Water regimen multi-source sensing and monitoring device and method

Abstract

The invention relates to the field of water conservancy monitoring, and discloses a water regimen multi-source sensing monitoring device and method, and the method comprises the following steps: collecting hydrological element data through a sensor module, removing abnormal values, and filling missing values; the preprocessed hydrological element data are fused, real-time hydrological element data processing and optimization are carried out by adopting Kalman filtering and particle filtering algorithms, and noise and inconsistency in multi-source data are eliminated; monitoring the data according to the fused data and the real-time environment; and based on historical data, performing quality evaluation and correction on the hydrological element data and the environmental monitoring data through Bayesian reasoning and deep learning. Through the hydrological element data processing and optimization further algorithm, the effect of remarkably improving the image quality under the low-light condition is achieved, the image collected under the low-light environment can be effectively recovered in combination with the optical transmission equation and Fourier transform, the image data is clear and easy to analyze, and the image quality is improved. Therefore, the visualization capability of hydrological monitoring is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of water conservancy monitoring, and in particular to a water regime multi-source sensing monitoring device and method. Background Art

[0002] In the field of water conservancy monitoring, video image data, as important auxiliary information, can provide real-time and intuitive visualization support for hydrological environment changes. In many practical applications, especially at night or in low-light environments, the images collected by the camera are often affected by low-light conditions, resulting in poor image quality and loss of details, making effective analysis and judgment difficult. Existing technologies generally use traditional image enhancement methods such as histogram equalization, contrast stretching or simple filtering, which leads to over-enhancement, noise amplification or loss of image details.

[0003] To improve image quality in low-light environments, technologies must be able to restore image detail and enhance image contrast and brightness, enabling accurate analysis and decision-making under a variety of lighting conditions. Image enhancement technology has become an important tool for improving image quality, but fully leveraging image enhancement algorithms in low-light scenarios, particularly restoring image detail without introducing noise, remains a technical challenge. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a water condition multi-source perception monitoring device and method, which solves the problems of traditional image enhancement methods such as histogram equalization, contrast stretching or simple filtering commonly used in the existing technology. However, these methods, while improving image brightness and contrast, will lead to the introduction of over-enhancement, noise amplification or loss of image details.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A water regime multi-source sensing monitoring method comprises the following steps:

[0006] Collect hydrological element data through the sensor module, clean it, remove outliers, fill in missing values, and calibrate the hydrological element data;

[0007] The pre-processed hydrological element data is integrated, and the Kalman filter and particle filter algorithms are used to process and optimize the real-time hydrological element data to eliminate noise and inconsistency in multi-source data;

[0008] Based on the fused data and real-time environmental monitoring data, data transmission is carried out through adaptive transmission protocols and multi-path redundancy mechanisms;

[0009] Based on historical data, quality assessment and correction of hydrological element data and environmental monitoring data are carried out through Bayesian reasoning and deep learning;

[0010] According to the corrected data and system operating status, the maximum power point tracking algorithm is used to optimize the power output of the solar panel, and the optimal control theory is used to dynamically adjust the charging and discharging process of the battery.

[0011] Furthermore, hydrological data is first collected through the sensor module and preprocessed. This includes cleaning, removing outliers, and filling missing values. The preprocessed data is dynamically corrected using a Kalman filter, and the data is fused using a particle filter algorithm. The Kalman filter adjusts the data using optimal estimation theory, while the particle filter is used to process the fusion of nonlinear and non-Gaussian data, ensuring that the processed data has higher accuracy.

[0012] Through the aforementioned hydrological element data processing and further optimization algorithms, the image quality under low-light conditions is significantly improved. Combined with the optical transmission equation and Fourier transform, images collected in low-light environments can be effectively restored, and the image data is clear and easy to analyze.

[0013] Preferably, the Kalman filter is used to dynamically correct the hydrological element data after optimal estimation and calibration, and the particle filter algorithm is used to process the fusion of nonlinear and non-Gaussian data.

[0014] Preferably, the hydrological element data processing and optimization further includes using infrared imaging technology and full-spectrum imaging algorithms, combined with optical transmission equations and Fourier transform to restore low-light images for intelligent AI identification of flow rate and floating objects.

[0015] Preferably, the transmission path selection algorithm based on the Markov process dynamically adjusts the transmission path in combination with factors such as network load and signal strength during data transmission.

[0016] Preferably, the pre-processed hydrological element data is fused, and after fusion, the following steps are performed:

[0017] Conduct data uncertainty analysis and further enhance the accuracy and stability of data fusion through extended Kalman filtering;

[0018] Combined with environmental monitoring data, the particle filter parameters are adjusted to determine the fusion trend in highly dynamic change scenarios.

[0019] Preferably, the multi-source data quality assessment and correction further comprises the following steps:

[0020] Through ensemble learning methods, historical data is trained and adaptive algorithms are used to perform dynamic corrections based on the anomaly detection results of real-time data collection.

[0021] After data quality correction, a quality assessment report is automatically generated to assist in identifying potential problems in the data and areas for improvement.

[0022] Preferably, the power output of the solar panels is optimized by combining the mains electricity with the solar energy and battery power supply in combination with the optimal control theory, so that the equipment can work continuously for no less than 30 days in the absence of light.

[0023] Preferably, the maximum power point tracking algorithm dynamically adjusts the operating voltage of the solar panel according to the real-time voltage and current data of the solar panel to maximize power output.

[0024] Preferably, the hydrological element data includes rainfall, water level, flow rate and video image data.

[0025] A water regime multi-source sensing and monitoring device, comprising:

[0026] Sensor module, used to collect hydrological element data;

[0027] Data preprocessing and fusion module, used to clean and correct the collected multi-source data and determine whether the data fusion is consistent;

[0028] Intelligent power management module, used to manage the intelligent charging and discharging of the battery;

[0029] A data transmission module is used to transmit data that is consistent with the judgment data fusion;

[0030] Video surveillance module, used to collect images and improve image quality through adaptive enhancement algorithms;

[0031] Data quality correction module, used to correct abnormal data in real time.

[0032] The present invention provides a multi-source water regime sensing and monitoring device and method. It has the following beneficial effects:

[0033] 1. The present invention further processes and optimizes hydrological element data to achieve a significant improvement in image quality under low-light conditions. Combined with optical transmission equations and Fourier transform, it can effectively restore images collected in low-light environments. The image data is clear and easy to analyze, thereby enhancing the visualization capability of hydrological monitoring.

[0034] 2. Through dynamic correction and fusion of multi-source data such as rainfall, water level, flow rate and video images, the present invention effectively eliminates noise and inconsistency in the data, improves the accuracy and reliability of hydrological data, and provides more accurate basic data for water conservancy monitoring.

[0035] 3. The present invention dynamically adjusts the operating voltage of the solar panel according to real-time voltage and current data through the maximum power point tracking algorithm. Combined with intelligent power management, it achieves an optimized combination of mains electricity, solar energy and batteries. It can continuously provide power even in the absence of sunlight, greatly improving the energy efficiency and self-sufficiency of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for multi-source water regime sensing and monitoring according to the present invention;

[0037] Figure 2 This is a module relationship architecture diagram of a water regime multi-source perception and monitoring device of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Please see the attached Figure 1 The embodiment of the present invention provides a method for multi-source sensing and monitoring of water conditions, comprising the following steps:

[0040] Collect hydrological element data through the sensor module, clean it, remove outliers, fill in missing values, and calibrate the hydrological element data;

[0041] The pre-processed hydrological element data is integrated, and the Kalman filter and particle filter algorithms are used to process and optimize the real-time hydrological element data to eliminate noise and inconsistency in multi-source data;

[0042] Based on the fused data and real-time environmental monitoring data, data transmission is carried out through adaptive transmission protocols and multi-path redundancy mechanisms;

[0043] Based on historical data, quality assessment and correction of hydrological element data and environmental monitoring data are carried out through Bayesian reasoning and deep learning;

[0044] According to the corrected data and system operating status, the maximum power point tracking algorithm is used to optimize the power output of the solar panel, and the optimal control theory is used to dynamically adjust the charging and discharging process of the battery.

[0045] The Kalman filter is used to dynamically correct the hydrological element data after optimal estimation and calibration, and the particle filter algorithm is used to process the fusion of nonlinear and non-Gaussian data.

[0046] Specifically, the Kalman filter uses the optimal estimation theory to dynamically correct the state of the system so that the state estimate at each time point can be close to the true value. By taking a weighted average of the predicted results and the actual measured values, the system error is reduced. In hydrological data processing, data such as water level, flow rate, and rainfall are affected by multiple factors. The use of the Kalman filter can eliminate the influence of sensor errors and update the estimated value in real time through a recursive iterative process. In each iteration, the state estimate is updated using the following formula:

[0047] ;

[0048] in, represents the state at time k; is the state transfer matrix; is the control input; is the process noise; Represents the system at the last point in time The state vector of The control matrix converts the external control input Mapped to state space.

[0049] Based on the above formula:

[0050] ;

[0051] in, For the moment The observed value of is the observation matrix, is the observation noise;

[0052] ; in: is the result of the calculation of the formula; refers to the covariance matrix of the forecast error; Mapping the state variables of the system into the measurement space; Observation Matrix The transposed matrix of represents the uncertainty or noise in the measurement itself.

[0053] The Kalman gain adjusts the weights between measurements and predictions via the covariance matrix, thereby reducing the error at each time step;

[0054] ; in: This is the current moment final, revised state estimates; Refers to the current state predicted based solely on the state at the previous moment before considering the current measurement value; The weight coefficient calculated by the first formula; At the current moment The actual measurement value obtained from the sensor; Actual measured value and predicted measured values The difference between .

[0055] This formula shows that by correcting the difference between the predicted value and the actual measured value and updating the system state, through these steps, the Kalman filter can correct the system state in real time and minimize the impact of noise on system estimation;

[0056] The particle filter algorithm is mainly used to process nonlinear and non-Gaussian data and perform weighted averaging based on the weights of these particles to obtain the optimal estimate of the system state. When complex situations cannot be handled by Kalman filtering, the particle filter works through the following steps:

[0057] First, initialize the particles and randomly generate a set of particles in the state space to represent the state;

[0058] Then use the state transition model to predict each particle and calculate the weight. The formula is as follows:

[0059] ;

[0060] in, is the weight of the particle, is the match between the particle and the observation, is the observation data;

[0061] Finally, resampling is performed based on the weight of the particles to ensure that particles with high weights have more samples, which is expressed by the following formula:

[0062] ;

[0063] The weighted average of the particles provides the final estimate of the system state. Particle filtering can handle complex non-Gaussian noise and is particularly suitable for hydrological data with rapid fluctuations, such as flow rate and precipitation. Combining Kalman filtering with particle filtering solves the challenge of processing different data types. Kalman filtering is primarily used to process data from linear systems, while particle filtering optimizes for nonlinear aspects of the system. By combining the two, the system can provide higher accuracy when processing complex hydrological data.

[0064] Hydrological element data processing and optimization further includes the use of infrared imaging technology and full-spectrum imaging algorithms, combined with optical transmission equations and Fourier transform to restore low-light images for intelligent AI to identify flow rate and floating objects.

[0065] Specifically, infrared imaging technology provides basic images in low-light environments by capturing infrared radiation of different wavelengths. It also uses thermal radiation imaging in the near-infrared or mid-infrared bands to provide effective visual support in the absence of sufficient natural light.

[0066] Secondly, full-spectrum imaging algorithms capture more image details by acquiring spectral data from infrared to ultraviolet wavelengths. This provides more reflectance information than traditional RGB imaging in hydrological and environmental monitoring. Optical transmission equations are used to model light attenuation and scattering effects in low-light images. The following formula is used to calculate the attenuation of light in a medium:

[0067] ;

[0068] in, is the intensity of the light after propagation, is the incident light intensity, is the attenuation coefficient of the medium, is the distance the light travels;

[0069] The optical transmission equation can restore image details lost due to scattering and attenuation based on physical attenuation mechanisms, improving low-light imaging. Finally, the Fourier transform is used to convert images from the time domain to the frequency domain. In the frequency domain, the high-frequency portion of the image typically contains detailed information, while the low-frequency portion contains general structure and contours. By performing frequency-domain analysis on an image using the Fourier transform, the high-frequency components of the image can be enhanced, improving image detail and contrast.

[0070] The Fourier transform process includes the following steps:

[0071] First, the low-light image is Fourier transformed to obtain a frequency domain representation;

[0072] Secondly, the high-frequency information in the frequency domain is enhanced to improve the clarity of the image;

[0073] Finally, the enhanced frequency domain image is converted back to the time domain through inverse Fourier transform to restore the high-quality image. This can effectively restore low-light images and provide clear and high-quality visual data support for complex hydrological monitoring and environmental monitoring scenarios.

[0074] The transmission path selection algorithm based on Markov process dynamically adjusts the transmission path during data transmission by combining factors such as network load and signal strength.

[0075] Specifically, Markov processes are used to model state transitions in the network. Each state in the network represents a different transmission condition, such as signal strength and network load. The system predicts the network state at the next moment based on the current network state, such as signal strength and bandwidth utilization, and evaluates the pros and cons of different transmission paths using the state transition probabilities of the Markov chain.

[0076] Secondly, network load and signal strength are important factors in selecting transmission paths. During actual data transmission, network load may increase as data traffic changes, causing transmission rates on certain paths to decrease. Signal strength directly affects data transmission quality; weaker signals increase transmission errors and reduce system transmission efficiency. Therefore, the system monitors network load and signal strength in real time, dynamically assesses the status of each transmission path, and uses Markov processes to predict changes in network status and decide whether to switch to another path.

[0077] Finally, dynamic path adjustment makes choices based on the above factors. When the network load of the current path is too high or the signal strength is weak, the system will select another path with lower load and stronger signal for data transmission, reducing packet loss and delay and optimizing the use of network resources.

[0078] The pre-processed hydrological feature data are fused and the following steps are performed after fusion:

[0079] Conduct data uncertainty analysis and further enhance the accuracy and stability of data fusion through extended Kalman filtering;

[0080] Combined with environmental monitoring data, the particle filter parameters are adjusted to determine the fusion trend in highly dynamic change scenarios.

[0081] Specifically, first, hydrological element data fusion integrates information and improves its accuracy by merging pre-processed data from different sources, and fuses various data sources through weighted averaging or other algorithms, with the aim of eliminating noise and maximizing effective information;

[0082] The uncertainty analysis component uses an extended Kalman filter to optimize the accuracy and stability of the data fusion results. The extended Kalman filter is a filtering method for nonlinear systems. It estimates the state of the system by linearizing the nonlinear system model, thereby improving the accuracy of the system state estimation. The EKF first performs a first-order Taylor expansion on the state equation and observation equation to generate a linearized model. Data correction is then implemented through state updates. Environmental monitoring data is then used to dynamically adjust the parameters of the particle filter. The particle filter can handle non-Gaussian noise and nonlinear system problems. Its basic principle is to estimate the state using a large number of particles, and the particle weights are updated based on the observed values. When dealing with highly dynamic scenarios, the number of particles and their update rules can be dynamically adjusted in combination with environmental monitoring data.

[0083] The quality assessment and correction of multi-source data also includes the following steps:

[0084] Through ensemble learning methods, historical data is trained and adaptive algorithms are used to perform dynamic corrections based on the anomaly detection results of real-time data collection.

[0085] After data quality correction, a quality assessment report is automatically generated to assist in identifying potential problems in the data and areas for improvement.

[0086] Specifically, historical data is trained using an ensemble learning method. This method combines multiple base learners, including random forests and support vector machines, to obtain a prediction model for evaluating the quality of real-time data.

[0087] Secondly, the anomaly detection results of the real-time collected data will be used as the basis for dynamic correction. The system uses an anomaly detection algorithm to monitor inconsistencies or outliers in the data in real time. Based on the detected anomalies, the adaptive algorithm will automatically adjust the correction strategy;

[0088] Finally, after completing the data quality correction, the system automatically generates a quality assessment report, including outliers, missing values, etc., and provides corresponding improvement suggestions. This report will help users identify data quality issues and provide direction for future data improvements.

[0089] The panel power output is optimized by combining the mains electricity with the solar energy and battery power supply in combination with the optimal control theory, so that the equipment can work continuously for no less than 30 days in the absence of sunlight.

[0090] Specifically, first, the mains electricity and solar energy complement each other in power supply. During the day, the system mainly relies on solar panels for power supply, converting solar energy into electricity and directly supplying the equipment. The remaining power is stored in the battery. At night or on cloudy days, when solar energy is insufficient, the battery provides power to ensure continuous operation of the equipment. If the battery power is low, the system will automatically switch to the mains electricity supply.

[0091] Secondly, in order to ensure that the equipment can work continuously for at least 30 days under no-light conditions, it is necessary to calculate the appropriate battery capacity through optimal control theory. Assume that the battery storage capacity is , the daily energy consumption of the equipment is , then the battery needs to meet the following formula:

[0092] ;

[0093] in, is the average daily power consumption of the equipment, and 30 is the number of consecutive working days;

[0094] Finally, the application of optimal control theory is used to dynamically adjust the charging and discharging process of the battery, so that the equipment can utilize solar energy under different working conditions and reasonably control the battery power to avoid overcharging or over-discharging, thereby extending the battery life. The optimal control problem is expressed by the following formula:

[0095] ;

[0096] in, System status including battery charge, The control input includes battery charge and discharge control, is the cost function, The goal of optimal control is to minimize the cost function so that the system can operate efficiently and keep the battery fully charged within a given time frame.

[0097] J: total cost to be minimized; : Control variable, i.e. the power obtained from the mains at time t; : state variable, i.e. the state of charge (SoC) of the battery at time t; : electricity price cost coefficient; :Battery status penalty weight; : Ideal battery state of charge target value; Based on the above, when describing how the battery charge x(t) changes over time, the following formula is used: ; : The rate of change of battery state of charge.

[0098] : total capacity of the battery; :Battery charge and discharge efficiency; : The predicted solar power generation at time t.

[0099] : Load power consumption of the device at time t; Finally, the judgment is made through two constraints; Constraint 1: State constraint (battery power limit): ; Constraint 2: Control constraint (charging power limit): .

[0100] The maximum power point tracking algorithm dynamically adjusts the operating voltage of the solar panel based on the real-time voltage and current data of the solar panel to maximize power output.

[0101] Specifically, first, real-time voltage and current data acquisition. The relationship between the output power of the solar panel and the voltage and current. The output power of the solar panel It can be expressed by the following formula:

[0102] ;

[0103] in, is power, is the voltage, By monitoring the voltage and current of the solar panels in real time, the system can obtain the instant power output of the solar panels at any time;

[0104] Next, dynamically adjust the operating voltage of the solar panel. To ensure maximum power output, the system needs to dynamically adjust the operating voltage of the solar panel. , which is the maximum power point voltage. This voltage value is closely related to the working environment of the solar panel, including temperature, light intensity, etc., and will change with changes in environmental conditions;

[0105] In order to find the maximum power point, the commonly used methods are the perturbation and observation method and the incremental derivative method. When the perturbation and observation method P>O: By periodically changing the operating voltage of the solar panel and observing the changes in power output, the location of the maximum power point is inferred;

[0106] When the power increases, the system will continue to adjust the voltage in the current direction; when the power decreases, the system will adjust the voltage direction to find the maximum power point. With voltage changes The relationship is:

[0107]

[0108] if , it means that the voltage needs to be adjusted in the current direction; if , then reverse adjustment is required.

[0109] Incremental derivative method (IncCond): By calculating the increments of current and voltage, it is determined whether the voltage needs to be adjusted. With voltage changes The ratio between If the power increases, the voltage will continue to be adjusted. For the incremental derivative method, the relationship between power change and voltage change is:

[0110] ;

[0111] If the power increases, the system adjusts the voltage to achieve maximum power output;

[0112] Finally, to maximize power output, the MPPT algorithm ensures that the solar panels always operate at the optimal voltage operating point under different light intensities and environmental conditions, maximizing power output through these dynamic adjustment strategies.

[0113] Hydrological element data include rainfall, water level, flow rate and video image data.

[0114] Specifically, rainfall refers to the amount of precipitation per unit time. It is one of the basic indicators in hydrological monitoring and is used to analyze water flow changes and flood prediction;

[0115] Water level represents the height between the surface of a water body and a reference point. Water level data is used to monitor changes in the depth of water bodies such as reservoirs, rivers, and lakes, thereby providing support for water resource management such as flood control and irrigation.

[0116] Flow velocity indicates the speed of water flow. Changes in flow velocity can reflect the dynamic characteristics of water flow, including hydrodynamic analysis, river channel design and water quality monitoring.

[0117] Video image data, acquired through on-site monitoring cameras or drones, is used to observe changes in the hydrological environment in real time. This data can assist in monitoring parameters such as flow and water levels in areas where direct measurement is difficult, in remote waters, or in areas experiencing dynamic changes. Image data can also be combined with computer vision technology for automated analysis, including estimating water levels or flow velocities through image recognition.

[0118] Please see the attached Figure 2 , a water regime multi-source sensing and monitoring device, comprising:

[0119] Sensor module, used to collect hydrological element data;

[0120] Data preprocessing and fusion module, used to clean and correct the collected multi-source data and determine whether the data fusion is consistent;

[0121] Intelligent power management module, used to manage the intelligent charging and discharging of the battery;

[0122] A data transmission module is used to transmit data that is consistent with the judgment data fusion;

[0123] Video surveillance module, used to collect images and improve image quality through adaptive enhancement algorithms;

[0124] Data quality correction module, used to correct abnormal data in real time.

[0125] Specifically, first, the sensor module collects hydrological element data in real time, including rainfall, water level, flow rate and video image data. The sensors include rainfall sensors, water level meters, flow meters and high-definition cameras for collection;

[0126] After data collection, the data stream enters the data preprocessing and fusion module, which cleans, corrects and fuses multi-source data. Kalman filtering and particle filtering algorithms are used to optimize data accuracy and consistency, ensuring high-quality output of sensor data.

[0127] The fused data enters the intelligent power management module, which dynamically adjusts the battery charging and discharging process according to the battery power status of the device to maintain sufficient power supply during uninterrupted operation. The processed data passes through the data transmission module, which adopts adaptive transmission protocols and redundancy mechanisms to ensure the real-time and integrity of data in multi-path data transmission, and ensure reliable data transmission even in an unstable network environment. When transmitting data, satellite IoT terminals can also be used to provide communication services by accessing the domestic master station system of the Asia-Pacific 6D Ku-band domestic high-throughput satellite to realize data interaction between IoT devices. It solves the problems of large workload and difficulty in laying supporting facilities such as optical fiber, network cables, and base stations in traditional projects, as well as insufficient coverage, insufficient regional targeting, over-reliance on ground communication equipment, and poor anti-destruction capabilities. The antenna type is a flat-panel antenna with frequency parameters of 13.75-14.5 GHz for transmission and 10.7-12.75 GHz for reception. The information rate is 1 Mbps for downlink and 256 Kbps for uplink. The protocol standards support TCP / IP and UDP, etc. The LED indicator can indicate the status of power supply, satellite link transmission and reception, and alarm. The external interfaces are RJ-45 network port and RS232. The channel carrier transmission communication system is TDMA, and the reception communication system is TDM.

[0128] For video surveillance data, when the data transmission module transmits data to the video surveillance module, it uses the adaptive enhancement algorithm to process the image, automatically adjust the image brightness, contrast and other parameters, improve the image quality, and ensure the image clarity and effectiveness in complex environments;

[0129] Finally, all collected and processed data enters the data quality correction module, which uses deep learning and Bayesian inference algorithms to correct abnormal data in real time and conduct data quality assessment to ensure data accuracy and reliability. The real-time acquisition, processing, transmission, and correction of hydrological data provide strong technical support for hydrological monitoring, environmental management, and disaster warning.

[0130] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A water regime multi-source perception monitoring method, characterized in that: The following steps are involved: Collect hydrological element data through the sensor module, clean it, remove outliers, fill in missing values, and calibrate the hydrological element data; The pre-processed hydrological element data is integrated, and the Kalman filter and particle filter algorithms are used to process and optimize the real-time hydrological element data to eliminate noise and inconsistency in multi-source data; Based on the fused data and real-time environmental monitoring data, data transmission is carried out through adaptive transmission protocols and multi-path redundancy mechanisms; Based on historical data, quality assessment and correction of hydrological element data and environmental monitoring data are carried out through Bayesian reasoning and deep learning; According to the corrected data and system operating status, the maximum power point tracking algorithm is used to optimize the power output of the solar panel, and the optimal control theory is used to dynamically adjust the charging and discharging process of the battery.

2. A water regime multi-source sensing and monitoring method according to claim 1, characterized in that: The Kalman filter is used to dynamically correct the hydrological element data after optimal estimation and calibration, and the particle filter algorithm is used to process the fusion of nonlinear and non-Gaussian data.

3. A water regime multi-source sensing and monitoring method according to claim 1, characterized in that: The hydrological element data processing and optimization further includes using infrared imaging technology and full-spectrum imaging algorithms, combined with optical transmission equations and Fourier transforms to restore low-light images for intelligent AI identification of flow rate and floating objects.

4. A water regime multi-source sensing and monitoring method according to claim 1, characterized in that: The transmission path selection algorithm based on Markov process dynamically adjusts the transmission path during data transmission by combining factors such as network load and signal strength.

5. The method for multi-source water regime sensing and monitoring according to claim 1, characterized in that: The pre-processed hydrological element data is fused, and the following steps are performed after fusion: Conduct data uncertainty analysis and further enhance the accuracy and stability of data fusion through extended Kalman filtering; Combined with environmental monitoring data, the particle filter parameters are adjusted to determine the fusion trend in highly dynamic change scenarios.

6. A water regime multi-source sensing and monitoring method according to claim 1, characterized in that: The multi-source data quality assessment and correction further includes the following steps: Through ensemble learning methods, historical data is trained and adaptive algorithms are used to perform dynamic corrections based on the anomaly detection results of real-time data collection. After data quality correction, a quality assessment report is automatically generated to assist in identifying potential problems in the data and areas for improvement.

7. The method for multi-source water regime sensing and monitoring according to claim 1, characterized in that: The panel power output optimization adopts the mains-to-electricity complementary solar energy and battery power supply mode combined with optimal control theory, allowing the equipment to work continuously for no less than 30 days in the absence of sunlight.

8. The method for multi-source water regime sensing and monitoring according to claim 1, characterized in that: The maximum power point tracking algorithm dynamically adjusts the operating voltage of the solar panel based on the real-time voltage and current data of the solar panel to maximize power output.

9. The method for multi-source water regime sensing and monitoring according to claim 1, characterized in that: The hydrological element data includes rainfall, water level, flow rate and video image data.

10. A water regime multi-source sensing and monitoring device, applied to a water regime multi-source sensing and monitoring method according to any one of claims 1 to 9, characterized in that: include: Sensor module, used to collect hydrological element data; Data preprocessing and fusion module, used to clean and correct the collected multi-source data and determine whether the data fusion is consistent; Intelligent power management module, used to manage the intelligent charging and discharging of the battery; A data transmission module is used to transmit data that is consistent with the judgment data fusion; Video surveillance module, used to collect images and improve image quality through adaptive enhancement algorithms; Data quality correction module, used to correct abnormal data in real time.

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