Drainage construction management system for high-density urban inland inundation prevention and control

Abstract

The invention relates to the technical field of drainage construction management, and discloses a drainage construction management system for high-density urban area waterlogging prevention and control, and the system comprises a catchment partitioning module which is used for collecting hydrological environment data of a target city and dividing the target city based on the hydrological environment data to obtain a plurality of catchment units; the data acquisition module is used for acquiring built environment data of the target city and obtaining a waterlogging factor set of each catchment unit based on the built environment data; the inland inundation statistics module is used for acquiring an inland inundation data set of the target city and obtaining the inland inundation density of each catchment unit based on the inland inundation data set; and the stability association module is used for associating the waterlogging factor set and the waterlogging density of each catchment unit to obtain a plurality of association sequences, and calculating a single-line explanation value of a single waterlogging factor in the waterlogging factor set of each association sequence to the waterlogging density and a collinear explanation value of a plurality of waterlogging factors to the waterlogging density.

Description

Technical Field

[0001] The present invention relates to the technical field of drainage construction management, and more specifically, to a drainage construction management system for preventing and controlling waterlogging in high-density urban areas. Background Art

[0002] With the global climate change and the acceleration of urbanization, waterlogging disasters in high-density urban areas are becoming increasingly severe, seriously threatening the safety of residents' lives and the sustainable development of cities. Traditional waterlogging prevention and control methods rely solely on the expansion of drainage pipe networks, without using scientific methods to find the key factors that cause waterlogging in the target city, and then conduct low-cost drainage construction based on the key factors. Therefore, the existing urban waterlogging prevention and control methods have problems such as high cost and poor effect. Summary of the invention

[0003] The present invention provides a drainage construction management system for preventing and controlling waterlogging in high-density urban areas, which solves the technical problems raised in the background technology.

[0004] The present invention provides a drainage construction management system for preventing and controlling waterlogging in high-density urban areas, comprising:

[0005] The water catchment zoning module is used to collect the hydrological environment data of the target city and divide the target city based on the hydrological environment data to obtain several water catchment units;

[0006] The data collection module is used to obtain the built environment data of the target city and obtain the waterlogging factor set of each catchment unit based on the built environment data;

[0007] The waterlogging statistics module is used to obtain the waterlogging dataset of the target city and obtain the waterlogging density of each catchment unit based on the waterlogging dataset;

[0008] The stationary association module is used to associate the waterlogging factor set of each watershed unit with the waterlogging density to obtain several association sequences, and calculate the single-line explanation value of the single waterlogging factor in the waterlogging factor set of each association sequence to the waterlogging density and the collinear explanation value of the multiple waterlogging factors to the waterlogging density;

[0009] The geographical association module is used to calculate the geographical effect scale of each waterlogging factor based on the multi-scale geographical weighted regression model, and weight the corresponding single-line explanation value and collinear explanation value of the geographical effect scale of the waterlogging factor to obtain the first updated explanation and the second updated explanation;

[0010] The waterlogging management module is used to determine characteristic waterlogging factors based on the first updated interpretation and the second updated interpretation, and to manage the drainage construction of the target city accordingly based on the characteristic waterlogging factors.

[0011] Furthermore, several water catchment units are obtained, including:

[0012] Obtain a plane image of the target city and rasterize the plane image to obtain a number of grid cells; collect the hydrological environment data of the grid cell in the xth row and yth column, the hydrological environment data including: elevation data, surface water flow direction, surface roughness, impervious area ratio, rainwater pipe network density and drainage pipe slope;

[0013] The flow rate of the grid cell in the xth row and yth column is calculated based on the hydrological environment data. The flow rate calculation formula is as follows: ;

[0014] in, represents the flow rate of the grid cell at row x and column y, represents the normalization coefficient, and denote the first weight parameter and the second weight parameter respectively, Represents the elevation data of the grid cell at row x and column y. represents the surface water flow direction of the grid cell in row x and column y, represents the surface roughness of the grid cell in row x and column y, Indicates the influence weight of surface roughness, represents the impervious area ratio of the grid cell in row x and column y, represents the stormwater pipe network density of the grid cell in row x and column y, represents the slope of the drainage pipe of the grid cell at row x and column y;

[0015] Based on the circulation of several grid units, a circulation matrix is ​​obtained; the circulation matrix is ​​processed by the Sobel operator to extract several edge features, and a water sink network is obtained based on the combination of several edge features; the water sink network is fitted to the plane image, and the plane image is segmented by the water sink network to obtain several sub-blocks, each of which is regarded as a water sink unit.

[0016] Furthermore, the waterlogging factor set of each catchment unit is obtained, including:

[0017] Built environment data of target cities include: drainage system data and drainage system-related data;

[0018] Drainage system data include: stormwater pipe network density, drainage pipe slope, stormwater inlet drainage capacity and stormwater storage capacity;

[0019] Drainage system-related data include: terrain slope, building density and green space coverage of the target city;

[0020] The drainage system data and drainage system related data of each watershed unit are obtained, and each data in the drainage system data and drainage system related data is used as the waterlogging factor of the watershed unit to obtain the waterlogging factor set of the watershed unit.

[0021] Furthermore, the waterlogging density of the catchment unit includes:

[0022] Obtain the first flood data from the private platform, the first flood data including: flood time and flood coordinates;

[0023] Obtain the second flood data of the pan-media platform, including:

[0024] Set preset keywords to filter the texts on the pan-media platform and obtain the target texts;

[0025] The target text is processed based on the NLP natural language model to obtain the second waterlogging data, which includes: waterlogging time and waterlogging coordinates;

[0026] The first waterlogging data and the second waterlogging data are merged to obtain complete waterlogging data;

[0027] The waterlogging density of each catchment unit is calculated based on the waterlogging events and waterlogging areas in the complete waterlogging data. The calculation formula of waterlogging density is as follows: ;

[0028] in, represents the waterlogging density of the j-th catchment unit, Indicates the number of first flood data, express The index of represents the waterlogging catchment area of ​​the ath first waterlogging data, represents the impact weight of the a-th first flood data, Indicates the number of the second flood data, express The index of represents the waterlogging coordinates of the bth second waterlogging data, represents the coordinate range of the ith catchment unit, represents the first indicator function, if , then the value is 1, otherwise the value is 0. represents the second indicator function, if , then the value is 1, otherwise the value is 0. represents the confidence coefficient of the pan-media platform, represents the bias constant term for waterlogging;

[0029] The confidence coefficient of the pan-media platform is determined based on the order of magnitude of the number of users of the corresponding pan-media platform, including: obtaining the order of magnitude of the number of users of the pan-media platform, weighting the preset benchmark confidence coefficient based on the order of magnitude, and obtaining .

[0030] Further, single-line explanations of values ​​include:

[0031] A single-line interpretation framework is established for each waterlogging factor in the waterlogging factor set. The single-line interpretation framework includes: discrete classification of the corresponding waterlogging factor and waterlogging density of the catchment unit of the corresponding waterlogging factor;

[0032] The discrete classification of waterlogging factors includes: generating S cluster centers by loading the s-th waterlogging factor, clustering the s-th waterlogging factor based on K clustering, obtaining S discrete classifications of the s-th waterlogging factor, and assigning discrete classification weights to each discrete classification;

[0033] The single-line explanation value of the s-th waterlogging factor is calculated for the S discrete classifications of the s-th waterlogging factor through the improved geographic factor detector model. The calculation formula of the improved factor detector model is as follows: ;

[0034] in, represents the single-line interpretation value of the sth waterlogging factor, represents the S discrete categories of the s-th waterlogging factor, express The index of represents the average waterlogging density of the catchment unit corresponding to the h-th discrete classification of the s-th waterlogging factor, represents the variance of waterlogging density of the catchment unit corresponding to the h-th discrete classification of the s-th waterlogging factor, represents the average waterlogging density of several catchment units in the target city, Represents the variance of waterlogging density of several catchment units in the target city.

[0035] Further, collinearity interpretation values ​​include:

[0036] The collinearity explanation value is calculated for any number of waterlogging factors through the improved geographic interactive detector model. The calculation formula of the improved geographic interactive detector model is as follows: ; ;

[0037] in, represents the collinearity explanation value of m waterlogging factors, represents the number of waterlogging factors, express The index of represents the weighted waterlogging density of the catchment unit corresponding to the jth waterlogging factor, It represents the variance of the single-line explanation value of m waterlogging factors; represents the number of water catchment units, express The index of represents the waterlogging density of the nth catchment unit, Represents the discrete classification weight of the jth waterlogging factor corresponding to the nth catchment unit.

[0038] Further, obtaining the first updated interpretation and the second updated interpretation includes:

[0039] There are J catchment units in the target city, each of which has a waterlogging density And the Z-term waterlogging factor, for each watershed unit, a regression watershed unit is established to obtain the initial multi-scale geographically weighted regression model. The calculation formula of the initial multi-scale geographically weighted regression model is as follows: ;

[0040] in, represents the waterlogging density of the j-th catchment unit, and denote the first local regression coefficient and the second local regression coefficient, respectively. represents the bias coefficient, The watershed unit representing the regression of the jth watershed unit , represents the number of waterlogging factors, express The index of represents the discrete classification weight of the zth waterlogging factor of the jth catchment unit;

[0041] By using spatial weighted least squares and The value is estimated, including: obtaining the spatial weighted weight based on the Gaussian kernel function, and the calculation formula of the Gaussian kernel function is as follows: ;

[0042] in, Indicates that the zth waterlogging factor is at the target regression point The scale weight of the j-th catchment unit is: represents the bandwidth of the zth waterlogging factor, represents the Euclidean distance between the jth catchment unit and the corresponding regressed catchment unit, represents the natural index;

[0043] Among them, the bandwidth of the waterlogging factor z The optimization is performed based on the AICc local weighted regression evaluation index, and the AICc local weighted regression evaluation index is as follows: ;

[0044] Based on the AICc local weighted regression evaluation index, the bandwidth of each waterlogging factor is obtained, and the Z waterlogging factor is processed based on the maximum-minimum value normalization to obtain the geographical effect scale of each waterlogging factor; the single-line explanation value and the collinear explanation value are weighted by the geographical effect scale to obtain the first updated explanation value and the second updated explanation value.

[0045] Furthermore, characteristic waterlogging factors include:

[0046] The first significant impact threshold is set, and the first significant impact threshold is compared with the first updated explanation value of each waterlogging factor. The waterlogging factor whose first updated explanation value is greater than the first significant impact threshold is taken as a dominant significant factor to obtain a dominant significant set.

[0047] Set the second significant impact threshold, traverse all combinations of waterlogging factors, and obtain the second updated explanation value corresponding to each combination. If the second updated explanation value is greater than the second significant impact threshold, take the waterlogging factor in the combination corresponding to the second updated explanation value as a potential significant factor to obtain a potential significant set.

[0048] The intersection of the dominant significant set and the latent significant set is obtained, and the waterlogging factors in the intersection are taken as characteristic waterlogging factors.

[0049] Furthermore, drainage construction in target cities is managed accordingly based on characteristic waterlogging factors, including:

[0050] The correlation between the built environment data and drainage construction of the target city corresponding to the characteristic waterlogging factors determined by experts, including positive correlation and negative correlation;

[0051] If the characteristic waterlogging factor is positively correlated, the built environment data of the target city corresponding to the characteristic waterlogging factor will be increased through drainage construction;

[0052] If the characteristic waterlogging factor is negatively correlated, the built environment data of the target city corresponding to the characteristic waterlogging factor will be reduced through drainage construction.

[0053] The beneficial effects of the present invention are:

[0054] 1. The present invention integrates government and pan-media multi-source data to achieve comprehensive collection and accurate assessment of urban waterlogging risks, ensuring the breadth and accuracy of data sources, thereby effectively making up for the shortcomings of traditional single data collection methods.

[0055] 2. The present invention adopts a multi-scale geographically weighted regression model and an improved factor detector to dynamically update the statistical explanatory power and spatial influence range of each waterlogging factor, effectively capturing the spatial heterogeneity within the city and providing solid technical support for the accurate identification of characteristic waterlogging factors.

[0056] 3. Through the innovative integration of multi-source data collection, geographic information processing and statistical models, a refined and quantitative assessment of urban waterlogging risks in high-density urban areas has been achieved. The interpretation values ​​are dynamically updated through watershed zoning, waterlogging factor screening and multi-scale geographically weighted regression, thus providing scientific, accurate and economical decision-making support for drainage construction and waterlogging prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a module diagram of a drainage construction management system for preventing and controlling waterlogging in high-density urban areas according to the present invention. DETAILED DESCRIPTION

[0058] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0059] like Figure 1 As shown, a drainage construction management system for waterlogging prevention and control in high-density urban areas includes:

[0060] The water catchment zoning module is used to collect the hydrological environment data of the target city and divide the target city based on the hydrological environment data to obtain several water catchment units;

[0061] The data collection module is used to obtain the built environment data of the target city and obtain the waterlogging factor set of each catchment unit based on the built environment data;

[0062] The waterlogging statistics module is used to obtain the waterlogging dataset of the target city and obtain the waterlogging density of each catchment unit based on the waterlogging dataset;

[0063] The stationary association module is used to associate the waterlogging factor set of each watershed unit with the waterlogging density to obtain several association sequences, and calculate the single-line explanation value of the single waterlogging factor in the waterlogging factor set of each association sequence to the waterlogging density and the collinear explanation value of the multiple waterlogging factors to the waterlogging density;

[0064] The geographical association module is used to calculate the geographical effect scale of each waterlogging factor based on the multi-scale geographical weighted regression model, and weight the corresponding single-line explanation value and collinear explanation value of the geographical effect scale of the waterlogging factor to obtain the first updated explanation and the second updated explanation;

[0065] The waterlogging management module is used to determine characteristic waterlogging factors based on the first updated interpretation and the second updated interpretation, and to manage the drainage construction of the target city accordingly based on the characteristic waterlogging factors.

[0066] In one embodiment of the present invention, a plurality of water collection units are obtained, including:

[0067] Obtain a plane image of the target city and rasterize the plane image to obtain a number of grid cells; collect the hydrological environment data of the grid cell in the xth row and yth column, the hydrological environment data including: elevation data, surface water flow direction, surface roughness, impervious area ratio, rainwater pipe network density and drainage pipe slope;

[0068] The flow rate of the grid cell in the xth row and yth column is calculated based on the hydrological environment data. The flow rate calculation formula is as follows: ;

[0069] in, represents the flow rate of the grid cell at row x and column y, represents the normalization coefficient, and denote the first weight parameter and the second weight parameter respectively, Represents the elevation data of the grid cell at row x and column y. represents the surface water flow direction of the grid cell in row x and column y, represents the surface roughness of the grid cell in row x and column y, Indicates the influence weight of surface roughness, represents the impervious area ratio of the grid cell in row x and column y, represents the stormwater pipe network density of the grid cell in row x and column y, represents the slope of the drainage pipe of the grid cell at row x and column y;

[0070] Based on the circulation of several grid units, a circulation matrix is ​​obtained; the circulation matrix is ​​processed by the Sobel operator to extract several edge features, and a water sink network is obtained based on the combination of several edge features; the water sink network is fitted to the plane image, and the plane image is segmented by the water sink network to obtain several sub-blocks, each of which is regarded as a water sink unit.

[0071] Specifically, the plane image of the target city is first obtained and rasterized to divide the entire city into multiple grid cells of fixed size. Each grid cell is an independent spatial sampling unit, and its size is carefully designed to ensure that it can fully reflect the local hydrological and topographic characteristics, providing a balanced and accurate spatial basis for subsequent data collection and calculation. Subsequently, a number of key hydrological environmental data are collected in each grid cell, including elevation, surface water flow direction, surface roughness, impervious area ratio, rainwater pipe network density, and drainage pipe slope. The above data reveal factors such as terrain undulation, water flow direction, surface friction resistance, hardening degree, and drainage system distribution, which are all important variables affecting the smoothness of water flow. In order to eliminate the dimensional differences between different data, all collected data are normalized, and by setting the first and second weight parameters, key indicators such as elevation and roughness are given different importance, so as to accurately calculate the flow value of each grid cell. Based on the flow values ​​of all grid cells, a flow matrix is ​​formed, which comprehensively reflects the comprehensive ability of water flow in various areas of the city. By using the Sobel operator to perform edge detection on the flow matrix, edge features representing water flow convergence or segmentation can be extracted. These features are further used to combine and generate a water network, and the network is fitted on the original plane image to achieve accurate segmentation of the urban space. Finally, each sub-block segmented by the water network is defined as an independent water unit. This method fully integrates spatial image processing and edge detection technology, and calculates the flow through comprehensive hydrological environmental data, achieving a detailed division of hydrological characteristics and drainage conditions in the city, providing accurate and reliable spatial units for subsequent waterlogging risk assessment and drainage construction management.

[0072] In one embodiment of the present invention, the waterlogging factor set of each catchment unit is obtained, including:

[0073] Built environment data of target cities include: drainage system data and drainage system-related data;

[0074] Drainage system data include: stormwater pipe network density, drainage pipe slope, stormwater inlet drainage capacity and stormwater storage capacity;

[0075] Drainage system-related data include: terrain slope, building density and green space coverage of the target city;

[0076] The drainage system data and drainage system related data of each watershed unit are obtained, and each data in the drainage system data and drainage system related data is used as the waterlogging factor of the watershed unit to obtain the waterlogging factor set of the watershed unit.

[0077] Specifically, in order to fully reveal the internal mechanism of the formation of waterlogging risk in the target city, the present claim discloses a detailed method for obtaining a waterlogging factor set for each watershed unit from built environment data. Specifically, the built environment data of the target city is divided into two categories: one is drainage system data, and the other is drainage system related data. First, the drainage system data mainly reflects the composition and performance parameters of the drainage infrastructure itself, including the density of the rainwater pipe network, the slope of the drainage pipe, the drainage capacity of the rainwater outlet, and the rainwater storage capacity. These parameters directly determine the drainage efficiency and operation stability of the city under heavy rain conditions. Secondly, the drainage system related data covers the external environmental factors that affect the operation of the drainage system, such as terrain slope, building density and green space coverage. The terrain slope affects the flow direction and convergence of rainwater, the building density is usually accompanied by an increase in the impervious area, and the green space coverage is conducive to the natural infiltration and absorption of rainwater. By collecting the above two types of data in each watershed unit and treating each of the data as an independent waterlogging factor, the present invention constructs a multidimensional waterlogging factor set. This factor set not only comprehensively reflects the actual situation of the drainage system and its operating environment, but also provides a detailed data basis for subsequent statistical analysis of urban waterlogging risks, stability correlation, and drainage construction optimization, thereby achieving a refined assessment of urban waterlogging risks.

[0078] In one embodiment of the present invention, the waterlogging density of the water catchment unit includes:

[0079] Obtain the first flood data from the private platform, the first flood data including: flood time and flood coordinates;

[0080] Obtain the second flood data of the pan-media platform, including:

[0081] Set preset keywords to filter the texts on the pan-media platform and obtain the target texts;

[0082] The target text is processed based on the NLP natural language model to obtain the second waterlogging data, which includes: waterlogging time and waterlogging coordinates;

[0083] The first waterlogging data and the second waterlogging data are merged to obtain complete waterlogging data;

[0084] The waterlogging density of each catchment unit is calculated based on the waterlogging events and waterlogging areas in the complete waterlogging data. The calculation formula of waterlogging density is as follows: ;

[0085] in, represents the waterlogging density of the j-th catchment unit, Indicates the number of first flood data, express The index of represents the waterlogging catchment area of ​​the ath first waterlogging data, represents the impact weight of the a-th first flood data, Indicates the number of the second flood data, express The index of represents the waterlogging coordinates of the bth second waterlogging data, represents the coordinate range of the ith catchment unit, represents the first indicator function, if , then the value is 1, otherwise the value is 0. represents the second indicator function, if , then the value is 1, otherwise the value is 0. represents the confidence coefficient of the pan-media platform, represents the bias constant term for waterlogging;

[0086] The confidence coefficient of the pan-media platform is determined based on the order of magnitude of the number of users of the corresponding pan-media platform, including: obtaining the order of magnitude of the number of users of the pan-media platform, weighting the preset benchmark confidence coefficient based on the order of magnitude, and obtaining .

[0087] It should be noted that in order to avoid the situation where the second waterlogging data of the pan-media platform cannot be obtained, or the second waterlogging data is empty, a bias constant term of waterlogging is set, and the bias constant term of waterlogging is a parameter based on manual customization.

[0088] Specifically, waterlogging information is obtained from two data sources, and this information is merged to accurately quantify the waterlogging risk of each watershed unit. First, the present invention collects the first waterlogging data from a private platform. These data contain the time and geographical coordinates of the waterlogging event, which usually come from the government or professional monitoring agencies and have high data credibility; at the same time, the second waterlogging data is also obtained from a pan-media platform. When collecting the second waterlogging data, the text of the pan-media platform is screened by preset keywords to obtain the target text, and then the natural language processing (NLP) model is used to parse the target text to extract the time and coordinate information of the waterlogging event. These two parts of data are strictly merged to eliminate duplication and noise to form a complete waterlogging data set. Next, for each watershed unit, the waterlogging density is calculated based on the complete data set. The specific process is as follows: First, for each event in the first waterlogging data, statistics are performed according to its waterlogging catchment area and pre-defined impact weight; at the same time, for each event in the second waterlogging data, an indicator function is used to determine whether it falls within the coordinate range of the current catchment unit, and a corresponding weight is assigned in combination with the confidence coefficient of the pan-media platform (the confidence coefficient is obtained by weighting the preset benchmark value according to the order of magnitude of the platform users). Finally, the weighted statistical results of the two types of data are added together to form the waterlogging density of the catchment unit.

[0089] In one embodiment of the present invention, a single line interprets the value, including:

[0090] A single-line interpretation framework is established for each waterlogging factor in the waterlogging factor set. The single-line interpretation framework includes: discrete classification of the corresponding waterlogging factor and waterlogging density of the catchment unit of the corresponding waterlogging factor;

[0091] The discrete classification of waterlogging factors includes: generating S cluster centers by loading the s-th waterlogging factor, clustering the s-th waterlogging factor based on K clustering, obtaining S discrete classifications of the s-th waterlogging factor, and assigning discrete classification weights to each discrete classification;

[0092] The single-line explanation value of the s-th waterlogging factor is calculated for the S discrete classifications of the s-th waterlogging factor through the improved geographic factor detector model. The calculation formula of the improved factor detector model is as follows: ;

[0093] in, represents the single-line interpretation value of the sth waterlogging factor, represents the S discrete categories of the s-th waterlogging factor, express The index of represents the average waterlogging density of the catchment unit corresponding to the h-th discrete classification of the s-th waterlogging factor, represents the variance of waterlogging density of the catchment unit corresponding to the h-th discrete classification of the s-th waterlogging factor, represents the average waterlogging density of several catchment units in the target city, Represents the variance of waterlogging density of several catchment units in the target city.

[0094] Specifically, the waterlogging factors are discretely classified, and the improved geographic factor detector model is used to calculate the single-line interpretation value, so as to quantify the explanatory power of each waterlogging factor on the waterlogging density of each catchment unit. First, in order to reflect the impact characteristics of each waterlogging factor in different regions, this embodiment discretizes each waterlogging factor using a clustering method. Specifically, for the s-th waterlogging factor, S cluster centers are generated by loading, and the factor data is divided into S discrete categories based on the K clustering algorithm. Each category not only represents the performance of the factor under a certain degree of influence, but is also assigned corresponding discrete classification weights, which reflect the concentration and volatility of the waterlogging density of the catchment units in each category. Next, for each discrete classification, the present invention uses an improved geographic factor detector model to calculate the single-line interpretation value of the s-th waterlogging factor. The calculation formula of the model includes: the average waterlogging density of the catchment unit corresponding to the discrete category, which reflects the waterlogging risk level under this category; the variance of the waterlogging density of the catchment unit within the discrete category, which reflects the fluctuation of the waterlogging density within this category; the average value and variance of the waterlogging density of the catchment unit in the entire target city are used as a global reference. By combining the statistical indicators of each discrete category and weighted summing them, this model can obtain a single-line explanation value, which reflects the explanatory power of the s-th waterlogging factor on the waterlogging density when it acts alone. In other words, the single-line explanation value not only reflects the local statistical characteristics of the factor data, but also fully considers the unevenness of spatial distribution, thus providing a solid basis for the subsequent quantitative analysis of waterlogging risks.

[0095] It should be noted that the factor detector is a statistical tool used to quantify the explanatory power of a single waterlogging factor on the spatial distribution of the target variable. The basic idea is to compare the ratio between the local variance of waterlogging density in each catchment unit and the overall variance of waterlogging density in the entire city under the factor division, so as to obtain an explanation value. The explanation value usually ranges from 0 to 1. The higher the value, the greater the spatial distribution difference that the waterlogging factor can explain, which in turn indicates that it has a strong individual impact on the occurrence of waterlogging. The factor detector provides a quantitative basis for subsequent screening and focus on waterlogging factors.

[0096] In one embodiment of the present invention, the collinear interpretation value includes:

[0097] The collinearity explanation value is calculated for any number of waterlogging factors through the improved geographic interactive detector model. The calculation formula of the improved geographic interactive detector model is as follows: ; ;

[0098] in, represents the collinearity explanation value of m waterlogging factors, represents the number of waterlogging factors, express The index of represents the weighted waterlogging density of the catchment unit corresponding to the jth waterlogging factor, It represents the variance of the single-line explanation value of m waterlogging factors; represents the number of water catchment units, express The index of represents the waterlogging density of the nth catchment unit, Represents the discrete classification weight of the jth waterlogging factor corresponding to the nth catchment unit.

[0099] Specifically, the collinear explanatory values ​​of multiple waterlogging factors are calculated by an improved geographic interactive detector model to quantify the joint explanatory power of multiple waterlogging factor combinations on waterlogging density in a catchment unit. Firstly, the present invention adopts an improved geographic interactive detector model for any m waterlogging factors, the core of which is to compare the weighted waterlogging density of each waterlogging factor in each catchment unit with the statistical fluctuations of the single-line explanatory values ​​of these factors.

[0100] Specifically, the model calculates the collinear explanation value through the following steps: For each waterlogging factor, the corresponding classification weight has been assigned in each watershed unit according to the discrete classification method. These weights reflect the performance of waterlogging density under different discrete levels and provide a basis for subsequent multi-factor combination analysis. For the jth waterlogging factor, the weighted waterlogging density is counted in each watershed unit, and the overall weighted mean and fluctuation degree (i.e., the variance of the single-line explanation value) are calculated as a measure of the imbalance of the impact of a single factor on waterlogging density.

[0101] The weighted waterlogging density of the above multiple factors is combined, and the formula comprehensively considers: the weighted waterlogging density corresponding to the m waterlogging factors, which reflects the joint effect of the factor combination in each watershed unit on the waterlogging density; at the same time, the statistical variance of the single-line explanation value of the m waterlogging factors is taken into consideration as an indicator to measure the synergistic effect between factors; combined with the number of each watershed unit and its corresponding discrete classification weight, a quantitative value reflecting the overall joint explanation ability is obtained, namely the collinear explanation value. The improved geographic interaction detector model can not only reveal the local impact of a single waterlogging factor, but also reflect the interaction and nonlinear synergistic effect between multiple factors, thereby more accurately describing the impact of multiple factor combinations on waterlogging density.

[0102] It should be noted that the interaction detector is an extension of the factor detector method, which is used to evaluate the joint effect of multiple combinations of waterlogging factors in explaining the spatial distribution of the target variable. It compares the fluctuation of weighted waterlogging density under the joint action of multiple factors with the fluctuation of each factor acting alone to determine whether there is a synergistic or inhibitory effect between these factors. If the explanatory power of the factor combination is significantly higher than the simple superposition of the explanatory power of each individual factor, it indicates that there is a positive interaction or nonlinear enhancement effect; otherwise, there may be a negative or complementary effect. The results of the interaction detector help to identify which factor combinations have a decisive impact on waterlogging risks in complex urban environments, providing a more comprehensive basis for system optimization and decision-making.

[0103] In one embodiment of the present invention, obtaining the first updated interpretation and the second updated interpretation includes:

[0104] There are J catchment units in the target city, each of which has a waterlogging density And the Z-term waterlogging factor, for each watershed unit, a regression watershed unit is established to obtain the initial multi-scale geographically weighted regression model. The calculation formula of the initial multi-scale geographically weighted regression model is as follows: ;

[0105] in, represents the waterlogging density of the j-th catchment unit, and denote the first local regression coefficient and the second local regression coefficient, respectively. represents the bias coefficient, The watershed unit representing the regression of the jth watershed unit , represents the number of waterlogging factors, express The index of represents the discrete classification weight of the zth waterlogging factor of the jth catchment unit;

[0106] By using spatial weighted least squares and The value is estimated, including: obtaining the spatial weighted weight based on the Gaussian kernel function, and the calculation formula of the Gaussian kernel function is as follows: ;

[0107] in, Indicates that the zth waterlogging factor is at the target regression point The scale weight of the j-th catchment unit is: represents the bandwidth of the zth waterlogging factor, represents the Euclidean distance between the jth catchment unit and the corresponding regressed catchment unit, represents the natural index;

[0108] Among them, the bandwidth of the waterlogging factor z The optimization is performed based on the AICc local weighted regression evaluation index, and the AICc local weighted regression evaluation index is as follows: ;

[0109] Based on the AICc local weighted regression evaluation index, the bandwidth of each waterlogging factor is obtained, and the Z waterlogging factor is processed based on the maximum-minimum value normalization to obtain the geographical effect scale of each waterlogging factor; the single-line explanation value and the collinear explanation value are weighted by the geographical effect scale to obtain the first updated explanation value and the second updated explanation value.

[0110] Specifically, by performing local regression analysis in each catchment unit and adopting regional weighted data processing methods, the spatial propagation range of each waterlogging factor can be precisely measured. By adopting strict local weighted regression technology and parameter optimization strategy, the statistical performance of each factor in different regions can match its actual spatial influence range, and the accurate extraction of the geographical action scale of the waterlogging factor is achieved. On this basis, the present invention further normalizes the obtained spatial influence scale, and uses it as a weighting factor to merge and correct the single and combined waterlogging factor interpretation values ​​obtained in traditional statistical analysis. The updated interpretation value not only retains the essential connotation of the factor's statistical interpretation of waterlogging density, but also fully reflects its spatial heterogeneity and diffusion characteristics, thereby providing a more scientific and targeted decision-making basis for waterlogging risk assessment and drainage construction management.

[0111] It should be noted that the multiscale geographically weighted regression (MGWR) model is an advanced spatial regression method. On the basis of local weighted regression, it allows each explanatory variable to have its own independent spatial scope, that is, its own bandwidth parameter. This feature enables the model to more finely reveal the differential impact of different waterlogging factors in various urban areas, thereby providing a more reliable basis for the spatial quantitative analysis of waterlogging risk. In the implementation process, the MGWR model performs local regression analysis on each watershed unit and uses spatial weighting technology to give higher weights to data with closer distances, thereby obtaining local regression coefficients. Unlike traditional geographically weighted regression, MGWR determines the optimal bandwidth for each waterlogging factor separately. This process usually uses information criteria (such as modified AICc) to evaluate a series of candidate bandwidths and select parameters that make the local model fit best and have reasonable complexity. The bandwidth obtained in this way not only reflects the statistical relationship between various waterlogging factors, but also intuitively represents their actual diffusion range and scale of action in space. By normalizing the spatial effect scale of each factor, the model further combines this information with the original statistical explanation value to form an updated explanation index, thereby achieving a comprehensive evaluation that takes into account both statistical explanatory power and spatial influence range.

[0112] It should be noted that it is an improved information criterion used to evaluate the balance between model goodness of fit and complexity in a local weighted regression model. Its core idea is to modify the traditional Akaike Information Criterion (AIC) to adapt to the deviations that may occur in a small sample size or local regression environment. By introducing a penalty term in the local model, this evaluation index can effectively avoid overfitting and help determine the optimal bandwidth parameter.

[0113] In one embodiment of the present invention, characteristic waterlogging factors include:

[0114] The first significant impact threshold is set, and the first significant impact threshold is compared with the first updated explanation value of each waterlogging factor. The waterlogging factor whose first updated explanation value is greater than the first significant impact threshold is taken as a dominant significant factor to obtain a dominant significant set.

[0115] Set the second significant impact threshold, traverse all combinations of waterlogging factors, and obtain the second updated explanation value corresponding to each combination. If the second updated explanation value is greater than the second significant impact threshold, take the waterlogging factor in the combination corresponding to the second updated explanation value as a potential significant factor to obtain a potential significant set.

[0116] The intersection of the dominant significant set and the latent significant set is obtained, and the waterlogging factors in the intersection are taken as characteristic waterlogging factors.

[0117] Specifically, the strategy used to identify and determine the characteristic waterlogging factors adopts a double threshold screening method to ensure that both the statistical explanatory power and the interaction between factors are fully considered. Specifically, a first significant impact threshold is first defined to compare the first updated explanation value of each waterlogging factor; any factor whose first updated explanation value exceeds the threshold is considered to have a significant impact at the single action level and is included in the dominant significant set. At the same time, a second significant impact threshold is also set. The second updated explanation value calculated by the combination of each waterlogging factor is traversed and screened. Any factor whose value exceeds the threshold is considered to have potential significance in the factor combination effect, thereby forming a potential significant set. Finally, the above two sets are intersected to obtain the final characteristic waterlogging factor.

[0118] In one embodiment of the present invention, corresponding management of drainage construction in a target city is performed based on characteristic waterlogging factors, including:

[0119] The correlation between the built environment data and drainage construction of the target city corresponding to the characteristic waterlogging factors determined by experts, including positive correlation and negative correlation;

[0120] If the characteristic waterlogging factor is positively correlated, the built environment data of the target city corresponding to the characteristic waterlogging factor will be increased through drainage construction;

[0121] If the characteristic waterlogging factor is negatively correlated, the built environment data of the target city corresponding to the characteristic waterlogging factor will be reduced through drainage construction.

[0122] Specifically, through expert judgment and data association, the positive and negative correlations between the built environment data of the target city and the drainage construction measures are clarified, so as to achieve targeted adjustments. Specifically, this scheme first relies on the characteristic waterlogging factors obtained by the above modules, combined with the comprehensive evaluation of the urban drainage system and its operating conditions by experts, to determine the correlation between each characteristic factor and the drainage construction parameters: if it is found through evaluation that a certain characteristic factor is positively correlated with the drainage construction index, that is, the increase in the value of this factor tends to lead to an increase in the risk of waterlogging, then the management strategy requires that the index be increased accordingly in the drainage construction (for example, increasing the density of the pipe network) to enhance the drainage capacity; conversely, if a certain characteristic factor is negatively correlated with the drainage construction index, the risk of waterlogging is alleviated by reducing this index (such as reducing the impervious area). In this way, through expert judgment and data feedback, the drainage construction management module can dynamically adjust the urban built environment according to the specific direction of action of the characteristic waterlogging factors, so as to achieve accurate and targeted waterlogging prevention and control.

[0123] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A drainage construction management system for waterlogging prevention and control in high-density urban areas, characterized in that: include: The water catchment zoning module is used to collect the hydrological environment data of the target city and divide the target city based on the hydrological environment data to obtain several water catchment units; The data collection module is used to obtain the built environment data of the target city and obtain the waterlogging factor set of each catchment unit based on the built environment data; The waterlogging statistics module is used to obtain the waterlogging dataset of the target city and obtain the waterlogging density of each catchment unit based on the waterlogging dataset; The stationary association module is used to associate the waterlogging factor set of each watershed unit with the waterlogging density to obtain several association sequences, and calculate the single-line explanation value of the single waterlogging factor in the waterlogging factor set of each association sequence to the waterlogging density and the collinear explanation value of the multiple waterlogging factors to the waterlogging density; The geographical association module is used to calculate the geographical effect scale of each waterlogging factor based on the multi-scale geographical weighted regression model, and weight the corresponding single-line explanation value and collinear explanation value of the geographical effect scale of the waterlogging factor to obtain the first updated explanation and the second updated explanation; The waterlogging management module is used to determine characteristic waterlogging factors based on the first updated interpretation and the second updated interpretation, and to manage the drainage construction of the target city accordingly based on the characteristic waterlogging factors.

2. A drainage construction management system for waterlogging prevention and control in high-density urban areas according to claim 1, characterized in that: Several water catchment units are obtained, including: Obtain a plane image of the target city and rasterize the plane image to obtain a number of grid cells; collect the hydrological environment data of the grid cell in the xth row and yth column, the hydrological environment data including: elevation data, surface water flow direction, surface roughness, impervious area ratio, rainwater pipe network density and drainage pipe slope; The flow rate of the grid cell in the xth row and yth column is calculated based on the hydrological environment data. The flow rate calculation formula is as follows: ； in, represents the flow rate of the grid cell at row x and column y, represents the normalization coefficient, and denote the first weight parameter and the second weight parameter respectively, Represents the elevation data of the grid cell at row x and column y. represents the surface water flow direction of the grid cell in row x and column y, represents the surface roughness of the grid cell in row x and column y, Indicates the influence weight of surface roughness, represents the impervious area ratio of the grid cell in row x and column y, represents the stormwater pipe network density of the grid cell in row x and column y, represents the slope of the drainage pipe of the grid cell at row x and column y; Based on the circulation of several grid units, a circulation matrix is ​​obtained; the circulation matrix is ​​processed by the Sobel operator to extract several edge features, and a water sink network is obtained based on the combination of several edge features; the water sink network is fitted to the plane image, and the plane image is segmented by the water sink network to obtain several sub-blocks, each of which is regarded as a water sink unit.

3. A drainage construction management system for waterlogging prevention and control in high-density urban areas according to claim 2, characterized in that: The waterlogging factor set of each catchment unit is obtained, including: Built environment data of target cities include: drainage system data and drainage system-related data; Drainage system data include: stormwater pipe network density, drainage pipe slope, stormwater inlet drainage capacity and stormwater storage capacity; Drainage system-related data include: terrain slope, building density and green space coverage of the target city; The drainage system data and drainage system related data of each watershed unit are obtained, and each data in the drainage system data and drainage system related data is used as the waterlogging factor of the watershed unit to obtain the waterlogging factor set of the watershed unit.

4. A drainage construction management system for waterlogging prevention and control in high-density urban areas according to claim 3, characterized in that: Waterlogging density of a catchment unit, including: Obtain the first flood data from the private platform, the first flood data including: flood time and flood coordinates; Obtain the second flood data of the pan-media platform, including: Set preset keywords to filter the texts on the pan-media platform and obtain the target texts; The target text is processed based on the NLP natural language model to obtain the second waterlogging data, which includes: waterlogging time and waterlogging coordinates; The first waterlogging data and the second waterlogging data are merged to obtain complete waterlogging data; The waterlogging density of each catchment unit is calculated based on the waterlogging events and waterlogging areas in the complete waterlogging data. The calculation formula of waterlogging density is as follows: ； in, represents the waterlogging density of the j-th catchment unit, Indicates the number of first flood data, express The index of represents the waterlogging catchment area of ​​the ath first waterlogging data, represents the impact weight of the a-th first flood data, Indicates the number of the second flood data, express The index of represents the waterlogging coordinates of the bth second waterlogging data, represents the coordinate range of the ith catchment unit, represents the first indicator function, if , then the value is 1, otherwise the value is 0. represents the second indicator function, if , then the value is 1, otherwise the value is 0. represents the confidence coefficient of the pan-media platform, represents the bias constant term for waterlogging; The confidence coefficient of the pan-media platform is determined based on the order of magnitude of the number of users of the corresponding pan-media platform, including: obtaining the order of magnitude of the number of users of the pan-media platform, weighting the preset benchmark confidence coefficient based on the order of magnitude, and obtaining .

5. A drainage construction management system for waterlogging prevention and control in high-density urban areas according to claim 4, characterized in that: A single line explains the value, including: A single-line interpretation framework is established for each waterlogging factor in the waterlogging factor set. The single-line interpretation framework includes: discrete classification of the corresponding waterlogging factor and waterlogging density of the catchment unit of the corresponding waterlogging factor; The discrete classification of waterlogging factors includes: generating S cluster centers by loading the s-th waterlogging factor, clustering the s-th waterlogging factor based on K clustering, obtaining S discrete classifications of the s-th waterlogging factor, and assigning discrete classification weights to each discrete classification; The single-line explanation value of the s-th waterlogging factor is calculated for the S discrete classifications of the s-th waterlogging factor through the improved geographic factor detector model. The calculation formula of the improved factor detector model is as follows: ； in, represents the single-line interpretation value of the sth waterlogging factor, represents the S discrete categories of the s-th waterlogging factor, express The index of represents the average waterlogging density of the catchment unit corresponding to the h-th discrete classification of the s-th waterlogging factor, represents the variance of waterlogging density of the catchment unit corresponding to the h-th discrete classification of the s-th waterlogging factor, represents the average waterlogging density of several catchment units in the target city, Represents the variance of waterlogging density of several catchment units in the target city.

6. A drainage construction management system for waterlogging prevention and control in high-density urban areas according to claim 5, characterized in that: Collinearity interpretation values, including: The collinearity explanation value is calculated for any number of waterlogging factors through the improved geographic interactive detector model. The calculation formula of the improved geographic interactive detector model is as follows: ； ； in, represents the collinearity explanation value of m waterlogging factors, represents the number of waterlogging factors, express The index of represents the weighted waterlogging density of the catchment unit corresponding to the jth waterlogging factor, It represents the variance of the single-line explanation value of m waterlogging factors; represents the number of water catchment units, express The index of represents the waterlogging density of the nth catchment unit, Represents the discrete classification weight of the jth waterlogging factor corresponding to the nth catchment unit.

7. A drainage construction management system for waterlogging prevention and control in high-density urban areas according to claim 6, characterized in that: The first updated explanation and the second updated explanation are obtained, including: There are J catchment units in the target city, each of which has a waterlogging density And the Z-term waterlogging factor, for each watershed unit, a regression watershed unit is established to obtain the initial multi-scale geographically weighted regression model. The calculation formula of the initial multi-scale geographically weighted regression model is as follows: ； in, represents the waterlogging density of the j-th catchment unit, and denote the first local regression coefficient and the second local regression coefficient, respectively. represents the bias coefficient, The watershed unit representing the regression of the j-th watershed unit , represents the number of waterlogging factors, express The index of represents the discrete classification weight of the zth waterlogging factor of the jth catchment unit; By using spatial weighted least squares and The value is estimated, including: obtaining the spatial weighted weight based on the Gaussian kernel function, and the calculation formula of the Gaussian kernel function is as follows: ； in, Indicates that the zth waterlogging factor is at the target regression point The scale weight of the j-th catchment unit is: represents the bandwidth of the zth waterlogging factor, represents the Euclidean distance between the jth catchment unit and the corresponding regressed catchment unit, represents the natural index; Among them, the bandwidth of the waterlogging factor z The optimization is performed based on the AICc local weighted regression evaluation index, and the AICc local weighted regression evaluation index is as follows: ； Based on the AICc local weighted regression evaluation index, the bandwidth of each waterlogging factor is obtained, and the Z waterlogging factor is processed based on the maximum-minimum value normalization to obtain the geographical effect scale of each waterlogging factor; the single-line explanation value and the collinear explanation value are weighted by the geographical effect scale to obtain the first updated explanation value and the second updated explanation value.

8. A drainage construction management system for waterlogging prevention and control in high-density urban areas according to claim 7, characterized in that: Characteristic waterlogging factors include: The first significant impact threshold is set, and the first significant impact threshold is compared with the first updated explanation value of each waterlogging factor. The waterlogging factor whose first updated explanation value is greater than the first significant impact threshold is taken as a dominant significant factor to obtain a dominant significant set. Set the second significant impact threshold, traverse all combinations of waterlogging factors, and obtain the second updated explanation value corresponding to each combination. If the second updated explanation value is greater than the second significant impact threshold, take the waterlogging factor in the combination corresponding to the second updated explanation value as a potential significant factor to obtain a potential significant set. The intersection of the dominant significant set and the latent significant set is obtained, and the waterlogging factors in the intersection are taken as characteristic waterlogging factors.

9. A drainage construction management system for waterlogging prevention and control in high-density urban areas according to claim 8, characterized in that: Based on the characteristic waterlogging factors, the drainage construction of the target city is managed accordingly, including: The correlation between the built environment data and drainage construction of the target city corresponding to the characteristic waterlogging factors determined by experts, including positive correlation and negative correlation; If the characteristic waterlogging factor is positively correlated, the built environment data of the target city corresponding to the characteristic waterlogging factor will be increased through drainage construction; If the characteristic waterlogging factor is negatively correlated, the built environment data of the target city corresponding to the characteristic waterlogging factor will be reduced through drainage construction.

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