + P 3,t , where P 4,t is the load demand at time t, and P 3,t is the discharge power of the energy storage battery at time t. The charge-discharge power constraint condition is: P 2,t ≤ P 2,max , P 3,t ≤ P 3,max , wherein P 2,max and P 3,max are the maximum charge and discharge powers of the energy storage battery, respectively. The adaptive sleep algorithm includes: the water quality sensor enters a deep sleep mode when the fluctuation rate of the water quality sensor is less than 5% for five consecutive samplings; the data analysis module adopts an event-driven wake-up mechanism and is activated only when there is water quality abnormality or a remote instruction is received; and the remote communication module switches to an eDRX power saving mode when there is no data transmission; The trigger condition for the water quality sensor to enter the deep sleep mode is that the fluctuation rate δ of the water quality sensor is less than 5% for k consecutive samplings, and the fluctuation rate δ satisfies the following relationship: wherein, is the most recent k samples of data, is the mean of the samples of data.

2. The low power consumption surface water monitoring system based on modular design according to claim 1, characterized in that: The U-shaped support frame is fixedly installed in the inside of the shore cabinet, sliding grooves are arranged on the inner wall of the U-shaped support frame, clamping strips are protruded on the two sides of the analysis unit, the clamping strips are slidingly installed in the sliding grooves, and the front side, the right side and the top of the shore cabinet are hingedly installed with protection doors.

3. The low power consumption surface water monitoring system based on modular design according to claim 1, characterized in that: The top of the water taking buoy is provided with a floating ball, and the bottom is provided with a fixed anchor, the fixed anchor is arranged at the bottom of the water area to be measured, and the pipe opening of the water taking pipe is provided with a screen for blocking larger volume impurities.

4. The low power consumption surface water monitoring system based on modular design according to claim 3, characterized in that: The water taking buoy is also provided with a buckle, the buckle is connected with a fixing rope, and the fixing rope is connected with a shore fixing point.

5. The low power consumption surface water monitoring system based on modular design of claim 1, wherein: The video monitoring unit comprises a rotating assembly and a camera, the rotating assembly comprises a mounting sleeve installed on the outer wall of the shore cabinet, a rotating motor is fixedly installed in the mounting sleeve, a worm is installed on the output end of the rotating motor, a rotating rod is installed at the bottom of the camera, the rotating rod is rotatably installed in the mounting sleeve, a worm wheel is installed on the rotating rod, and the worm wheel is meshedly connected with the worm.

6. The low power consumption surface water monitoring system based on modular design of claim 1, wherein: The measuring tank, the drain pipe and the water inlet pipe are all installed with electromagnetic valves which are electrically connected with the centralized control unit, the inside of the shore cabinet and the protection doors are both installed with flame retardant layers, and recessed handles are arranged on the two sides of the shore cabinet.

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