Ecological-friendly water level dynamic monitoring and adjusting system

Abstract

The invention discloses an eco-friendly water level dynamic monitoring and adjusting system which comprises a sensing layer, a data transmission and processing layer, a decision and control layer and a user interaction layer. The sensing layer comprises a water level data acquisition module, an environmental parameter acquisition module and a visual monitoring module; the data transmission and processing layer comprises a data transmission module, a data processing and analysis module and a data interface module; the decision and control layer comprises an intelligent decision support system module, a self-adaptive adjustment mechanism module and an automatic control system module, and the intelligent decision support system module is responsible for analyzing the water level change trend and predicting the future water level based on historical data and real-time data and considering multiple factors such as seasonal changes and rainfall modes; an eco-friendly water level adjustment scheme is formulated, and the self-adaptive adjustment mechanism module is responsible for adjusting a water level adjustment strategy according to the actual situation and monitoring data of the wetland ecosystem; the user interaction layer comprises a user interaction module and an alarm and notification module.

Description

Technical Field

[0001] The present invention relates to the technical field of water level adjustment, and particularly to an eco-friendly water level dynamic monitoring and adjustment system. Background Art

[0002] Wetlands provide a suitable living environment for numerous organisms and are a treasure trove of biodiversity. Wetland plants, animals, and microbial communities are interdependent, forming a complex ecological system network. Wetlands can regulate water sources, conserve water quality, store floodwaters and prevent droughts, and have a regulatory effect on the climate. They can absorb and store large amounts of water, acting as a buffer during floods and releasing water during droughts to maintain the stability of the ecosystem. By reasonably controlling the wetland water level, the balance of the wetland ecosystem can be maintained, ensuring the healthy development of wetland plants, animals, and microbial communities. By dynamically monitoring and adjusting the wetland water level, the natural purification and circulation of wetland water bodies can be promoted, improving water quality.

[0003] In the prior art, during actual operation, the operation of water level adjustment equipment may cause certain interference to the wetland ecosystem. For example, frequent water level adjustments may damage the growth environment of wetland vegetation and affect the habitat and reproduction of aquatic organisms. Therefore, an eco-friendly water level dynamic monitoring and adjustment system is proposed. Summary of the Invention

[0004] The purpose of the present invention is to solve the drawback in the prior art that the operation of water level adjustment equipment may cause certain interference to the wetland ecosystem, and to propose an eco-friendly water level dynamic monitoring and adjustment system.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] An eco-friendly water level dynamic monitoring and adjustment system includes a perception layer, a data transmission and processing layer, a decision-making and control layer, and a user interaction layer;

[0007] The perception layer includes a water level data acquisition module, an environmental parameter acquisition module, and a visual monitoring module. The water level data acquisition module is responsible for collecting water level data in real time through water level sensors deployed at key positions in waters such as wetlands, rivers, and lakes, adopting a non-contact or low-interference design to ensure the accuracy of data collection and the minimum interference to biological habitats. The environmental parameter acquisition module is responsible for comprehensively monitoring water quality (such as dissolved oxygen, pH value, temperature, etc.), soil humidity, and meteorological parameters (such as rainfall, wind speed, etc.) using environmental sensors, providing comprehensive ecological environment data support for intelligent decision-making. The visual monitoring module is responsible for periodically or as needed capturing macroscopic changes in the water area ecosystem through cameras and drone inspections, providing high-resolution images and videos to help managers understand the overall situation of the wetland ecosystem;

[0008] The data transmission and processing layer includes a data transmission module, a data processing and analysis module, and a data interface module. The data transmission module is responsible for using low-power wide-area network technologies such as LoRa and NB-IoT to achieve real-time transmission of sensor data, ensuring the timeliness and accuracy of the data, and providing reliable data support for intelligent decision-making. The data processing and analysis module is responsible for collecting, storing, and processing data from the perception layer using a cloud computing platform, performing data processing and prediction, and mining the potential value in the data to provide a scientific basis for water level adjustment. The data interface module is responsible for providing API interfaces to provide data access interfaces for third-party applications, promoting data sharing and cross-platform collaboration, expanding the application scope of the system, and improving the flexibility and scalability of the system;

[0009] The decision-making and control layer includes an intelligent decision support system module, an adaptive adjustment mechanism module, and an automatic control system module. The intelligent decision support system module is responsible for analyzing the water level change trend based on historical data and real-time data, predicting the future water level, and considering various factors such as seasonal changes and rainfall patterns to formulate an eco-friendly water level adjustment plan. The adaptive adjustment mechanism module is responsible for adjusting the water level adjustment strategy according to the actual situation of the wetland ecosystem and the monitoring data. For example, during the vigorous growth period of wetland vegetation, reduce the frequency and amplitude of water level adjustment to protect the growth environment of the vegetation. The automatic control system module is responsible for automatically adjusting the water level adjustment facilities (such as gates, pumps, etc.) according to the instructions output by the decision support system module to achieve dynamic adjustment of the water level;

[0010] The user interaction layer includes a user interaction module and an alarm and notification module. The user interaction module is responsible for providing an interface for users through a mobile application / Web platform. The interface includes functions such as real-time water level information, ecological environment reports, and adjustment strategy feedback. The alarm and notification module is responsible for automatically sending alarm messages to relevant management personnel and stakeholders when the water level is abnormal or the ecological environment indicators exceed the threshold;

[0011] The data transmission module transmits the data collected by the perception layer to the data processing and analysis module in real time. The data processing and analysis module provides the processing results to the intelligent decision support system module and the user interaction module. The intelligent decision support system module receives the data from the data processing and analysis module, analyzes and predicts by combining historical data and real-time data, formulates a water level adjustment plan, and sends the instructions to the automatic control system module. The adaptive adjustment mechanism module adjusts the water level adjustment strategy according to the actual situation of the wetland ecosystem and the monitoring data collected by the perception layer. When the water level is abnormal or the ecological environment indicators exceed the threshold, the alarm and notification module receives the alarm information from the intelligent decision support system module.

[0012] The above technical solution further includes:

[0013] Further, the water level data acquisition module includes a water level sensor and a water level data acquisition unit, the environmental parameter acquisition module includes a water quality sensor, a soil humidity sensor, a meteorological sensor and an environmental parameter data acquisition unit, the visual monitoring module includes a camera, a drone, an image processing unit and a data storage unit. After the image and video data captured by the camera and the drone are processed by the image processing unit, they are stored in the data storage unit, and the image and video data are subjected to correlation analysis with the water level data and the environmental parameter data.

[0014] Further, the data processing and analysis module includes a data collection unit, a data storage unit, a data processing unit, a data analysis unit and a data visualization and reporting unit. The data collection unit is responsible for receiving data from the perception layer and performing preliminary data verification and preprocessing. The data storage unit is responsible for providing cloud storage services and performing data cloud storage to ensure the secure storage and efficient access of data. The data processing unit is responsible for data cleaning, denoising and format conversion. The data analysis unit is responsible for using big data analysis technology to aggregate, classify and correlate analyze the data. The data visualization and reporting unit is responsible for providing data visualization tools and report generation functions to help managers understand and analyze the data. After the data collection unit receives data from the perception layer, it transfers it to the data storage unit for storage. The data processing unit reads data from the data storage unit and performs cleaning, denoising and format conversion processing. The data processing unit transfers the processed data to the data analysis unit. The data prediction unit transfers the analysis result to the data visualization and reporting unit. The data visualization and reporting unit visually displays the analysis result in the form of charts, reports, etc. and transfers it to the user interaction module.

[0015] Furthermore, the intelligent decision support system module includes a data collection unit, a data processing unit, a prediction model unit, and a solution formulation unit. The data collection unit is responsible for collecting the required data from the data processing and analysis module. The data processing unit is responsible for cleaning, integrating, and formatting the collected data to meet the subsequent analysis requirements. The prediction model unit is responsible for constructing and training a prediction model based on historical data and real-time data to predict the future water level. The solution formulation unit is responsible for formulating a water level adjustment plan according to the prediction results and ecological protection requirements, and conducting plan evaluation and optimization. The data collection unit transfers the collected data to the data processing unit. After cleaning, integrating, and formatting the original data, the data processing unit transfers it to the prediction model unit. The data processing and analysis module transfers the analysis results (such as water level change trends, seasonal change characteristics, etc.) to the prediction model unit. The prediction model unit combines real-time data and external factors (such as rainfall patterns, etc.) to construct and train a prediction model. The prediction model unit transfers the prediction results to the solution formulation unit. The solution formulation unit formulates a water level adjustment plan according to the prediction results and ecological protection requirements, and conducts plan evaluation and optimization.

[0016] Furthermore, the adaptive adjustment mechanism module includes a monitoring data collection unit, a data analysis unit, a strategy adjustment decision unit, and a strategy monitoring and evaluation unit. The monitoring data collection unit is responsible for collecting real-time monitoring data of the wetland ecosystem, including water level, water quality, vegetation growth status, soil humidity, etc. The data analysis unit is responsible for analyzing the collected monitoring data and extracting key information, such as the health status of the wetland ecosystem, vegetation growth trends, etc. The strategy adjustment decision unit is responsible for formulating and adjusting the water level adjustment strategy based on the data analysis results, combined with the characteristics and protection objectives of the wetland ecosystem. The strategy monitoring and evaluation unit is responsible for continuously monitoring the implementation effect of the strategy, evaluating the effectiveness of the strategy and the stability of the wetland ecosystem, and providing a basis for subsequent adjustments. The monitoring data collection unit transfers the collected real-time monitoring data to the data analysis unit. The data analysis unit transfers the analysis results to the strategy adjustment decision unit. The strategy adjustment decision unit formulates or adjusts the water level adjustment strategy based on the analysis results. The strategy monitoring and evaluation unit continuously monitors the implementation effect of the strategy and feeds back the evaluation results to the data analysis unit and the strategy adjustment decision unit.

[0017] Furthermore, the automatic control system module includes an instruction receiving unit, an instruction parsing unit, a device status monitoring unit, an automatic adjustment execution unit, and a feedback and monitoring unit. The instruction receiving unit is responsible for receiving water level adjustment instructions from the decision support system module and the adaptive adjustment mechanism module. The instruction parsing unit parses the received instructions to clarify the specific requirements for water level adjustment. The device status monitoring unit monitors the working status of the water level adjustment facilities in real time, including parameters such as the opening degree, flow rate, and pressure. The automatic adjustment execution unit automatically adjusts the operation of the water level adjustment facilities according to the parsed instructions and the real-time monitored device status. The feedback and monitoring unit feeds back the actual operation status of the water level adjustment facilities and the water level change situation to the decision support system module and the adaptive adjustment mechanism module, and continuously monitors the water level adjustment process.

[0018] Furthermore, the alarm and notification module includes a threshold setting and monitoring unit, an alarm information generation unit, a recipient management unit, an alarm information sending unit, and an alarm record and tracking unit. The threshold setting and monitoring unit is responsible for presetting the thresholds of water level and ecological environment indicators, and continuously monitoring the actual values of water level and ecological environment indicators. When it is monitored that the indicators exceed the thresholds, the alarm information generation unit is responsible for generating alarm information. The recipient management unit is responsible for maintaining the recipient list, including the contact information of relevant management personnel and stakeholders. The alarm information sending unit selects an appropriate communication method according to the recipient list and sends the alarm information to relevant personnel. The alarm record and tracking unit records the detailed information of each alarm and provides a tracking function. The threshold setting and monitoring unit continuously monitors the actual values of water level and ecological environment indicators and compares them with the preset thresholds. When the indicators exceed the thresholds, it triggers the alarm information generation unit to generate alarm information. When generating alarm information, the alarm information generation unit will query the recipient management unit to obtain the contact information of relevant management personnel and stakeholders. After sending the alarm information, the alarm information sending unit transmits relevant information (such as alarm time, abnormal indicators, recipients, etc.) to the alarm record and tracking unit for recording.

[0019] Furthermore, the prediction model unit constructs and trains a prediction model based on historical data and real-time data to predict the future water level. The specific steps are as follows:

[0020] Data preparation: Collect historical water level data, including the observed values of time series, and preprocess the data, such as filling missing values and handling outliers.

[0021] Stationarity test: Plot the time series graph to observe whether it shows stationarity. Use the ADF (Augmented Dickey-Fuller) test for stationarity testing. If the time series is non-stationary, differencing processing is required until it becomes a stationary series. The number of differencing times is the d parameter in the ARIMA model;

[0022] Model parameter determination: Plot the ACF graph (autocorrelation graph) and PACF graph (partial autocorrelation graph). According to the trailing and truncating characteristics of the ACF graph and PACF graph, determine the p (number of autoregressive terms) and q (number of moving average terms) parameters in the ARIMA model. You can also use criteria such as AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) for model selection to determine the best combination of p, d, and q parameters;

[0023] Model establishment: Based on the determined p, d, and q parameters, establish the ARIMA model. The mathematical expression of the ARIMA model is: Y(t) = c + φ1Y(t - 1) + φ2Y(t - 2) + … + φpY(t - p) + εt + θ1ε(t - 1) + θ2ε(t - 2) + … + θqε(t - q), where Y(t) is the value of the time series, t is the time point, c is the constant term, φ1, φ2,..., φp are autoregressive coefficients, εt is the disturbance term, and θ1, θ2,..., θq are moving average coefficients.

[0024] Model testing: Conduct residual analysis on the established ARIMA model to check whether the residuals meet the assumption conditions such as normality and independence. If the residual analysis does not meet the requirements, the model parameters need to be readjusted;

[0025] Prediction and evaluation: Use the established ARIMA model to predict the water level in the future for a period of time, and evaluate the accuracy of the prediction results. You can use indicators such as mean square error (MSE) and mean absolute error (MAE) for measurement.

[0026] The present invention has the following beneficial effects:

[0027] 1. In the present invention, based on historical data and real-time data, analyze the water level change trend, predict the future water level, and consider factors such as seasonal changes and rainfall patterns to formulate an ecologically friendly water level adjustment plan, simulate the laws of natural water level changes, ensure that the water level adjustment is coordinated with natural processes. At the same time, according to the actual situation of the wetland ecosystem and monitoring data, flexibly adjust the water level adjustment strategy to avoid excessive adjustment frequency or untimely adjustment.

[0028] 2. In the present invention, a camera / drone inspection is set up for visual inspection on a regular basis or as needed to capture the macroscopic changes in the water ecosystem. These devices can provide high-resolution images and videos to help managers understand the overall condition of the wetland ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 FIG. is a system block diagram of an eco-friendly water level dynamic monitoring and adjustment system proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] Please refer to Figure 1 As shown, the present invention is an eco-friendly water level dynamic monitoring and adjustment system, including a sensing layer, a data transmission and processing layer, a decision-making and control layer, and a user interaction layer;

[0032] The sensing layer includes a water level data acquisition module, an environmental parameter acquisition module, and a visual monitoring module. The water level data acquisition module is responsible for collecting water level data in real time through water level sensors deployed at key positions in waters such as wetlands, rivers, and lakes, adopting a non-contact or low-interference design to ensure the accuracy of data collection and the minimum interference to biological habitats. The environmental parameter acquisition module is responsible for comprehensively monitoring water quality (such as dissolved oxygen, pH value, temperature, etc.), soil humidity, and meteorological parameters (such as rainfall, wind speed, etc.) using environmental sensors to provide comprehensive ecological environment data support for intelligent decision-making. The visual monitoring module is responsible for capturing the macroscopic changes in the water ecosystem regularly or as needed through cameras and drone inspections, providing high-resolution images and videos to help managers understand the overall condition of the wetland ecosystem;

[0033] The data transmission and processing layer includes a data transmission module, a data processing and analysis module, and a data interface module. The data transmission module is responsible for using low-power wide-area network technologies such as LoRa and NB-IoT to achieve real-time transmission of sensor data, ensuring the timeliness and accuracy of data, and providing reliable data support for intelligent decision-making. The data processing and analysis module is responsible for collecting, storing, and processing data from the perception layer using a cloud computing platform, performing data processing and prediction, and mining the potential value in the data to provide a scientific basis for water level adjustment. The data interface module is responsible for providing API interfaces to provide data access interfaces for third-party applications, promoting data sharing and cross-platform collaboration, expanding the application scope of the system, and improving the flexibility and scalability of the system;

[0034] The decision-making and control layer includes an intelligent decision support system module, an adaptive adjustment mechanism module, and an automatic control system module. The intelligent decision support system module is responsible for analyzing the water level change trend based on historical data and real-time data, predicting the future water level, and considering various factors such as seasonal changes and rainfall patterns to formulate an eco-friendly water level adjustment plan. The adaptive adjustment mechanism module is responsible for adjusting the water level adjustment strategy according to the actual situation of the wetland ecosystem and monitoring data. For example, during the period of vigorous growth of wetland vegetation, reduce the frequency and amplitude of water level adjustment to protect the growth environment of the vegetation. The automatic control system module is responsible for automatically adjusting the water level adjustment facilities (such as gates, pumps, etc.) according to the instructions output by the decision support system module to achieve dynamic adjustment of the water level;

[0035] The user interaction layer includes a user interaction module and an alarm and notification module. The user interaction module is responsible for providing an interface for users through a mobile application / Web platform. The interface includes functions such as real-time water level information, ecological environment report, and adjustment strategy feedback. The alarm and notification module is responsible for automatically sending alarm information to relevant management personnel and stakeholders when the water level is abnormal or the ecological environment indicators exceed the threshold;

[0036] The data transmission module transmits the data collected by the perception layer to the data processing and analysis module in real time. The data processing and analysis module provides the processing results to the intelligent decision support system module and the user interaction module. The intelligent decision support system module receives the data from the data processing and analysis module, analyzes and predicts by combining historical data and real-time data, formulates a water level adjustment plan, and sends the instructions to the automatic control system module. The adaptive adjustment mechanism module adjusts the water level adjustment strategy according to the actual situation of the wetland ecosystem and monitoring data collected by the perception layer. When the water level is abnormal or the ecological environment indicators exceed the threshold, the alarm and notification module receives the alarm information from the intelligent decision support system module.

[0037] In one embodiment, for the above water level data acquisition module, the water level data acquisition module includes a water level sensor and a water level data acquisition unit. The environmental parameter acquisition module includes a water quality sensor, a soil humidity sensor, a meteorological sensor, and an environmental parameter data acquisition unit. The visual monitoring module includes a camera, a drone, an image processing unit, and a data storage unit. The image and video data captured by the camera and the drone are processed by the image processing unit and then stored in the data storage unit. The image and video data are subjected to correlation analysis with the water level data and the environmental parameter data.

[0038] In one embodiment, for the above data processing and analysis module, the data processing and analysis module includes a data collection unit, a data storage unit, a data processing unit, a data analysis unit, and a data visualization and reporting unit. The data collection unit is responsible for receiving data from the perception layer and performing preliminary data verification and preprocessing. The data storage unit is responsible for providing cloud storage services, performing data cloud storage, and ensuring the secure storage and efficient access of data. The data processing unit is responsible for performing data cleaning, denoising, and format conversion. The data analysis unit is responsible for using big data analysis techniques to aggregate, classify, and perform correlation analysis on the data. The data visualization and reporting unit is responsible for providing data visualization tools and report generation functions to help management personnel understand and analyze the data. After the data collection unit receives data from the perception layer, it transmits the data to the data storage unit for storage. The data processing unit reads the data from the data storage unit and performs cleaning, denoising, and format conversion processing. The data processing unit transmits the processed data to the data analysis unit. The data prediction unit transmits the analysis results to the data visualization and reporting unit. The data visualization and reporting unit visually displays the analysis results in the form of charts, reports, etc. and transmits them to the user interaction module.

[0039] In one embodiment, for the above-mentioned intelligent decision support system module, the intelligent decision support system module includes a data collection unit, a data processing unit, a prediction model unit, and a solution formulation unit. The data collection unit is responsible for collecting the required data from the data processing and analysis module. The data processing unit is responsible for cleaning, integrating, and formatting the collected data to meet the subsequent analysis requirements. The prediction model unit is responsible for constructing and training a prediction model based on historical data and real-time data to predict the future water level. The solution formulation unit is responsible for formulating a water level adjustment plan according to the prediction results and ecological protection requirements, and conducting plan evaluation and optimization. The data collection unit transfers the collected data to the data processing unit. After the data processing unit cleans, integrates, and formats the original data, it transfers the data to the prediction model unit. The data processing and analysis module transfers the analysis results (such as water level change trends, seasonal change characteristics, etc.) to the prediction model unit. The prediction model unit combines real-time data and external factors (such as rainfall patterns, etc.) to construct and train a prediction model. The prediction model unit transfers the prediction results to the solution formulation unit. The solution formulation unit formulates a water level adjustment plan according to the prediction results and ecological protection requirements, and conducts plan evaluation and optimization.

[0040] In one embodiment, for the above-mentioned adaptive adjustment mechanism module, the adaptive adjustment mechanism module includes a monitoring data collection unit, a data analysis unit, a strategy adjustment decision unit, and a strategy monitoring and evaluation unit. The monitoring data collection unit is responsible for collecting real-time monitoring data of the wetland ecosystem, including water level, water quality, vegetation growth status, soil humidity, etc. The data analysis unit is responsible for analyzing the collected monitoring data and extracting key information, such as the health status of the wetland ecosystem, vegetation growth trends, etc. The strategy adjustment decision unit is responsible for formulating and adjusting the water level adjustment strategy based on the data analysis results, combined with the characteristics and protection objectives of the wetland ecosystem. The strategy monitoring and evaluation unit is responsible for continuously monitoring the implementation effect of the strategy, evaluating the effectiveness of the strategy and the stability of the wetland ecosystem, and providing a basis for subsequent adjustments. The monitoring data collection unit transfers the collected real-time monitoring data to the data analysis unit. The data analysis unit transfers the analysis results to the strategy adjustment decision unit. The strategy adjustment decision unit formulates or adjusts the water level adjustment strategy based on the analysis results. The strategy monitoring and evaluation unit continuously monitors the implementation effect of the strategy and feeds back the evaluation results to the data analysis unit and the strategy adjustment decision unit.

[0041] In one embodiment, for the above-mentioned automatic control system module, the automatic control system module includes an instruction receiving unit, an instruction parsing unit, a device status monitoring unit, an automatic adjustment execution unit, and a feedback and monitoring unit. The instruction receiving unit is responsible for receiving water level adjustment instructions from the decision support system module and the adaptive adjustment mechanism module. The instruction parsing unit parses the received instructions to clarify the specific requirements for water level adjustment. The device status monitoring unit monitors the working status of the water level adjustment facilities in real time, including parameters such as the opening degree, flow rate, and pressure. The automatic adjustment execution unit automatically adjusts the operation of the water level adjustment facilities according to the parsed instructions and the real-time monitored device status. The feedback and monitoring unit feeds back the actual operation status of the water level adjustment facilities and the water level change situation to the decision support system module and the adaptive adjustment mechanism module, and continuously monitors the water level adjustment process.

[0042] In one embodiment, for the above-mentioned alarm and notification module, the alarm and notification module includes a threshold setting and monitoring unit, an alarm information generation unit, a recipient management unit, an alarm information sending unit, and an alarm record and tracking unit. The threshold setting and monitoring unit is responsible for presetting the thresholds of water level and ecological environment indicators, and continuously monitoring the actual values of water level and ecological environment indicators. When it is monitored that the indicators exceed the thresholds, the alarm information generation unit is responsible for generating alarm information. The recipient management unit is responsible for maintaining the recipient list, including the contact information of relevant management personnel and stakeholders. The alarm information sending unit selects an appropriate communication method according to the recipient list and sends the alarm information to relevant personnel. The alarm record and tracking unit records the detailed information of each alarm and provides a tracking function. The threshold setting and monitoring unit continuously monitors the actual values of water level and ecological environment indicators and compares them with the preset thresholds. When the indicators exceed the thresholds, it triggers the alarm information generation unit to generate alarm information. When generating alarm information, the alarm information generation unit will query the recipient management unit to obtain the contact information of relevant management personnel and stakeholders. After sending the alarm information, the alarm information sending unit transfers relevant information (such as alarm time, abnormal indicators, recipients, etc.) to the alarm record and tracking unit for recording.

[0043] In one embodiment, for the above-mentioned prediction model unit, the prediction model unit constructs and trains a prediction model based on historical data and real-time data to predict the future water level. The specific steps are as follows:

[0044] Data preparation: Collect historical water level data, including the observed values of time series, and preprocess the data, such as filling missing values and handling outliers;

[0045] Stationarity test: Plot the time series graph to observe whether it shows stationarity. Use the ADF (Augmented Dickey-Fuller) test for stationarity testing. If the time series is non-stationary, differencing processing is required until it becomes a stationary series. The number of differencing times is the d parameter in the ARIMA model;

[0046] Model parameter determination: Plot the ACF graph (autocorrelation function graph) and PACF graph (partial autocorrelation function graph). According to the trailing and truncating characteristics of the ACF graph and PACF graph, determine the p (number of autoregressive terms) and q (number of moving average terms) parameters in the ARIMA model. You can also use criteria such as AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) for model selection to determine the best combination of p, d, and q parameters;

[0047] Model establishment: According to the determined p, d, and q parameters, establish an ARIMA model. The mathematical expression of the ARIMA model is: Y(t) = c + φ1Y(t - 1) + φ2Y(t - 2) + … + φpY(t - p) + εt + θ1ε(t - 1) + θ2ε(t - 2) + … + θqε(t - q), where Y(t) is the value of the time series, t is the time point, c is the constant term, φ1, φ2, …, φp are autoregressive coefficients, εt is the disturbance term, and θ1, θ2, …, θq are moving average coefficients.

[0048] Model test: Conduct residual analysis on the established ARIMA model to check whether the residuals meet the assumption conditions such as normality and independence. If the residual analysis does not meet the requirements, the model parameters need to be readjusted;

[0049] Prediction and evaluation: Use the established ARIMA model to predict the water level for a period of time in the future, and evaluate the accuracy of the prediction results. You can use indicators such as mean square error (MSE) and mean absolute error (MAE) to measure;

[0050] Suppose we have a set of historical water level data. After the stationarity test, it is found that first-order differencing processing is required. Then, plot the ACF and PACF graphs. It is found that the ACF graph is significantly non-zero at lags 1 and 2, while the PACF graph is significantly non-zero at lag 1 and then decays rapidly. Based on this information, we can preliminarily determine the parameters of the ARIMA model as p = 1, d = 1, q = 1, that is, the ARIMA(1,1,1) model;

[0051] Next, establish an ARIMA(1,1,1) model and predict the water level for a period of time in the future. The prediction results can be compared with the actual observed values to evaluate the accuracy of the model.

[0052] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and permutations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An eco-friendly water level dynamic monitoring and adjustment system, characterized in that: It includes the perception layer, data transmission and processing layer, decision-making and control layer, and user interaction layer; The perception layer includes a water level data acquisition module, an environmental parameter acquisition module and a visual monitoring module. The water level data acquisition module is responsible for collecting water level data in real time through water level sensors deployed at key locations in the water area. The environmental parameter acquisition module is responsible for using environmental sensors to comprehensively monitor water quality, soil moisture and meteorological parameters. The visual monitoring module is responsible for capturing macroscopic changes in the water ecosystem regularly or on demand through camera and drone inspections. The data transmission and processing layer includes a data transmission module, a data processing and analysis module, and a data interface module. The data transmission module is responsible for realizing the real-time transmission of sensor data. The data processing and analysis module is responsible for using the cloud computing platform to collect, store and process data from the perception layer, and perform data processing and prediction. The data interface module is responsible for providing an API interface to provide a data access interface for third-party applications. The decision-making and control layer includes an intelligent decision support system module, an adaptive adjustment mechanism module and an automatic control system module. The intelligent decision support system module is responsible for analyzing the trend of water level changes based on historical data and real-time data, predicting future water levels, and formulating eco-friendly water level adjustment plans by considering seasonal changes and rainfall patterns. The adaptive adjustment mechanism module is responsible for adjusting the water level adjustment strategy according to the actual situation and monitoring data of the wetland ecosystem. The automatic control system module is responsible for automatically adjusting the water level adjustment facilities according to the instructions output by the decision support system module to achieve dynamic adjustment of the water level. The user interaction layer includes a user interaction module and an alarm and notification module. The user interaction module is responsible for providing an interface for users, and the alarm and notification module is responsible for automatically sending alarm information to relevant managers and stakeholders when the water level is abnormal or the ecological environment index exceeds the threshold; The data transmission module transmits the data collected by the perception layer to the data processing and analysis module in real time. The data processing and analysis module provides the processing results to the intelligent decision support system module and the user interaction module. The intelligent decision support system module receives the data from the data processing and analysis module, analyzes and predicts the historical data and real-time data, formulates a water level adjustment plan, and sends instructions to the automatic control system module. The adaptive adjustment mechanism module adjusts the water level adjustment strategy according to the actual situation and monitoring data of the wetland ecosystem collected by the perception layer. When the water level is abnormal or the ecological environment index exceeds the threshold, the alarm and notification module receives the alarm information from the intelligent decision support system module.

2. The eco-friendly water level dynamic monitoring and adjustment system according to claim 1, characterized in that: The water level data acquisition module includes a water level sensor and a water level data acquisition unit, the environmental parameter acquisition module includes a water quality sensor, a soil moisture sensor, a meteorological sensor and an environmental parameter data acquisition unit, and the visual monitoring module includes a camera, a drone, an image processing unit and a data storage unit. The images and video data captured by the camera and the drone are processed by the image processing unit and stored in the data storage unit, and the image and video data are correlated and analyzed with the water level data and the environmental parameter data.

3. The eco-friendly water level dynamic monitoring and adjustment system according to claim 1, characterized in that: The data processing and analysis module includes a data collection unit, a data storage unit, a data processing unit, a data analysis unit and a data visualization and reporting unit. The data collection unit is responsible for receiving data from the perception layer and performing preliminary data verification and preprocessing. The data storage unit is responsible for providing cloud storage services and performing data cloud storage. The data processing unit is responsible for data cleaning, denoising and format conversion. The data analysis unit is responsible for using big data analysis technology to aggregate, classify and perform correlation analysis on data. The data visualization and reporting unit is responsible for providing data visualization tools and report generation functions to help managers understand and analyze data. After receiving data from the perception layer, the data collection unit passes it to the data storage unit for storage. The data processing unit reads data from the data storage unit and performs cleaning, denoising and format conversion. The data processing unit passes the processed data to the data analysis unit. The data prediction unit passes the analysis results to the data visualization and reporting unit. The data visualization and reporting unit visualizes the analysis results and passes them to the user interaction module.

4. The eco-friendly water level dynamic monitoring and adjustment system according to claim 1, characterized in that: The intelligent decision support system module includes a data collection unit, a data processing unit, a prediction model unit and a program formulation unit. The data collection unit is responsible for collecting required data from the data processing and analysis module. The data processing unit is responsible for cleaning, integrating and formatting the collected data. The prediction model unit is responsible for building and training a prediction model based on historical data and real-time data to predict future water levels. The program formulation unit is responsible for formulating a water level adjustment plan based on the prediction results and ecological protection needs, and evaluating and optimizing the plan. The data collection unit passes the collected data to the data processing unit. The data processing unit cleans, integrates and formats the original data and then passes it to the prediction model unit. The data processing and analysis module passes the analysis results to the prediction model unit. The prediction model unit combines real-time data and external factors to build and train a prediction model. The prediction model unit passes the prediction results to the program formulation unit. The program formulation unit formulates a water level adjustment plan based on the prediction results and ecological protection needs, and evaluates and optimizes the plan.

5. The eco-friendly water level dynamic monitoring and adjustment system according to claim 1, characterized in that: The adaptive adjustment mechanism module includes a monitoring data collection unit, a data analysis unit, a strategy adjustment decision unit and a strategy monitoring and evaluation unit. The monitoring data collection unit is responsible for collecting real-time monitoring data of the wetland ecosystem. The data analysis unit is responsible for analyzing the collected monitoring data and extracting key information. The strategy adjustment decision unit is responsible for formulating and adjusting the water level adjustment strategy based on the data analysis results and in combination with the characteristics and protection goals of the wetland ecosystem. The strategy monitoring and evaluation unit is responsible for continuously monitoring the implementation effect of the strategy, evaluating the effectiveness of the strategy and the stability of the wetland ecosystem. The monitoring data collection unit transmits the collected real-time monitoring data to the data analysis unit, and the data analysis unit transmits the analysis results to the strategy adjustment decision unit. The strategy adjustment decision unit formulates or adjusts the water level adjustment strategy based on the analysis results. The strategy monitoring and evaluation unit continuously monitors the implementation effect of the strategy and feeds back the evaluation results to the data analysis unit and the strategy adjustment decision unit.

6. The eco-friendly water level dynamic monitoring and adjustment system according to claim 1, characterized in that: The automatic control system module includes an instruction receiving unit, an instruction parsing unit, an equipment status monitoring unit, an automatic adjustment execution unit and a feedback and monitoring unit. The instruction receiving unit is responsible for receiving water level adjustment instructions from the decision support system module and the adaptive adjustment mechanism module. The instruction parsing unit parses the received instructions to clarify the specific requirements for water level adjustment. The equipment status monitoring unit monitors the working status of the water level adjustment facility in real time. The automatic adjustment execution unit automatically adjusts the operation of the water level adjustment facility according to the parsed instructions and the real-time monitored equipment status. The feedback and monitoring unit feeds back the actual operating status and water level changes of the water level adjustment facility to the decision support system module and the adaptive adjustment mechanism module, and continuously monitors the water level adjustment process.

7. The eco-friendly water level dynamic monitoring and adjustment system according to claim 1, characterized in that: The alarm and notification module includes a threshold setting and monitoring unit, an alarm information generating unit, a receiver management unit, an alarm information sending unit and an alarm recording and tracking unit. The threshold setting and monitoring unit is responsible for presetting the thresholds of water level and ecological environment indicators, and continuously monitoring the actual values ​​of water level and ecological environment indicators. When the indicators are monitored to exceed the thresholds, the alarm information generating unit is responsible for generating alarm information. The receiver management unit is responsible for maintaining the receiver list, including the contact information of relevant managers and stakeholders. The alarm information sending unit selects a suitable communication method according to the receiver list and sends the alarm information to the relevant personnel. The alarm recording and tracking unit records the detailed information of each alarm and provides a tracking function. The threshold setting and monitoring unit continuously monitors the actual values ​​of water level and ecological environment indicators and compares them with the preset thresholds. When the indicators exceed the thresholds, the alarm information generating unit is triggered to generate alarm information. When generating the alarm information, the alarm information generating unit will query the receiver management unit to obtain the contact information of the relevant managers and stakeholders. After sending the alarm information, the alarm information sending unit will pass the relevant information to the alarm recording and tracking unit for recording.

8. The eco-friendly water level dynamic monitoring and adjustment system according to claim 4, characterized in that: The prediction model unit constructs and trains a prediction model based on historical data and real-time data to predict future water levels. The specific steps are as follows: Data preparation: Collect historical water level data, including time series observations, and preprocess the data; Stationarity test: Draw a time series graph to see if it is stationary. Use the ADF test to test for stationarity. If the time series is not stationary, it needs to be differentiated until it becomes a stationary series. The number of differences is the d parameter in the ARIMA model. Model parameter determination: Draw the ACF graph (autocorrelation graph) and PACF graph (partial autocorrelation graph), and determine the p and q parameters in the ARIMA model based on the tailing and truncation characteristics of the ACF graph and PACF graph; Model establishment: According to the determined p, d, q parameters, an ARIMA model is established. The mathematical expression of the ARIMA model is: Y(t)=c+φ1Y(t-1)+φ2Y(t-2)+…+φpY(tp)+εt+θ1ε(t-1)+θ2ε(t-2)+…+θqε(tq), where Y(t) is the value of the time series, t is the time point, c is the constant term, φ1, φ2,..., φp are autoregressive coefficients, εt is the interference term, and θ1, θ2,..., θq are moving average coefficients; Model testing: Perform residual analysis on the established ARIMA model to check whether the residuals meet the assumptions. If the residual analysis does not meet the requirements, the model parameters need to be readjusted. Prediction and evaluation: Use the established ARIMA model to predict the water level in the future.

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