Modularized design-based low-power-consumption surface water monitoring station device and system

Abstract

The invention relates to a modular design-based low-power surface water monitoring station device and system, and belongs to the technical field of water quality monitoring, the device integrates a shoreside 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, and through the modular design, functions can be flexibly adjusted according to specific monitoring requirements; independent operation of each unit is realized, the fault positioning and repairing process is greatly simplified, and the maintenance efficiency is remarkably improved; the system covers a water quality detection module, a multi-mode sampling module, a data analysis module, a video monitoring module, an energy management module, a remote communication module, a fault self-diagnosis module and the like, all the modules work cooperatively, stable operation of the system in a complex environment is ensured, efficient utilization of photovoltaic power generation is achieved by optimizing a charging and discharging strategy, and the system has a wide application prospect. The continuous operation capability of the system under the condition of insufficient illumination is guaranteed, the resource utilization efficiency is improved, and the operation cost is reduced.

Description

Technical Field

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

[0002] With the growing demand for surface water monitoring, traditional surface water monitoring station devices have exposed many defects and shortcomings in practical applications. In existing technologies, surface water monitoring stations typically adopt an integrated design, integrating water sampling, analysis, and monitoring functions into a single device. This integrated design results in fixed device functions, making it difficult to flexibly adjust or expand according to specific needs. Sensors cannot be quickly replaced or added based on changes in monitoring targets. The internal structure of the device is complex, and the various functional modules are interdependent. Once a module fails, the repair and replacement process is cumbersome and costly, and troubleshooting and repair are inefficient. Furthermore, traditional equipment typically adopts a high-power design and lacks energy-saving optimization, resulting in high operating costs. This makes sustained and stable operation difficult, especially in remote areas or when power supply is insufficient.

[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 of the Invention

[0004] The purpose of the present invention 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 object, the present invention provides the following technical solutions:

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

[0007] A shore cabinet, wherein a protective door is hingedly mounted on the surface of the shore cabinet and a mounting plate is mounted inside the shore cabinet;

[0008] A water quality detection unit, comprising a measuring pool fixedly mounted on the mounting plate, with a plurality of water quality sensors electrically connected to a centralized control unit provided on the top of the measuring pool, wherein monitoring ends of the water quality sensors are plugged into the water inside the measuring pool, and the measuring pool is fixedly connected to a water inlet pipe and a drain pipe extending to the outside of the shore cabinet;

[0009] An analysis unit, the analysis unit being electrically connected to the centralized control unit and mounted in the shore cabinet via a U-shaped support frame, the input end of the analysis unit being connected to the data output end of the water quality sensor, for performing real-time analysis of the water quality data collected by the water quality detection unit;

[0010] A sampling unit, the sampling unit comprising a water sampling buoy disposed in the water area to be measured, the water sampling buoy being provided with a water pump, the water pump being provided with a water outlet and a water inlet, the water outlet being connected to the water inlet pipe, the water inlet being connected to a water sampling pipe, the water sampling pipe extending into the water area to be measured to collect water samples;

[0011] A video monitoring unit, which is installed on the outer wall of the shore cabinet and is used to monitor the environmental conditions around the water area in real time. The video monitoring unit is electrically connected to the centralized control unit;

[0012] A power supply unit, comprising a photovoltaic panel mounted on top of the shore cabinet, the photovoltaic panel being connected to an energy storage battery mounted inside the shore cabinet via a converter;

[0013] The centralized control unit is used to receive and process data from the water quality detection unit, the analysis unit and the video monitoring unit, and make decisions.

[0014] As a preferred technical solution of the present invention, the U-shaped support frame is fixedly installed inside the shore cabinet, and sliding grooves are provided on both sides of the inner wall of the U-shaped support frame. Card strips are protruding on both sides of the analysis unit, and the card strips are slidably installed in the sliding grooves. Protective doors are hingedly installed on the front, right side and top of the shore cabinet.

[0015] As a preferred technical solution of the present invention, the water intake buoy is provided with a float on the top and a fixed anchor on the bottom. The fixed anchor is set at the bottom of the water area to be measured, and the mouth of the water collection pipe is provided with a partition net for blocking larger impurities.

[0016] As a preferred technical solution of the present invention, the water intake buoy is further provided with a buckle, the buckle is connected to a fixing rope, and the fixing rope is connected to a fixed point on the shore.

[0017] As a preferred technical solution of the present invention, the video surveillance unit includes a rotating component and a camera, the rotating component includes 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 gear is installed on the rotating rod, and the worm gear is meshed with the worm gear.

[0018] As a preferred technical solution of the present invention, solenoid valves electrically connected to the centralized control unit are installed between the measuring pool and the drainage pipe and the water inlet pipe, flame retardant layers are installed on the inner sides of the shore cabinet and the protective door, and grooved handles are provided on both sides of the shore cabinet.

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

[0020] A water quality detection module, comprising a measuring tank and a water quality sensor, wherein a monitoring end of the water quality sensor is inserted into the measuring tank for collecting water quality data in real time;

[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 measured by a fixed anchor and a float. The water pump extracts water samples through the water sampling pipe and transports them to the measurement tank. The start and stop of the water pump is dynamically controlled by a centralized control unit based on an adaptive sampling algorithm.

[0022] The data analysis module is in communication with the water quality detection module and is used to analyze the 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 the centralized control unit via an Ethernet communication module and is used 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] Energy management module, including photovoltaic panels, energy storage batteries and dynamic power consumption controller. The photovoltaic panels are installed on the top of the shore cabinet and connected to the energy storage batteries through a converter. The dynamic power consumption controller dynamically adjusts the system power consumption mode according to the energy storage battery power and ambient light intensity.

[0025] Remote communication module, used to compress and transmit water quality data, abnormal alarm signals and image streams to the cloud platform and receive remote control commands;

[0026] A fault self-diagnosis module is used to monitor the noise level of water quality sensors, verify equipment operating parameters, and identify hardware anomalies by fusing water quality sensor data through a Kalman filter algorithm.

[0027] Among them, the centralized control unit includes a microprocessor and an edge computing chip, which are used to 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 at a preset time according to the sampling cycle set by the user to perform sampling operations. After sampling is completed, the water quality detection module introduces the water sample into the measuring 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, and automatically wakes up and starts the device.

[0028] As a preferred technical solution of the present invention, 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 the window length W is dynamically adjusted according to the sampling frequency to satisfy the following relationship:

[0029] W=max(10,0.2×f)

[0030] Where f is the current sampling frequency;

[0031] If the sampling data exceeds the range of [μ-2σ,μ+2σ] for three consecutive times, an abnormal alarm will be triggered and fed back to the centralized control unit. The centralized control unit will fit the seasonal change curve based on historical data and dynamically adjust the threshold boundary. The seasonal change curve is fitted to historical data through the 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 power is less than 30%, the video monitoring module is turned off and the water quality sampling period is extended to twice the baseline value; when the ambient light intensity is less than 200W / m 2 When the data is in the range of 0 to 1, the energy storage battery is used 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 to optimize the charging and discharging strategy;

[0033] The formula for the LSTM neural network to estimate photovoltaic power generation in the next 24 hours is as follows:

[0034]

[0035] Where, P t is the predicted value of power generation in the next 24 hours; P t-i is the historical power generation data, where n is the time window, i is the historical time step, representing the i-th hour; M j,t is the meteorological data, including temperature, humidity and cloud cover data, m is the total number of meteorological features, j represents different meteorological features; η is the photovoltaic panel efficiency parameter, including the attenuation coefficient and tilt angle data of the photovoltaic panel. i 、b j , γ is the weight coefficient of the model;

[0036] Based on the LSTM neural network, we predict the photovoltaic power generation in the next 24 hours. In combination with the status of the energy storage battery and the load demand, we design a dynamic optimization model. The optimization goal is to maximize the power generation utilization rate of the photovoltaic panel while minimizing the cycle loss of the energy storage battery and the peak-to-valley difference rate of the power grid. The dynamic optimization model formula is as follows:

[0037]

[0038] Where T is the total number of time steps in the optimization cycle; C t is the electricity price at time t; P 1,t is the power purchased from the grid at time t; λ is the weight coefficient of energy storage loss; η' is the charging efficiency of the energy storage battery; P 2,t is 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 condition is: E min ≤E t ≤E max , where 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;

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

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

[0043] The charge and discharge power constraint condition is: P 2,t ≤P 2,max , P 3,t ≤P 3,max , where P 2,max and P 3,max 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 samples is less than 5%; the data analysis module adopts an event-driven wake-up mechanism and is only activated when the water quality is abnormal or a remote command is received; the remote communication module switches to the eDRX power saving mode when there is no data transmission;

[0045] 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 in k consecutive samplings is less than 5%, and the fluctuation rate δ satisfies the following relationship:

[0046]

[0047] Where x t-k:t is the most recent k sampling data, μ t-k:t is the mean of the sampled data.

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

[0049] The present invention adopts a modular design for the surface water monitoring station device, and can flexibly adjust the functions of the device according to specific monitoring needs, such as increasing or decreasing the types of water quality sensors to adapt to different monitoring targets. Each unit can operate independently, which is convenient for rapid fault location and repair. If a problem occurs in a unit, only the unit needs to be replaced or repaired without overhauling the entire device, making maintenance work simpler and more efficient. In addition, the modular design allows the device to be transported in parts and installed independently, reducing installation time and transportation costs.

[0050] The low-power surface water monitoring system based on modular design of the present invention covers multiple modules such as water quality detection, multimodal sampling, data analysis, video monitoring, energy management, remote communication and fault self-diagnosis. The modules work together to ensure the stable operation of the system in complex environments. By reasonably adjusting the system power consumption mode and optimizing the charging and discharging strategy, the efficient utilization of photovoltaic power generation is ensured, and it adapts to different lighting conditions and environmental changes, and ensures the system's ability to continue operating under insufficient lighting conditions, it not only improves resource utilization efficiency, but also reduces operating costs and extends the life of the energy storage battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of a low-power surface water monitoring station device based on modular design according to the present invention;

[0052] Figure 2 Schematic diagram of the installation of the analysis unit and the U-shaped support frame of the present invention;

[0053] Figure 3 is a schematic diagram of a video surveillance unit of the present invention;

[0054] Among them, 1-shore cabinet, 11-protective door, 12-mounting plate, 2-water quality detection unit, 21-measuring pool, 22-water quality sensor, 23-water inlet pipe, 24-drain pipe, 25-solenoid valve, 3-analysis unit, 31-card strip, 4-sampling unit, 41-water buoy, 42-float, 43-fixed anchor, 44-fixed rope, 5-video monitoring unit, 51-rotating assembly, 52-camera, 53-mounting sleeve, 54-rotating motor, 55-worm, 56-rotating rod, 57-worm gear 57, 6-photovoltaic panel, 13-U-shaped support frame, 131-chute. DETAILED DESCRIPTION

[0055] The following is a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the specific embodiments given here are only used to illustrate and explain the present invention and cannot be used to limit the present invention.

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

[0057] A shore cabinet 1, a protective door 11 is hingedly installed on the surface of the shore cabinet 1, and a mounting plate 12 is installed inside the shore cabinet 1;

[0058] The water quality detection unit 2 includes a measuring tank 21 fixedly mounted on the mounting plate 12. A plurality of water quality sensors 22 electrically connected to the centralized control unit are provided on the top of the measuring tank 21. The monitoring ends of the water quality sensors 22 are plugged into the water inside the measuring tank 21. The measuring tank 21 is fixedly connected to a water inlet pipe 23 and a drain pipe 24 extending to the outside of the shore cabinet 1.

[0059] Analysis unit 3, which 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 the analysis unit 3 is connected to the data output end of the water quality sensor 22, and is used to perform real-time analysis of the water quality data collected by the water quality detection unit 2;

[0060] The sampling unit 4 includes a water sampling buoy 41 provided in the water area to be tested, a water pump is provided in the water sampling buoy 41, and the water pump is provided with a water outlet and a water inlet, the water outlet is connected to the water inlet pipe 23, and the water inlet is connected to the water sampling pipe, and the water sampling pipe extends into the water area to be tested to collect water samples;

[0061] Video monitoring unit 5, which is installed on the outer wall of the shore cabinet 1 and is used to monitor the environmental conditions around the water area in real time. The 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, and the photovoltaic panel 6 is connected to the energy storage battery installed inside the shore cabinet 1 through a converter;

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

[0064] The outer surface of the shore cabinet 1 is hingedly installed with a protective door 11, which can be opened to facilitate maintenance and operation of the equipment; the water quality detection unit 2 includes multiple water quality sensors 22 for real-time monitoring of various parameters of water quality, including but not limited to pH value, dissolved oxygen, turbidity, conductivity and water temperature, etc., with the characteristics of rapid response, and can be quickly adjusted and provide accurate data in different water environments; the analysis unit 3 is responsible for real-time analysis of the data collected by the water quality detection unit 2, and 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 the safe range, the system automatically generates an alarm and promptly notifies the management personnel; sampling unit 4; video monitoring unit 5 is used to monitor the environmental conditions around the water area in real time, and can be equipped with a high-definition camera 52. The image of camera 52 can be transmitted to the remote monitoring center synchronously with the water quality data, helping managers to obtain complete water environment information; the power supply unit is responsible for providing a stable power supply for the entire monitoring station, the photovoltaic panel 6 provides electrical energy to the system, the converter converts it into a form suitable for power supply, and the energy storage battery stores energy for use at night or on cloudy days to ensure 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 use environment; the centralized control unit is used to receive and uniformly process data from the water quality detection unit 2, the analysis unit 3 and the video monitoring unit 5, and 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 these data.

[0065] The present invention adopts a modular design for the surface water monitoring station device, and can flexibly adjust the functions of the device according to specific monitoring needs, such as increasing or decreasing the types of water quality sensors 22 to adapt to different monitoring targets. Each unit can operate independently, which is convenient for rapid fault location and repair. If a problem occurs in a unit, only the unit needs to be replaced or repaired without overhauling the entire device, making maintenance work simpler and more efficient. In addition, the modular design allows the device to be transported in parts 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, and sliding grooves 131 are provided on both sides of the inner wall of the U-shaped support frame 13. A clamping strip 31 is protruded on both sides of the analysis unit 3, and the clamping strip 31 is slidably installed in the sliding groove 131. The front, right side and top of the shore cabinet 1 are all hingedly installed with protective doors 11.

[0067] The sliding installation method of the slide groove 131 and the card strip 31 facilitates the rapid 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 fails, it can be quickly disassembled for inspection and repair, reducing the time for troubleshooting and repair, reducing system downtime, and improving equipment availability. In addition, the slide groove 131 and the card strip 31 can be standardized to ensure compatibility between different modules and facilitate integration with other devices or systems.

[0068] The front, right side and top of the shore cabinet 1 are hingedly installed with protective doors 11 for unilateral maintenance of each unit. Among them, the front protective door is mainly used for maintaining the water quality detection unit 2 and the analysis unit 3; the right side protective door is mainly used for maintaining the sampling unit 4; and the top protective door is mainly used for maintaining the centralized control unit and the power supply unit.

[0069] In a preferred embodiment of the present invention, a water intake buoy 41 is provided with a float 42 on the top and a fixed anchor 43 on the bottom. The fixed anchor 43 is located at the bottom of the water area to be measured, and a partition net is provided at the mouth of the water sampling pipe to block larger impurities.

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

[0071] The float 42 provides buoyancy, allowing the buoy to float stably on the water surface, while the fixed anchor 43 fixes the buoy to the bottom of the water, reducing the swaying and rotation of the buoy in the water, and reducing the equipment swaying caused by water flow impact, wind and waves and other factors. The two work together to ensure the stable position of the buoy in the water. Even under complex hydrological conditions, such as turbulent water flow, wind and waves, etc., it can remain relatively still, providing a stable water intake platform for water quality monitoring and ensuring the stability of the buoy under complex hydrological conditions.

[0072] In a preferred embodiment of the present invention, the video surveillance unit 5 includes a rotating component 51 and a camera 52. The rotating component 51 includes a mounting sleeve 53 installed on the outer wall of the shore cabinet 1. A rotating motor 54 is fixedly installed in the mounting sleeve 53. A worm 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 in the mounting sleeve 53. A worm gear 57 is installed on the rotating rod 56, and the worm gear 57 is meshed with the worm 55.

[0073] When the rotating motor 54 is started, its output end drives the worm 55 to rotate, and the worm 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, so that the camera 52 can flexibly adjust the angle, facilitate real-time monitoring of the environment around the monitoring station, expand the monitoring range, ensure comprehensive coverage of the environment around the water area, monitor the environment around the water area in real time, capture abnormal situations, provide complete environmental information, and adapt 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 drainage pipe 24 and the water inlet pipe 23, a flame retardant layer is installed on the inner side of the shore cabinet 1 and the protective door 11, and grooved handles are provided on both sides of the shore cabinet 1.

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

[0076] Water quality detection module, the water quality detection module includes a measuring tank 21 and a water quality sensor 22. The monitoring end of the water quality sensor 22 is inserted into the measuring tank 21 to collect water quality data of the water body in real time;

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

[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 measured by a fixed anchor 43 and a float 42. The water pump extracts water samples through the water sampling pipe and delivers them to the measurement tank 21. The start and stop of the water pump is dynamically controlled by the centralized control unit based on the adaptive sampling algorithm.

[0079] The data analysis module is in communication with the water quality detection module and is used to analyze the 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 the 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 uses real-time images captured by camera 52 to build an analysis model. If it detects any vandalism, it immediately triggers an alarm and reports the relevant information to the higher-level platform. Furthermore, when the device is deployed in tributaries, camera 52 can also assist in water environment analysis, such as identifying abnormal water conditions like muddy water, thereby achieving dual monitoring of water safety and water quality.

[0082] Energy management module, including photovoltaic panels 6, energy storage batteries and dynamic power consumption controller. Photovoltaic panels 6 are 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 power and ambient light intensity.

[0083] Remote communication module, used to compress and transmit water quality data, abnormal alarm signals and image streams to the cloud platform and receive remote control commands;

[0084] A fault self-diagnosis module is used to monitor the noise level of the water quality sensor 22, verify equipment operating parameters, and identify hardware anomalies by fusing data from the water quality sensor 22 using a Kalman filter algorithm;

[0085] Among them, the centralized control unit includes a microprocessor and an edge computing chip, which are used to 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 at a preset time according to the sampling cycle set by the user to perform sampling operations. After the sampling is completed, the water quality detection module introduces the water sample into the measuring tank 21 to collect water quality data, and feeds the output water quality data back to the data analysis module. After the sampling and data reporting are completed, the device enters sleep mode until it receives a manual wake-up command or the next sampling cycle, and automatically wakes up and starts the device.

[0086] Users can customize the sampling cycle according to the application scenario. The default is usually 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 manual real-time wake-up function. Users can wake up the device at any time according to needs to perform equipment maintenance or other operations. It can effectively balance power consumption and real-time requirements to ensure that the device runs efficiently in a low-power state.

[0087] The low-power surface water monitoring system based on modular design achieves real-time, accurate and intelligent water quality monitoring through the synergy of various modules. The modular design not only improves the flexibility and scalability of the system, but also reduces maintenance costs and power consumption, ensuring the stable operation of the system in various environments, monitoring multiple water quality parameters in real time, and providing comprehensive water quality information; dynamically controlling the sampling frequency and position to adapt to different hydrological conditions; quickly identifying abnormal situations and generating alarm signals through intelligent algorithms; optimizing energy utilization to ensure that the system can still operate normally in insufficient light; remote management and operation to improve the intelligence level of the system; automatically detecting and diagnosing faults to reduce equipment downtime and maintenance costs; coordinating the operation of various modules to ensure the efficient operation of the system; optimizing system power consumption through event-driven wake-up mechanism and deep sleep mode, making the system have significant advantages in the field of water quality monitoring, able to meet the needs of different users, and provide 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 in the current window, and the window length W is dynamically adjusted according to the sampling frequency to satisfy the following relationship:

[0089] W=max(10,0.2×f)

[0090] Where f is the current sampling frequency;

[0091] If the sampling data exceeds the range of [μ-2σ, μ+2σ] for three consecutive times, an abnormal alarm will be triggered and fed back to the centralized control unit. The centralized control unit will fit the seasonal change curve based on historical data and dynamically adjust the threshold boundary. The seasonal change curve is fitted to historical data through the 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] Combined with the sliding window dynamic threshold algorithm, it can quickly respond to changes in water quality parameters, identify anomalies, and ensure the real-time and accuracy of monitoring data; by dynamically adjusting the threshold boundaries, it can adapt to seasonal changes in water quality parameters, reduce false alarms and missed alarms, and improve the reliability of anomaly detection; combined with historical data to fit the seasonal change curve, 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 power is less than 30%, the video monitoring module is turned off and the water quality sampling period is extended to 2 times the baseline value; when the ambient light intensity is less than 200W / m 2When the power is supplied by the energy storage battery, the calculation frequency of the data analysis module is limited; the photovoltaic power generation in the next 24 hours is predicted through the LST M neural network, and the charging and discharging strategy is optimized;

[0094] The formula for the LSTM neural network to estimate photovoltaic power generation in the next 24 hours is as follows:

[0095]

[0096] Where, P t is the predicted value of power generation in the next 24 hours; P t-i is the historical power generation data, where n is the time window, i is the historical time step, representing the i-th hour; M j,t is the meteorological data, including temperature, humidity and cloud cover data, m is the total number of meteorological features, j represents different meteorological features; η is the efficiency parameter of the photovoltaic panel 6, including the attenuation coefficient and tilt angle data of the photovoltaic panel 6. i 、b j , γ is the weight coefficient of the model;

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

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

[0099]

[0100] Where T is the total number of time steps in the optimization cycle; C t is the electricity price at time t; P 1,t is the power purchased from the grid at time t; λ is the weight coefficient of energy storage loss; η' is the charging efficiency of the energy storage battery; P 2,t is 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 charge and discharge power constraints;

[0102] The energy storage battery capacity constraint is: E min ≤E t ≤E max , where E t is the remaining capacity of the energy storage battery at time t, Emin and E max are the minimum and maximum capacities of the energy storage battery, respectively;

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

[0104] 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, P 3,t is the discharge power of the energy storage battery at time t;

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

[0106] P t The photovoltaic power generation in the next 24 hours is predicted by the LSTM neural network, which reflects the potential of photovoltaic power generation in different time periods in the future, including peak and valley periods. The dynamic optimization model is based on P t The predicted value is combined with other parameters such as the energy storage battery status, load demand, electricity price, etc. to reasonably arrange the charging and discharging time of the energy storage battery to maximize the utilization rate of photovoltaic power generation. When the photovoltaic power generation is predicted to be high, photovoltaic power generation is used first to meet the load demand, and the excess electricity is stored in the energy storage battery. When the photovoltaic power generation is predicted to be low, the energy storage battery is used first to discharge to meet the load demand and reduce the purchase of electricity from the grid.

[0107] In optimizing the charging and discharging strategy for photovoltaic power generation, constraints ensure that the dynamic optimization model is feasible in actual operation. The energy storage battery capacity constraint ensures 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 photovoltaic panel 6; the load demand constraint ensures that the load demand is met; and the charge and discharge power constraint ensures that the charge and discharge power of the energy storage system does not exceed its maximum limit. Through the constraints, the optimization model can find the optimal charge and discharge strategy while meeting the 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 samples is less than 5%; the data analysis module adopts an event-driven wake-up mechanism and is only activated when the water quality is abnormal or a remote command is received; the remote communication module switches to the eDRX power saving mode when there is no data transmission;

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

[0111]

[0112] Where x t-k:t is the most recent k sampling data, μ t-k:t is the mean of the sampled data.

[0113] Through event-driven wake-up mechanism and deep sleep mode, the system power consumption is significantly reduced. It is only activated when the water quality is abnormal or a remote command is received, ensuring the timely processing of critical data, reducing unnecessary energy consumption, and extending the operation time of the device. It is especially 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 thereof based on the present invention. The variations and modifications made by ordinary technicians in this industry through the present invention without making groundbreaking innovations all fall within the scope of protection of the present invention.

Claims

1. A low-power surface water monitoring station device based on modular design, characterized by: include: A shore cabinet, wherein a protective door is hingedly mounted on the surface of the shore cabinet and a mounting plate is mounted inside the shore cabinet; A water quality detection unit, comprising a measuring pool fixedly mounted on the mounting plate, with a plurality of water quality sensors electrically connected to a centralized control unit provided on the top of the measuring pool, wherein monitoring ends of the water quality sensors are plugged into the water inside the measuring pool, and the measuring pool is fixedly connected to a water inlet pipe and a drain pipe extending to the outside of the shore cabinet; An analysis unit, the analysis unit being electrically connected to the centralized control unit and mounted in the shore cabinet via a U-shaped support frame, the input end of the analysis unit being connected to the data output end of the water quality sensor, for performing real-time analysis of the water quality data collected by the water quality detection unit; A sampling unit, the sampling unit comprising a water sampling buoy disposed in the water area to be measured, the water sampling buoy being provided with a water pump, the water pump being provided with a water outlet and a water inlet, the water outlet being connected to the water inlet pipe, the water inlet being connected to a water sampling pipe, the water sampling pipe extending into the water area to be measured to collect water samples; A video monitoring unit, which is installed on the outer wall of the shore cabinet and is used to monitor the environmental conditions around the water area in real time. The video monitoring unit is electrically connected to the centralized control unit; A power supply unit, comprising a photovoltaic panel mounted on top of the shore cabinet, the photovoltaic panel being connected to an energy storage battery mounted inside the shore cabinet via a converter; The centralized control unit is used to receive and process data from the water quality detection unit, the analysis unit and the video monitoring unit, and make decisions.

2. The low-power surface water monitoring station device based on modular design according to claim 1 is characterized by: The U-shaped support frame is fixedly installed inside the shore cabinet, and sliding grooves are opened on both sides of the inner wall of the U-shaped support frame. Card strips are protruding on both sides of the analysis unit, and the card strips are slidably installed in the sliding grooves. Protective doors are hingedly installed on the front, right side and top of the shore cabinet.

3. The low-power surface water monitoring station device based on modular design according to claim 1, characterized in that: The top of the water intake buoy is provided with a float ball, and the bottom is provided with a fixed anchor, and the fixed anchor is arranged at the bottom of the water area to be measured. The pipe mouth of the water sampling pipe is provided with a partition net for blocking larger impurities.

4. The low-power surface water monitoring station device based on modular design according to claim 3 is characterized by: The water intake buoy is also provided with a buckle, the buckle is connected to a fixing rope, and the fixing rope is connected to a fixed point on the shore.

5. The low-power surface water monitoring station device based on modular design according to claim 1, characterized in that: The video surveillance unit includes a rotating component and a camera. The rotating component includes 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 gear is installed on the rotating rod, and the worm gear is meshed with the worm.

6. The low-power surface water monitoring station device based on modular design according to claim 1, characterized in that: Solenoid valves electrically connected to the centralized control unit are installed between the measuring pool and the drainage pipe and the water inlet pipe. Flame retardant layers are installed on the inner sides of the shore cabinet and the protective door. Grooved handles are provided on both sides of the shore cabinet.

7. The low-power surface water monitoring system based on modular design is characterized by: The system is used to implement a low-power surface water monitoring station device based on modular design as described in any one of claims 1 to 6, and the system comprises: A water quality detection module, comprising a measuring tank and a water quality sensor, wherein a monitoring end of the water quality sensor is inserted into the measuring tank for collecting water quality data in real time; 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 measured by a fixed anchor and a float. The water pump extracts water samples through the water sampling pipe and transports them to the measurement tank. The start and stop of the water pump is dynamically controlled by a centralized control unit based on an adaptive sampling algorithm. The data analysis module is in communication with the water quality detection module and is used to analyze the water quality data and generate abnormal alarm signals through a sliding window dynamic threshold algorithm; The video surveillance module includes a rotatable camera and a motion detection unit. The camera is connected to the centralized control unit via an Ethernet communication module and is used 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. Energy management module, including photovoltaic panels, energy storage batteries and dynamic power consumption controller. The photovoltaic panels are installed on the top of the shore cabinet and connected to the energy storage batteries through a converter. The dynamic power consumption controller dynamically adjusts the system power consumption mode according to the energy storage battery power and ambient light intensity. Remote communication module, used to compress and transmit water quality data, abnormal alarm signals and image streams to the cloud platform and receive remote control commands; Fault self-diagnosis module, used to monitor the noise level of water quality sensors, verify equipment operating parameters, and identify hardware anomalies by fusing water quality sensor data through the Kalman filter algorithm; Among them, the centralized control unit includes a microprocessor and an edge computing chip, which are used to 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 at a preset time according to the sampling cycle set by the user to perform sampling operations. After sampling is completed, the water quality detection module introduces the water sample into the measuring 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, and automatically wakes up and starts the device.

8. The low-power surface water monitoring system based on modular design according to claim 7 is characterized in that: 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 the window length W is dynamically adjusted according to the sampling frequency to meet the following relationship: W=max(10,0.2×f) 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 will be triggered and fed back to the centralized control unit. The centralized control unit will fit the seasonal change curve based on historical data and dynamically adjust the threshold boundary. The seasonal change curve is fitted to historical data through the 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.

9. The low-power surface water monitoring system based on modular design according to claim 7, characterized in that: The strategy of the dynamic power consumption controller to dynamically adjust the system power consumption mode is as follows: when the energy storage battery power is less than 30%, the video monitoring module is turned off and the water quality sampling period is extended to twice the baseline value; when the ambient light intensity is less than 200W / m 2 When the data is in the range of 0 to 1, the energy storage battery is used 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 to optimize the charging and discharging strategy; The formula for the LSTM neural network to estimate photovoltaic power generation in the next 24 hours is as follows: Where, P t is the predicted value of power generation in the next 24 hours; P t-i is the historical power generation data, where n is the time window, i is the historical time step, representing the i-th hour; M j,t is the meteorological data, including temperature, humidity and cloud cover data, m is the total number of meteorological features, j represents different meteorological features; η is the photovoltaic panel efficiency parameter, including the attenuation coefficient and tilt angle data of the photovoltaic panel. i 、b j , γ is the weight coefficient of the model; Based on the LSTM neural network, we predict the photovoltaic power generation in the next 24 hours. In combination with the status of the energy storage battery and the load demand, we design a dynamic optimization model. The optimization goal is to maximize the power generation utilization rate of the photovoltaic panel while minimizing the cycle loss of the energy storage battery and the peak-to-valley difference rate of the power grid. The dynamic optimization model formula is as follows: Where T is the total number of time steps in the optimization cycle; C t is the electricity price at time t; P 1,t is the power purchased from the grid at time t; λ is the weight coefficient of energy storage loss; η' is the charging efficiency of the energy storage battery; P 2,t is the charging power of the energy storage battery at time t; 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; The energy storage battery capacity constraint condition is: E min ≤E t ≤E max , where 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 constraint condition is: P pv,t ≤P pv,max , where P pv,t is the power generation of the photovoltaic panel at time t, P pv,max is the maximum output power of the photovoltaic panel; The load demand constraint condition is: P 4,t ≤P pv,t +P 1,t +P 3,t , where P 4,t is the load demand at time t, P 3,t is the discharge power of the energy storage battery at time t; The charge and discharge power constraint condition is: P 2,t ≤P 2,max , P 3,t ≤P 3,max , where P 2,max and P 3,max are the maximum charging and discharging power of the energy storage battery respectively.

10. The low-power surface water monitoring system based on modular design according to claim 7, characterized in that: The adaptive sleep algorithm includes: the water quality sensor enters deep sleep mode when the fluctuation rate of five consecutive samples is less than 5%; the data analysis module uses an event-driven wake-up mechanism and is only activated 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; 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 in k consecutive samplings is less than 5%, and the fluctuation rate δ satisfies the following relationship: Where x t-k:t is the most recent k sampling data, μ t-k:t is the mean of the sampled data.

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