A dynamic planning method and system for hydrological monitoring points

Abstract

The present invention discloses a method and system for dynamic planning of hydrological monitoring points, which relate to the field of hydrological monitoring technology. The method comprises: collecting parameters of hydrological monitoring points in a target area and formulating an initial point planning scheme. Based on the prediction of the future time zone, a hydrological dynamic prediction is performed on the area to obtain a hydrological prediction result. According to the result, the initial point plan is adjusted for redundancy to generate a first adjustment scheme. Based on the monitoring needs and the prediction results, the point blind spots are corrected to form a second adjustment scheme. Combined with geographic feature data and damage risk thresholds, compensation adjustments are made to damaged points to generate a third adjustment scheme. The above adjustment schemes are integrated, and global optimization is performed to generate an optimized layout of hydrological monitoring points. The technical problems of poor planning flexibility and low accuracy of hydrological monitoring points in the prior art are solved, and the technical effects of improving the planning flexibility and accuracy of hydrological monitoring points and improving the quality of hydrological monitoring are achieved.

Description

Technical Field

[0001] The present application relates to the field of hydrological monitoring technology, and in particular to a method and system for dynamic planning of hydrological monitoring points. Background Art

[0002] In the application scenario of hydrological monitoring, with the increasing intensification of climate change and human activities, the dynamic changes in regional hydrological conditions are becoming increasingly complex, and the problems of traditional monitoring point layout are becoming increasingly prominent. The reasonable layout of hydrological monitoring points is the key to accurately capturing regional hydrological dynamics, but traditional point layouts often rely on historical data and lack predictions of future hydrological trends, resulting in redundancy, blind spots, and point damage in the monitoring point layout. Existing hydrological monitoring systems fail to fully consider regional geographical characteristics and future changes in hydrological dynamics when planning points, making it difficult to respond to sudden hydrological events in real time. As a result, they are unable to effectively optimize resource allocation and ensure the comprehensiveness and accuracy of monitoring data.

[0003] At present, relevant technologies have technical problems such as poor planning flexibility and low accuracy of hydrological monitoring points. Summary of the Invention

[0004] The present application provides a method and system for dynamic planning of hydrological monitoring points, which adopts the method of collecting the parameters of the hydrological monitoring points in the target area and formulating an initial point planning scheme. Based on the prediction of the future time zone, a hydrological dynamic prediction is made for the area to obtain the hydrological prediction results. According to the results, the initial point plan is adjusted for redundancy to generate a first adjustment plan. Based on the monitoring needs and the prediction results, the point blind spots are corrected to form a second adjustment plan. Combining the geographical feature data and the damage risk threshold, compensatory adjustments are made to the damaged points to generate a third adjustment plan. By integrating the above adjustment plans, the initial plan is globally optimized to generate an optimized layout of hydrological monitoring points, which realizes multi-dimensional flexible adjustment of the hydrological monitoring points through the hydrological dynamic prediction results, and achieves the technical effect of improving the planning flexibility and accuracy of the hydrological monitoring points and improving the quality of hydrological monitoring.

[0005] This application provides a method for dynamic planning of hydrological monitoring points, including:

[0006] Collect the parameters of the hydrological monitoring points in the target area to obtain an initial point planning scheme; perform hydrological dynamic prediction on the target area based on a preset future time zone to obtain a hydrological dynamic prediction result; perform redundancy adjustment on the hydrological monitoring points of the initial point planning scheme based on the hydrological dynamic prediction result to obtain a first adjustment scheme for point planning; based on the hydrological dynamic prediction result, perform blind spot correction on the hydrological monitoring points of the initial point planning scheme according to the monitoring demand analysis channel and the hydrological monitoring point addition decision channel to obtain a second adjustment scheme for point planning; based on the regional geographic feature data set of the target area and the hydrological dynamic prediction result, perform damage compensation on the hydrological monitoring points of the initial point planning scheme according to the point damage risk threshold to obtain a third adjustment scheme for point planning; perform global optimization on the initial point planning scheme according to the first adjustment scheme, the second adjustment scheme and the third adjustment scheme to generate a hydrological monitoring point optimization result.

[0007] This application also provides a hydrological monitoring point dynamic planning system, including:

[0008] An initial point planning scheme acquisition module is used to collect the hydrological monitoring point parameters of the target area to obtain an initial point planning scheme; a hydrological dynamic prediction result acquisition module is used to perform hydrological dynamic prediction on the target area based on a preset future time zone to obtain a hydrological dynamic prediction result; a point planning first adjustment scheme acquisition module is used to perform redundancy adjustment of the hydrological monitoring points on the initial point planning scheme based on the hydrological dynamic prediction result to obtain a first adjustment scheme for point planning; a second adjustment scheme acquisition module is used to adjust the hydrological monitoring points according to the monitoring needs based on the hydrological dynamic prediction result. The analytical channel and the decision channel for adding hydrological monitoring points correct the blind spots of the initial point planning scheme to obtain the second adjustment scheme of the point planning; the third adjustment scheme acquisition module is used to compensate for the damage of the hydrological monitoring points of the initial point planning scheme according to the point damage risk threshold based on the regional geographic feature data set of the target area and the hydrological dynamic prediction result, and obtain the third adjustment scheme of the point planning; the global optimization module is used to globally optimize the initial point planning scheme according to the first adjustment scheme of the point planning, the second adjustment scheme of the point planning and the third adjustment scheme of the point planning to generate a hydrological monitoring point optimization result.

[0009] The present application proposes a method and system for dynamic planning of hydrological monitoring points. First, the parameters of the hydrological monitoring points in the target area are collected to formulate an initial point planning scheme. Based on the prediction of the future time zone, a hydrological dynamic prediction is made for the area to obtain the hydrological prediction results. According to the results, the initial point plan is adjusted for redundancy to generate a first adjustment plan. Based on the monitoring needs and the prediction results, the point blind spots are corrected to form a second adjustment plan. Combining the geographical feature data and the damage risk threshold, compensatory adjustments are made to the damaged points to generate a third adjustment plan. The above adjustment plans are integrated, the initial plan is globally optimized, and the optimized hydrological monitoring point layout is generated. The hydrological monitoring points are flexibly adjusted in multiple dimensions through the hydrological dynamic prediction results, thereby achieving the technical effect of improving the planning flexibility and accuracy of the hydrological monitoring points and improving the quality of hydrological monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A schematic diagram of a flow chart of a method for dynamic planning of hydrological monitoring points provided in an embodiment of the present application;

[0012] Figure 2 A structural diagram of a dynamic planning system for hydrological monitoring points provided in an embodiment of the present application.

[0013] Explanation of the accompanying symbols: initial point planning scheme acquisition module 10, hydrological dynamic prediction result acquisition module 20, point planning first adjustment scheme acquisition module 30, second adjustment scheme acquisition module 40, third adjustment scheme acquisition module 50, global optimization module 60. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The present application embodiment provides a method for dynamic planning of hydrological monitoring points, such as Figure 1 As shown, the method includes:

[0018] Step S100 collects the parameters of the hydrological monitoring points in the target area to obtain an initial point planning scheme. Specifically, when collecting the parameters of the hydrological monitoring points in the target area to obtain the initial point planning scheme, the target area scope and monitoring factors are first clarified. Based on the factors, the appropriate equipment type, measurement range, and accuracy requirements are selected. At the same time, relevant information is consulted to obtain historical monitoring point distribution, hydrological data change trends, and regional geographic information. Next, field data collection is carried out. The latitude and longitude and altitude of the points are accurately measured using GPS. The location information in the river is recorded. The equipment model, parameters, installation time, operating status, and maintenance history are recorded in detail. The surrounding environmental information such as land use type, vegetation cover, pollution sources, and positional relationships with other geographic factors are observed and recorded. The collected data is then classified and organized into a database, classified by dimensions such as monitoring factors to ensure that the data is searchable. Then, a GIS software or mapping tool is used to draw the initial point planning map and annotate the relevant information. The plan is preliminarily evaluated from the aspects of the rationality of the point distribution and the coverage of the hydrological characteristic area. Problem areas are marked, and finally, the initial point planning scheme is obtained.

[0019] Step S200 performs a hydrological dynamics forecast for the target area based on a preset future time zone, obtaining hydrological dynamics forecast results. Specifically, a hydrological monitoring sensor network is first deployed in the target area to collect and preprocess real-time hydrological data. Meteorological forecast data for the preset future time zone, including precipitation, temperature, wind speed and direction, is obtained from meteorological agencies and matched to the locations of hydrological monitoring stations. Geographical data, such as topography, water system distribution, and soil type, is then aggregated and digitized for integration and analysis within a geographic information system (GIS) platform. Next, an appropriate hydrological model is selected based on regional characteristics and research objectives. Parameters are determined and validated using historical hydrological and meteorological data. Finally, different scenarios for the future time zone are configured in conjunction with meteorological forecast data. Real-time hydrological, meteorological, and geographic data are then input into the model for simulation calculations based on the preset scenarios. During the process, operational status is monitored to predict future trends in water levels, flow rates, water quality, and other factors. Finally, statistical indicators of the forecast results are analyzed, visualized, and uncertainty analysis is performed to account for the influence of various factors. Hydrological dynamics forecast results are then obtained, providing a basis for subsequent work.

[0020] In one possible implementation, a hydrological dynamic prediction is performed for the target area based on a preset future time zone to obtain a hydrological dynamic prediction result. Step S200 further includes step S210, where real-time hydrological parameters of the target area are collected to obtain real-time hydrological status information. Specifically, advanced hydrological monitoring equipment is installed at key locations in the target area, such as the confluence of a river's main stream and tributaries, the inlet and outlet of a reservoir, and typical groundwater observation wells. For water level monitoring, high-precision pressure sensors or ultrasonic sensors are used. These sensors can sense minute changes in water level in real time and collect and transmit data at a high frequency (e.g., once per second). Flow monitoring uses Doppler flowmeters or ADCP (Acoustic Doppler Current Profiler) to accurately measure the velocity profile of water flow and calculate real-time flow data based on the cross-sectional information of the river. In terms of water quality monitoring, multi-parameter water quality monitors are used to synchronously monitor parameters such as water temperature, pH, dissolved oxygen, and conductivity. The collected data is sent to the data center via wired or wireless transmission. During the data collection process, the equipment is monitored for quality in real time. Once abnormal data fluctuations or transmission interruptions are found, the fault diagnosis and repair program is immediately started to ensure the continuity and accuracy of the data. The real-time hydrological parameters collected are summarized and integrated to build a real-time hydrological database. The data are preliminarily analyzed and processed, such as calculating the rate of change of water level and instantaneous change of flow, so as to obtain characteristic information of real-time hydrological status. By drawing real-time data change curves, the changing trends of parameters such as water level and flow over time are intuitively displayed, helping analysts to quickly understand the dynamic characteristics of the current hydrological status. At the same time, data mining algorithms are used to compare and analyze the hydrological data of the same period in history to find out the abnormal points or potential laws of the current data, providing basic information for subsequent hydrological dynamic prediction.

[0021] Step S220: Loading weather forecast data for the target area based on the preset future time zone. Specifically, meteorological forecast data for the target area in a preset future time zone is obtained from professional meteorological departments, meteorological data service platforms, or numerical weather prediction models. Data sources usually provide data in multiple formats, which need to be unified and converted to make them compatible with hydrological data and geographic feature data. The data includes precipitation forecast information, such as the temporal distribution, amount, and spatial distribution of rainfall; temperature forecast, covering maximum and minimum temperatures and their changing trends; wind direction and speed forecast, clarifying the changes in wind direction and speed at different altitudes. Data interface technology is used to achieve automated downloading and loading of meteorological data to ensure data timeliness and integrity. The loaded meteorological forecast data is preprocessed to remove possible erroneous data or outliers. For example, data points that significantly deviate from historical meteorological data patterns or do not conform to meteorological principles are marked and corrected. The accuracy and reliability of the data are improved by comparing and verifying with data from meteorological stations in surrounding areas. At the same time, a quality assessment index system for meteorological data is established to quantitatively evaluate the accuracy and resolution of the data, so that factors affecting data quality can be considered in subsequent hydrological dynamic predictions. Appropriate weight adjustments can be made to parts of the data with lower quality, or data interpolation, smoothing, and other methods can be used to optimize the data.

[0022] Step S230, based on the real-time hydrological status information and the meteorological forecast data, the hydrological dynamic prediction is performed according to the regional geographic feature data set to obtain the hydrological dynamic prediction result. Specifically, the real-time hydrological status information, meteorological forecast data and regional geographic feature data set are fused and processed, and according to the geographic information system (GIS) technology, the hydrological data and meteorological data are spatially matched and associated with the geographic feature data to build an integrated data model. For example, precipitation data is combined with terrain data to analyze the precipitation distribution and runoff formation process in different terrain parts, and the data is standardized to meet the input requirements of the hydrological prediction model, including data format, unit unification, data range adjustment and other operations. At the same time, according to the prediction target and time scale, the data is reasonably divided and reorganized into time series so that the model can accurately capture the time correlation and causal relationship between the data, and select the appropriate hydrological data according to the hydrogeological characteristics, meteorological conditions and prediction needs of the target area. Dynamic prediction models, common models include hydrological models based on physical mechanisms, statistical models, and artificial intelligence models. For models based on physical mechanisms, such as SWAT (Soil and Water Assessment Tool) and MIKESHE, it is necessary to set model parameters according to the actual situation of the region, such as the soil permeability coefficient, vegetation interception parameters, river roughness coefficient, etc. Statistical models need to determine appropriate statistical distribution functions and model parameters, such as the autoregressive order and moving average order in ARIMA (autoregressive moving average model). Artificial intelligence models such as neural network models need to set network structure, training algorithm parameters, etc., use historical data to pre-train the model and optimize parameters to improve the model's prediction performance and adaptability, input the prepared data into the selected prediction model, and start the model for calculation. During the model operation, the calculation status of the model and the rationality of the output results are monitored in real time, and the intermediate results of the model output are analyzed and verified to ensure the accuracy of the model calculation process. According to the preset future time zone, the model gradually calculates the changes in hydrological elements at different time steps, such as the rising or falling trend of water level, the increase or decrease in flow rate, and the prediction of water quality changes. The results of the model output are post-processed, including data inversion, error correction and visualization of results. By drawing charts of the prediction results and generating prediction reports, the hydrological dynamic prediction results are presented to provide a scientific basis for relevant decision-making and research.

[0023] Step S300: Based on the hydrological dynamic prediction results, the initial point planning scheme is adjusted for redundancy in hydrological monitoring points to obtain a first adjustment scheme for the point planning. Specifically, relevant data for each monitoring point is first extracted from the hydrological dynamic prediction results, and historical monitoring data for the initial points is collated. After cleaning and preprocessing, the data is classified and archived. Next, a suitable fitting model, such as linear regression or time series analysis, is selected to perform fitting analysis on the historical and predicted data to obtain fitting relationships and parameters. Based on the fitting results, twin point clusters are identified, and location proximity analysis is performed on the points within the clusters. The redundancy coefficient is calculated based on multiple factors, and a threshold is determined to determine redundant points. An adjustment strategy is then formulated based on the redundancy detection results. Points are retained in critical areas and their parameters or functions are adjusted. Redundant points in non-critical areas are considered for removal or functional transfer. The initial plan is adjusted according to the strategy to generate a first adjustment scheme. The rationality of the scheme is then verified and optimized through simulation analysis and expert evaluation to ensure that monitoring requirements are met, redundancy is avoided, and efficiency and resource utilization are improved.

[0024] In one possible implementation, the initial point planning scheme is adjusted for redundancy of the hydrological monitoring points based on the hydrological dynamic prediction results to obtain a first adjustment plan for the point planning. Step S300 further includes step S310, which performs monitoring parameter fitting on each hydrological monitoring point in the initial point planning scheme based on the hydrological dynamic prediction results to determine multiple point monitoring fitting results. Specifically, a rich data resource is obtained from the hydrological dynamic prediction results, including information such as water level change predictions, flow change trends, and water quality component fluctuations at different time steps. For each hydrological monitoring point in the initial point planning scheme, the relevant prediction data is extracted, and the existing historical monitoring data of each point is sorted out. These data are quality checked to eliminate abnormal values ​​caused by factors such as instrument failure and environmental interference. The data are standardized to have a unified dimension and format to facilitate subsequent analysis and calculation. For example, the water level data is uniformly converted to meters, and the flow data is converted to cubic meters per second. Historical data and predicted data are arranged and combined according to the time series to construct a data set for each point, preparing for subsequent fitting work. According to the characteristics of the hydrological data and the research purpose, an appropriate fitting method is selected. Common fitting methods include linear fitting methods, which are suitable for situations where the data presents a relatively simple linear relationship; for data with periodic or seasonal variation characteristics, harmonic analysis fitting or seasonal ARIMA model can be used for fitting. A corresponding fitting model is constructed for each monitoring point. For example, for a water level monitoring point, if its water level data shows obvious seasonal variation in a year, a seasonal ARIMA model can be constructed to determine the model parameters (such as seasonal cycle, autoregressive order, moving average order, etc.). The model parameters are estimated through optimization algorithms such as the least squares method, so that the model can describe the relationship between historical monitoring data and predicted data as accurately as possible. After model calculation, the monitoring parameter fitting curve and the coefficients of the fitting equation for each point are obtained, which constitute the fitting results of multiple point monitoring and provide basic data for subsequent redundancy detection.

[0025] Step S320, based on the multiple point monitoring fitting results, the initial point planning scheme is subjected to redundancy detection, and monitoring point redundancy detection results are generated. Specifically, based on the multiple point monitoring fitting results, similarity analysis is performed between each point. The correlation coefficient is used as a measurement indicator to calculate the correlation coefficient between each two point monitoring fitting results. The correlation coefficient can reflect the similarity between the two data sequences in terms of change trends and amplitudes. For flow monitoring points, if the correlation coefficient of the flow fitting data sequences of the two points is close to 1, it indicates that they are very similar in flow change patterns. In addition to the correlation coefficient, other similarity indicators can also be calculated, such as Euclidean distance, dynamic time warping distance, etc. The Euclidean distance can measure the numerical difference between the monitoring data of two points at the same time point; the dynamic time warping distance is suitable for processing situations where the data sequence may be stretched or offset on the time axis, and more accurately evaluate the similarity of the data. By comprehensively applying these similarity indicators, a comprehensive quantitative analysis of the similarity between each point is performed. Based on the calculation results of the similarity index, a reasonable threshold is set to determine whether there is redundancy. If the correlation coefficient between two points is greater than or equal to the set point monitoring predetermined twin degree threshold, and they are also relatively close in geographical location, it is preliminarily determined that there may be redundant point pairs. Further analysis and verification of the possible redundant points are carried out, combined with regional geographical characteristics, hydrological function requirements and other factors. For example, in an area with relatively stable water flow and relatively uniform hydrological characteristics, if the monitoring data of two points are highly similar and the locations are adjacent, then these two points are likely to be redundant. After detailed analysis and judgment, the specific information of the redundant points is determined, and the redundant detection results of the monitoring points are generated to clarify which points may be redundant and the degree of redundancy, providing an accurate basis for subsequent adjustments.

[0026] Step S330, based on the redundant detection results of the monitoring points, redundancy adjustment is performed to generate the first adjustment plan for the point planning. Specifically, according to the redundant detection results of the monitoring points, a scientific and reasonable redundant adjustment strategy is formulated. For points determined to be redundant, it is necessary to comprehensively consider various factors to decide the adjustment method. If the area where the redundant points are located has important hydrological research value or special hydrological changes may occur, even if there is redundancy, one of the points can be retained, and its monitoring equipment can be upgraded or the monitoring parameter settings can be optimized. For example, in an area close to the source of a river and with sensitive ecological environment, although the monitoring data of the two points are similar, in order to more comprehensively grasp the impact of hydrological changes at the source on the downstream, one point can be retained and the monitoring of water quality microbial indicators can be increased. For those points in non-critical areas with more obvious redundancy, it can be considered Remove it or adjust it to a location that needs more monitoring. When formulating the adjustment strategy, it is also necessary to consider cost factors, data continuity, and the impact on the layout of the entire monitoring network. According to the formulated adjustment strategy, the initial point planning scheme is actually adjusted, and the redundant points to be removed are deleted from the scheme, and the retained points are adjusted accordingly. The parameters or functions are optimized. After the adjustment is completed, the new point planning scheme is comprehensively inspected and evaluated to ensure that the adjusted scheme meets the needs of hydrological monitoring while achieving a reasonable layout of monitoring points and reducing redundancy. After repeated inspections and optimizations, the first adjustment plan for point planning is finally generated, providing a more scientific and efficient point layout basis for subsequent hydrological monitoring work.

[0027] In one possible implementation, redundancy detection is performed on the initial point planning scheme based on the multiple point monitoring fitting results to generate monitoring point redundancy detection results. Step S320 further includes step S321, performing pairwise twin identification based on the multiple point monitoring fitting results to obtain multiple point monitoring twin degrees. Specifically, key characteristic parameters of each point are extracted from the multiple point monitoring fitting results, such as data change trends, fluctuation amplitudes, mean values, and other information. These parameters are organized into a format that can be calculated and compared, preparing data for subsequent twin degree calculations. For each hydrological monitoring point, a one-to-one comparison is performed with other points to ensure that no combination is missed. During the comparison process, considering that different types of monitoring data (such as water level, flow, water quality, etc.) may have different dimensions and ranges of variation, data standardization or normalization is required to make different data comparable. An appropriate calculation method is used to determine the twin degree of point monitoring. A commonly used method is based on the data similarity algorithm to calculate the similarity of the monitoring data of two points in the feature space. For example, the similarity of data change trends can be measured by calculating the Pearson correlation coefficient. If the change trends of the water level data series of two points in the same time interval are highly consistent, the Pearson correlation coefficient will approach 1. At the same time, combined with the similarity calculation of other characteristic parameters, such as the similarity of fluctuation amplitude can be evaluated by calculating the difference in standard deviation, and the mean similarity is directly compared by comparing the data mean. Taking these factors into consideration, the twin degree value of each point combination is obtained by weighted summation or constructing a comprehensive indicator function, thereby obtaining the twin degree of multiple point monitoring.

[0028] Step S322: Determine whether the twin degrees of the multiple point monitoring are greater than or equal to the predetermined twin degrees of the point monitoring, and determine multiple twin point clusters that are greater than or equal to the predetermined twin degrees of the point monitoring. Specifically, the calculated twin degrees of the multiple point monitoring are compared with the predetermined twin degrees of the point monitoring. The predetermined twin degree is a measurement standard determined based on experience, statistical analysis of historical data, or expert knowledge, and is used to determine which point combinations have a high similarity. Each twin degree value is checked one by one. If it is greater than or equal to the predetermined twin degree, the corresponding two points are classified into a twin point cluster. For example, if the predetermined twin degree is 0.8, when the twin degree of two points is 0.9, the two points are classified into the same twin point cluster. After comparing and screening all point combinations, multiple twin point clusters are determined, each cluster containing two hydrological monitoring points with a high similarity.

[0029] Step S323 , performing proximity analysis of intra-cluster point positions based on the multiple twin point clusters to determine multiple intra-cluster point redundancy coefficients. Specifically, the precise geographic location information of the two hydrological monitoring points within each twin point cluster is obtained, including latitude and longitude coordinates, altitude, etc. For points in linear geographical environments such as rivers, their relative positional relationships with upstream and downstream areas, river banks, etc. must also be clarified, and the location information must be digitized for subsequent calculations and analysis. For example, the latitude and longitude coordinates are converted into coordinate values ​​in a plane rectangular coordinate system to facilitate the calculation of location proximity indicators such as the distance between the two points. The redundancy coefficient of the points within the cluster is determined based on factors such as the actual distance between the points, the relative orientation, and the geographical environment characteristics. If the two points are close to each other and are in a similar geographical environment (such as on the same side of the river bank or in the same section of a gentle river channel), the redundancy coefficient will be higher. The redundancy coefficient can be calculated by establishing a mathematical model. For example, parameters such as distance weight and environmental similarity weight are set, and a redundancy coefficient value between 0 and 1 is calculated through weighted summation. This calculation is performed for the points within each twin point cluster to determine the redundancy coefficients of multiple points within the cluster.

[0030] Step S324: Determine whether the multiple intra-cluster point redundancy coefficients are greater than or equal to an intra-cluster point redundancy threshold, and determine an intra-cluster point redundancy coefficient that is greater than or equal to the intra-cluster point redundancy threshold. Specifically, the multiple calculated intra-cluster point redundancy coefficients are compared with the intra-cluster point redundancy threshold. The threshold is also a standard determined based on experience, data analysis, etc., and is used to determine whether a point is redundant. If a certain intra-cluster point redundancy coefficient is greater than or equal to the threshold, it is identified as a coefficient with redundant characteristics. All redundant coefficients are checked and identified, and an intra-cluster point redundancy coefficient that is greater than or equal to the intra-cluster point redundancy threshold is determined.

[0031] Step S325, based on the redundancy coefficient of the points within the identified cluster, the initial point planning scheme is point mapped to obtain the monitoring point redundancy detection result. Specifically, based on the redundancy coefficient of the points within the identified cluster, the initial point planning scheme is point mapped. Points with redundant characteristics are marked and associated in the planning scheme, and their relevant information, such as point number, location information, redundancy coefficient size, etc., is recorded. Through mapping, the redundant point situation existing in the initial point planning scheme is fully reflected, thereby obtaining the monitoring point redundancy detection result. The result provides an accurate basis for subsequent point adjustment and optimization, and helps to improve the rationality and scientificity of the layout of hydrological monitoring points.

[0032] Step S400, based on the hydrological dynamic prediction results, the initial point planning scheme is corrected for the hydrological monitoring point blind spots according to the monitoring demand analysis channel and the hydrological monitoring point addition decision channel to obtain the second adjustment plan for the point planning. Specifically, the hydrological dynamic prediction results are used to detect hydrological fluctuations at each location, and the fluctuation coefficient reflecting the hydrological instability of each location is calculated. At the same time, the hydrological risk prediction is carried out in combination with historical data, geographic information, human activities and other factors to obtain the risk coefficient. The fluctuation coefficient and risk coefficient are input into the monitoring demand analysis channel, and the hydrological monitoring demand at each location is calculated based on the predetermined fluctuation weight and risk weight. Then, it is determined whether the demand reaches the predetermined value, and a demand distribution map is drawn in the geographic information system to determine the area with low demand as a potential blind spot. The blind spot is confirmed by combining historical data and field surveys, and then remote sensing and field measurements are used to collect the geographical feature data of the blind spot, including terrain height, slope, vegetation cover, soil type and hydrological related information. Then, based on the blind spot detection results and geographic feature data, the location, number and type of new points are determined by adding decision channels, such as setting up water level monitoring points in low-lying areas and water quality monitoring points in areas with dense vegetation. The number of new points is reasonably planned based on the blind spot situation. Finally, the information of the new points is incorporated into the initial plan and simulated for verification. After optimization and adjustment, the second adjustment plan for the point planning is generated to improve the layout of the monitoring points.

[0033] In a possible implementation, based on the hydrological dynamic prediction results, the initial point planning scheme is corrected for the blind spots of the hydrological monitoring points according to the monitoring demand analysis channel and the decision channel for adding hydrological monitoring points, and a second adjustment plan for the point planning is obtained. Step S400 further includes step S410, based on the monitoring demand analysis channel, the hydrological monitoring demand analysis is performed according to the hydrological dynamic prediction results to determine the hydrological monitoring demand of multiple locations. Specifically, key information is extracted from the hydrological dynamic prediction results, including data such as the predicted values ​​of water level changes, flow change trends, and the possibility of water quality changes at different locations at different time points in the future. The data is classified and sorted, and grouped according to location information so that demand analysis can be performed for each location later. At the same time, the data is smoothed and outliers are removed to improve the accuracy and reliability of the data. For example, some obviously unreasonable data points caused by temporary sensor failures or model errors are corrected or deleted. The monitoring demand analysis channel is used, which contains an algorithm model built based on historical monitoring data and expert experience. The sorted hydrological data of each location is input into the channel to start the demand analysis calculation process. In the analysis channel, first, the water level change severity index of each location is calculated based on the water level change prediction data, such as using statistical quantities such as standard deviation to measure. For the flow change trend, its rate and amplitude of change are analyzed. Combined with the law of flow change under different seasons and climatic conditions, corresponding weights are assigned for comprehensive evaluation. In terms of water quality changes, factors such as the possible spread of pollution sources, the self-purification capacity of water bodies, and the degree of impact on the surrounding ecological environment and human activities are considered. The factors are integrated according to a predetermined algorithm, combined with the predetermined hydrological fluctuation weight and the predetermined hydrological risk weight, and through mathematical operations such as weighted summation, the hydrological monitoring demand of multiple locations is accurately calculated. Each location corresponds to a demand value, which reflects the urgency of hydrological monitoring at that location.

[0034] Step S420, determines whether the hydrological monitoring demand of the plurality of locations is greater than or equal to the predetermined hydrological monitoring demand, and generates a hydrological monitoring demand location distribution. Specifically, the calculated hydrological monitoring demand of the plurality of locations is compared with the predetermined hydrological monitoring demand one by one. The predetermined hydrological monitoring demand is a standard value determined based on the hydrological importance classification of the target area, water resources management goals, and relevant policies and regulations. For those locations whose demand is greater than or equal to the predetermined value, they are recorded, and by marking them on a geographic information system (GIS) platform, the location information that meets the conditions is integrated to form a hydrological monitoring demand location distribution. In the marking process, different colors or symbols can be used to distinguish according to the level of demand, so as to intuitively show the spatial distribution difference of demand. For example, areas with higher demand can be marked in red, and areas with lower demand but still meeting the conditions can be marked in yellow, thereby clearly showing which areas have a more prominent demand for hydrological monitoring.

[0035] Step S430, based on the hydrological monitoring demand location distribution, the initial point planning scheme is used to identify vacant monitoring points, and obtain point blind spot detection results. Specifically, the generated hydrological monitoring demand location distribution is superimposed and compared with the initial point planning scheme, and the monitoring coverage of each position in the demand location distribution in the initial point planning scheme is carefully checked. For those locations that exist in the demand location distribution but have no corresponding monitoring points in the initial point planning scheme or the monitoring points are sparse and cannot meet the monitoring needs, they are identified as vacant monitoring points. Through precise geographic coordinate matching and spatial analysis algorithms, the specific location information and range of these vacant points are determined to obtain point blind spot detection results. This process needs to consider factors such as the effective monitoring radius of the monitoring point, monitoring accuracy, and monitoring requirements of different hydrological elements. For example, for water quality monitoring, it is necessary to consider the diffusion range of the pollution source and the mixing of the water flow to determine a reasonable monitoring blind spot range.

[0036] Step S440: Geographic feature collection is performed based on the point blind spot detection results to obtain point blind spot geographic feature data. Specifically, based on the point blind spot detection results, specific areas where geographic feature collection is required are determined. For these areas, a variety of geographic information collection technologies are comprehensively applied, using satellite remote sensing images to obtain macroscopic geographic feature information, such as the overall morphology of the topography, vegetation coverage and type, etc. More accurate data such as terrain height and slope are obtained through ground measurement equipment, such as GPS measuring instruments and topographic mapping instruments. Information closely related to hydrology, such as groundwater level, soil texture and permeability, can be collected through groundwater monitoring wells, soil sampling and analysis, etc. During the collection process, targeted sampling is performed according to a certain grid density or based on the topographic and geomorphological characteristics to ensure that the collected data can accurately reflect the geographic features of the point blind spot. Real-time quality control is performed on the collected data to check the accuracy and completeness of the data, and supplementary collection or data correction is carried out in a timely manner.

[0037] Step S450 , based on the point blind spot detection result and the geographical feature data of the point blind spot, and according to the additional decision channel of the hydrological monitoring point, a second adjustment plan for the point planning is generated. Specifically, a large amount of historical hydrological monitoring data, geographical feature data and corresponding successful point addition case data are collected. The historical hydrological monitoring data include actual monitoring records of water level, flow and water quality in different periods and different regions; the geographical feature data covers topography, vegetation, soil and other information; the point addition case data records in detail the circumstances under which the point addition was carried out, the location and type of the addition, and the subsequent effects. Key features are extracted from the collected data, such as the hydrological characteristics corresponding to different terrain types (mountains, plains, river valleys, etc.), the correlation characteristics between vegetation coverage and water quality changes, the characteristics of the impact of soil properties on water flow, etc. Each case is annotated, and the annotated content includes whether it is an effective point addition case (that is, whether the monitoring effect has been improved, specific needs have been met, etc.). The annotated data is used for model training, and machine learning algorithms such as neural network algorithms or decision tree algorithms are used for training. In the neural network training, appropriate parameters such as the number of network layers, number of nodes and learning rate are set. By inputting feature data and corresponding annotation information, the network weights are continuously adjusted so that the model can learn the relationship between different feature combinations and the addition of new points. The decision tree algorithm constructs decision branches based on indicators such as information gain, corresponding various feature condition combinations to point addition strategies. Through repeated training and verification, the model parameters are optimized to improve the model's accuracy and generalization ability. The point blind spot detection results and geographic feature data are input into the trained model. The model makes analytical decisions based on the learned knowledge and experience, taking into account various factors. For example, in point blind spots in mountainous areas, if the terrain is steep, vegetation is sparse, and the groundwater level fluctuates greatly, the model will recommend adding flow and water level monitoring points at key locations such as valley exits based on the rules and patterns obtained from training. Based on the decision results output by the model, the initial point planning scheme is adjusted and optimized to generate a second adjustment plan for the point planning. During the adjustment process, the collaborative work of the new points and the original points is fully considered to ensure the rationality and effectiveness of the entire monitoring network and improve the overall level of hydrological monitoring.

[0038] In one possible implementation, based on the monitoring demand analysis channel, the hydrological monitoring demand analysis is performed according to the hydrological dynamic prediction results, and the hydrological monitoring demand of multiple locations is determined. Step S410 further includes step S411, performing hydrological fluctuation detection on each location of the target area according to the hydrological dynamic prediction results to obtain hydrological fluctuation coefficients of multiple locations. Specifically, detailed data about each location in the target area is obtained from the hydrological dynamic prediction results, including a data sequence of water level, flow, water quality and other factors that change over time. For each location, these data are arranged in chronological order and pre-processed to remove possible outliers, such as data points that are obviously inconsistent with the actual situation due to instrument failure or data transmission errors. The noise interference in the data is reduced by a data smoothing algorithm to improve the accuracy and reliability of the data. Taking water level data as an example, if the water level data at a certain moment is too different from the data at the previous and next adjacent moments and does not conform to the law of water flow changes, further inspection and verification or correction is required, using appropriate statistical methods. To calculate the hydrological fluctuation coefficient, for water level fluctuation, calculate the standard deviation or variance of water level changes within a certain period of time. The larger the standard deviation, the more severe the water level fluctuation. For example, select one month as the time period, and calculate the standard deviation of daily water level data at a certain location within this month as the water level fluctuation coefficient. For flow fluctuation, the average value of the flow change rate or the coefficient of variation of the flow can be calculated. In the calculation process, the influence of different seasons, climatic conditions and geographical environmental factors on fluctuations is taken into account. For example, the fluctuation of river flow in rainy season is usually large, and corrections are made by setting seasonal adjustment factors to more accurately reflect the actual hydrological fluctuation situation of each location, thereby obtaining hydrological fluctuation coefficients for multiple locations.

[0039] Step S412, based on the hydrological dynamic prediction results, the hydrological risk of each location in the target area is predicted to obtain hydrological risk coefficients for multiple locations. Specifically, based on the hydrological dynamic prediction results, in-depth analysis of various factors that may cause hydrological risks is conducted. In terms of flood risk, factors such as rainfall, topography, river basin characteristics, and water conservancy engineering facilities are considered. If the predicted rainfall exceeds the carrying capacity of a river in a certain area in a short period of time, and the area is low-lying and the river channel is narrow, then the flood risk will be high. In terms of water quality risk, attention is paid to factors such as the distribution of pollution sources, water flow conditions, and surrounding human activities. For example, locations close to industrial areas with slow water flow may face a higher risk of water pollution. For drought risk, analysis is conducted in combination with rainfall forecasts, soil types, groundwater levels, and vegetation coverage, such as soil water retention capacity. Regions with poor water quality, low groundwater levels and sparse vegetation have a high risk of drought. A corresponding risk assessment model is constructed to calculate the hydrological risk coefficient. For flood risk, a flood simulation model based on hydrological principles can be used in combination with historical flood event data for calculation. By inputting data such as rainfall and topography, the possibility of flood occurrence and inundation range can be simulated, and the flood risk coefficient is determined based on the simulation results. For water quality risk, a pollutant diffusion model is used in combination with parameters such as pollution source intensity and water body self-purification capacity for calculation. Drought risk can be determined by establishing a drought indicator system and comprehensively considering factors such as rainfall, evaporation, and soil moisture content. After calculation, the hydrological risk coefficients of multiple locations in the target area are obtained.

[0040] Step S413, input the hydrological fluctuation coefficients of the multiple locations and the hydrological risk coefficients of the multiple locations into the monitoring demand analysis channel to obtain the hydrological monitoring demand of the multiple locations, wherein the monitoring demand analysis channel includes predetermined hydrological fluctuation weights and predetermined hydrological risk weights. Specifically, the calculated hydrological fluctuation coefficients of the multiple locations and the hydrological risk coefficients of the multiple locations are input into the monitoring demand analysis channel, which pre-sets predetermined hydrological fluctuation weights and predetermined hydrological risk weights. The weights are determined based on the assessment of the importance of different hydrological factors and past monitoring experience. For example, if an area often suffers from flood disasters, the weights related to flood risks may be set relatively high. Through expert evaluation, data analysis, historical case summary and other methods, different weight values ​​are reasonably determined to reflect the different degrees of impact of hydrological fluctuations and risks on monitoring needs in specific areas or situations. In the monitoring demand analysis channel, the fluctuation coefficients and the hydrological risk coefficients are adjusted according to the set weights. The risk coefficient is weightedly calculated using the mathematical method of weighted summation. The hydrological fluctuation coefficient of each location is multiplied by the predetermined hydrological fluctuation weight, and the hydrological risk coefficient is multiplied by the predetermined hydrological risk weight. The two are then added together to obtain the hydrological monitoring demand at each location. For example, the hydrological fluctuation coefficient of a certain location is 0.6, and the predetermined hydrological fluctuation weight is 0.4; the hydrological risk coefficient is 0.8, and the predetermined hydrological risk weight is 0.6. The hydrological monitoring demand at this location is 0.6×0.4+0.8×0.6=0.72. Through this calculation method, the corresponding hydrological monitoring demand is determined for each location in the target area, so that subsequent point planning adjustments and other work can be carried out according to the demand.

[0041] Step S500, based on the regional geographic feature dataset of the target area and the hydrological dynamic prediction results, the initial point planning scheme is compensated for the damage to the hydrological monitoring points according to the point damage risk threshold, and the third adjustment plan for the point planning is obtained. Specifically, the geographic feature dataset of the target area and the hydrological dynamic prediction results are first integrated, and information related to the point damage risk such as topography, soil, vegetation, etc. is extracted from the geographic dataset. Combined with the water level, flow changes and extreme weather information in the hydrological prediction, a data file is established for each monitoring point. Then, a point damage risk prediction model is constructed, integrating the influencing mechanism of geographical and hydrological factors, using machine learning or physical models, and after historical data training and verification, the damage risk of each point is predicted to obtain a risk coefficient. These coefficients are then compared with the set threshold, and the points that are greater than or equal to the threshold are marked to determine the distribution of predicted damaged points, while a comprehensive judgment is made in combination with the specific geographical environment. Then, based on the geographic feature dataset, the geographical environment around the damaged points is analyzed, and compensation strategies are formulated for different situations such as complex terrain, unstable geology, or large flood impacts, such as relocating points, strengthening foundations, or adding flood control facilities. The initial plan is then adjusted according to the strategy to generate a third adjustment plan. Finally, GIS and hydrological models are used to simulate the safety and monitoring effects of the points under different conditions. If there are any problems, optimization and improvement are carried out until the safety and monitoring requirements are met, and the final plan is determined.

[0042] In one possible implementation, based on the regional geographic feature data set of the target area and the hydrological dynamic prediction results, the initial point planning scheme is compensated for damage to the hydrological monitoring points according to the point damage risk threshold to obtain a third adjustment scheme for the point planning. Step S500 further includes step S510, based on the hydrological dynamic prediction results, performing damage risk prediction on each hydrological monitoring point in the initial point planning scheme to obtain multiple point damage risk coefficients. Specifically, various key information is extracted from the hydrological dynamic forecast results, including but not limited to water level change amplitude predictions, flow peak predictions, and the probability and intensity predictions of extreme weather events (such as rainstorms, floods, and hurricanes). For each hydrological monitoring point within the initial point planning scheme, relevant data on the surrounding geographical environment is obtained, such as geographical characteristics such as terrain height, slope, and geological structure stability at the location, as well as distance information from water bodies such as rivers and lakes. At the same time, historical records of events related to point damage that have occurred at the point and surrounding areas are compiled, such as water level heights that have been impacted by floods and the impact range of geological disasters. These data are integrated as reference data and processed according to a unified data format to ensure data accuracy and completeness, making full preparations for subsequent risk prediction calculations. A point damage risk prediction model is constructed, which comprehensively considers the impact of multiple factors on the point damage risk. For example, a statistical analysis method is used to establish a relationship model between the water level change amplitude and the point damage probability. If the predicted water level rise exceeds a certain threshold and the point is located in a low-lying area, the damage risk will increase accordingly. For peak flow prediction, the impact of water flow on the points is analyzed in combination with factors such as the width, depth and water flow speed of the river, and the damage risk is then assessed. When considering extreme weather events, the probability distribution of damage caused to the points by weather events such as rainstorms and hurricanes of different intensities is statistically calculated based on historical data and incorporated into the model calculation. The collected data is input into the model, and through the model's calculation logic, the weight relationship of various factors is comprehensively considered to calculate a quantitative damage risk coefficient for each hydrological monitoring point, thereby obtaining damage risk coefficients for multiple points. These coefficients can intuitively reflect the degree of damage risk that each point may face in the future.

[0043] Step S520, determine whether the damage risk coefficients of the multiple points are greater than or equal to the point damage risk threshold, and establish a predicted damaged point distribution. Specifically, the calculated damage risk coefficients of the multiple points are compared and analyzed one by one with the pre-set point damage risk threshold. The point damage risk threshold is a measurement standard determined based on engineering experience, industry standards, and historical data statistical analysis. If the damage risk coefficient of a certain point is greater than or equal to the threshold, it means that the point is more likely to be damaged in the future. These points that meet the conditions are recorded and sorted, and marked on a map or geographic information system (GIS) according to the geographical location information of the points. The information of these points is integrated to form a predicted damaged point distribution. The distribution of these points with higher damage risks is displayed in a visual way, so that it can be clearly seen which areas of monitoring points have greater safety hazards, providing a clear target area for subsequent targeted point layout compensation.

[0044] Step S530: Based on the regional geographic feature dataset and the predicted distribution of damaged points, point layout compensation is performed to generate a third adjustment plan for the point planning. Specifically, based on the regional geographic feature dataset, a detailed geographic feature analysis is conducted for each point in the predicted distribution of damaged points and its surrounding environment. For points located in mountainous areas with steep terrain slopes and unstable geological structures, engineering measures are considered for reinforcement, such as building slope protection and retaining walls, to enhance the stability of the point and prevent damage from geological disasters such as landslides. For points near rivers and prone to flooding, based on historical flood data, river topography, and water flow direction, the point is relocated to a higher elevation or safer location, or flood control facilities are constructed for the point, such as building flood control dikes and installing waterproof protective covers. For areas with loose soil and prone to subsidence, foundation reinforcement measures are implemented to ensure the stability of the point foundation. At the same time, based on the monitoring function requirements of the point and the characteristics of the surrounding geographical environment, the installation method and layout of the monitoring equipment are replanned to improve the adaptability and reliability of the point in complex geographical environments. Based on the formulated point layout compensation strategy, the initial point planning scheme is adjusted and optimized. The points that need to be relocated will be rearranged in a new safe location and equipped with appropriate monitoring equipment; the points that need to be reinforced or have additional protective facilities will be subject to corresponding engineering renovations and equipment updates. During the adjustment process, the adjusted plan will be simulated and verified using tools such as geographic information systems and hydrological simulation software. The feasibility and monitoring effect of the new point layout plan will be simulated under different hydrological conditions and geographical environments to check whether the risk of point damage can be effectively reduced and the accuracy and continuity of hydrological monitoring data can be guaranteed. If problems are found during the simulation process, such as the existence of new monitoring blind spots or unsatisfactory compensation measures, the plan will be further optimized and adjusted in a timely manner. After repeated verification and improvement, the third adjustment plan for point planning will be generated to make the layout of hydrological monitoring points more scientific, reasonable, safe and reliable.

[0045] Step S600: Based on the first, second, and third adjustment plans for the point planning, the initial point planning scheme is globally optimized to generate optimized hydrological monitoring point results. Specifically, the data for the first, second, and third adjustment plans for the point planning are first aggregated, cleaned, and standardized, and a comprehensive evaluation index system is established, covering aspects such as monitoring coverage, data accuracy, reasonable point locations, and cost-effectiveness. A mathematical model is constructed by determining weights and calculating evaluation scores. Conflicts during the integration process are then addressed, such as analyzing the objectives of each plan, visualizing conflicting areas using GIS, and coordinating solutions after comprehensive evaluation, such as adjusting point locations, parameters, or functions. Iterative optimization is then performed, using a hydrological model to simulate the performance of optimized points under different hydrological conditions and actual environments. Any deficiencies are adjusted based on the simulation results, and the process is repeated until the goals are met. Finally, the optimized hydrological monitoring point scheme is determined, detailed information is archived, and the results are visualized. The results are compared with the initial scheme to summarize lessons learned, providing a reference for subsequent work and completing the optimization and improvement of the hydrological monitoring point layout.

[0046] The embodiment of the present application adopts the method of collecting the parameters of the hydrological monitoring points in the target area and formulating the initial point planning scheme. Based on the prediction of the future time zone, the hydrological dynamic prediction is performed on the area to obtain the hydrological prediction results. According to the results, the initial point plan is adjusted for redundancy to generate a first adjustment plan. Based on the monitoring needs and the prediction results, the point blind spots are corrected to form a second adjustment plan. Combining the geographical feature data and the damage risk threshold, compensatory adjustments are made to the damaged points to generate a third adjustment plan. The above adjustment plans are integrated, the initial plan is globally optimized, and the optimized hydrological monitoring point layout is generated. The hydrological monitoring points are flexibly adjusted in multiple dimensions through the hydrological dynamic prediction results, thereby achieving the technical effect of improving the planning flexibility and accuracy of the hydrological monitoring points and improving the quality of hydrological monitoring.

[0047] In the above, refer to Figure 1 A method for dynamic planning of hydrological monitoring points according to an embodiment of the present invention is described in detail. Figure 2 A hydrological monitoring point dynamic planning system according to an embodiment of the present invention is described.

[0048] A hydrological monitoring point dynamic planning system according to an embodiment of the present invention is used to address the technical issues of poor planning flexibility and low accuracy for hydrological monitoring points in the prior art. By using hydrological dynamic prediction results to flexibly adjust hydrological monitoring points in multiple dimensions, the system achieves the technical effect of increasing planning flexibility and accuracy for hydrological monitoring points and improving the quality of hydrological monitoring. The hydrological monitoring point dynamic planning system includes: an initial point planning scheme acquisition module 10, a hydrological dynamic prediction result acquisition module 20, a point planning first adjustment scheme acquisition module 30, a second adjustment scheme acquisition module 40, a third adjustment scheme acquisition module 50, and a global optimization module 60.

[0049] The initial point planning scheme acquisition module 10 is used to collect hydrological monitoring point parameters of the target area and obtain an initial point planning scheme.

[0050] The hydrological dynamic prediction result acquisition module 20 is used to perform hydrological dynamic prediction on the target area based on a preset future time zone to obtain a hydrological dynamic prediction result.

[0051] The point planning first adjustment scheme acquisition module 30 is used to perform redundancy adjustment of the hydrological monitoring points on the initial point planning scheme based on the hydrological dynamic prediction result to obtain the point planning first adjustment scheme.

[0052] The second adjustment scheme acquisition module 40 is used to correct the blind spots of the hydrological monitoring points of the initial point planning scheme based on the hydrological dynamic prediction results, according to the monitoring demand analysis channel and the hydrological monitoring point addition decision channel, to obtain the second adjustment scheme of the point planning.

[0053] The third adjustment scheme acquisition module 50 is used to compensate for the damage to the hydrological monitoring points in the initial point planning scheme based on the regional geographic feature dataset of the target area and the hydrological dynamic prediction results according to the point damage risk threshold, and obtain the third adjustment scheme of the point planning.

[0054] The global optimization module 60 is used to perform global optimization on the initial point planning scheme according to the first point planning adjustment scheme, the second point planning adjustment scheme and the third point planning adjustment scheme, and generate a hydrological monitoring point optimization result.

[0055] The specific configuration of the hydrological dynamic prediction result acquisition module 20 will be described in detail below. As described above, the hydrological dynamic prediction is performed on the target area based on the preset future time zone to obtain the hydrological dynamic prediction result. The hydrological dynamic prediction result acquisition module 20 further includes: a real-time hydrological state information acquisition unit, which is used to collect real-time hydrological parameters of the target area and obtain real-time hydrological state information; a meteorological forecast data loading unit, which is used to load meteorological forecast data for the target area based on the preset future time zone; and a hydrological dynamic prediction unit, which is used to perform hydrological dynamic prediction based on the regional geographic feature dataset based on the real-time hydrological state information and the meteorological forecast data to obtain the hydrological dynamic prediction result.

[0056] The specific configuration of the point planning first adjustment scheme acquisition module 30 will be described in detail below. As described above, the initial point planning scheme is adjusted for redundancy of the hydrological monitoring points based on the hydrological dynamic prediction results to obtain the first adjustment scheme of the point planning. The point planning first adjustment scheme acquisition module 30 further includes: a monitoring fitting result determination unit, the monitoring fitting result determination unit is used to perform monitoring parameter fitting on each hydrological monitoring point in the initial point planning scheme based on the hydrological dynamic prediction results, and determine multiple point monitoring fitting results; a redundancy detection unit, the redundancy detection unit is used to perform redundancy detection on the initial point planning scheme based on the multiple point monitoring fitting results, and generate a monitoring point redundancy detection result; a redundancy adjustment unit, the redundancy adjustment unit is used to perform redundancy adjustment based on the monitoring point redundancy detection result, and generate the first adjustment scheme of the point planning.

[0057] Wherein, based on the multiple point monitoring fitting results, the initial point planning scheme is subjected to redundancy detection to generate monitoring point redundancy detection results, and the redundancy detection unit further includes: a twin identification subunit, the twin identification subunit is used to perform twin identification in pairs according to the multiple point monitoring fitting results to obtain multiple point monitoring twin degrees; a predetermined twin degree monitoring subunit, the predetermined twin degree monitoring subunit is used to judge whether the multiple point monitoring twin degrees are greater than or equal to the point monitoring predetermined twin degree, and determine multiple twin point clusters greater than or equal to the point monitoring predetermined twin degree; proximity analysis Subunit, the proximity analysis subunit is used to perform proximity analysis of the point positions within the cluster based on the multiple twin point clusters, and determine multiple intra-cluster point redundancy coefficients; redundancy coefficient judgment subunit, the redundancy coefficient judgment subunit is used to judge whether the multiple intra-cluster point redundancy coefficients are greater than or equal to the intra-cluster point redundancy threshold, and determine an identified intra-cluster point redundancy coefficient that is greater than or equal to the intra-cluster point redundancy threshold; point mapping subunit, the point mapping subunit is used to perform point mapping on the initial point planning scheme based on the identified intra-cluster point redundancy coefficient to obtain the monitoring point redundancy detection result.

[0058] The specific configuration of the second adjustment scheme acquisition module 40 will be described in detail below. As described above, based on the hydrological dynamic prediction results, the initial point planning scheme is corrected for the hydrological monitoring point blind spot according to the monitoring demand analysis channel and the hydrological monitoring point addition decision channel to obtain the second adjustment scheme for the point planning. The second adjustment scheme acquisition module 40 further includes: a position hydrological monitoring demand determination unit, which is used to perform hydrological monitoring demand analysis based on the monitoring demand analysis channel and the hydrological dynamic prediction results to determine the hydrological monitoring demand of multiple positions; a hydrological monitoring demand position distribution generation unit, which is used to determine whether the hydrological monitoring demand of the multiple positions is greater than or equal to the predetermined hydrological monitoring demand. Demand degree, generating a hydrological monitoring demand location distribution; a point blind spot detection result acquisition unit, the said point blind spot detection result acquisition unit is used to identify vacant monitoring points in the initial point planning scheme based on the said hydrological monitoring demand location distribution, and obtain point blind spot detection results; a geographical feature acquisition unit, the said geographical feature acquisition unit is used to collect geographical features based on the said point blind spot detection results, and obtain geographical feature data of the point blind spots; a point planning second adjustment plan generation unit, the said point planning second adjustment plan generation unit is used to generate the point planning second adjustment plan according to the addition of a decision channel to the hydrological monitoring point based on the said point blind spot detection results and the said point blind spot geographical feature data.

[0059] Wherein, based on the monitoring demand analysis channel, the hydrological monitoring demand analysis is performed according to the hydrological dynamic prediction result, and the hydrological monitoring demand degrees of multiple locations are determined. The location hydrological monitoring demand degree determination unit further includes: a location hydrological fluctuation coefficient acquisition subunit, the location hydrological fluctuation coefficient acquisition subunit is used to perform hydrological fluctuation detection at each location of the target area according to the hydrological dynamic prediction result, and obtain multiple location hydrological fluctuation coefficients; a location hydrological risk coefficient acquisition subunit, the location hydrological risk coefficient acquisition subunit is used to perform hydrological risk prediction at each location of the target area according to the hydrological dynamic prediction result, and obtain multiple location hydrological risk coefficients; a location hydrological monitoring demand degree acquisition subunit, the location hydrological monitoring demand degree acquisition subunit is used to input the multiple location hydrological fluctuation coefficients and the multiple location hydrological risk coefficients into the monitoring demand analysis channel, and obtain the multiple location hydrological monitoring demand degrees, wherein the monitoring demand analysis channel includes a predetermined hydrological fluctuation weight and a predetermined hydrological risk weight.

[0060] The specific configuration of the third adjustment scheme acquisition module 50 will be described in detail below. As described above, based on the regional geographic feature data set of the target area and the hydrological dynamic prediction result, the initial point planning scheme is compensated for the damage of the hydrological monitoring points according to the point damage risk threshold, and the third adjustment scheme of the point planning is obtained. The third adjustment scheme acquisition module 50 further includes: a damage risk prediction unit, the damaged risk prediction unit is used to predict the damage risk of each hydrological monitoring point in the initial point planning scheme based on the hydrological dynamic prediction result, and obtain multiple point damage risk coefficients; a predicted damaged point distribution establishment unit, the predicted damaged point distribution establishment unit is used to determine whether the multiple point damage risk coefficients are greater than or equal to the point damage risk threshold, and establish a predicted damaged point distribution; a point layout compensation unit, the point layout compensation unit is used to perform point layout compensation according to the predicted damaged point distribution based on the regional geographic feature data set, and generate the third adjustment scheme of the point planning.

[0061] A hydrological monitoring point dynamic planning system provided by an embodiment of the present invention can execute a hydrological monitoring point dynamic planning method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0062] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0063] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A dynamic planning method for hydrological monitoring points, characterized in that: The method comprises: Collect the hydrological monitoring point parameters in the target area and obtain the initial point planning scheme; Performing hydrological dynamic prediction on the target area based on a preset future time zone to obtain a hydrological dynamic prediction result; Based on the hydrological dynamic prediction result, the initial point planning scheme is adjusted for hydrological monitoring point redundancy to obtain a first point planning adjustment scheme; Based on the hydrological dynamic prediction results, the initial point planning scheme is corrected for the hydrological monitoring point blind spots according to the monitoring demand analysis channel and the hydrological monitoring point addition decision channel to obtain a second adjustment plan for the point planning; Based on the regional geographic feature dataset of the target area and the hydrological dynamic prediction result, compensating the hydrological monitoring point damage in the initial point planning scheme according to the point damage risk threshold to obtain a third adjustment scheme for the point planning; According to the first adjustment plan for point planning, the second adjustment plan for point planning and the third adjustment plan for point planning, the initial point planning plan is globally optimized to generate hydrological monitoring point optimization results.

2. The method according to claim 1, wherein Performing a hydrological dynamic prediction for the target area based on a preset future time zone to obtain a hydrological dynamic prediction result, including: Collecting real-time hydrological parameters of the target area to obtain real-time hydrological status information; Loading weather forecast data for the target area based on the preset future time zone; Based on the real-time hydrological status information and the meteorological forecast data, a hydrological dynamic prediction is performed according to the regional geographic feature data set to obtain the hydrological dynamic prediction result.

3. The method according to claim 1, wherein Based on the hydrological dynamic prediction result, the initial point planning scheme is adjusted for hydrological monitoring point redundancy to obtain a first point planning adjustment scheme, including: Based on the hydrological dynamic prediction results, monitoring parameters of each hydrological monitoring point in the initial point planning scheme are fitted to determine multiple point monitoring fitting results; Performing redundancy detection on the initial point planning scheme based on the multiple point monitoring fitting results to generate a monitoring point redundancy detection result; Redundancy adjustment is performed based on the redundancy detection result of the monitoring points to generate a first adjustment plan for the point planning.

4. The method according to claim 3, wherein Performing redundancy detection on the initial point planning scheme based on the multiple point monitoring fitting results to generate a monitoring point redundancy detection result, including: Perform twin recognition on a pairwise basis according to the multiple point monitoring fitting results to obtain multiple point monitoring twin degrees; Determining whether the twinning degrees of the plurality of point monitoring are greater than or equal to a predetermined twinning degree of the point monitoring, and determining a plurality of twin point clusters having a twinning degree greater than or equal to the predetermined twinning degree of the point monitoring; Performing proximity analysis of point positions within the cluster based on the multiple twin point clusters to determine multiple point redundancy coefficients within the cluster; Determining whether the plurality of intra-cluster point redundancy coefficients are greater than or equal to an intra-cluster point redundancy threshold, and determining an identification intra-cluster point redundancy coefficient that is greater than or equal to the intra-cluster point redundancy threshold; The initial point planning scheme is point mapped based on the point redundancy coefficient within the identification cluster to obtain the monitoring point redundancy detection result.

5. The method according to claim 1, wherein Based on the hydrological dynamic prediction results, the initial point planning scheme is corrected for the hydrological monitoring point blind spots according to the monitoring demand analysis channel and the hydrological monitoring point addition decision channel to obtain a second adjustment scheme for the point planning, including: Based on the monitoring demand analysis channel, performing hydrological monitoring demand analysis according to the hydrological dynamic prediction results to determine the hydrological monitoring demand of multiple locations; Determining whether the hydrological monitoring demand of the plurality of locations is greater than or equal to a predetermined hydrological monitoring demand, and generating a hydrological monitoring demand location distribution; Based on the location distribution of the hydrological monitoring requirements, identifying the missing monitoring points in the initial point planning scheme to obtain point blind spot detection results; Geographical feature collection is performed based on the point blind spot detection result to obtain geographical feature data of the point blind spot; Based on the point blind spot detection results and the geographical feature data of the point blind spot, and in accordance with the establishment of a decision channel at the hydrological monitoring point, a second adjustment plan for the point planning is generated.

6. The method according to claim 5, wherein Based on the monitoring demand analysis channel, performing hydrological monitoring demand analysis according to the hydrological dynamic prediction results, and determining the hydrological monitoring demand of multiple locations, including: Performing hydrological fluctuation detection at each location in the target area according to the hydrological dynamic prediction result to obtain hydrological fluctuation coefficients at multiple locations; Performing hydrological risk prediction for each location in the target area according to the hydrological dynamic prediction result to obtain hydrological risk coefficients for multiple locations; The hydrological fluctuation coefficients of the multiple locations and the hydrological risk coefficients of the multiple locations are input into the monitoring demand analysis channel to obtain the hydrological monitoring demand degrees of the multiple locations, wherein the monitoring demand analysis channel includes predetermined hydrological fluctuation weights and predetermined hydrological risk weights.

7. The method according to claim 1, wherein Based on the regional geographic feature dataset of the target area and the hydrological dynamic prediction result, the initial point planning scheme is compensated for hydrological monitoring point damage according to the point damage risk threshold to obtain a third point planning adjustment scheme, including: Based on the hydrological dynamic prediction results, damage risk prediction is performed on each hydrological monitoring point in the initial point planning scheme to obtain damage risk coefficients for multiple points; Determining whether the damage risk coefficients of the plurality of points are greater than or equal to the point damage risk threshold, and establishing a predicted damaged point distribution; Based on the regional geographic feature data set, point layout compensation is performed according to the predicted distribution of damaged points to generate a third adjustment plan for the point planning.

8. A dynamic planning system for hydrological monitoring points, characterized in that: The system is used to implement the method for dynamic planning of hydrological monitoring points according to any one of claims 1 to 7, and the system comprises: An initial point planning scheme acquisition module is used to collect hydrological monitoring point parameters in the target area and obtain an initial point planning scheme; A hydrological dynamic prediction result acquisition module, configured to perform hydrological dynamic prediction on the target area based on a preset future time zone to obtain a hydrological dynamic prediction result; a first adjustment scheme acquisition module for point planning, the first adjustment scheme acquisition module for point planning being used to perform redundancy adjustment of hydrological monitoring points on the initial point planning scheme based on the hydrological dynamic prediction result to obtain a first adjustment scheme for point planning; A second adjustment scheme acquisition module is used to correct the blind spots of the hydrological monitoring points in the initial point planning scheme based on the hydrological dynamic prediction result and the monitoring demand analysis channel and the hydrological monitoring point addition decision channel to obtain a second adjustment scheme for the point planning; A third adjustment scheme acquisition module is configured to compensate for damage to the hydrological monitoring points in the initial point planning scheme based on the regional geographic feature dataset of the target area and the hydrological dynamic prediction result according to the point damage risk threshold, thereby obtaining a third adjustment scheme for the point planning; A global optimization module is used to globally optimize the initial point planning scheme according to the first adjustment scheme of the point planning, the second adjustment scheme of the point planning and the third adjustment scheme of the point planning, and generate a hydrological monitoring point optimization result.

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