Visual river and lake water area health ecological supervision and management system

Abstract

The invention relates to the technical field of environment intelligent supervision, and discloses a visual river and lake water area health ecological supervision and management system. A data acquisition and feature extraction module of the system is responsible for acquiring multi-source ecological monitoring data of water areas of rivers and lakes, extracting spatial change features of motion trails of aquatic organisms and a time sequence of water quality parameters from the data, and identifying abnormal features according to an index variation mode corresponding to a predefined ecological health level; and generating an ecological health abnormity response label set associated with the specific geographic position. And the health state analysis module obtains key information of the marked water area section based on the label set, analyzes a change track and a deviation interval of the health state in the supervision time frame, and determines a health state lagging time period. And the evaluation delay processing module is used for extracting related time information to calculate a time difference according to the water area subareas related to the lagging time period, screening out the water area subareas which are not evaluated completely and sorting the water area subareas, and generating an adjustable configuration scheme of the monitoring time period of the water area subareas.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental intelligent supervision, in particular to a visual river and lake water area health ecological supervision and management system. BACKGROUND

[0002] In the past 20 years, with the increasing emphasis on ecological environment, the concept of river health has emerged, and its connotation has been continuously clarified and improved. River health issues have become a hot topic in exploring the renewable maintenance of aquatic ecosystems, biodiversity maintenance, and corresponding river ecological restoration, protection, and management. Rivers, as an important part of the Earth's ecological system, not only provide water for production, life, and ecology for humans, but also play an irreplaceable role in maintaining the Earth's water cycle, energy balance, and climate change. However, since the late 20th century, rapid population growth and increasing human activities, such as industrial wastewater discharge, agricultural non-point source pollution, excessive water resource development and utilization, and riverbank vegetation destruction, have led to local, regional, and even global water ecological and environmental problems, such as river drying up, depletion, biodiversity loss, and water pollution, which have severely damaged the structure and function of rivers.

[0003] In order to better understand the current situation of rivers and predict their future trends, river and lake health evaluation is particularly important. Using the established river health evaluation index system, we can understand the current actual situation of rivers and make reasonable predictions about future trends. River and lake health evaluation aims to comprehensively understand the health status of rivers and lakes, quickly and accurately identify existing problems, and timely and in-depth diagnose the causes of the problems, thereby establishing a perfect health record. This not only provides scientific and reasonable basis for implementing river and lake management and protection, but also helps the public to deeply understand the real health status of rivers and lakes, and promotes the active performance of responsibilities by river and lake managers at all levels and relevant departments. In a word, river and lake health evaluation is the core content of river and lake management, and is also the key means to test whether the river and lake management system is truly "famous" and "practical". Through the study and evaluation of river health, we can more scientifically formulate river protection and management strategies and achieve sustainable development of river ecosystems.

[0004] Traditional water area monitoring methods usually rely on manual on-site sampling and laboratory analysis. Staff need to personally go to each sampling point of the river and lake water area, use professional tools to collect water samples, soil samples, and observe and record vegetation coverage data. Then, the collected samples are taken back to the laboratory for detection and analysis using various complex instruments and equipment to obtain relevant environmental parameter data.

[0005] This traditional monitoring method has many obvious shortcomings. First, the response speed is slow. From field sampling to laboratory analysis, to the final acquisition of data results, it often takes a long time. During this period, the ecological status of rivers and lakes may have changed, resulting in data that cannot accurately reflect the current actual situation in a timely manner. For example, for some sudden water pollution incidents, the traditional monitoring method is difficult to detect and respond in a timely manner. By the time the problem is detected, the pollution may have spread, causing greater damage to the ecological environment.

[0006] The cost is high. Manual field sampling requires a large amount of manpower, and workers need to have professional knowledge and skills, which increases the cost of manpower. At the same time, the tools and equipment required for sampling and the instruments and equipment required for laboratory analysis are relatively expensive, and a certain cost is also required for sample transportation. In addition, in order to ensure the accuracy and representativeness of the monitoring data, multiple sampling needs to be carried out in different areas and time periods, which further increases the cost of investment.

[0007] It is difficult to achieve large-area supervision. Due to the limitations of manpower, material resources and time, the traditional monitoring method cannot comprehensively and carefully monitor large areas of rivers and lakes. The distribution of sampling points is relatively sparse, and it is difficult to cover every corner of the river and lake, so the ecological status of some areas cannot be monitored in a timely manner, and there are monitoring blind spots, which is extremely disadvantageous for a comprehensive understanding of the ecological health status of rivers and lakes. With the increasing demand for river and lake management, this traditional monitoring method is increasingly difficult to meet the needs of actual work, and it is urgent to replace it with a more efficient, accurate and comprehensive monitoring method. SUMMARY

[0008] The purpose of the present application is to provide a visual river and lake water area health ecological supervision and management system to solve the problems raised in the above background art.

[0009] To achieve the above purpose, the present application provides a visual river and lake water area health ecological supervision and management system, which comprises: A data acquisition and feature extraction module is used to acquire multi-source ecological monitoring data of rivers and lakes, extract spatial variation features of aquatic organism activity trajectories and time series of water quality parameters from the multi-source ecological monitoring data, identify abnormal features based on pre-defined index variation patterns corresponding to ecological health levels, and generate an ecological health abnormal response label set, wherein each label is associated with a specific geographic location; A health status analysis module is used to acquire the baseline health level, initial monitoring time point and final evaluation time point of each labeled water area segment based on the ecological health abnormal response label set, analyze the change trajectory and deviation interval of the health status within the supervision time frame, and determine the health status lag period; The evaluation delay processing module is configured to extract the duration of each subzone at the end of the two consecutive monitoring periods and the evaluation completion time point of the ecological indicators at the end of the monitoring period, calculate the time difference between the time points and the end time of the current monitoring period to determine whether the evaluation is completed, screen out the water subzones whose evaluation is not completed, and prioritize the water subzones according to the evaluation delay duration, and generate a water subzone monitoring period adjustable configuration scheme based on the sorting result.

[0010] Preferably, the ecological health anomaly response label set includes a code for uniquely identifying a health anomaly event, a code representing an anomaly category, and accurate spatial coordinates of a monitoring point; the health state lag time period covers the duration of state response delay, the length of health recovery process lag, and the time interval from anomaly event triggering to health rhythm deviation; the water subzone monitoring period adjustable configuration scheme includes a water subzone priority sequence based on evaluation delay, an adjustment amount of monitoring period length, and a time point at which a scheduling control parameter takes effect; the monitoring period execution instruction includes a condition for triggering period switching, a time point at which a synchronous recovery state is identified, and a determination condition for spatial structure compression.

[0011] Preferably, the step of generating the ecological health anomaly response label set further comprises: Collecting multi-source ecological monitoring data, and for each monitoring point, calculating the spatial coordinate change of the water organism activity track and the duration of the water quality parameter time series; Based on the spatial coordinate difference and the time series length, deriving the spatial offset distance and the time change period of each monitoring point to form an offset and change time sequence parameter set; Using the offset and change time sequence parameter set, analyzing the parameter fluctuation of the monitoring point in the health state change process, counting the number of abnormal fluctuations, and establishing a correlation model between the number of abnormal fluctuations and the change period, thereby calculating the ecological health fluctuation frequency; Comparing the ecological health fluctuation frequency with a preset threshold, identifying the monitoring points with excessive fluctuation frequency, and associating their abnormal behaviors with geographical locations to generate the ecological health anomaly response label set.

[0012] Preferably, the step of determining the health state lag time period further comprises: Obtaining the labeled water subzone from the ecological health anomaly response label set, extracting the reference health level, the initial monitoring time point and the final evaluation time point of each subzone within the monitoring period, calculating the time interval between the initial response time and the final evaluation time, and generating health key time interval data; Based on the health key time interval data, combined with the total number and numerical distribution of the monitoring points in the supervision period, the total coverage and density level of the health status are analyzed to obtain health density distribution information; According to the health density distribution information, the time interval distribution of the position points before, during and after the state change in the health key time interval is calculated, the health density offset characteristic value is derived, the position of the density abnormal section in the supervision period is identified, and the health density offset time period is generated; Based on the health density offset time period, the relevance of the time period and the health state change section in the supervision period is evaluated, the continuous section where the health state change exists is filtered out, and the health behavior delay occurrence area is marked to form the health state lag time period.

[0013] Preferably, the step of generating the water area partition monitoring period adjustable configuration scheme further comprises: According to the water area partition number specified in the health state lag time period, the static continuous time length and the evaluation completion time point of the terminal ecological index of each partition at the monitoring late stage in the continuous two supervision periods are extracted, the time difference value between the evaluation completion time point and the current cycle monitoring end time point is calculated, and the water area partition end section evaluation lag information is obtained; Based on the water area partition end section evaluation lag information, it is judged whether the evaluation lag value exceeds the preset evaluation completion benchmark threshold value, the water area partition where the evaluation is not completed is filtered out, and the terminal index waiting time of the corresponding partition is extracted. According to the waiting time, a water area partition evaluation waiting priority sequence is generated; According to the sorting in the water area partition evaluation waiting priority sequence, an additional monitoring time period is allocated to each water area partition, the monitoring time period length is adjusted within the total monitoring time length, the water area partition number and the adjusted monitoring duration are recorded, and a water area partition monitoring period adjustable configuration scheme is generated.

[0014] Preferably, the system comprises: The supervision period management module is used to check the spatial distribution mode of the biological population in the real-time supervision period using the initial parameters in the water area partition monitoring period adjustable configuration scheme, and identify the starting time point of the continuous recovery of a group of adjacent monitoring areas. If all starting time points are earlier than the threshold time point of the supervision plan switching, it is recorded as a synchronous recovery event, and a supervision period execution instruction is generated; The dynamic adjustment module is used to dynamically adjust the monitoring frequency and spatial coverage range in combination with the supervision period execution instruction and the health state lag time period, and output an optimized supervision strategy.

[0015] Preferably, the step of generating the supervision period execution instruction further comprises: The water area partition starting configuration time value recorded in the water area partition monitoring period adjustable configuration scheme is called, the spatial distance distribution of the ecological indicators in the corresponding partition monitoring area in the real-time supervision period is detected, a continuous improvement starting time point of a group of adjacent monitoring points in the monitoring section is identified, and a monitoring point improvement starting time set is generated; According to the time points of the improvement actions of the adjacent monitoring points in the monitoring point improvement starting time set, it is judged whether all the improvement times are earlier than the threshold time point of the supervision preparation switching in the water area partition monitoring period adjustable configuration scheme. If the condition is met, it is marked as a synchronous improvement state, and the water area partition number and the state information are associated, and a supervision period execution instruction is generated.

[0016] Preferably, the process of judging whether all the improvement times are earlier than the threshold time point of the supervision preparation switching in the water area partition monitoring period adjustable configuration scheme comprises: calculating the average time difference of the adjacent monitoring point improvement time points, and comparing the average time difference with the switching time difference threshold. If the average time difference is less than the switching time difference threshold, it is determined that it is earlier than the threshold time point of the supervision preparation switching.

[0017] Preferably, the method further comprises the following steps: The period number marked in the supervision period execution instruction is called, the health trajectory data of the terminal monitoring point is screened, the change trend of the health indicator is compared with the end section length, if the health level continuously improves and has not reached a stable state, the additional time required to reach a predetermined health point is calculated, and the end section control period is updated to obtain an end section monitoring continuous regulation result; The step of obtaining the end section monitoring continuous regulation result further comprises: The supervision period number marked in the supervision period execution instruction is extracted, the trajectory data of the terminal monitoring point in the monitoring later section under the corresponding period is screened, the continuous change sequence and the time stamp from the end section starting time to the health indicator stable point are extracted, and an end section health trajectory sequence is generated; Based on the end section health trajectory sequence, the health change trend of the terminal monitoring point in the end section length is analyzed, the health amplitude value of the sequence end section is extracted, and compared with the end section duration. If the health level continuously rises and has not entered a stable interval, the additional time length required for the monitoring point to reach a predetermined health point is calculated, and an end section remaining health time interval is generated; The water area partition required additional time value in the end section remaining health time interval is called, the real-time end section monitoring control period is updated, the originally configured end section monitoring end time point is adjusted, the control interval of the water area partition end section is re-set, and an end section monitoring continuous regulation result is obtained; The end section monitoring continuous regulation result comprises a remaining health path extension length, a terminal monitoring point health completion prediction period, and a monitoring retention recommended time window.

[0018] Preferably, the step of obtaining the end-stage monitoring duration control result further comprises: Extracting the supervision cycle number marked from the supervision cycle execution instruction, screening the trajectory data of the terminal monitoring point in the monitoring late stage in the corresponding cycle, extracting the continuous change sequence and timestamp from the health index stable point to the end stage starting time, and generating the end stage health trajectory sequence; Based on the end stage health trajectory sequence, the health change trend of the terminal monitoring point in the end stage duration is analyzed, the health increment value of the sequence end stage is extracted, and compared with the end stage duration, if the health level continuously rises and does not enter the stable interval, the remaining health time distance of the end stage is calculated, and the remaining health time distance of the end stage is generated. The remaining health time distance of the end stage is called, the remaining health time distance of the end stage is updated, the original configuration of the end stage monitoring end time point is adjusted, the control interval of the water area partition end stage is reset, and the end stage monitoring duration control result is obtained. The end stage monitoring duration control result includes the remaining health path duration, the terminal monitoring point health completion prediction period, and the monitoring retention recommended time window.

[0019] Compared with the prior art, the beneficial effects of the present application are: The data acquisition and feature extraction module of the present application has powerful functions. When collecting multi-source ecological monitoring data, it can comprehensively collect various information of river and lake water area through various advanced sensor devices, including but not limited to water quality parameters, aquatic organism activity trajectory, water level change, meteorological data, etc. These sensors are like "antennae" distributed in the river and lake, which can capture every subtle change of the water ecological environment in real time.

[0020] For the spatial change feature extraction of aquatic organism activity trajectory, the module uses advanced image recognition and tracking technology. Through the deployment of high-definition cameras and intelligent image analysis systems in the middle of the river and lake, different types of aquatic organisms can be identified, and their swimming path, habitat area change and other information can be accurately recorded. For example, for the migration route of fish, the system can clearly draw the migration trajectory of fish in different seasons and different time periods, thereby providing detailed data for the study of the ecological habits of aquatic organisms.

[0021] In terms of time series extraction of water quality parameters, high-precision water quality monitoring sensors are used to continuously monitor water temperature, dissolved oxygen, pH value, chemical oxygen demand and other key indicators, and arrange these data in chronological order to form a complete time series. In this way, the trend of water quality parameters changing with time can be directly observed, such as the influence of high temperature in summer on the content of dissolved oxygen.

[0022] Based on predefined variation patterns of indicators corresponding to ecological health levels, this module can quickly and accurately identify abnormal features. By comparing real-time monitored data with pre-set standard patterns, an anomaly identification mechanism is immediately triggered once data deviates from the normal range. Furthermore, each abnormal feature generates a corresponding set of ecological health anomaly response tags. These tags are associated with specific geographical locations, like attaching a "location tag" to each anomaly point, enabling staff to quickly pinpoint the specific area where the anomaly occurred, clarify the type and severity of the anomaly, and provide clear direction for subsequent governance and restoration work.

[0023] The health status analysis module, based on a set of ecological health anomaly response tags, conducts an in-depth analysis of the health status of rivers and lakes. It first obtains the baseline health level, initial monitoring time point, and final assessment time point for each labeled water segment. By analyzing this key information, a health record is established for each water segment.

[0024] Advanced data modeling and analysis algorithms were used to analyze the trajectory of changes in water health status. Ecological indicator data from different time points were integrated to create a health status change curve. This curve visually demonstrates how the health status of a body of water evolves over time—whether it gradually improves, remains stable, or tends to deteriorate. For example, analysis of the curve reveals that after implementing a series of ecological protection measures, water quality indicators gradually improved, biodiversity increased, and the health status of a certain section of water was significantly enhanced.

[0025] Analyzing deviations provides another perspective on the health of aquatic waters. It assesses the degree of fluctuation in health status by calculating the range of deviation between actual monitoring data and baseline health levels. If water quality parameters in a particular water segment frequently exceed the normal fluctuation range over a period of time, it indicates that the ecosystem of that area may be disturbed by external factors, such as illegal discharge from nearby factories or agricultural non-point source pollution. This method allows for the timely detection of potential ecological risks.

[0026] Identifying the health status lag period is crucial for timely understanding of river and lake ecological changes. When the health status of a certain water area fails to meet expected improvement targets or shows a deteriorating trend within a certain timeframe, this period can be designated as the health status lag period. This information allows managers to adjust their management strategies promptly, increase monitoring of the area, and implement targeted governance measures, such as increasing water purification facilities and strengthening pollution source control, to ensure the healthy development of river and lake ecosystems.

[0027] The assessment delay processing module plays a crucial role in optimizing the allocation of monitoring resources within the entire system. It performs detailed temporal information extraction for the water zones involved in the health status lag period. By accurately extracting the duration of final ecological indicators in the later stages of monitoring and the assessment completion time for each zone across two consecutive monitoring cycles, it obtains the temporal dynamic information of the ecological assessment for each zone.

[0028] Calculating the time difference between these time points and the end of the current monitoring cycle is a key method for determining whether the assessment is complete. If the time difference is zero or negative, it indicates that the assessment of that area has been completed within the current monitoring cycle; conversely, if the time difference is positive, it indicates that the assessment has been delayed. This quantitative method allows for the clear identification of water areas where assessments have not been completed.

[0029] The water areas whose assessments were not yet completed were prioritized, taking into full account factors such as the assessment delay time. Areas with longer delays had higher priority because these areas may have more complex ecological problems that require immediate attention and treatment. An adjustable monitoring schedule scheme for each water area, generated based on the prioritization results, allows for flexible adjustments to monitoring time and resource allocation according to the actual conditions of different areas. For high-priority areas, monitoring frequency can be increased, with more human, material, and technical resources invested to ensure timely understanding of ecological changes and buy valuable time to address ecological problems. For lower-priority areas, resources can be allocated rationally while ensuring basic monitoring needs are met, maximizing the utilization of monitoring resources and effectively improving the efficiency and effectiveness of river and lake water area supervision, thus ensuring the health and stability of river and lake ecosystems. Attached Figure Description

[0030] Figure 1 This is a schematic diagram illustrating the working principle of the visualized river and lake water health and ecological supervision and management system described in this invention. Figure 2 A flowchart explaining the constituent elements such as ecological health anomaly response labels; Figure 3 A flowchart of the steps to determine the lag time period of health status. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1This invention provides a visualized river and lake water health and ecological supervision and management system. The system includes: a data acquisition and feature extraction module, a health status analysis module, and an assessment delay processing module. The data acquisition and feature extraction module is responsible for collecting multi-source ecological monitoring data of rivers and lakes, such as acquiring aquatic biological activity trajectories and water quality parameters through sensor networks. This module extracts spatial variation features and time series from the data, identifies abnormal features based on predefined ecological health level indicator variation patterns, and generates a set of ecological health anomaly response labels. Each label is associated with a specific geographical location, providing basic data for subsequent analysis. The health status analysis module receives the label set as input, obtains the baseline health level, initial monitoring time point, and final assessment time point for each labeled water segment, analyzes the change trajectory and deviation range of the health status within the supervision time frame, thereby determining the health status lag time period and revealing the phenomenon of delayed health response. The assessment delay processing module extracts the final ecological indicator data of each water area involved in the lag period during the continuous monitoring cycle, calculates the time difference between the assessment completion time and the end time of the current cycle, determines the assessment completion status, filters incomplete areas and sorts them by priority according to the delay duration, and finally generates an adjustable configuration scheme for the monitoring period of the water area to achieve the optimal allocation of monitoring resources.

[0033] Example 1: See Figure 2 In practical implementation, the data acquisition and feature extraction module collects multi-source ecological monitoring data through a sensor network consisting of fixed monitoring stations and mobile monitoring equipment deployed in river and lake waters. Fixed monitoring stations are equipped with underwater sonar and high-definition cameras to capture images of aquatic life activities. Mobile monitoring equipment includes unmanned vessels and buoys carrying multi-parameter water quality probes. The multi-source ecological monitoring data includes latitude and longitude coordinate sequences of aquatic life activity trajectories and time series of water quality parameters. The water quality parameter time series involves equally spaced sampling values ​​of indicators such as dissolved oxygen content, pH value, and turbidity. For each monitoring point, the spatial coordinate change of the aquatic life activity trajectory is calculated using the Euclidean distance formula between continuous trajectory points for point-by-point difference calculation. Simultaneously, the duration of the water quality parameter time series is calculated by extracting the difference between the start and end timestamps of each parameter sequence. The spatial coordinate change and duration together constitute the original feature vector of the monitoring point.

[0034] Based on spatial coordinate differences and time series length, the spatial offset distance of each monitoring point is calculated using the straight-line distance between the center point of the trajectory point set and the initial point. The time variation period is extracted using a periodic analysis algorithm for water quality parameter sequences to obtain the periodic value corresponding to the dominant frequency, forming a set of offset and variation time series parameters. This set is stored as a structured data table containing monitoring point number, spatial offset distance, and time variation period. Using this set, the parameter fluctuations of monitoring points during changes in health status are analyzed. The variance of parameters within each window is calculated using the sliding time window method. The number of abnormal fluctuations is counted by setting a variance threshold to identify the number of windows exceeding the threshold. A correlation model between the number of abnormal fluctuations and the variation period is established. A linear regression method is used to fit the relationship between the number of fluctuations and the period length, thereby calculating the ecological health fluctuation frequency as the average number of abnormal fluctuations per unit time. The frequency of ecological health fluctuations is compared with a preset threshold, which is determined based on quantile analysis of historical health data. Monitoring points exceeding the frequency limit are identified by comparing their point-by-point frequency values ​​with the threshold. Their abnormal behavior is then correlated with their geographical location by querying the coordinate records in the geographic information system (GIS) using the numbers of the exceeding monitoring points. This generates a set of ecological health anomaly response tags, which includes a UUID code to uniquely identify the health anomaly event, a classification code representing the anomaly category, and the latitude and longitude coordinates of the monitoring point in the WGS84 coordinate system. The health status lag time encompasses the duration of the status response delay, calculated through the time difference between the event occurrence time and the system response time. The length of the health recovery process lag is obtained through deviation analysis of the recovery curve and the standard recovery template. The time interval from the triggering of the anomaly event to the shift in the health rhythm is obtained through health indicator phase change detection.

[0035] In some embodiments, the adjustable configuration scheme for water area zoning monitoring periods includes generating a water area zoning priority sequence based on assessment delay by sorting it in reverse order of delay duration; determining the adjustment amount of the monitoring period length by multiplying the delay duration by the unit monitoring time; and using the start time of the next monitoring cycle as the effective time point for scheduling control parameters. The monitoring cycle execution instructions include conditions for triggering cycle switching determined by a monitoring point coverage threshold; the time when the synchronization recovery status is identified is determined by the consistency detection of the recovery times of adjacent monitoring points; and the determination condition for spatial structure compression is calculated by the rate of change of monitoring point distribution density. In a specific implementation, the data acquisition module receives real-time sensor data streams through a distributed message queue, the feature extraction process uses a streaming computing framework for real-time processing, and the generated ecological health anomaly response tag set is stored in a time-series database and a spatial index is established.

[0036] It is understandable that the collection frequency of multi-source ecological monitoring data is set in a tiered manner based on the area and ecological sensitivity of rivers and lakes. Key monitoring areas use a minute-level collection frequency, while general areas use an hourly collection frequency. The anomaly category codes in the ecological health anomaly response label set adopt a hierarchical coding system. The first-level code represents the broad category of anomaly, such as biological anomaly or water quality anomaly, while the second-level code represents the specific anomaly type, such as abnormal fish aggregation or a sudden drop in dissolved oxygen. In practical implementation, the calculation of spatial offset distance takes into account the topographic factors of the water area, using digital elevation model data to perform topographic correction on the straight-line distance. The analysis of temporal variation cycles uses the Fast Fourier Transform algorithm to extract the main periodic components from the water quality parameter sequence.

[0037] Optionally, the calculation of the frequency of ecological health fluctuations employs a weighted average method, assigning weight coefficients to the fluctuation frequency of different parameters based on their ecological importance. These weight coefficients are pre-set by the expert evaluation system. The update mechanism for the offset and change time-series parameter set adopts a rolling update mode, retaining the latest parameter historical records for trend analysis. In specific implementation, a time decay factor is introduced into the statistics of abnormal fluctuation frequencies, assigning higher statistical weights to recent abnormal fluctuations, making the frequency of ecological health fluctuations more reflective of the current state. Specifically, the time decay factor is a weighting coefficient used to assign different statistical weights based on the time elapsed since the occurrence of the abnormal fluctuation; recent abnormal fluctuations are given higher weights, while older ones are given lower weights. The time decay factor is obtained through analysis of the time series of historical abnormal fluctuation data. When calculating parameter fluctuations using the sliding time window method, the system dynamically adjusts the weight coefficients based on the time difference between the current time point and the time point of fluctuation occurrence, making recent fluctuations have a greater impact on the calculation of the current ecological health fluctuation frequency. For the statistics of abnormal fluctuations, introducing a time decay factor can make the frequency of ecological health fluctuations more accurately reflect the current state of rivers and lakes, avoid the excessive influence of historical abnormal fluctuations on the current assessment, and thus improve the real-time performance and accuracy of abnormal feature identification.

[0038] In some embodiments, the preset threshold is determined using a dynamic threshold adjustment algorithm, which automatically adjusts the threshold size based on seasonal variations and hydrological conditions. The dynamic threshold adjustment algorithm establishes a threshold curve based on the statistical distribution characteristics of historical data from the same period. Smoothing is introduced into the calculation of spatial coordinate changes at monitoring points; a Kalman filter algorithm is used to denoise the original trajectory coordinates, eliminating coordinate jumps caused by measurement errors. It can be understood that the duration calculation of the water quality parameter time series considers data gaps; linear interpolation is used to fill in the missing data periods before calculating the complete duration.

[0039] In practical implementation, the correlation model is established using a multiple regression method. In addition to the time variation period, environmental factors such as water temperature and flow velocity are introduced as auxiliary variables to improve the accuracy of predicting the frequency of ecological health fluctuations. The process of establishing the correlation model is as follows: First, the system collects the number of abnormal fluctuations, the time variation period, and the corresponding environmental monitoring data for each monitoring point, including auxiliary variables such as water temperature, flow velocity, dissolved oxygen content, pH value, and turbidity. Next, a multiple regression analysis method is used, with the number of abnormal fluctuations as the dependent variable and the time variation period, water temperature, flow velocity, dissolved oxygen content, pH value, and turbidity as independent variables, for regression fitting. This model quantifies the correlation strength and direction between the above variables and the number of abnormal fluctuations, thus establishing a mathematical model capable of predicting the number of abnormal fluctuations based on the time period and multiple environmental conditions. The generation process of the ecological health abnormal response label set includes a manual review process. Labels automatically generated by the system must be confirmed by domain experts before being officially added to the database. The manual review process allows experts to annotate and correct labels online through a web management interface. The coordinate records of the geographic information system adopt a hierarchical storage structure. The basic layer stores the static coordinates of the monitoring points, while the dynamic layer updates the movement trajectory of the monitoring points in real time.

[0040] Optionally, uncertainty analysis is introduced into the calculation of the health status lag time. Multiple possible lag time distributions are generated through Monte Carlo simulation, and the median of the distribution is taken as the final lag time value. A validity verification mechanism is set up for the generation of execution instructions during the monitoring cycle, performing logical conflict detection and resource availability checks on newly generated instructions. This validity verification mechanism is implemented through a rule engine. In specific implementation, the determination criteria for spatial structure compression are calculated using cluster analysis. After density clustering of the monitoring point distribution, the change rate of compactness indicators for each category is calculated.

[0041] It is understandable that the calculation of spatial coordinate changes in the activity trajectories of aquatic organisms differentiates between different types of organisms. Fish trajectories and benthic organism trajectories employ different movement characteristic parameters, including average movement speed and activity range radius. The duration analysis of water quality parameter time series differentiates between parameter types. Chemical parameter sequences and biological parameter sequences use different period extraction algorithms. Chemical parameter sequences utilize spectral analysis, while biological parameter sequences employ behavioral pattern recognition. In practical implementation, the counting of abnormal fluctuations is constrained by a minimum time interval to prevent repeated counting of continuous fluctuations within a short period. This minimum time interval is set as a configurable parameter based on the parameter characteristics.

[0042] Optionally, the storage of the ecological health anomaly response tag set adopts a distributed columnar database, supporting efficient spatial and temporal range queries. The database establishes a composite index including spatial and temporal indexes. The generation of the monitoring point start time set is improved by setting a time synchronization mechanism, using a network time protocol to unify the timestamps of all monitoring devices, ensuring consistency of time data across devices. In specific implementation, the transmission of offset and change time series parameter sets adopts a binary encoding format to reduce the amount of data transmitted over the network. The binary encoding format includes fields such as data header identifier, number of parameters, and parameter value array.

[0043] Example 2: See Figure 3In practical implementation, the health status analysis module obtains labeled water segments from the ecological health anomaly response tag set, which is derived from the output of the data acquisition and feature extraction module. Each labeled water segment is associated with a set of monitoring points with ecological health anomaly response tags. The baseline health level of each water segment within the monitoring period is extracted by querying the historical health database to obtain the average index value of that segment under normal conditions. The initial monitoring time point is parsed from the ecological health anomaly response tag set to obtain the first trigger timestamp of each abnormal event. The final assessment time point is extracted from the assessment log records to obtain the time information of the most recent completed health assessment. The time interval between the initial response time and the final assessment time is calculated by directly subtracting the timestamps to generate key health time interval data. This data records the time span from the occurrence of an anomaly to the most recent assessment for each water segment. Based on the key health time interval data, combined with the total number and numerical distribution of monitoring points within the monitoring period, the total coverage area of ​​the health status is analyzed by calculating the area of ​​the smallest convex polygon containing the abnormal monitoring points. The density level is calculated by the number of abnormal monitoring points per unit area, yielding health density distribution information, which includes spatial coverage area and point density values. Based on health density distribution information, the time interval distribution of locations before, during, and after state changes within the critical health time interval is calculated. Locations before state changes correspond to the initial monitoring time point, locations during state changes correspond to the inflection point of health index changes, and locations after state changes correspond to the final assessment time point. The time interval distribution is obtained by statistically analyzing the time differences between these three locations. The health density shift feature value is derived by calculating the standard deviation of the time intervals. The location of density anomaly segments within the monitoring period is identified by finding outliers in the time interval distribution, generating health density shift time periods. These shift time periods mark consecutive periods within the monitoring period where the time intervals are abnormally large or small. Further, this is achieved by calculating the standard deviation of the time interval distribution of locations before, during, and after state changes within the critical health time interval. The system first extracts the initial monitoring time point, the inflection point of health index changes, and the final assessment time point for each water segment within the monitoring period, calculates the time interval sequence between these locations, and then performs statistical analysis on this time interval sequence, obtaining the standard deviation as the health density shift feature value. Identifying density anomalies is accomplished by analyzing outliers in the time interval distribution. The system uses statistical methods (such as box plots or Z-scores) to identify time intervals that deviate from the normal range, thereby locating density anomalies in the monitoring period.Based on the health density offset time period, the correlation between time segments and health status change segments within the monitoring period is assessed using Pearson correlation coefficient analysis. Continuous segments with delayed health status changes are screened out. Weakly correlated time periods are identified by setting a correlation coefficient threshold and marked as areas where health behavior delays occur, forming a health status lag time period. The health status lag time period includes the start time and duration of the delayed segment.

[0044] For a specified water area zone number within the health status lag period, the system extracts the static duration of the final ecological indicators for each water area in the later stages of monitoring over two consecutive monitoring cycles. The final ecological indicators refer to the core health parameters at the end of the monitoring cycle. The static duration is determined by analyzing the continuous time period during which the indicator values ​​remain stable within a threshold range. The assessment completion time is parsed from the workflow log of the assessment system. The time difference between the assessment completion time and the end time of the current monitoring cycle is calculated using timestamp subtraction to obtain the final assessment lag information for each water area zone. This final assessment lag information quantifies the assessment delay degree of each water area zone. Based on the final assessment lag information, it is determined whether the assessment lag value exceeds a preset assessment completion benchmark threshold. The assessment completion benchmark threshold is set to a fixed time length according to the operating specifications. Water areas where assessment is not completed are selected by comparing the lag value with the threshold. The waiting time for the corresponding final indicators of the water areas is extracted and sorted in descending order of waiting time to generate a water area zone assessment waiting priority sequence. The water areas ranked higher in the water area assessment waiting priority sequence indicate the most severe assessment delay. Based on the priority sequence of water area zoning assessments, additional monitoring time slots are allocated to each water area using a priority-weighted algorithm. Higher-priority water areas receive a larger proportion of additional monitoring time. The length of monitoring time slots is adjusted within the total monitoring duration by reallocating fixed total monitoring resources. The water area zoning number and the adjusted monitoring duration are recorded, generating an adjustable configuration scheme for water area zoning monitoring time slots. This scheme includes a mapping table between the water area zoning number list and the adjusted monitoring duration. In implementation, a sliding window mechanism is introduced to calculate the health status lag time slot, dynamically updating the boundary range of the delay segment. The sliding window size is proportional to the monitoring period length. The storage of water area zoning end-of-phase assessment lag information adopts a time-series database structure, supporting rapid retrieval of historical delay records by time range.

[0045] In some embodiments, the calculation of health density distribution information considers the geographical boundary constraints of water area zones, and spatial intersection analysis restricts the distribution of monitoring points to the polygonal range of the zones. The analysis of the duration of quiescence of final-stage ecological indicators in the later stages of monitoring employs a rate-of-change detection algorithm; when the rate of change of an indicator remains below a set threshold, it is determined to have entered a quiescent state. The calculation of the time difference between the completion time of the assessment and the end time of the current monitoring cycle incorporates unified time zone processing, converting all timestamps to the standard time zone before calculation. It can be understood that the generation of the priority sequence for water area zone assessment is subject to a minimum delay constraint; only zones with delays exceeding the minimum time threshold are sorted, avoiding unnecessary resource adjustments.

[0046] Optionally, the storage of key health time interval data adopts a compressed encoding format to reduce storage space usage while retaining millisecond-level precision in time intervals. The density anomaly segment identification algorithm employs an adaptive threshold method, dynamically adjusting the anomaly judgment threshold based on the statistical characteristics of the overall time interval distribution. In specific implementation, Pearson correlation coefficient analysis sets a moving time window, calculating local correlation coefficients within the sliding window to improve the spatiotemporal accuracy of correlation analysis. The marking of delayed health behavior occurrence areas incorporates a manual confirmation process, displaying delayed segments through a visual interface for expert review and confirmation.

[0047] In some embodiments, the allocation of additional monitoring time periods employs a linear programming optimization model, with the goal of minimizing the total delay time to solve for the optimal allocation scheme. The adjustable configuration scheme for water area zoning monitoring time periods has a delayed activation mechanism, applying configuration changes only at the start of a new monitoring cycle. It can be understood that the calculation of the final indicator waiting time differentiates between indicator types, setting different waiting time weight coefficients for different categories of ecological indicators. In specific implementation, the total number of monitoring points and numerical distribution analysis within the monitoring cycle employs a spatial gridding method, dividing the water area into regular grids and then counting the number of monitoring points within each grid.

[0048] Optionally, the correlation assessment of health status change segments incorporates multi-dimensional features, including spatial distribution and hydrological features in addition to temporal features. The extraction and validity verification of water area zoning numbers ensures that the numbers exist in the zoning list currently managed by the system. In practical implementation, the setting of the assessment completion benchmark threshold supports hierarchical configuration, allowing different threshold standards to be set according to the importance of different water area zonings. The calculation of health density offset feature values ​​incorporates normalization processing to make the feature values ​​of different water area zonings comparable.

[0049] It is understandable that upper and lower limits are set for adjusting the monitoring period length to ensure that the adjusted monitoring duration is within a reasonable operational range. The generation of the adjustable configuration scheme for water area monitoring periods includes version control information, recording the historical record of each configuration change. In specific implementation, the formation of areas where health behavior delays occur considers seasonal influencing factors, and the delay judgment criteria are corrected by introducing a seasonal adjustment factor. A secondary confirmation process is set up for incomplete water area screening, and a manual review mechanism is initiated for boundary delay situations. Specifically, the seasonal adjustment factor is a correction coefficient based on seasonal variation patterns, used to adjust the judgment threshold for the lag period of health status. Based on the technical solution, the seasonal adjustment factor is obtained by analyzing the health density distribution information and environmental data of different seasons in historical monitoring cycles. The system automatically adjusts the threshold parameters in the delay judgment criteria according to seasonal factors (such as water temperature changes, rainfall patterns, etc.). For correcting the delay judgment criteria, introducing the seasonal adjustment factor can eliminate the interference of seasonal factors on the judgment of health status delay, making the delay judgment more consistent with the actual ecological change patterns of rivers and lakes, and improving the scientificity and adaptability of identifying the lag period of health status.

[0050] Optionally, the analysis of time interval distribution employs kernel density estimation to generate continuous probability distribution curves for anomaly segment identification. The update of lag information at the end of the water area zoning assessment uses an event-driven mechanism, triggering a recalculation of lag information immediately upon completion of a new assessment. In practical implementation, the allocation of additional monitoring time periods considers equipment load balancing to avoid excessive concentration of monitoring resources in certain water area zonings. The output of the health status lag time period includes a confidence index, reflecting the reliability of the delay judgment.

[0051] Example 3: In specific implementation, the monitoring cycle management module calls the initial parameters recorded in the adjustable configuration scheme of the water area zoning monitoring period. The adjustable configuration scheme of the water area zoning monitoring period includes the water area zoning start configuration time value, the monitoring period length adjustment amount, and scheduling control parameters. The initial parameters are used to initialize the operating environment of the current monitoring cycle. The spatial distribution pattern of biological populations in the real-time monitoring cycle is checked by fusing remote sensing image interpretation with ground monitoring point data. The spatial distribution pattern of biological populations includes species richness distribution map and population density heat map. The starting time point of continuous recovery of a group of adjacent monitoring areas is identified by time series clustering algorithm to identify monitoring area groups with similar recovery trends. If all starting time points are earlier than the threshold time point of the monitoring plan switch, they are recorded as synchronous recovery events. Synchronous recovery events mean that multiple adjacent areas have started the ecological recovery process before the plan switch and generate a monitoring cycle execution command. The monitoring cycle execution command includes the cycle switch trigger condition, the synchronous recovery status identifier, and the spatial structure compression parameters.

[0052] The specific implementation process includes calling the initial configuration time value of the water area zoning monitoring period recorded in the adjustable configuration scheme. The initial configuration time value represents the activation time point of each zoning in the current monitoring cycle. The spatial distance distribution of ecological indicators within the corresponding zoning monitoring area in the real-time monitoring cycle is calculated using the Euclidean distance matrix method. Ecological indicators include water quality parameters and biological activity indicators, and the spatial distance distribution reflects the degree of ecological similarity between monitoring points. A set of continuous improvement start times for a group of adjacent monitoring points within the monitoring segment is identified using a change point detection algorithm to identify the turning points in indicator trends, generating a set of monitoring point improvement start times. This set contains the mapping relationship between monitoring point numbers and improvement timestamps.

[0053] Based on the improvement start time set of adjacent monitoring points, determine whether all improvement times are earlier than the threshold time for monitoring preparation switching in the adjustable configuration scheme of water area zoning monitoring periods. The determination process uses a time window comparison method, and the formula for calculating the average time difference between adjacent monitoring point improvement times is: in: Indicates the average time difference. The number of adjacent monitoring point pairs and For a pair of adjacent monitoring points, the improvement time point is used. The average time difference is then considered. With switching time difference threshold Comparison, switching time difference threshold Based on historical operational data, if the average time difference is less than the switching time difference threshold, it is determined to be earlier than the monitoring preparation switching threshold time point. If the condition is met, it is marked as a synchronous improvement state. The synchronous improvement state means that multiple monitoring points start the ecological improvement process almost simultaneously, and the water area zoning number and status information are associated to generate a monitoring cycle execution instruction. The monitoring cycle execution instruction is used to guide the cycle switching decision.

[0054] The dynamic adjustment module combines the execution command of the monitoring cycle with the health status lag time period, which is derived from the output of the health status analysis module. Dynamic adjustment of the monitoring frequency is achieved by recalculating the sampling interval, taking into account equipment performance and energy consumption constraints. Spatial coverage adjustment is accomplished by optimizing the distribution density of monitoring points. The optimized monitoring strategy is output, which includes new monitoring parameter settings and resource allocation schemes.

[0055] In some embodiments, the analysis of spatial distribution patterns of biological populations incorporates species mobility factors, employing larger-scale spatial aggregation analysis for highly mobile species. A minimum time interval constraint is set for identifying the starting time points of continuous recovery in adjacent monitoring areas to avoid misjudging short-term fluctuations as recovery initiation. The threshold time points for switching monitoring plans are dynamically adjusted according to seasonal changes, with a more lenient threshold setting used during the rainy season. It is understood that the recording of synchronous recovery events includes a confidence score, calculated based on the quantity and quality of monitoring areas participating in the recovery. The spatial distance distribution calculation of ecological indicators uses a weighted Euclidean distance formula, assigning different weight coefficients to different ecological indicators according to their importance. Data quality verification is implemented in the generation of the monitoring point improvement starting time set, using only data points with confidence scores higher than the threshold. The average time difference calculation considers the spatial distribution density of monitoring points, employing stricter time consistency standards for high-density areas. The generation of monitoring cycle execution instructions includes a fallback mechanism, automatically degrading the execution plan when system resources are insufficient.

[0056] In some embodiments, the dynamic adjustment of monitoring frequency adopts a gradual adjustment strategy to avoid data discontinuity caused by sudden changes in monitoring frequency. Spatial coverage optimization introduces cost constraints to find the optimal coverage scheme within budget limitations. The optimized supervision strategy output includes a version identifier to facilitate tracking the history of strategy changes. It can be understood that the duration verification of the synchronization improvement status setting requires the improvement status to persist for a certain period before it is confirmed as effective. The storage of the initial configuration time value of the water area partition adopts a distributed consistency protocol to ensure time synchronization among multiple nodes. The update of the monitoring point improvement start time set adopts an event-driven mechanism, updating the set in real time when a new improvement point is detected. In specific implementations, the setting of the switching time difference threshold supports hierarchical configuration, using different threshold standards for different types of water area partitions. The execution of the dynamic adjustment module has a warm-up period, and new supervision strategies are applied gradually to avoid system oscillations.

[0057] In some embodiments, the definition of adjacent monitoring points considers hydrological connectivity, and spatial adjacency is corrected using water flow direction data. The spatial distance distribution analysis of ecological indicators incorporates a time dimension, calculating a spatiotemporal joint distance matrix. The identification of the starting time point for continuous improvement within a monitoring segment employs a multi-scale sliding window algorithm, adapting to improvement processes of varying lengths. It can be understood that the associated information of synchronous improvement status includes improvement intensity indicators, quantifying the degree of improvement at each monitoring point. The transmission of monitoring cycle execution instructions uses an encryption protocol to ensure the security of instruction transmission. Dynamic adjustment of monitoring frequency considers equipment lifecycle, avoiding premature equipment failure due to overuse. A transition period is set for spatial coverage adjustments, gradually migrating monitoring equipment to reduce service interruptions. The optimized monitoring strategy generates an impact assessment report, predicting potential changes in effects caused by strategy changes.

[0058] It is understandable that the inspection of spatial distribution patterns of biological populations includes an outlier filtering mechanism to eliminate distribution patterns that clearly do not conform to ecological laws. The starting time point records of continuous recovery in adjacent monitoring areas include geolocation codes, supporting spatial querying and analysis. Weather forecast data is incorporated into the calculation of the threshold time point for switching monitoring plans, considering the impact of meteorological factors on switching timing. In specific implementation, the storage of the set of monitoring point improvement starting times adopts a time-series database structure, supporting efficient time-range queries. A buffer is set between the comparison of the average time difference and the switching time difference threshold to avoid frequent state switching in boundary situations. The marking of synchronous improvement states incorporates a manual confirmation process, providing an expert review channel for the automatic marking results. Quality checkpoints are set in the output of the dynamic adjustment module to verify the rationality and feasibility of the new strategy. The optimized monitoring strategy is deployed using a canary deployment model, first piloted in selected areas before full-scale rollout.

[0059] Example 4: In specific implementation, the process of determining whether all improvement times are earlier than the threshold time point for monitoring preparation switching in the adjustable configuration scheme of water area zoning monitoring periods adopts a distributed time comparison algorithm. The improvement times are derived from the timestamp sequence recorded in the set of improvement start times of monitoring points, and the threshold time point for monitoring preparation switching is parsed from the scheduling control parameter segment of the adjustable configuration scheme of water area zoning monitoring periods. The average time difference between the improvement times of adjacent monitoring points is calculated through time series difference operation. The average time difference reflects the time dispersion of improvement actions of a group of monitoring points, and is compared with the switching time difference threshold, which is set to a fixed time interval based on historical operation data. If the average time difference is less than the switching time difference threshold, it is determined to be earlier than the monitoring preparation switching threshold time point, and the determination result triggers the synchronization improvement status marking operation. The system calls the cycle number marked in the monitoring cycle execution instruction. The monitoring cycle execution instruction is generated by the monitoring cycle management module, and the cycle number uniquely identifies a monitoring cycle instance. The health trajectory data of the final monitoring points is filtered by querying the time series records in the health database. The final monitoring points refer to the active monitoring points at the end of the monitoring cycle, and the health trajectory data includes the correspondence between timestamps and health indicator values. A trend analysis algorithm is used to compare the changing trends of health indicators with the duration of the final monitoring period. The duration of the final monitoring period refers to the time from the start of the later monitoring phase to the current moment. The slope value of the changing trends of health indicators is calculated using a linear fitting method. If the health level continues to improve but has not reached a stable state, the make-up time required to reach the predetermined health point is calculated using an extrapolation prediction model. The predetermined health point is set as a fixed threshold according to ecological standards. The make-up time represents the length of time required to reach the target state from the current state, and the final control cycle is updated. The final control cycle manages the time window parameters of the later monitoring phase, resulting in the continuous regulation results of the final monitoring. The continuous regulation results of the final monitoring include the adjusted monitoring end time and continuous monitoring recommendations.

[0060] In implementation, the average time difference between improvement time points of adjacent monitoring points is calculated using a sliding window mechanism, with the window size adaptively adjusted according to the monitoring point density. Comparison between improvement time points and threshold time points incorporates time zone standardization, converting all timestamps to Coordinated Universal Time (UTC) before calculation. A dynamic adjustment mechanism is implemented for switching time difference thresholds, employing different threshold standards based on the ecological sensitivity level of the water area zoning. The parsing of cycle numbers in the monitoring cycle execution instructions includes format verification to ensure conformity with the system-defined coding standards. The selection criteria for final monitoring points include data integrity and monitoring activity indicators, selecting only monitoring points with satisfactory data quality for analysis. The query of health trajectory data optimizes the index structure, establishing a composite index of timestamps and monitoring point numbers to improve query efficiency. The analysis of health indicator trends employs a multi-model fusion method, combining linear regression and exponential smoothing algorithms to improve the accuracy of trend judgment.

[0061] The calculation of the final phase duration considers the phase division of the monitoring cycle, and the starting point of the final phase is determined by the cycle configuration parameters. A confidence interval is set for judging the continuous improvement of health levels, requiring that the indicator values ​​at multiple consecutive time points show an upward trend. The identification of a stable state is achieved through coefficient of variation analysis; when the indicator fluctuation range is less than a set threshold, a stable state is determined. The setting of predetermined health points supports tiered standards, using different health target values ​​for different categories of water areas. The calculation of make-up time introduces uncertainty assessment, generating a time prediction interval rather than a single value. Updates to the final control cycle employ a transaction mechanism to ensure the atomicity and consistency of cycle parameter changes. The storage of continuous control results for the final monitoring includes version management, recording the parameter change history for each control measure. A re-verification mechanism is set up for the judgment process where the improvement time point is earlier than the threshold time point, initiating a secondary verification process for boundary cases. The comparison results between the average time difference and the switching time difference threshold are recorded in the judgment log, including input parameters and judgment details. Synchronous improvement status marking operations are associated with timestamps and operator information, enabling complete status change auditing. When filtering final monitoring points by cycle number, a validity check is performed, filtering out expired or discontinued monitoring point records. The comparative analysis of health trajectory data includes outlier handling, smoothing or removing values ​​that significantly deviate from the normal range. The correlation analysis between the duration of the final segment and the trend of health indicators introduces a weighting factor, assigning higher weight to recent data. The time-compensation calculation model undergoes periodic accuracy verification, adjusting model parameters based on historical prediction errors.

[0062] Referring to Table 1, the threshold time point management for monitoring preparation switching supports manual overwrite functionality, allowing administrators to temporarily adjust the threshold under special circumstances. Time synchronization verification of acquisition settings for adjacent monitoring points ensures consistency of timestamps across different monitoring devices. The average time difference calculation result is rounded to the second level to balance computational efficiency and accuracy. A buffer range is set for conditions determining whether a condition is earlier than the monitoring preparation switching threshold time point to avoid frequent state switching near the threshold threshold. The association query between cycle number and health trajectory data uses join optimization to reduce database query response time. The judgment criteria for continuous improvement in health level support custom configuration, allowing adjustment of judgment conditions based on different water characteristics. The generation of continuous control results for the final monitoring stage includes an impact assessment, predicting the impact of control measures on monitoring resource allocation.

[0063] Table 1: Parameter Table for Determining Improvement Time Parameter name Parameter description Data source Value range Improvement time point Timestamp of the start of ecological indicators improvement at the monitoring point Set of improvement start time at the monitoring point Time format Threshold time point Critical time point for supervision plan switching Adjustable configuration scheme of water area partition monitoring period Time format Average time difference Average interval of improvement time points of adjacent monitoring points Time series calculation Non-negative time interval Switching time difference threshold Time tolerance threshold for determining synchronous improvement System configuration parameter Positive time interval In some embodiments, the comparison between the improved time point and the threshold time point adopts a batch processing mode, processing multiple water area determination requests at once. Average time difference calculation supports parallel processing, distributing the calculation of time differences after grouping monitoring points. The application of switching time difference thresholds considers the differences between weekdays and holidays, setting different threshold standards. The parsing of the monitoring cycle execution instructions includes security checks, verifying the instruction signature to prevent tampering. The query of health trajectory data for the final monitoring points sets time range restrictions to avoid loading too much historical data and impacting performance. The analysis of health indicator change trends introduces seasonal adjustments to eliminate the interference of periodic fluctuations on trend judgment. The determination of the final segment duration supports dynamic adjustment, updating the final segment interval in real time according to the actual monitoring progress.

[0064] Optionally, the results of the improvement time point being earlier than the threshold time point are visualized, displaying the judgment status and key indicators through a dashboard. Intermediate results are recorded during the average time difference calculation process for easy troubleshooting and algorithm optimization. Modifications to the time difference threshold are logged, enabling full traceability of parameter changes. The cache for monitoring cycle execution commands uses an incremental update strategy, updating only the changed data. The selection criteria for final monitoring points support combined queries, allowing multiple selection criteria to be specified simultaneously. Health trajectory data is stored in a columnar structure, improving the compression rate and query efficiency of time series data. The push of continuous control results from the final monitoring phase supports multiple channels, sending control notifications simultaneously via message queues and emails. The collection frequency of improvement time points is consistent with the monitoring data update frequency to ensure real-time performance. Threshold time point settings consider system processing latency, reserving sufficient safety margins. The calculation results of the average time difference undergo statistical significance testing to exclude accidental time clustering phenomena. Priority is set for the processing of monitoring cycle execution commands, with commands from important water area zones processed first. Machine learning methods are introduced into the health trajectory data analysis of final monitoring points to automatically identify abnormal change patterns. The assessment of continuously improving health levels is combined with an expert knowledge base and incorporates the experience and rules of field experts. The application of the results of end-stage monitoring and continuous control is implemented using a phased release mechanism, first verifying the effectiveness in pilot areas before full-scale rollout.

[0065] Optionally, the comparison between the improvement time point and the threshold time point supports fuzzy matching, allowing the setting of a time error range. The average time difference calculation considers the spatial distribution characteristics of monitoring points, employing a relaxed time consistency standard for distant monitoring points. The adjustment of the switching time difference threshold introduces an adaptive algorithm, automatically optimizing the threshold based on historical judgment accuracy. The generation of execution instructions during the supervision cycle is equipped with an anti-duplicate mechanism to avoid generating instructions with the same content repeatedly. The query results of health trajectory data from the final monitoring points are cached to reduce frequent database access. The analysis of health indicator change trends sets trend intensity indicators to quantify the significance and reliability of trends. Feedback on the continuous control results of the final monitoring phase collects user evaluations to continuously optimize the control algorithm and parameter settings. The judgment process where the improvement time point is earlier than the threshold time point is embedded in the workflow engine to automate the judgment process. The average time difference calculation results are normalized to eliminate the impact of the number of monitoring points on the results. The maintenance of the switching time difference threshold supports version management, recording the historical records and reasons for threshold changes. The transmission of execution instructions during the supervision cycle uses a reliable messaging mechanism to ensure that instructions are delivered and processed sequentially. The health trajectory data analysis at the final monitoring points sets a data quality threshold, analyzing only data records that meet the quality requirements. The assessment of continuously improving health levels incorporates multi-indicator fusion, comprehensively judging based on the changing trends of multiple relevant indicators.

[0066] Example 5: In specific implementation, the marked supervision cycle number is extracted from the supervision cycle execution instruction. The supervision cycle execution instruction is generated by the supervision cycle management module and stored in the system instruction library. The supervision cycle number adopts the encoding structure of "year-region code-serial number". The trajectory data of the final monitoring point in the monitoring period of the corresponding cycle is filtered by time range query. The final monitoring point refers to the monitoring point that is still actively collecting data in the last stage of the supervision cycle. The monitoring period is defined as the last 30% of the time interval of the supervision cycle. The trajectory data includes a timestamp sequence and the corresponding set of health indicator values. The continuous change sequence and timestamp from the start time of the last period to the stable point of the health indicator are extracted. The start time of the last period is calculated proportionally according to the total duration of the supervision cycle. The stable point of the health indicator is determined by detecting whether the fluctuation range of the indicator value is continuously less than the threshold. The final health trajectory sequence is generated. The final health trajectory sequence is a set of continuous data points with time as the horizontal axis and health indicator value as the vertical axis. Based on the final health trajectory sequence, the analysis of the health change trend of the final monitoring point within the final period uses the least squares method for linear fitting. The final period duration is the time length from the start of the final period to the current analysis time, and the health change trend is quantified by the slope value of the fitted straight line. The health increase value of the final period is extracted by calculating the index changes of the last few data points. The health increase value reflects the recent rate of improvement in health status and is compared with the final period duration, which is the total time span covered by the final health trajectory sequence. If the health level continues to rise but has not entered a stable range, the judgment of a continuous rise in health level requires that the index values ​​at multiple consecutive time points are higher than the previous point. The stable range is determined by statistical analysis of historical data to determine the index fluctuation range. The calculation of the supplementary time required for the monitoring point to reach the predetermined health point is achieved through a trend extrapolation algorithm. The predetermined health point is the system's preset health status target value, and the supplementary time represents the time required to reach the target at the current rate of improvement. The remaining health time interval of the final period is generated, which includes the mapping relationship between the monitoring point number and the predicted required time value.

[0067] The system retrieves the required supplementary time value for the water area zone from the remaining health time interval at the end of the monitoring period. This remaining health time interval is stored in the system's prediction result database, with the supplementary time value stored in minutes. Updating the real-time end-of-life monitoring control cycle is achieved by modifying the cycle configuration parameters. The end-of-life monitoring control cycle manages the time window settings for the later stages of monitoring. It adjusts the original configured end-of-life monitoring end time point, extending the end time point backward by the supplementary time, and resets the control interval for the final stage of the water area zone. The control interval defines the start and end time range of the monitoring operation, resulting in the continuous control results for end-of-life monitoring. These results include the adjusted time parameters and execution recommendations. The continuous control results for end-of-life monitoring include the remaining health path continuation time (the additional monitoring time required to predict achieving the health target), the predicted health completion period for the final monitoring point (the specific time interval expected to achieve the health status target), and the recommended monitoring maintenance time window (the recommended time window for continuing monitoring operations).

[0068] In practical implementation, the extraction of monitoring cycle numbers undergoes format verification to ensure that the numbers conform to the system-defined coding specifications. Data integrity checks are implemented for the selection of final monitoring points, requiring the missing rate of trajectory data to be below a set threshold. The calculation of time intervals for the later monitoring phase supports custom ratios, allowing different interval ratios to be set for different types of water areas. The determination of the start time of the final phase considers the monitoring data collection frequency to ensure that the start time aligns with the data collection points. The detection of stable points for health indicators uses a sliding window algorithm, calculating the standard deviation and coefficient of variation of indicator values ​​within the window. The generation of the final health trajectory sequence undergoes data smoothing, employing a moving average method to eliminate random fluctuation interference. The analysis of health change trends sets confidence levels, only accepting trends with significance above a threshold. The extraction of health increase values ​​supports dynamic window size, automatically adjusting the calculation window width based on data density. The comparison between the final phase duration and the health increase value introduces standardization processing to eliminate the influence of different indicator units. The judgment of a continuous rise in health levels sets a minimum increase requirement to avoid minor fluctuations being misjudged as a continuous rise. The determination of stable intervals uses dynamic thresholds, adjusting the interval range based on the historical fluctuation characteristics of the monitoring points. The calculation of supplementary duration takes into account trend decay factors, introducing a decay factor to correct simple extrapolation results. The storage of the remaining health time interval at the end of the period includes a timestamp, recording the specific time point of the prediction calculation. Access to the supplementary time value required for a water area zone is subject to permission verification, ensuring that only authorized modules can access the prediction data. The update of the real-time end-of-period monitoring control cycle adopts a transaction mechanism to ensure the atomicity of cycle parameter changes. Adjustments to the end time point of end-of-period monitoring are subject to a maximum extension limit to prevent indefinite extension of the monitoring cycle. A notification mechanism is triggered after the control interval of the end of the water area zone is reset, sending configuration change reminders to relevant management personnel. The calculation of the remaining health path continuation duration undergoes rationality verification, and abnormally long prediction results are subject to manual review. The generation of the final monitoring point health completion prediction period includes probability assessment, providing prediction intervals at different confidence levels. The determination of the recommended monitoring maintenance time window considers resource constraints, prioritizing the monitoring needs of key areas when monitoring resources are limited.

[0069] In some embodiments, the extraction of monitoring period numbers supports batch processing, handling data filtering requests for multiple periods at once. Trajectory data queries for final monitoring points employ distributed computing, dividing the large dataset into multiple nodes for parallel processing. The time interval definition for the later monitoring period supports absolute time settings, allowing direct specification of specific start and end times. The generation of the final health trajectory sequence includes outlier detection, correcting or removing values ​​that significantly deviate from the normal range. Analysis of health change trends incorporates a nonlinear model, providing special handling for exponential or logarithmic growth patterns. The calculation of health increase values ​​supports weighted averaging, assigning higher weights to recent data to improve predictive sensitivity. The final duration comparison uses a dynamic threshold, automatically adjusting the comparison standard based on the historical performance of monitoring points. The judgment of a continuous rise in health levels combines multiple indicators, requiring multiple relevant indicators to show an upward trend before confirming improvement. Statistics for stable intervals use a rolling window, dynamically updating the interval range using data from the most recent period. The prediction of supplementary duration incorporates a machine learning model, using historical data to train the prediction algorithm and improve accuracy. The storage of the remaining health time interval in the final stage uses a compressed format to reduce storage space usage. The call to the supplementary time value required for water area partitioning is optimized through caching, and a caching strategy is implemented for frequently accessed data. Updates to the real-time terminal monitoring control cycle support version rollback, allowing restoration to previous configurations in case of problems. Adjustments to the terminal monitoring end time point consider equipment maintenance plans, avoiding planned maintenance periods. The control interval for the terminal phase of water area partitioning is reset with conflict detection to avoid conflicts with the operation times of other systems. The output of the remaining health path continuation duration includes an uncertainty range, providing the possible fluctuation range of the predicted value. The generation of the predicted health completion period for the terminal monitoring point supports scenario analysis, providing multiple sets of prediction results under different assumptions. The determination of the recommended monitoring time window incorporates cost-benefit analysis, seeking a balance between monitoring effectiveness and resource consumption.

[0070] Optionally, the extraction and recording of monitoring cycle numbers are logged to facilitate tracking of detailed information about the data filtering process. The final monitoring point selection results are visualized, with eligible monitoring points highlighted on a map. The time interval calculation for the later monitoring phase supports manual adjustment, allowing experts to fine-tune the interval range based on actual conditions. Quality indicators are set for the generation of the final health trajectory sequence, and a re-collection process is initiated for sequences that do not meet the data quality standards. The analysis results of health change trends are verified by experts, ensuring the accuracy of trend judgments through manual review. The calculation of health increase values ​​supports multiple statistical methods, providing options for multiple increase indicators such as mean and median. The comparison of the final duration introduces relative value evaluation, comparing the duration with the total cycle length to obtain a relative proportion. The judgment of a continuous rise in health levels sets duration requirements, requiring the upward trend to be maintained for a sufficiently long time before confirmation. The determination of stable intervals considers seasonal factors, using different interval standards for different seasons. Sensitivity analysis is performed on the prediction of supplementary duration to assess the impact of changes in key parameters on the prediction results. The transmission of the remaining health time interval in the final phase uses an encryption protocol to ensure the security of the prediction data during transmission. Access rates for the supplementary time values ​​required for water area zones are limited to prevent excessively frequent queries from impacting system performance. Impact assessments are performed on updates to the real-time terminal monitoring control cycle to predict the impact of configuration changes on other parts of the system. Adjustments to the terminal monitoring end time point support gradual changes, adjusting the end time in stages to reduce system impact. An operation report is generated after the control interval for the terminal phase of the water area zone is reset, detailing the changes and expected effects. The calculation of the remaining health path duration is cross-validated, using different methods to calculate results and comparing them. The generation of the health completion prediction period for the terminal monitoring point supports export functionality, allowing prediction results to be exported as standard format files. The determination of the recommended monitoring time window considers external factors, incorporating environmental information such as weather forecasts to optimize the recommended results.

[0071] It is understandable that the monitoring cycle number marked in the monitoring cycle execution instruction is bound to the specific water area zone to ensure the accuracy of data screening. The trajectory data collection frequency of the final monitoring point remains consistent throughout the monitoring period to avoid analytical bias caused by different sampling intervals. The continuous change sequence from the start of the final phase to the stable point of the health index requires temporal continuity; missing data segments are filled in using interpolation methods. The application of the least squares method in the health change trend analysis requires a sufficient number of data points; if insufficient, it automatically switches to a simpler analysis method. The comparison between the health increase value and the duration of the final phase considers the data distribution characteristics, using non-parametric comparison methods for non-normally distributed data. A verification mechanism is set up for the judgment of a continuous rise in health level, using the change directions of multiple independent indicators to mutually corroborate each other. The determination of the stable interval adopts the principle of statistical control charts, using the mean plus or minus several times the standard deviation to define the interval range. The calculation of the supplementary time introduces attenuation correction to consider the trend that the improvement rate may gradually slow down over time. An index is established for storing the remaining health time interval of the final phase, supporting fast retrieval by monitoring point number and time range. The call to the supplementary time value required by the water area zone undergoes validity checks to ensure that the time value is within a reasonable range. The update and approval process for the real-time terminal monitoring control cycle is set up, and significant changes require approval from the responsible personnel. Adjustments to the terminal monitoring end time are subject to resource availability checks to ensure sufficient resources are available for extended monitoring periods. After resetting the control interval for the terminal phase of the water area, relevant documentation is updated to ensure consistency between system documentation and actual configuration. The output of the remaining health path duration includes the calculation basis, explaining the data and methods used for prediction. The generation of the health completion prediction period for the final monitoring point takes into account the work calendar to exclude the impact of holidays on monitoring data. The recommended monitoring time window is optimized through calculations to minimize resource consumption while meeting monitoring objectives.

[0072] Optionally, the extraction of the monitoring period number supports fuzzy matching, allowing the corresponding period to be located even when the number is partially missing. The trajectory data analysis of the final monitoring points is automated, periodically executing analysis tasks to generate the latest results. The time interval definition for the later monitoring phase supports multiple modes, providing various definition methods such as proportional or fixed duration. The generation of the final health trajectory sequence undergoes data standardization to eliminate dimensional differences between different monitoring points. The analysis results of health change trends include a quality score to help users assess the reliability of the trend analysis. The calculation of health increase values ​​supports rolling updates, updating the increase value in real time as new data arrives. The comparison results of the final duration are visualized, presenting the comparison conclusions intuitively through charts. A warning mechanism is set up for the judgment of continuously rising health levels, issuing an alert promptly when the upward trend is interrupted. The statistics of stable intervals support grouped calculations, statistically calculating interval ranges separately for different types of monitoring points. The prediction of supplementary duration is corrected in real time, adjusting the prediction model based on the deviation between the latest data and the predicted value. The management of the remaining health time interval in the final phase supports historical queries, allowing users to view prediction records at any time point. Access to water zone zoning requires supplementary time values, and access times and purposes are audited and logged. Updates to the real-time terminal monitoring control cycle support a test mode, allowing changes to be validated in a test environment before formal application. Adjustments to the terminal monitoring end time point undergo dependency checks to ensure changes do not affect the operation of other related systems. After the control interval for the terminal phase of a water zone is reset, related systems are updated synchronously to maintain configuration consistency across systems. The calculation of the remaining health path duration supports manual correction, allowing experts to adjust the automatic calculation results based on experience. The generation of the terminal monitoring point health completion prediction period provides multiple output formats to meet the needs of different use cases.

[0073] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A visualized river and lake water health and ecological supervision and management system, characterized in that, The system includes: The data acquisition and feature extraction module is used to collect multi-source ecological monitoring data of rivers and lakes, extract spatial variation features of aquatic biological activity trajectories and time series of water quality parameters from the multi-source ecological monitoring data, identify abnormal features based on the indicator variation patterns corresponding to the predefined ecological health level, and generate a set of ecological health abnormal response tags, where each tag is associated with a specific geographical location. The health status analysis module is used to obtain the baseline health level, initial monitoring time point and final assessment time point for each labeled water area based on the ecological health anomaly response tag set, analyze the trajectory and deviation range of health status changes within the monitoring time frame, and determine the health status lag time period. The assessment delay processing module is used to extract the duration of the final ecological indicators in the later stage of monitoring and the assessment completion time point for each water area involved in the health status lag period in two consecutive monitoring cycles. It calculates the time difference between these time points and the end time of the current monitoring cycle to determine whether the assessment is completed, filters out water areas whose assessment is not completed, and prioritizes them according to their assessment delay duration. Based on the ranking results, it generates an adjustable configuration scheme for the monitoring period of the water area.

2. The method for visual supervision and management of the health and ecology of rivers and lakes according to claim 1, characterized in that, The ecological health anomaly response tag set includes a code for uniquely identifying health anomaly events, a code representing the anomaly category, and the precise spatial coordinates of the monitoring point; the health status lag time period covers the duration of the status response delay, the length of the health recovery process lag, and the time interval between the triggering of the anomaly event and the shift in the health rhythm; the adjustable configuration scheme for the water area zoning monitoring period includes a water area zoning priority sequence based on the assessment delay, the adjustment amount of the monitoring period length, and the time point at which the scheduling control parameters take effect; the supervision cycle execution command includes the conditions for triggering cycle switching, the time when the synchronous recovery status is identified, and the judgment conditions for spatial structure compression.

3. The method for visual supervision and management of the health and ecology of rivers and lakes according to claim 1, characterized in that, The step of generating the ecological health anomaly response label set further includes: Collect multi-source ecological monitoring data, and for each monitoring point, calculate the spatial coordinate changes of aquatic biological activity trajectories and the duration of water quality parameter time series; Based on the spatial coordinate difference and the time series length, the spatial offset distance and time change period of each monitoring point are derived to form a set of offset and change time series parameters. Using the aforementioned set of offset and change time series parameters, the parameter fluctuations of monitoring points during the process of health status change are analyzed, the number of abnormal fluctuations is counted, and a correlation model between the number of abnormal fluctuations and the change cycle is established, thereby calculating the frequency of ecological health fluctuations. By comparing the frequency of ecological health fluctuations with preset thresholds, monitoring points with excessive fluctuation frequencies are identified, and their abnormal behaviors are associated with geographical locations to generate a set of ecological health abnormal response labels.

4. The method for visualizing the health and ecological supervision of rivers and lakes according to claim 3, characterized in that, The step of determining the lag time period of health status further includes: The water sections marked by the ecological health anomaly response tags are obtained, and the baseline health level, initial monitoring time point and final assessment time point of each section within the monitoring period are extracted. The time interval between the initial response time and the final assessment time is calculated to generate health key time interval data. Based on data from key health time intervals, combined with the total number and numerical distribution of monitoring points within the monitoring period, the overall coverage and density level of health status are analyzed to obtain health density distribution information. Based on the health density distribution information, the time interval distribution of the position points before, during and after the state change within the critical health time interval is calculated, the health density shift feature value is derived, and the position of the density anomaly segment in the monitoring period is identified to generate the health density shift time period. Based on the health density offset time period, the correlation between time segments and health status change segments within the monitoring period is assessed, continuous segments with delayed health status changes are screened out and marked as areas where health behavior is delayed, forming health status lag time periods.

5. The method for visualizing the health and ecological supervision of rivers and lakes according to claim 4, characterized in that, The step of generating an adjustable configuration scheme for water area zoning monitoring time periods further includes: Based on the water area zone number specified in the health status lag time period, extract the static duration of the final ecological indicators of each zone in the later stage of monitoring and the assessment completion time point in two consecutive monitoring cycles. Calculate the time difference between the assessment completion time point and the monitoring end time point of the current cycle to obtain the final assessment lag information of the water area zone. Based on the lag information of the final assessment of water area zones, it is determined whether the assessment lag value exceeds the preset assessment completion benchmark threshold, the water area zones that have not completed the assessment are screened out, and the waiting time of the final indicators of the corresponding zones is extracted, sorted by waiting time, and a priority sequence of water area zone assessment waiting is generated. Based on the priority sequence of water area assessment, an additional monitoring time period is allocated to each water area. The length of the monitoring time period is adjusted within the total monitoring duration. The water area number and the adjusted monitoring duration are recorded to generate an adjustable configuration scheme for the monitoring time period of the water area.

6. The method for visualizing the health and ecological supervision of rivers and lakes according to claim 5, characterized in that, The system includes: The monitoring cycle management module is used to check the spatial distribution pattern of biological populations in the real-time monitoring cycle using the initial parameters in the adjustable configuration scheme of the water area zoning monitoring period, and to identify the starting time point of continuous recovery of a group of adjacent monitoring areas. If all starting time points are earlier than the threshold time point for switching the monitoring plan, they are recorded as synchronous recovery events and a monitoring cycle execution instruction is generated. The dynamic adjustment module is used to dynamically adjust the monitoring frequency and spatial coverage by combining the execution instructions of the monitoring cycle and the lag time of the health status, and output the optimized monitoring strategy.

7. The method for visual supervision and management of the health and ecology of rivers and lakes according to claim 6, characterized in that, The step of generating the supervision cycle execution instruction further includes: Call the water zone start configuration time value recorded in the adjustable configuration scheme of water zone monitoring period, detect the spatial distance distribution of ecological indicators in the corresponding zone monitoring area in the real-time monitoring cycle, identify the continuous improvement start time point of a group of adjacent monitoring points in the monitoring segment, and generate a set of monitoring point improvement start times. Based on the improvement action time of adjacent monitoring points in the set of monitoring point improvement start times, determine whether all improvement times are earlier than the threshold time point for supervision preparation switching in the adjustable configuration scheme of water area zoning monitoring period. If the condition is met, mark it as synchronous improvement status, associate the water area zoning number with the status information, and generate supervision cycle execution instructions.

8. The method for visual supervision and management of the health and ecology of rivers and lakes according to claim 7, characterized in that, The process of determining whether all improvement times are earlier than the threshold time point for monitoring preparation switching in the adjustable configuration scheme of water area zoning monitoring time periods includes: calculating the average time difference between the improvement times of adjacent monitoring points, and comparing the average time difference with the switching time difference threshold. If the average time difference is less than the switching time difference threshold, it is determined to be earlier than the monitoring preparation switching threshold time point.

9. The method for visual supervision and management of the health and ecology of rivers and lakes according to claim 1, characterized in that, The method further includes the following steps: Call the cycle number marked in the supervision cycle execution instruction, filter the health trajectory data of the final monitoring point, compare the trend of health indicator changes with the final duration, if the health level continues to improve but has not reached a stable state, calculate the make-up time required to reach the predetermined health point, update the final control cycle, and obtain the final monitoring continuous regulation results. The step of obtaining the terminal monitoring and continuous regulation results further includes: Extract the marked monitoring cycle number from the monitoring cycle execution instruction, filter the trajectory data of the final monitoring point in the monitoring stage under the corresponding cycle, extract the continuous change sequence and timestamp from the start time of the final stage to the stable point of health indicators, and generate the final stage health trajectory sequence. Based on the final health trajectory sequence, the health change trend of the final monitoring point within the final period is analyzed, the health increase value of the final segment is extracted and compared with the duration of the final segment. If the health level continues to rise and has not entered the stable range, the supplementary time required for the monitoring point to reach the predetermined health point is calculated, and the remaining health time interval of the final segment is generated. The system calls the supplementary time value required for the water area zone in the remaining health time interval of the last segment, updates the real-time last segment monitoring control cycle, adjusts the original configured last segment monitoring end time point, resets the control interval of the last segment of the water area zone, and obtains the continuous regulation result of the last segment monitoring. The results of the continuous regulation of the final monitoring stage include the duration of the remaining health pathway, the predicted time period for the completion of health at the final monitoring point, and the recommended time window for monitoring maintenance.

10. The method for visual supervision and management of the health and ecology of rivers and lakes according to claim 1, characterized in that, The step of obtaining the terminal monitoring and continuous regulation results further includes: Extract the marked monitoring cycle number from the monitoring cycle execution instruction, filter the trajectory data of the final monitoring point in the monitoring stage under the corresponding cycle, extract the continuous change sequence and timestamp from the start time of the final stage to the stable point of health indicators, and generate the final stage health trajectory sequence. Based on the final health trajectory sequence, the health change trend of the final monitoring point within the final period is analyzed, the health increase value of the final segment is extracted and compared with the duration of the final segment. If the health level continues to rise and has not entered the stable range, the supplementary time required for the monitoring point to reach the predetermined health point is calculated, and the remaining health time interval of the final segment is generated. The system calls the supplementary time value required for the water area zone in the remaining health time interval of the last segment, updates the real-time last segment monitoring control cycle, adjusts the original configured last segment monitoring end time point, resets the control interval of the last segment of the water area zone, and obtains the continuous regulation result of the last segment monitoring. The results of the continuous regulation of the final monitoring stage include the duration of the remaining health pathway, the predicted time period for the completion of health at the final monitoring point, and the recommended time window for monitoring maintenance.

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