Water conservancy project management system based on GIS

Abstract

The invention relates to the technical field of water conservancy data processing, in particular to a GIS-based water conservancy project management system, which comprises a non-stationary data decomposition module, a nested autoregression modeling module, a space-time dynamic reconstruction module, a risk quantitative mapping module and a risk decision optimization module. According to the method, long-term changes and short-term fluctuations are quantified and synthesized term by term through hydrological time series data in trend fitting and fluctuation amplitude extraction, the precision and timeliness of time series prediction are improved, dynamic integration of data of multiple monitoring points is achieved in combination with geographic information matching and spatial difference value distribution analysis, and the accuracy and timeliness of time series prediction are improved. According to the method, the spatial distribution of water resources is accurately predicted, the correlation degree between regions is quantified, risk index quantification is combined with a spatial interpolation technology, so that risk region division is more detailed, the pertinence of emergency management is optimized, and the scientificity and execution efficiency of risk decision are further improved based on scheduling data analysis and influence factor quantification.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy data processing, and in particular to a water conservancy project management system based on GIS. Background Art

[0002] The field of water conservancy data processing technology refers to the technical field of collecting, storing, analyzing and applying various types of data in water conservancy projects using computer technology, information systems and data management methods. This field covers water resources monitoring, dispatching management, project operation monitoring, disaster warning and other aspects, aiming to improve the intelligence and information level of water conservancy projects. Through the efficient processing and analysis of hydrological data, geographic information, equipment operation status and other information, it is possible to achieve optimal allocation of water resources, project safety assurance and decision support, and further improve the scientificity and efficiency of water conservancy management.

[0003] Among them, the water conservancy project management system refers to an information system developed based on water conservancy data processing technology, which is mainly used to comprehensively manage the construction, operation, maintenance and monitoring process of water conservancy projects. Its uses include real-time collection of water conservancy data, project operation status monitoring, resource scheduling optimization, risk warning and emergency management, etc., to help managers achieve efficient management and decision support for water conservancy projects.

[0004] In the data processing process of existing technologies, the time series analysis method is relatively simple, and it fails to effectively distinguish long-term trends from short-term fluctuations, making it difficult to achieve refined predictions, which affects the accuracy of water resource scheduling. Spatial data processing does not fully combine dynamic monitoring point information, and the correlation between water resources in different regions is difficult to quantify, and it is impossible to reflect the real-time changes in spatial distribution. In terms of risk management, existing technologies are extensive in risk indicator extraction and regional division, and cannot achieve accurate identification and classification of risk areas, affecting the timeliness and effectiveness of emergency management, which may lead to safety hazards in water conservancy projects. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a water conservancy project management system based on GIS.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: The water conservancy project management system based on GIS includes:

[0007] The non-stationary data decomposition module segments the monitoring data of dam structure and gate operation based on the original hydrological time series data, separates the data into high-frequency and low-frequency data segment by segment, removes the noise components with energy below the threshold, reorganizes the remaining components and performs residual analysis, calculates the energy distribution of the components and identifies outliers, and removes the abnormal components to form a hydrological modal function component set;

[0008] The nested autoregressive modeling module, based on the set of hydrological mode function components, fits trend parameters item by item for the low-frequency components of the dam structure and calculates trend values, extracts the fluctuation amplitude and fluctuation parameters of the high-frequency components of the gate operation, performs weighted synthesis, combines and reconstructs the trend values and fluctuation values to generate a time-series reconstruction prediction value;

[0009] The spatio-temporal dynamic reconstruction module, based on the time-series reconstruction prediction value, matches the geographical coordinates of the dam structure monitoring points with those of the channel water conveyance, calculates the spatial difference distribution value among multiple monitoring points, performs fitting analysis in combination with the basin geographical information to generate a water resource spatial distribution prediction value, calculates the water resource correlation degree among multiple regions through regional spatial data and quantifies the spatial correlation parameters to obtain the water resource spatial correlation degree parameters;

[0010] The risk quantification mapping module, based on the water resource spatial distribution prediction value and the water resource spatial correlation degree parameters, matches the monitoring data of the dam structure and the pump station equipment, extracts risk indicators item by item and calculates the quantification values, performs spatial interpolation processing on the risk quantification values and fits and reconstructs them in combination with the land use data to obtain a water resource risk spatial distribution map;

[0011] The risk decision optimization module, based on the water resource risk spatial distribution map, delimits the risk level areas of the dam structure and the pump station equipment, identifies the risk areas and extracts the risk values and hot spot parameters, decomposes the hot spot parameters and the reservoir operation data, calculates the contribution degree of the differential influencing factors, and reconstructs the risk management optimization parameters.

[0012] The set of hydrological mode function components includes high-frequency components, low-frequency components, noise components, and energy distribution anomaly components. The time-series reconstruction prediction value includes trend values, fluctuation values, and time-series reconstruction data. The water resource spatial distribution prediction value includes spatial difference distribution values, geographical information fitting analysis results, and multi-region water resource correlation parameters. The water resource spatial correlation degree parameter is specifically the water resource correlation degree between regions. The water resource risk spatial distribution map includes risk indicator quantification values, spatial interpolation results, and land use data fitting reconstruction maps. The risk management optimization parameters include risk level areas, risk values, hot spot parameters, and contribution degrees of differential influencing factors.

[0013] As a further solution of the present invention, the obtaining steps of the set of hydrological mode function components are specifically as follows:

[0014] Segment the monitoring data of the dam structure and the gate operation, divide the original hydrological time series data into multiple sub-data intervals according to time windows, perform Fourier transform on the data interval by interval to decompose it into high-frequency components and low-frequency components, and generate high-frequency and low-frequency component sets;

[0015] Calculate the energy of multiple components in the high-frequency and low-frequency component sets, eliminate the noise components with energy lower than the set threshold, retain the components with effective energy, and generate a set of components with effective energy;

[0016] Perform margin analysis on the set of components with effective energy, using the formula:

[0017]

[0018] Calculate the margin distribution energy between multiple components, eliminate the abnormal energy components and reorganize the components with effective margin analysis, and reform a set of hydrological mode function components of the time series data;

[0019] Among them, E r represents the reorganized margin energy, E i represents the energy of the i-th component, w i represents the weight of the i-th component, and n represents the total number of components.

[0020] As a further solution of the present invention, the steps for obtaining the predicted value of the time series reconstruction are specifically as follows:

[0021] Based on the set of hydrological mode function components, fit the trend parameters item by item for the low-frequency components of the dam structure, calculate the trend value of each low-frequency component, and generate a set of low-frequency trend values of the dam structure;

[0022] Based on the set of hydrological mode function components, calculate the fluctuation amplitude and fluctuation parameters of the high-frequency components of the gate operation, and perform weighted synthesis of multiple fluctuation amplitudes and fluctuation parameters, using the formula:

[0023]

[0024] Calculate the weighted fluctuation value and generate the high-frequency fluctuation value of the gate operation;

[0025] Among them, A w represents the weighted fluctuation value, f i represents the fluctuation amplitude of the i-th high-frequency component, p i represents the fluctuation parameter of the corresponding component, w i represents the weighting factor, and n represents the number of high-frequency components;

[0026] Merge the set of low-frequency trend values of the dam structure and the high-frequency fluctuation value of the gate operation, and calculate and generate the predicted value of the time series reconstruction through linear reconstruction of the component time series.

[0027] As a further solution of the present invention, the steps for obtaining the predicted value of the spatial distribution of water resources are specifically as follows:

[0028] Reconstruct the predicted value based on the time series, match the monitoring points of the dam structure with the geographical coordinates of the channel water conveyance, use GIS technology to determine the distance between each monitoring point and the nearest water conveyance point, and generate the spatial coordinate matching result;

[0029] Based on the spatial coordinate matching result, adopt the spatial statistical analysis method, calculate the standard deviation and mean of the geographical distances between the monitoring points, and use the formula:

[0030]

[0031] Generate the spatial difference distribution data;

[0032] Among them, S d represents the spatial difference distribution value, x i , y i represent the coordinates of the i-th monitoring point, represents the average value of the coordinates of all monitoring points, and n represents the number of monitoring points;

[0033] Compare and analyze the spatial difference distribution data with the topographic and geomorphic data, adopt a linear regression model, refer to the influence of the topographic and geomorphic features on the water resource distribution, and perform spatial analysis in combination with GIS technology to calculate the predicted value of the water resource spatial distribution.

[0034] As a further solution of the present invention, the steps for obtaining the water resource spatial correlation degree parameter are specifically as follows:

[0035] From the predicted value of the water resource spatial distribution, adopt the spatial autocorrelation analysis method to evaluate the fitting degree of the water resource distribution between different regions, and generate the spatial autocorrelation analysis result;

[0036] Based on the spatial autocorrelation analysis result, evaluate the water resource mobility and dependence between regions, and use the formula:

[0037]

[0038] Calculate and generate the analysis result of the water resource correlation degree between regions;

[0039] Among them, C ij represents the water resource correlation degree between regions i and j, R i represents the water resource supply of region i, R j represents the water resource supply of region j, D i represents the water resource demand of region i, D j represents the water resource demand of region j, α is the key weight for the water resource supply, β is the adjustment coefficient for the water resource demand difference, and γ is a adjustment parameter for smoothing the denominator calculation to avoid the denominator being zero;

[0040] Using the analysis results of the water resource correlation degree among the regions, a weighted network model is adopted. Each region is regarded as a node, and at the same time, the water resource correlation degree among the regions is used as the weight of the edge connecting the nodes. Through network analysis, the water resource spatial correlation degree parameters are calculated.

[0041] As a further solution of the present invention, the specific steps for obtaining the water resource risk spatial distribution map are as follows:

[0042] Based on the predicted values of the water resource spatial distribution and the water resource spatial correlation degree parameters, the monitoring data of the dam structure and the pumping station equipment are matched, and a spatial contrast analysis method is used to identify key risk indicators, and a risk indicator identification result is generated;

[0043] Using the data extracted from the risk indicator identification result, statistical analysis techniques are applied to calculate the risk quantification value, and the formula is used:

[0044]

[0045] Generate a risk quantification result;

[0046] Among them, Q i represents the quantification value of the i-th risk, r ij represents the risk index of the i-th risk at the j-th monitoring point, w j is the weight factor of the j-th monitoring point, a kj is the adjustment coefficient corresponding to the monitoring point, n represents the total number of monitoring points for risk assessment in the target area, and m represents the number of coefficients used to adjust the risk calculation;

[0047] Using the risk quantification result, spatial interpolation processing is carried out, combined with land use data for fitting, and spatial data analysis is carried out through a geographic information system to reconstruct the risk distribution and generate a water resource risk spatial distribution map.

[0048] As a further solution of the present invention, the specific steps for obtaining the risk management optimization parameters are as follows:

[0049] Based on the water resource risk spatial distribution map, risk level areas of the dam structure and the pumping station equipment are delimited, risk values of multiple areas are extracted, the risk level of each area is calibrated, and a risk level area identification result is generated;

[0050] Using the risk level area identification result, hot spot parameters in the risk area are identified, including the highest risk point and key risk factors, and correlation analysis is carried out with the reservoir operation data to calculate the influence of the hot spot parameters, and the formula is used:

[0051]

[0052] Generate a differential influence factor analysis result;

[0053] Among them, C d represents the contribution degree of the differential influencing factor, p k is the hot-spot parameter identified from the regional identification, and ΔR k is the difference between the hot-spot parameter and the baseline reservoir operation data, and α k is the adjustment coefficient of each parameter, and S k is the normalization coefficient of the hot-spot parameter. n represents the total number of hot-spot parameters identified in the risk area;

[0054] Using the above analysis results of the differential influencing factors, identify the key influencing factors, determine the weights of multiple factors through systematic evaluation, classify and rank the differential influencing factors, and generate the optimized parameters for risk management.

[0055] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0056] In the present invention, through the hydrological time series data in trend fitting and fluctuation amplitude extraction, the long-term changes and short-term fluctuations are quantified and synthesized item by item, improving the accuracy and timeliness of time series prediction. Combining geographical information matching and spatial difference distribution analysis, the dynamic integration of multi-monitoring point data is realized, the spatial distribution of water resources is accurately predicted, and the correlation degree between regions is quantified. The combination of risk index quantification and spatial interpolation technology makes the division of risk areas more detailed and optimizes the pertinence of emergency management. Based on the analysis of operation data and quantification of influencing factors, the scientificity and execution efficiency of risk decision-making are further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is the system flow chart of the present invention;

[0058] Figure 2 is the flow chart of the acquisition steps of the hydrological mode function component set of the present invention;

[0059] Figure 3 is the flow chart of the acquisition steps of the predicted value of time series reconstruction of the present invention;

[0060] Figure 4 is the flow chart of the acquisition steps of the predicted value of the spatial distribution of water resources of the present invention;

[0061] Figure 5 is the flow chart of the acquisition steps of the spatial correlation degree parameter of water resources of the present invention;

[0062] Figure 6 is the flow chart of the acquisition steps of the spatial distribution map of water resources risk of the present invention;

[0063] Figure 7 is the flow chart of the acquisition steps of the optimized parameters for risk management of the present invention. Detailed implementation mode

[0064] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0066] Embodiment 1

[0067] Please refer to Figure 1 , the water conservancy project management system based on GIS includes:

[0068] The non-stationary data decomposition module segments the monitoring data of the dam structure and the gate operation based on the original hydrological time series data, separates the data into high-frequency and low-frequency components segment by segment, eliminates the noise components with energy lower than the threshold, recombines the remaining components with the residual analysis, calculates the energy distribution of the components and identifies the outliers, and eliminates the abnormal components to form a set of hydrological mode function components;

[0069] The nested autoregressive modeling module fits the trend parameters item by item for the low-frequency components of the dam structure and calculates the trend values based on the set of hydrological mode function components, extracts the fluctuation amplitude and fluctuation parameters of the high-frequency components of the gate operation, performs weighted synthesis, and combines and reconstructs the trend values and the fluctuation values to generate a time series reconstruction prediction value;

[0070] The spatio-temporal dynamic reconstruction module matches the geographical coordinates of the dam structure monitoring points with the channel water conveyance based on the time series reconstruction prediction value, calculates the spatial difference distribution value between multiple monitoring points, performs fitting analysis in combination with the basin geographical information, generates a water resource spatial distribution prediction value, calculates the water resource correlation degree between multiple regions through the regional spatial data and quantifies the spatial correlation parameters, and obtains the water resource spatial correlation degree parameters;

[0071] Based on the predicted values of the spatial distribution of water resources and the parameters of the spatial correlation degree of water resources, the risk quantification mapping module matches the monitoring data of the dam structure and pumping station equipment, extracts risk indicators item by item and calculates the quantification values, performs spatial interpolation processing on the risk quantification values and fits and reconstructs them in combination with land use data to obtain the spatial distribution map of water resources risks;

[0072] Based on the spatial distribution map of water resources risks, the risk decision-making optimization module delimits the risk level areas of the dam structure and pumping station equipment, identifies the risk areas and extracts the risk values and hotspot parameters, decomposes the hotspot parameters and the reservoir operation data, calculates the contribution degree of the differential influencing factors, and reconstructs the risk management optimization parameters.

[0073] The set of hydrological modal function components includes high-frequency components, low-frequency components, noise components, and energy distribution anomaly components. The predicted values of time series reconstruction include trend values, fluctuation values, and time series reconstruction data. The predicted values of the spatial distribution of water resources include spatial difference distribution values, geographical information fitting analysis results, and multi-regional water resources correlation parameters. The spatial correlation degree parameter of water resources is specifically the degree of water resources correlation between regions. The spatial distribution map of water resources risks includes risk indicator quantification values, spatial interpolation results, and land use data fitting reconstruction maps. The risk management optimization parameters include risk level areas, risk values, hotspot parameters, and the contribution degree of differential influencing factors.

[0074] Please refer to Figure 2 , and the specific steps for obtaining the set of hydrological modal function components are as follows:

[0075] Segment the monitoring data of the dam structure and gate operation, divide the original hydrological time series data into multiple sub-data intervals according to time windows, and perform Fourier transform on the data in each interval to decompose it into high-frequency and low-frequency components, generating high-frequency and low-frequency component sets;

[0076] Through time series analysis, the data is divided into multiple time windows, each time window containing continuous monitoring data of the dam structure and gate operation. After these data are segmented, they can be used for further frequency analysis. By performing Fourier transform on each data segment, high-frequency and low-frequency data components are obtained. This separation is for further analysis and elimination of noise. The high-frequency part usually contains rapidly changing abnormal data or noise, while the low-frequency part reflects the main trends and periodic changes of the data. After separating these two types of data, the key changes and potential problems in the hydrological data can be seen more clearly.

[0077] Calculate the energy of multiple components in the high-frequency and low-frequency component sets, eliminate the noise components with energy lower than the set threshold, and retain the components with effective energy, generating a set of components with effective energy;

[0078] First, calculate the energy of each component. The energy calculation is completed by the sum of squares of the components. This is a standard signal processing method used to evaluate the strength or power of a signal. Then, by setting an energy threshold, eliminate those components with energy lower than this threshold, which are usually noise or invalid data. The remaining high-energy components are considered signals containing useful information. This step is crucial for subsequent data analysis because it determines the quality and reliability of the data entering the analysis stage.

[0079] Conduct a residual analysis on the set of components with effective energy, using the formula:

[0080]

[0081] Calculate the residual distribution energy among multiple components, eliminate abnormal energy components, and reorganize the components with effective residual analysis to reform the component set of the hydrological mode function of the time-series data.

[0082] Among them, E r represents the reorganized residual energy, E i represents the energy of the i-th component, w i represents the weight of the i-th component, and n represents the total number of components.

[0083] Formula:

[0084]

[0085] The benefit of the formula is that it provides a method for quantitative analysis of the remaining energy, can effectively identify and eliminate abnormal energy components in the data, and thus ensure the accuracy of data reorganization.

[0086] Detailed explanation of the formula and the derivation process of formula calculation:

[0087] In the residual analysis, first, it is necessary to calculate the energy E i of each component. For example, if the energy of one component is 30 and that of another is 40, and the weights are 0.6 and 0.4 respectively, substitute them into the formula:

[0088] First, calculate the sum of squares of energy

[0089] Then, calculate the square of the sum of weighted energy

[0090] Square of the sum of weights

[0091] Finally, calculate the residual energy

[0092] The results show that through margin analysis, the main energy components in the data can be effectively identified and abnormal energy components can be eliminated, providing an accurate basis for data recombination, thus ensuring the reliability of the set of hydrological mode function components.

[0093] Please refer to Figure 3 , and the specific steps for obtaining the predicted value of time series reconstruction are as follows:

[0094] Based on the set of hydrological mode function components, the trend parameters of the low-frequency components of the dam structure are fitted item by item, the trend value of each low-frequency component is calculated, and a set of low-frequency trend values of the dam structure is generated;

[0095] In this process, the data of each low-frequency component are divided by time window and analyzed one by one. By fitting these low-frequency data to a preset mathematical model, the respective trend parameters can be obtained. These parameters reflect the long-term change trend of the data. Conducting such fitting calculations item by item can ensure that the time series analysis of the data is more accurate and targeted. Through this method, each trend value is obtained by processing the actual observed data through a mathematical model, and these trend values are aggregated into a set of low-frequency trend values of the dam structure.

[0096] Based on the set of hydrological mode function components, calculate the fluctuation amplitude and fluctuation parameters of the high-frequency components of the gate operation, and synthesize the multiple fluctuation amplitudes and fluctuation parameters by weighting, using the formula:

[0097]

[0098] Calculate the weighted fluctuation value to generate the high-frequency fluctuation value of the gate operation;

[0099] Among them, A w represents the weighted fluctuation value, f i represents the fluctuation amplitude of the i-th high-frequency component, p i represents the fluctuation parameter of the corresponding component, w i represents the weighting factor, and n represents the number of high-frequency components;

[0100] Formula:

[0101]

[0102] The advantage of the formula is that by weighting and combining the fluctuation amplitude and fluctuation parameter of each component, the actual fluctuation situation during the gate operation can be evaluated more accurately. At the same time, the introduction of the weighting factor can adjust the influence of each component, so that the final fluctuation value is closer to the actual operation state.

[0103] Detailed explanation of the formula and the derivation process of formula calculation:

[0104] Suppose there are three high-frequency components with fluctuation amplitudes of f1 = 2.0, f2 = 1.5, f3 = 3.0 respectively, corresponding fluctuation parameters of p1 = 0.5, p2 = 0.3, p3 = 0.7, and weight factors of w1 = 1, w2 = 0.5, w3 = 0.8. Calculate according to the formula:

[0105]

[0106] A w ≈0.707 + 0.402 + 1.647;

[0107] A w ≈2.756;

[0108] This result indicates that the fluctuation value obtained through weighted synthesis calculation is 2.756. This value can comprehensively consider the influence of different fluctuation amplitudes and parameters, and more accurately reflect the high-frequency fluctuation characteristics of the gate operation.

[0109] Merge the low-frequency trend value set of the dam structure with the high-frequency fluctuation value of the gate operation, and calculate and generate the time series reconstruction prediction value through the linear reconstruction of the component time series.

[0110] In this process, it is necessary to accurately align the time series to ensure that the trend value and the fluctuation value are merged within the same time frame. By using the method of linear reconstruction to combine the two, the generated prediction value can simultaneously reflect the long-term change trend of the dam structure and the instantaneous fluctuation of the gate operation, thus obtaining a more comprehensive monitoring data analysis result. These reconstructed time series prediction values provide a scientific basis for subsequent decision-making.

[0111] Please refer to Figure 4 , and the specific steps for obtaining the predicted value of the spatial distribution of water resources are as follows:

[0112] Based on the time series reconstruction prediction value, match the monitoring points of the dam structure with the geographical coordinates of the channel water conveyance, and use GIS technology to determine the distance between each monitoring point and the nearest water conveyance point to generate the spatial coordinate matching result;

[0113] First, use GIS technology to determine the geographical distance between each monitoring point and the nearest water conveyance point. This involves accurately measuring and recording the geographical coordinates of each monitoring point. The geographical coordinate dataset in GIS must be the latest and most accurate to ensure the accuracy of distance measurement. At the same time, the selection of each monitoring point should consider the influence of terrain and landform, as well as possible error sources such as atmospheric conditions and GPS signal interference. Through data processing and analysis using GIS software, including coordinate conversion and distance calculation, the accuracy and practicality of the monitoring data are ensured, so that the spatial coordinate matching result has high reliability and provides a solid foundation for the next spatial interpolation analysis.

[0114] Based on the spatial coordinate matching results, the standard deviation and mean of the geographical distances between the monitoring points are calculated using the spatial statistical analysis method, with the formula:

[0115]

[0116] Generate spatial difference distribution data;

[0117] Among them, S d represents the spatial difference distribution value, x i , y i represent the coordinates of the i-th monitoring point, represents the average value of the coordinates of all monitoring points, and n represents the number of monitoring points;

[0118] Formula:

[0119]

[0120] The advantage of the formula is that it provides a method to quantify the spatial differences between monitoring points, enabling the specific evaluation of the distance differences between different monitoring points and the analysis of the statistical characteristics of spatial data based on this.

[0121] Detailed explanation of the formula and the formula calculation derivation process:

[0122] Suppose there are 5 monitoring points with coordinates (2,3), (3,5), (5,8), (9,2), (7,6) respectively, and the average coordinates are (5.2,4.8). Substitute them into the formula for calculation:

[0123] First step, calculate the sum of the squares of the distances from each point to the average point, and the results are respectively:

[0124] 2.68, 0.68, 10.28, 13.68, 2.68; Second step, sum to get the sum of the squares of the total distances as 29.6;

[0125] Third step, use the formula

[0126] The result shows that the average spatial difference between the monitoring points is 7.4, which indicates that there are significant position differences between different monitoring points in space, and this is very important for understanding the influence of regional geographical and environmental factors on the monitoring data.

[0127] Compare and analyze the spatial difference distribution data with the topographic and geomorphic data, adopt a linear regression model, refer to the influence of the topographic and geomorphic features on the water resource distribution, and conduct spatial analysis in combination with GIS technology to calculate the predicted value of the water resource spatial distribution.

[0128] First, the collected spatial difference distribution data needs to be matched and analyzed with the basin topography and geomorphology data stored in the geographic information system. This includes unifying the data formats of both and geocoding the spatial data to ensure data consistency and comparability. Through the topographic and geomorphic impact analysis model, the potential impact of topography on water resource distribution is evaluated. This model needs to integrate data such as the slope, height, and geological type of the terrain. Through linear regression analysis, the relationship between topographic factors and water resource distribution can be established. The analysis results will help predict the water resource distribution under different topographic conditions. Further analysis also includes considerations of factors such as rainfall and soil water absorption rate, ensuring the comprehensiveness and accuracy of the prediction results. The predicted values of water resource spatial distribution will provide a scientific basis for water resource management and planning.

[0129] Please refer to Figure 5 , and the steps for obtaining the water resource spatial correlation degree parameters are specifically as follows:

[0130] From the predicted values of water resource spatial distribution, using the spatial autocorrelation analysis method, evaluate the fitting degree of water resource distribution between different regions, and generate the spatial autocorrelation analysis results;

[0131] Use the spatial autocorrelation analysis method to predict the spatial distribution of water resources. By calculating the autocorrelation coefficient, the water resource distribution pattern and similarity between different regions can be determined. The level of the autocorrelation coefficient indicates the similarity or difference degree of water resource distribution between regions, which is crucial for understanding the distribution characteristics of water resources between regions. Through specific autocorrelation analysis, it can be obtained which regions have similar water resource distributions and which have significant differences. This method relies on the geographic information system and statistical software for spatial data processing and analysis, ensuring the accuracy and practicality of the analysis. The generated spatial autocorrelation analysis results can intuitively display the distribution law and correlation characteristics of water resources in space.

[0132] Based on the spatial autocorrelation analysis results, evaluate the water resource mobility and dependence between regions, using the formula:

[0133]

[0134] Calculate and generate the analysis results of water resource correlation degree between regions;

[0135] Among them, C ij represents the water resource correlation degree between regions i and j, R i represents the water resource supply volume of region i, R j represents the water resource supply volume of region j, D i represents the water resource demand volume of region i, D jRepresents the water resource demand of region j. α is the key weight for water resource supply, β is the adjustment coefficient for the difference in water resource demand, and γ is an adjustment parameter used to smooth the denominator calculation to avoid a zero denominator;

[0136] Formula:

[0137]

[0138] The advantage of the formula is that by adjusting the parameters α, β, and γ, the influence of supply and demand and spatial distance on the correlation degree can be flexibly adjusted, increasing the applicability and flexibility of the model, and allowing for a more detailed simulation of the water resource mobility and dependence between regions.

[0139] Detailed explanation of the formula and the derivation process of the formula calculation:

[0140] First, it is necessary to obtain the water resource supply R of each region i and the demand D i . These data can be obtained through regional hydrological surveys and consumption records. α is the weight set according to the key nature of resource supply, and its value is determined through previous data analysis. β and γ are adjusted according to the demand difference and the need to avoid a zero denominator. Through example data, assume R i = 100 cubic meters per day, R j = 150 cubic meters per day, D i = 120 cubic meters per day, D j = 130 cubic meters per day, α = 0.8, β = 1.5, γ = 2. Substituting these values into the formula gives:

[0141]

[0142] The result shows a relatively high water resource correlation degree between regions i and j, which means that these two regions have a strong mutual dependence in water resource supply and demand. The increase in the correlation degree value reflects a stronger need for resource sharing and mobility.

[0143] Using the analysis results of the water resource correlation degree between regions, a weighted network model is adopted. Each region is regarded as a node, and at the same time, the water resource correlation degree between regions is used as the weight of the edge connecting the nodes. Through network analysis, the water resource spatial correlation degree parameter is calculated.

[0144] The water resource correlation between regions is quantified by obtaining and analyzing indicators such as water resource usage data, emissions, and recycling rates of each region. The data of each region is updated and recorded in real time through monitoring stations and satellite data, and then analyzed by specialized data processing software to ensure the accuracy and timeliness of the data. This data processing includes data cleaning, normalization, and outlier detection, so as to use this data as weights in the model. The weights of the edges connecting the nodes are dynamically adjusted according to the actual situation to reflect the actual situation of water resource utilization and dependence between different regions. The process of calculating the water resource spatial correlation parameters involves multiple steps, including the preliminary setting of weights, weight adjustment based on previous data patterns, and model verification, etc. Each step requires detailed data support and complex calculation processes to determine the weight coefficients between the regional nodes, so as to construct a weighted network model, thereby realizing the water resource management and optimal allocation between regions.

[0145] Please refer to Figure 6 , the specific steps for obtaining the water resource risk spatial distribution map are as follows:

[0146] Based on the predicted values of water resource spatial distribution and water resource spatial correlation parameters, match the monitoring data of the dam structure and pumping station equipment, and use the spatial contrast analysis method to identify key risk indicators and generate risk indicator identification results;

[0147] Through comprehensive analysis using geographic information systems and hydrological simulation software, for the data of each monitoring point, use spatial data analysis techniques to perform data normalization and outlier processing respectively according to the monitoring equipment with different locations and functions, and then compare the data at different times and locations through statistical methods to ensure the accuracy and relevance of the data. The generated risk indicator identification results can accurately reflect the key risks in actual water resource management.

[0148] Using the data extracted from the risk indicator identification results, apply statistical analysis techniques to calculate the risk quantification value, using the formula:

[0149]

[0150] Generate risk quantification results;

[0151] Among them, Q i represents the quantification value of the i-th risk, r ij represents the risk index of the i-th risk at the j-th monitoring point, w j is the weight factor of the j-th monitoring point, a kj is the adjustment coefficient corresponding to the monitoring point, n represents the total number of monitoring points for risk assessment in the target area, and m represents the number of coefficients used to adjust the risk calculation;

[0152] Formula:

[0153]

[0154] The benefit of the formula is that it can accurately quantify the risk level of a specific area by combining the risk indices and weight factors of each monitoring point, while taking into account the environmental impact factors of the monitoring points.

[0155] Detailed explanation of the formula and the derivation process of the formula calculation:

[0156] Set the number of monitoring points as n = 5 and the adjustment coefficient m = 3. Assume the risk index of the monitoring point is r ij are 20, 15, 10, 25, 30 respectively, and the weight factor w j is 0.2, 0.3, 0.1, 0.25, 0.15, and the adjustment coefficient a kj The sum is 10, 8, 12, 7, 9. Then the quantification value calculation of the i-th risk is:

[0157]

[0158] The result shows that the comprehensive risk quantification value of this area is 2.4387, indicating a relatively high risk and the need to take corresponding preventive measures.

[0159] Using the risk quantification results, conduct spatial interpolation processing, fit with land use data, conduct spatial data analysis through a geographic information system, reconstruct the risk distribution, and generate a spatial distribution map of water resource risks.

[0160] This model will take into account the impacts of various natural and human factors. Through this method, the obtained risk quantification results will be used as the input data for the next spatial interpolation processing. For spatial interpolation processing, this step mainly uses geostatistical methods such as the Kriging interpolation algorithm to fill in the areas without direct observation data. Through appropriate interpolation methods, the risk distribution can be made smoother. In the specific interpolation process, the risk values and their geographical locations of neighboring observation points will be considered to ensure spatial continuity and verisimilitude. Fit with land use data, and the land use data provides additional characterization factors such as land cover type and development degree, etc., which are all key factors affecting water resource risks. By combining these data, the risk distribution of water resources can be predicted and simulated more accurately, generating a spatial distribution map of water resource risks. This chart will intuitively display the risk levels of each area, providing a scientific basis for decision-makers.

[0161] Please refer to Figure 7 , and the specific steps for obtaining the risk management optimization parameters are as follows:

[0162] Based on the spatial distribution map of water resource risks, delimit the risk - level areas of dike structures and pumping station equipment, extract the risk values of multiple areas, calibrate the risk level of each area, and generate the risk - level area identification results;

[0163] First, the data in the map needs to be quantified. Through spatial analysis using GIS software, high - risk areas and low - risk areas are identified, and the data is compared with flood events to verify the effectiveness of the risk areas. By this method, the risk - level areas of dike structures and pumping station equipment are delimited. The accuracy of this area division is directly related to the effective implementation of subsequent flood - control measures. Then, extract the risk values of each area. These values need to be obtained through on - site measurement and previous data analysis to ensure the accuracy and timeliness of the data. This process involves data collection, processing, and analysis. Finally, calibrate the risk level of each area, use a standardized evaluation model to analyze this data, and the generated risk - level area identification results will be used to guide the actual construction and maintenance of flood - control projects.

[0164] Using the risk - level area identification results, identify the hot - spot parameters in the risk areas, including the highest - risk point and key risk factors, conduct correlation analysis with reservoir operation data, calculate the influence of the hot - spot parameters, and use the formula:

[0165]

[0166] Generate the analysis results of differential influencing factors;

[0167] Among them, C d represents the contribution degree of differential influencing factors, p k is the hot - spot parameter identified from area identification, ΔR k is the difference between the hot - spot parameter and the baseline reservoir operation data, α k is the adjustment coefficient of each parameter, S k is the standardization coefficient of the hot - spot parameter, and n represents the total number of hot - spot parameters identified in the risk area;

[0168] Formula:

[0169]

[0170] The benefit of the formula is that it can quantify the influence of hot - spot parameters in different risk - level areas. By improving the accuracy and adaptability of the model, it provides precise data support for reservoir operation, making risk management more targeted and forward - looking.

[0171] Detailed explanation of the formula and the formula calculation derivation process:

[0172] Consider a practical scenario, where p kThe risk assessment value representing a specific risk area. Assume that p in a certain high-risk area k is 85, and ΔR k is the deviation of the risk value from the baseline, set to 20%, and α k and S k are the adjustment coefficient and the normalization coefficient respectively, with values of 5 and 95. The calculation process is as follows:

[0173]

[0174] This result indicates that the degree of influence of the given risk area on the overall reservoir operation is 0.034, which is a relatively small influence value, indicating that although the risk level of this area is high, due to effective risk management measures, its actual impact on reservoir operation is low.

[0175] Utilize the analysis results of differential influencing factors to identify key influencing factors, determine the weights of multiple factors through systematic evaluation, classify and rank the differential influencing factors, and generate optimized parameters for risk management.

[0176] First, conduct data sorting and classification. Initially screen and exclude influencing factors through systematic software tools. This stage mainly relies on statistical data processing software to perform basic deviation, frequency, and pattern analysis on various data. Subsequently, based on the results of these basic data analyses, combined with industry standards and previous experience databases, further weight distribution is carried out for these factors. When setting the weights, consider the actual influence and variability of the factors. This requires the use of multi-factor analysis models and weight adjustment algorithms. Through this series of analyses and calculations, determine which factors are key influencing factors to optimize various parameters of risk management. These parameters will directly affect the subsequent risk assessment and the formulation of management strategies.

[0177] The above is only a preferred embodiment of the present invention, and it does not impose other forms of restrictions on the present invention. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. The GIS-based water conservancy project management system is characterized by: The system comprises: The non-stationary data decomposition module segments the monitoring data of dam structure and gate operation based on the original hydrological time series data, separates the data into high-frequency and low-frequency data segment by segment, removes the noise components with energy below the threshold, reorganizes the remaining components and performs residual analysis, calculates the energy distribution of the components and identifies outliers, and removes the abnormal components to form a hydrological modal function component set; The nested autoregressive modeling module fits trend parameters item by item to the low-frequency components of the dam structure and calculates trend values ​​based on the hydrological modal function component set, extracts the fluctuation amplitude and fluctuation parameters of the high-frequency components of the gate operation, performs weighted synthesis, merges and reconstructs the trend value and the fluctuation value, and generates a time series reconstruction prediction value; The spatiotemporal dynamic reconstruction module reconstructs the predicted value based on the time series, matches the geographical coordinates of the dam structure monitoring points with the channel water delivery, calculates the spatial difference distribution values ​​between multiple monitoring points, performs fitting analysis in combination with the basin geographic information, generates the predicted value of water resource spatial distribution, calculates the degree of water resource correlation between multiple regions through regional spatial data, quantifies the spatial correlation parameters, and obtains the water resource spatial correlation parameters; The risk quantification mapping module matches the monitoring data of the dam structure and the pump station equipment based on the water resource spatial distribution prediction value and the water resource spatial correlation parameter, extracts risk indicators item by item and calculates quantitative values, performs spatial interpolation processing on the risk quantitative values ​​and reconstructs them in combination with land use data fitting to obtain a water resource risk spatial distribution map; Based on the spatial distribution map of water resource risks, the risk decision optimization module delineates the risk level areas of dam structures and pump station equipment, identifies risk areas and extracts risk values ​​and hot spot parameters, decomposes hot spot parameters and reservoir scheduling data, calculates the contribution of differentiated influencing factors, and reconstructs risk management optimization parameters.

2. The GIS-based water conservancy project management system according to claim 1 is characterized in that: The hydrological modal function component set includes high-frequency components, low-frequency components, noise components, and energy distribution anomaly components. The time series reconstruction prediction value includes trend value, fluctuation value, and time series reconstruction data. The water resource spatial distribution prediction value includes spatial difference distribution value, geographic information fitting analysis results, and multi-regional water resource correlation parameters. The water resource spatial correlation parameter is specifically the degree of water resource correlation between regions. The water resource risk spatial distribution map includes risk indicator quantification values, spatial interpolation results, and land use data fitting reconstruction map. The risk management optimization parameters include risk level areas, risk values, hotspot parameters, and the contribution degree of differentiated influencing factors.

3. The GIS-based water conservancy project management system according to claim 2 is characterized in that: The steps for obtaining the hydrological mode function component set are specifically as follows: The monitoring data of dam structure and gate operation are segmented, the original hydrological time series data is divided into multiple sub-data intervals according to the time window, and the data is decomposed into high-frequency components and low-frequency components by Fourier transform in each interval to generate a set of high-frequency and low-frequency components; Calculating energy of multiple components in the high-frequency and low-frequency component sets, removing noise components whose energy is lower than a set threshold, retaining energy-effective components, and generating an energy-effective component set; The residual analysis is performed on the energy effective component set using the formula: Calculate the residual distribution energy between multiple components, remove the abnormal energy components and reorganize the effective components of residual analysis to re-form the hydrological modal function component set of time series data; Among them, E r represents the recombination excess energy, E i represents the energy of the i-th component, w i represents the weight of the i-th component, and n represents the total number of components.

4. The GIS-based water conservancy project management system according to claim 3 is characterized in that: The steps for obtaining the time series reconstruction prediction value are specifically as follows: Based on the hydrological modal function component set, the trend parameters of the low-frequency components of the dam structure are fitted item by item, the trend value of each low-frequency component is calculated, and a low-frequency trend value set of the dam structure is generated; Based on the hydrological modal function component set, the fluctuation amplitude and fluctuation parameters of the high-frequency components of the gate operation are calculated, and the multiple fluctuation amplitudes and fluctuation parameters are weighted and synthesized using the formula: Calculate the weighted fluctuation value and generate the high-frequency fluctuation value of gate operation; Among them, A w represents the weighted volatility value, f i represents the fluctuation amplitude of the i-th high-frequency component, p i represents the fluctuation parameter of the corresponding component, w i represents the weighting factor, n represents the number of high-frequency components; The low-frequency trend value set of the dam structure and the high-frequency fluctuation value of the gate operation are combined, and the time series reconstruction prediction value is calculated and generated through linear reconstruction of the component time series.

5. The GIS-based water conservancy project management system according to claim 4 is characterized in that: The steps for obtaining the predicted value of water resource spatial distribution are specifically as follows: Based on the time series reconstructed prediction value, the monitoring points of the dam structure are matched with the geographical coordinates of the channel water delivery, and the distance between each monitoring point and the nearest water delivery point is determined by using GIS technology to generate spatial coordinate matching results; Based on the spatial coordinate matching results, the spatial statistical analysis method is used to calculate the standard deviation and mean of the geographical distance between monitoring points using the formula: Generate spatial difference distribution data; Among them, S d Represents the spatial difference distribution value, x i ,y i represents the coordinates of the ith monitoring point, represents the average value of the coordinates of all monitoring points, and n represents the number of monitoring points; The spatial difference distribution data is compared and analyzed with the topographic data, and a linear regression model is used to refer to the impact of topography on water resource distribution, and spatial analysis is performed in combination with GIS technology to calculate the predicted value of water resource spatial distribution.

6. The GIS-based water conservancy project management system according to claim 5 is characterized in that: The steps for obtaining the water resource spatial correlation parameter are specifically as follows: Based on the predicted values ​​of spatial distribution of water resources, a spatial autocorrelation analysis method is used to evaluate the fitting degree of water resource distribution between differentiated regions and generate spatial autocorrelation analysis results; Based on the results of the spatial autocorrelation analysis, the water resources mobility and dependence between regions were evaluated using the formula: Calculate and generate the results of regional water resources correlation analysis; Among them, C ij represents the water resources correlation between regions i and j, R i represents the water supply in region i, R j represents the water supply in region j, D i represents the water resource demand of region i, D j represents the water demand of region j, α is the critical weight for water supply, β is the adjustment coefficient for the difference in water demand, and γ is an adjustment parameter used to smooth the denominator calculation to avoid the denominator being zero; Utilizing the analysis results of the water resources correlation between regions, a weighted network model is adopted, each region is regarded as a node, and the water resources correlation between regions is used as the weight of the edge connecting the nodes. Through network analysis, the water resources spatial correlation parameters are calculated.

7. The GIS-based water conservancy project management system according to claim 6 is characterized in that: The steps for obtaining the water resource risk spatial distribution map are specifically as follows: Based on the water resource spatial distribution prediction value and the water resource spatial correlation parameter, the monitoring data of the dam structure and the pump station equipment are matched, and the key risk indicators are identified by using a spatial comparative analysis method to generate a risk indicator identification result; Using the data extracted from the risk indicator identification results, the risk quantification value is calculated using statistical analysis techniques, using the formula: Generate risk quantification results; Among them, Q i represents the quantitative value of the i-th risk, r ij represents the risk index of the i-th risk at the j-th monitoring point, w j is the weight factor of the jth monitoring point, a kj is the adjustment coefficient corresponding to the monitoring point, n represents the total number of monitoring points for risk assessment in the target area, and m represents the number of coefficients used to adjust the risk calculation; The risk quantification results are used to perform spatial interpolation processing, combined with land use data for fitting, and spatial data analysis is performed through a geographic information system to reconstruct the risk distribution and generate a spatial distribution map of water resource risks.

8. The GIS-based water conservancy project management system according to claim 7 is characterized in that: The steps for obtaining the risk management optimization parameters are specifically as follows: Based on the water resource risk spatial distribution map, risk level areas of dam structures and pump station equipment are delineated, risk values ​​of multiple areas are extracted, the risk level of each area is calibrated, and risk level area identification results are generated; Using the risk level area identification results, identify the hotspot parameters in the risk area, including the highest risk point and key risk factors, perform correlation analysis with reservoir operation data, and calculate the influence of hotspot parameters using the formula: Generate analysis results of differentiated influencing factors; Among them, C d Indicates the contribution of differentiation influencing factors, p k is the hotspot parameter identified from the region, ΔR k is the difference between the hotspot parameters and the baseline reservoir operation data, α k is the adjustment coefficient of each parameter, S k is the standardized coefficient of the hotspot parameter, n represents the total number of hotspot parameters identified in the risk area; Utilize the analysis results of the differentiated influencing factors to identify key influencing factors, determine the weights of multiple factors through systematic evaluation, classify and sort the differentiated influencing factors, and generate risk management optimization parameters.

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