A rural sewage treatment strategy evaluation matching method, system, terminal and medium

Abstract

The application provides a rural sewage treatment strategy evaluation matching method, system, terminal and medium, and belongs to the technical field of rural sewage treatment. The method comprises the following steps: obtaining building data and population household statistical data of each township; calculating and integrating the household building plot coefficient results of township granularity based on the building data and population household statistical data of each township to obtain a township granularity household building plot coefficient result list; obtaining each population aggregation area classification result based on the township granularity household building plot coefficient result list and a preset population aggregation area scale estimation classifier; inputting the population aggregation area classification result into a preset rural aggregation area sewage treatment strategy applicability determination model for strategy matching to obtain the rural aggregation area sewage treatment strategy corresponding to each aggregation area. The application can accurately match the most suitable sewage treatment strategy for rural aggregation areas of different scales and characteristics.

Description

Technical Field

[0001] This invention relates to the field of rural sewage treatment technology, specifically to a method, system, terminal, and medium for assessing and matching rural sewage treatment strategies. Background Technology

[0002] Rural domestic sewage treatment is a core component of rural living environment improvement, and its importance is becoming increasingly prominent. It is not only a key measure to improve the rural ecological environment and enhance the quality of life for farmers, but also an important part of promoting agricultural and rural modernization and implementing the rural revitalization strategy.

[0003] However, my country's long-standing urban-rural divide has resulted in a significant gap between rural and urban areas in terms of sewage treatment. Urban areas possess well-established laws, regulations, standards, and sewage treatment facilities, while rural areas generally lack drainage channels and sewage treatment systems. Large amounts of untreated domestic sewage are discharged directly, severely polluting water sources and causing ecological and environmental problems such as cyanobacterial blooms. Furthermore, rural pollution sources are dispersed and difficult to centralize, residents have weak environmental awareness, and the economy is relatively underdeveloped. These factors collectively constitute a huge challenge for rural domestic sewage treatment.

[0004] The prominent problems currently existing in the advancement of rural domestic sewage treatment are as follows: First, rural areas are vast and their populations are scattered. The top-down, manual reporting mechanism for population concentration areas cannot accurately determine the boundaries of these areas, making data accuracy difficult to guarantee. Large-scale field surveys would lead to enormous operational costs. Second, the granularity of rural population data is limited, basic spatial data resources are weak, and spatializing population data is difficult, making it hard to meet the needs of rural domestic sewage treatment for analyzing rural population concentration. Third, there is insufficient research on quantitative analysis methods for developing rural sewage treatment plans, and a lack of automated evaluation and matching processes to support decision-making. This makes it difficult to achieve precise and scientific treatment in the planning, design, and implementation stages of sewage treatment projects, thus affecting the treatment effectiveness.

[0005] Therefore, how to quickly and accurately match the most suitable sewage treatment strategy for rural settlements of different sizes and characteristics is a technical problem that urgently needs to be solved. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a method, system, terminal, and medium for evaluating and matching rural sewage treatment strategies, in order to solve the technical problems existing in the prior art.

[0007] The technical solution adopted in this invention is as follows:

[0008] The first aspect of this application provides a method for evaluating and matching rural sewage treatment strategies, including:

[0009] Obtain building data and population / household statistics for each township;

[0010] Using the building data and population statistics of each township, calculate and integrate to obtain a list of average building patch coefficients per household at the township level;

[0011] Based on the list of average building patch coefficients per household at the township level and the preset population agglomeration area size calculation classifier, the classification results of each population agglomeration area are obtained.

[0012] The classification results of each population cluster area are input into a preset rural cluster area sewage treatment strategy applicability judgment model for strategy matching to obtain the rural cluster area sewage treatment strategy corresponding to each cluster area.

[0013] Furthermore, the calculation and integration of building data and population statistics from various townships to obtain a list of average building patch coefficients per household at the township level includes:

[0014] Obtain the total number of households in each administrative village within each township from the aforementioned population and household statistics;

[0015] Obtain the surface data of each building from the building data of each township;

[0016] Buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area;

[0017] The spatial graphic data of each cluster area are divided into administrative regions to obtain the total number of building patches in each administrative village within each township;

[0018] Based on the total number of households in each administrative village within each township, the total number of building plots in each administrative village within each township, and the total number of administrative villages within each township, the coefficient of the average number of building plots per household in each township is calculated.

[0019] The average number of building patches per household in each township is integrated to obtain a list of average building patch coefficients per household at each township level.

[0020] Furthermore, the buffer analysis of the surface data of each building to obtain spatial graphic data of each cluster area includes:

[0021] The spatial distribution characteristics of the surface data of each building were analyzed using the weighted kernel density method to obtain the buffer distance parameter;

[0022] Based on the buffer distance parameter, buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area.

[0023] Furthermore, the calculation of the average number of building plots per household in each township, based on the total number of households in each administrative village within each township, the total number of building plots in each administrative village within each township, and the total number of administrative villages within each township, includes:

[0024] The average number of building plots per household in each administrative village within each township is calculated by comparing the total number of building plots in each administrative village within each township with the total number of households in each administrative village within each township.

[0025] The average number of building plots per household in each administrative village within each township is added together to obtain the total average number of building plots per household in each administrative village within each township.

[0026] The coefficient of the average number of building plots per household in each township is obtained by calculating the ratio of the sum of the average number of building plots per household in each administrative village to the total number of administrative villages in each township.

[0027] Furthermore, the list of average building patch coefficients per household based on the township granularity and the preset population agglomeration area size calculation classifier are used to obtain the classification results of each population agglomeration area. The preset population agglomeration area size calculation classifier includes: a population agglomeration area size calculation module and a population agglomeration area size classification module.

[0028] The list of average building patch coefficients per household based on the township granularity, along with a preset population agglomeration area size calculation classifier, yields classification results for each population agglomeration area, including:

[0029] The population agglomeration area scale calculation module obtains the number of building patches in each population agglomeration area, and obtains the average building patch coefficient of each agglomeration area from the list of average building patch coefficients per household at the township level according to the name of the township to which each agglomeration area belongs.

[0030] The number of building patches in each population cluster area is calculated by ratioing the number of building patches per household to the corresponding average building patch coefficient, and the number of households in each population cluster area is obtained.

[0031] The population cluster size classification module matches the number of households in each population cluster with a preset population cluster grading system to obtain the classification results of each population cluster.

[0032] Furthermore, the classification results of each population cluster area are input into a preset rural cluster area sewage treatment strategy applicability judgment model for strategy matching to obtain the rural cluster area sewage treatment strategy corresponding to each cluster area. The preset rural cluster area sewage treatment strategy applicability judgment model includes: an indicator judgment module and a strategy evaluation and matching module.

[0033] The step of inputting the classification results of each population cluster into a preset rural cluster wastewater treatment strategy applicability judgment model to obtain the rural cluster wastewater treatment strategy corresponding to each cluster includes:

[0034] The index determination module obtains the cluster size factor of each population cluster classification result, and determines the type of the current cluster based on the cluster size factor of each population cluster classification result.

[0035] If the current cluster belongs to the first cluster type, then the governance strategy matched by the strategy evaluation and matching module is the centralized treatment-compliant emission mode.

[0036] If the current cluster belongs to the second cluster type, then the spatial distance factor and terrain factor of the current cluster are obtained. The indicator determination module determines whether the current cluster is located within 3km of an urban built-up area or a cluster with existing sewage treatment facilities, and whether it meets the pipeline construction cost requirements, based on the spatial distance factor and terrain factor.

[0037] If the judgment result is False, then the matching strategy is determined by the strategy evaluation matching module and the matching governance strategy is the centralized treatment-compliant emission mode.

[0038] If the judgment result is True, then the matching module will evaluate the strategy and match the governance strategy as the centralized processing-management mode.

[0039] If the current cluster belongs to the third cluster type, then the land resource factor of the current cluster is obtained. Based on the land resource factor, the indicator determination module determines whether the current cluster meets the requirements of per capita land return area greater than 0.3 mu and the area of ​​other water body types in the region other than receiving water bodies is 0.

[0040] If the judgment result is True, then the matching strategy is matched as a decentralized processing-resource utilization mode through the strategy evaluation matching module.

[0041] If the judgment result is False, then the matching strategy is determined by the strategy evaluation matching module and the matching governance strategy is the decentralized treatment-compliant emission mode.

[0042] Furthermore, the number of households in the first cluster area belongs to a cluster area type greater than 200;

[0043] The number of households in the second cluster type is greater than 20 and less than or equal to 200.

[0044] The number of households in the third cluster area is less than 20.

[0045] A second aspect of this application provides a rural sewage treatment strategy evaluation and matching system, the system comprising:

[0046] Data acquisition module: Acquires building data and population / household statistics for each township;

[0047] Calculation and integration module: Utilizes the building data and population statistics of each township to calculate and integrate the results to obtain a list of average building patch coefficients per household at the township level;

[0048] Classification and prediction module: Based on the list of average building patch coefficients per household at the township level and the preset population agglomeration area size calculation classifier, the classification results of each population agglomeration area are obtained;

[0049] Strategy determination module: Input the classification results of each population cluster into the preset rural cluster wastewater treatment strategy applicability determination model for strategy matching, and obtain the rural cluster wastewater treatment strategy corresponding to each cluster.

[0050] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to invoke the program instructions and execute the step instructions as described in the first aspect of this application.

[0051] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps and instructions as described in the first aspect of this application.

[0052] As can be seen from the above technical solution, the beneficial technical effects of the present invention are as follows:

[0053] 1. By comprehensively considering various factors such as the distribution of buildings, number of households, spatial distance, topography and land resources in various towns and villages, this invention can accurately match the most suitable sewage treatment strategy for rural clusters of different sizes and characteristics.

[0054] 2. By scientifically and rationally formulating wastewater treatment strategies, this invention can promote the sustainable development of rural areas, help protect rural water resources, improve water resource utilization efficiency, promote rural ecological civilization construction, and contribute to the green development of the rural economy and the virtuous cycle of the ecological environment. Attached Figure Description

[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0056] Figure 1 This is a flowchart illustrating the technical process of an embodiment of the present invention.

[0057] Figure 2 This is a working framework diagram of the population agglomeration area size calculation classifier according to an embodiment of the present invention;

[0058] Figure 3 This is a working framework diagram of the rural cluster area sewage treatment strategy applicability determination model according to an embodiment of the present invention;

[0059] Figure 4 This is a system framework diagram of an embodiment of the present invention;

[0060] Figure 5 This is a schematic diagram of a terminal according to an embodiment of the present invention. Detailed Implementation

[0061] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0062] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0063] Example

[0064] The working principle of the embodiments is explained in detail below:

[0065] Buildings serve as the carriers of human production and daily life. The distribution of population clusters is highly correlated with the spatial distribution of buildings. Utilizing building spatial data, and fully integrating building spatial distribution characteristics with demographic statistics, allows for the rapid extraction of population clusters and improves the accuracy and refinement of scale classification. Rural domestic sewage discharge is positively correlated with the number of households and building scale in settlements. The scale of sewage discharge, facility construction, and management costs in population clusters influence the selection of rural domestic sewage treatment measures. Decision-making and implementation of scientifically sound rural domestic sewage treatment measures require detailed and reliable information on population clusters.

[0066] like Figure 1 As shown in one embodiment, a technical process for evaluating and matching rural wastewater treatment strategies includes:

[0067] Prepare basic data, including building data, population and household statistics, topographic data (digital elevation model), land use classification data, and current status data of sewage treatment facilities.

[0068] The extraction and scale calculation of clustered areas consist of three parts: First, the construction of a clustered area extraction method based on building characteristics. This involves designing and constructing a method based on building area data buffer fusion, and using building scale weighted kernel density analysis to determine buffer parameters before obtaining building clustered area data. Second, the design and calculation of the average number of building patches per household coefficient. This involves designing the average number of building patches per household coefficient, using building data and population and household statistics to calculate and integrate the average number of building patches per household coefficient results at the township level. Third, the construction of a population clustered area scale calculation classifier. This involves obtaining coefficients based on the township name to which the clustered area belongs, using the average number of building patches per household coefficient results list to complete the inverse calculation of the number of households in the population clustered area, and designing classification rules for scale classification, thus completing the construction of the population clustered area scale calculation classifier.

[0069] Construction of a model for judging the applicability of governance strategies: First, a system of influencing factors for wastewater treatment decisions is constructed. This involves fully analyzing the key factors considered in various wastewater treatment strategies and constructing a system of judgment indicators that includes four aspects: cluster size, topography, spatial distance, and land resources. Second, based on the above indicator system, a decision tree model is used to construct a model for judging the applicability of wastewater treatment strategies in rural clusters.

[0070] To provide decision support, basic data is incorporated into the overall process, enabling the selection of wastewater treatment strategies for rural clusters in each cluster area, and completing the evaluation and matching of treatment strategies.

[0071] In one embodiment, a method for evaluating and matching rural wastewater treatment strategies specifically includes:

[0072] Step 1: Obtain building data and population / household statistics for each township;

[0073] Step 2: Using the building data and population statistics of each township, calculate and integrate the results to obtain a list of average building patch coefficients per household at the township level;

[0074] Step 3: Based on the list of average building patch coefficients per household at the township level and the preset population agglomeration area size calculation classifier, obtain the classification results for each population agglomeration area;

[0075] Step 4: Input the classification results of each population cluster into the preset rural cluster wastewater treatment strategy applicability judgment model for strategy matching to obtain the rural cluster wastewater treatment strategy corresponding to each cluster.

[0076] Specifically, in step 1, it is necessary to prepare basic data, including building data, population and household statistics, topographic data (digital elevation model), land use classification data, and current status data of sewage treatment facilities.

[0077] In step 2, the building data and population statistics of each township are used to calculate and integrate the results to obtain a list of average building patch coefficients per household at the township level.

[0078] Specifically, the total number of households in each administrative village within each township is obtained from the aforementioned population and household statistics.

[0079] Obtain the surface data of each building from the building data of each township;

[0080] Buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area;

[0081] The spatial graphic data of each cluster area are divided into administrative regions to obtain the total number of building patches in each administrative village within each township;

[0082] Based on the total number of households in each administrative village within each township, the total number of building plots in each administrative village within each township, and the total number of administrative villages within each township, the coefficient of the average number of building plots per household in each township is calculated.

[0083] The average number of building patches per household in each township is integrated to obtain a list of average building patch coefficients per household at each township level.

[0084] Specifically, buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area, including:

[0085] The spatial distribution characteristics of the surface data of each building were analyzed using the weighted kernel density method to obtain the buffer distance parameter;

[0086] Based on the buffer distance parameter, buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area.

[0087] In one embodiment, the core method for extracting clustered areas is buffer analysis. Buffer analysis refers to a spatial analysis method that automatically creates a buffer polygon layer within a certain width around a point, line, or area spatial graphic, and uses the resulting polygon range for analysis. It is one of the spatial analysis tools used to solve proximity problems. The proximity shown by the buffer can be used to describe the degree of closeness between two features in geographic space. Buffer distance is the core parameter in buffer analysis. Based on the buffer results constructed according to the buffer distance, buffers with intersecting relationships are merged, while buffers without intersecting relationships retain their original shape. This allows for the merging of multiple buffers, forming results that conform to the spatial distribution patterns of features and fully reflect the proximity relationships of features. Building data is area data, and its spatial graphic shape is mostly polygonal. Its minimum bounding rectangle has the characteristic of varying axis lengths. Using building area data for buffer analysis can more accurately express the spatial morphological characteristics of clustered areas, more accurately determine and precisely divide the boundaries of clustered areas, and improve the extraction accuracy of clustered areas.

[0088] Buffer distance is a crucial parameter in the cluster extraction process through buffer analysis. To clarify the specific value of this parameter, in one embodiment, a weighted kernel density analysis method is used to analyze the spatial distribution characteristics of building data. The optimal solution for the buffer distance parameter is determined by combining the kernel density analysis results. In this embodiment, the weighted kernel density model shows that the cluster distribution characteristics are nonlinear and cannot be analyzed using linear equations, i.e., there is no prior knowledge to determine the probability density function of each factor, requiring the use of nonparametric estimation methods for distribution analysis. Let X1, X2, ..., X... n For independent and identically distributed samples of the unit variable X, the kernel density estimate of the probability density function of X is:

[0089]

[0090] In the formula: k(x) is the kernel function, and k(x)≥0. h n Let n be the window width, or simply window width or bandwidth, and let n be the total number of independent and identically distributed samples of the unit variable X, where X1, X2, ..., Xn are the independent and identically distributed samples of the unit variable X. n ;

[0091] When calculated on a spatial graph, the predicted density at the (x,y) position is determined by the following formula:

[0092]

[0093] For dist i<radius

[0094] In the formula: i = 1, 2, ..., n are the input point data, pop i This is the population field value at point i, which can also be understood as a weighted field, dist. i It is the distance between point i and position (x,y), and radius is the search radius.

[0095] Based on building data, the KernelDensity function is used to obtain the building layout kernel density index by taking building spatial data as input data, selecting "total building area" as the weighting weight, setting the search radius, and then using the natural breakpoint method to segment the data. The segmented data is compared with the results of buffers constructed under multiple different buffer distances, and finally the optimal value of the buffer distance parameter is determined to be 30m.

[0096] In this embodiment, the Buffer function in the arcpy site package is used in the Python development environment to take the building surface graphic data as input data, set the output path, set the buffer distance parameter to 30m, set the buffer direction, buffer endpoint type, fusion method and other parameters, and then execute the function to complete the extraction of the cluster area graphic data.

[0097] Buildings are the spatial carriers of population. There is a strong correlation between the building volume and the population and number of households. The number of households is an important statistic often used to represent the population in sewage treatment decision-making. According to the data of the seventh population census of a certain city, there is a conversion coefficient of 2.5 between the number of households and the number of people, that is, the average number of people per household is 2.5. In terms of the correspondence between buildings and the number of households, there are significant differences between buildings in rural areas and buildings in urban areas: (1) The relationship between building patches and the number of households in rural areas shows a common phenomenon of "one-to-one" or "many-to-one", that is, a building courtyard composed of one or more building patches belongs to the same household; (2) The relationship between building patches and the number of households in urban areas shows the main feature of "one-to-many", that is, multiple households live together in the main building represented by the same building patch and hold independent property rights. The two show completely opposite correspondences. Since this invention focuses on rural agglomeration areas, in order to eliminate the interference of urban buildings on the coefficient of the average number of building patches per household as much as possible, by comparing the relationship between urban areas and urban management units, the building volume and population data of the community scope are eliminated.

[0098] In one embodiment, the step of calculating the average number of building patches per household in each township based on the total number of households in each administrative village within each township, the total number of building patches in each administrative village within each township, and the total number of administrative villages within each township includes:

[0099] The average number of building plots per household in each administrative village within each township is calculated by comparing the total number of building plots in each administrative village within each township with the total number of households in each administrative village within each township.

[0100] The average number of building plots per household in each administrative village within each township is added together to obtain the total average number of building plots per household in each administrative village within each township.

[0101] The coefficient of the average number of building plots per household in each township is obtained by calculating the ratio of the sum of the average number of building plots per household in each administrative village to the total number of administrative villages in each township.

[0102] In one embodiment, the coefficient for the average number of building plots per household in each township is calculated based on the total number of households in each administrative village within each township, the total number of building plots in each administrative village within each township, and the total number of administrative villages within each township. This coefficient is expressed by the following formula:

[0103]

[0104] In the formula: I Per_household_buildingblock (x) is the function for calculating the average number of building plots per household in township x, where x is the x-th township within the range, Num(x) is the total number of administrative villages contained in township x, and n is the n-th administrative village within the township, n = 1, 2, 3, ..., Num(x). All_building_block (n) represents the total number of building patches within the administrative village n. All_household (n) represents the total number of households within the administrative village n.

[0105] In one embodiment, based on population and household statistics, the population and household count of each level of administrative unit is obtained. Taking each township as the aggregation unit, the ratio of the total number of building plots in each administrative village within the township to the total number of population and households is summed and divided by the total number of administrative villages within the township to obtain the coefficient of the average number of building plots per household in each township. The coefficient is then stored in the list of the coefficient of the average number of building plots per household to complete the coefficient calculation.

[0106] In step 3, based on the list of average building patch coefficients per household at the township level and the preset population agglomeration area size calculation classifier, the classification results of each population agglomeration area are obtained, including:

[0107] The population agglomeration area scale calculation module obtains the number of building patches in each population agglomeration area, and obtains the average building patch coefficient of each agglomeration area from the list of average building patch coefficients per household at the township level, according to the name of the township to which each agglomeration area belongs.

[0108] The number of building patches in each population cluster area is calculated by ratioing the number of building patches per household to the corresponding average building patch coefficient, and the number of households in each population cluster area is obtained.

[0109] The population cluster size classification module matches the number of households in each population cluster with a preset population cluster grading system to obtain the classification results of each population cluster.

[0110] Specifically, the size of densely populated areas directly affects wastewater discharge. Therefore, a population agglomeration area size calculation and classifier is constructed to calculate and classify the population size characteristics of densely populated areas. The population agglomeration area size calculation and classifier includes a "population agglomeration area size calculation module" and a "population agglomeration area size classification module." The working framework diagram of the population agglomeration area size calculation and classifier is shown below. Figure 2 As shown.

[0111] The population agglomeration area size calculation module obtains the number of building patches in each population agglomeration area and, according to the name of the township to which each agglomeration area belongs, obtains the average building patch coefficient per household for each agglomeration area. In one embodiment, the population agglomeration area size calculation module is based on building spatial data. It uses the FeatureToPoint_management function in the arcpy site package to obtain the center point of the building area patch, converting the building area patch features into point features for easy counting and statistics. Then, using the spatial range data of the population agglomeration area, it performs spatial association and regional statistics through the SpatialJoin function to obtain the number of building patches in each population agglomeration area. Based on the statistical results of the number of building patches in each population agglomeration area, it traverses the agglomeration area, reads the township attribute information where the agglomeration area is located, and queries the list of average building patch coefficient per household according to the township name information to obtain the average building patch coefficient per household corresponding to the agglomeration area. It calculates the ratio of the number of building patches in the population agglomeration area to the average building patch coefficient per household to obtain the number of households, thereby completing the population agglomeration area size calculation.

[0112] The population agglomeration area size classification module matches the number of households in each population agglomeration area with a preset population agglomeration area classification system to obtain the classification results of each population agglomeration area. In one embodiment, the population agglomeration area size classification module matches the population agglomeration area size calculation results with the preset population agglomeration area classification system to obtain the classification results of each population agglomeration area. In this embodiment, the preset population agglomeration area classification system is combined with the rural sewage treatment common user number size classification system, using 20 households and 200 households as interval division values ​​to construct a three-category classification system: ① population agglomeration area with more than 200 households; ② population agglomeration area with 20 to 200 households; ③ population agglomeration area with less than 20 households.

[0113] In this embodiment, the population agglomeration area size calculation module and the population agglomeration area size classification module together constitute the population agglomeration area size calculation classifier. The classifier has a built-in list of average building patch coefficients per household and classification rules. By inputting population agglomeration area data and building space data, the population agglomeration area size calculation and classification can be completed.

[0114] In step 4, the classification results of each population cluster area are input into a preset rural cluster area sewage treatment strategy applicability judgment model for strategy matching, thereby obtaining the corresponding rural cluster area sewage treatment strategy for each cluster area, such as... Figure 3 As shown.

[0115] The step of inputting the classification results of each population cluster into a preset rural cluster wastewater treatment strategy applicability judgment model to obtain the rural cluster wastewater treatment strategy corresponding to each cluster includes:

[0116] The index determination module obtains the cluster size factor of each population cluster classification result, and determines the type of the current cluster based on the cluster size factor of each population cluster classification result.

[0117] In one embodiment, the cluster size factor is classified into three levels according to the commonly used scale classification system for rural domestic sewage treatment: High-level, Middle-level, and Low-level. The classification method is as follows:

[0118]

[0119] In the formula: x represents the cluster area variable x, Scale(x) is the function for calculating the number of households in cluster area x, and Level... Scale (x) is a calculation function for the size type of cluster x.

[0120] If the current cluster belongs to the first cluster type High-level, then the governance strategy matched by the strategy evaluation and matching module is the centralized treatment-compliant emission mode.

[0121] If the current cluster belongs to the Middle-level cluster type, then the spatial distance factor and terrain factor of the current cluster are obtained. The indicator determination module determines whether the current cluster is located within 3km of an urban built-up area or a cluster with existing sewage treatment facilities, and whether it meets the pipeline construction cost requirements, based on the spatial distance factor and terrain factor.

[0122] If the judgment result is False, then the matching strategy is determined by the strategy evaluation matching module and the matching governance strategy is the centralized treatment-compliant emission mode.

[0123] If the judgment result is True, then the matching module will evaluate the strategy and match the governance strategy as the centralized processing-management mode.

[0124] In one embodiment, the spatial distance factor: In the process of domestic sewage treatment, the spatial distance factor refers to the three-dimensional distance index of the cluster area with the participation of elevation information. It is used to calculate the three-dimensional spatial distance between the center point of cluster area A and the center point of cluster area B, or the three-dimensional spatial distance between the center point of cluster area A and the center point of urban built-up area C. The calculation formula is as follows:

[0125]

[0126] In the formula: i represents a population-concentrated area, j represents a population-concentrated area or urban built-up area, Point(i) and Point(j) represent the center point information corresponding to each area, X, Y, and Z represent the x-axis coordinates, y-axis coordinates, and z-axis coordinates, respectively. 3D (i,j) is the function for calculating the three-dimensional spatial distance between the center point of region i and the center point of region j.

[0127] In one embodiment, topographic factors: Rural areas often have more complex topographic surface features compared to urban areas. The topographic slope index and surface roughness index directly affect the ease of pipeline laying and directly impact the cost accounting of sewage treatment methods. The topographic slope index is a slope measurement between points, specifically the slope value between the center point of cluster A and the center point of cluster B, or the slope value between the center point of cluster A and the center point of urban built-up area C. The calculation formula is as follows:

[0128]

[0129] In the formula: i represents a densely populated area, j represents a densely populated area or urban built-up area, Point(i) and Point(j) are the center point information corresponding to each area, X, Y, and Z are the x-axis coordinates, y-axis coordinates, and z-axis coordinates, respectively, and Slope... point (i,j) is the function for calculating the slope between the center point of region i and the center point of region j.

[0130] Surface roughness refers to the ratio of the Earth's surface area to its projected area within a specific region. The surface roughness index reflects the variations in surface undulation and the degree of erosion. Roughness is a macroscopic topographic factor that reflects the variations in topography and the degree of erosion, and is an important quantitative indicator for measuring the degree of surface erosion. Surface roughness is calculated using Digital Elevation Model (DEM) data. First, the DEM data is segmented using the minimum bounding rectangle formed by cluster A and cluster B, or the minimum bounding rectangle formed by cluster A and urban built-up area C, to form input variables. Then, the mean of the surface roughness calculation results is calculated using the following formula:

[0131]

[0132] In the formula: i represents a densely populated area, j represents a densely populated area or urban built-up area, MBR(i,j) is the minimum bounding rectangle construction function for the two areas, and DEM slope (a) is the slope data within the range of DEM data, obtained by cropping the slope results generated from the minimum bounding rectangle a. Average(b) is the function for calculating the average value of b. Surface-roughness (i,j) represents the surface roughness index within the minimum bounding rectangle of regions i and j.

[0133] If the current cluster belongs to the third cluster type (Low-level), then the land resource factor of the current cluster is obtained. Based on the land resource factor, the indicator determination module determines whether the current cluster meets the requirements of per capita land return area greater than 0.3 mu and the area of ​​other water body types in the region, excluding receiving water bodies, is 0.

[0134] If the judgment result is True, then the matching strategy is matched as a decentralized processing-resource utilization mode through the strategy evaluation matching module.

[0135] If the judgment result is False, then the matching strategy is determined by the strategy evaluation matching module and the matching governance strategy is the decentralized treatment-compliant emission mode.

[0136] In one embodiment, land resource factors include three categories: the total area of ​​receiving bodies within the region, the per capita area returned to farmland, and the area of ​​other water bodies within the region. These factors are used to assess whether the current region possesses the basic conditions for the resource utilization of rural domestic sewage. Receiving bodies for the resource utilization of rural domestic sewage include cultivated land, fruit orchards, grasslands, forests, oxidation ponds, ecological ditches, ecological wetlands, hydroponic systems, etc. Combining national land change survey land data and ecological water body data, receiving bodies are categorized into receiving land and receiving water bodies. Furthermore, all water bodies other than receiving water bodies are classified into other water body types.

[0137] The total absorption area of ​​receiving bodies within the region is determined through spatial overlay analysis. This involves identifying receiving land and water bodies that have spatial overlap with the current cluster area, forming a set of receiving bodies corresponding to the current cluster area, and summing their total areas. The area of ​​receiving land and water bodies is calculated based on their own boundaries. The formula for calculating the total absorption area of ​​receiving bodies within the region is as follows:

[0138]

[0139] In the formula: S Totall-acceptor (x) is a function for calculating the total absorption area of ​​the receiving bodies within the region corresponding to cluster area x, S Acceptor-soil (i) represents the area of ​​the i-th receiving land plot, num Acceptor-soil (x) represents the number of receiving land parcels within the region corresponding to cluster x, i = 1, 2, 3, ..., num. Acceptor-soil (x), S Acceptor-water (j) represents the area of ​​the j-th receiving water body, num Acceptor-water (x) represents the number of receiving water body patches within the region corresponding to cluster x, j = 1, 2, 3, ..., num Acceptor-water (x).

[0140] The per capita land return area is calculated based on the total land absorption area within the region, combined with population data of the agglomeration area. The per capita land return area is a crucial quantitative indicator of the current resource utilization capacity of the agglomeration area. The calculation formula is as follows:

[0141]

[0142] In the formula: S Per-capita-acceptor (x) is the function for calculating the per capita area of ​​returned farmland in cluster area x, S Totall-acceptor (x) represents the total area of ​​the receiving bodies within the region corresponding to cluster area x, num household (x) represents the total number of households within the cluster area x, and k represents the average population coefficient per household.

[0143] When a cluster area overlaps with other water body types besides oxidation ponds, ecological ditches, ecological wetlands, and hydroponic systems, the adoption of resource-based utilization methods for rural domestic sewage treatment within the area may lead to pollution of these other water bodies. Therefore, the area of ​​other water bodies within the area is a veto indicator for the current cluster area to be suitable for resource utilization. The calculation formula is as follows:

[0144]

[0145] In the formula: S Other-water (x) is a function for calculating the area of ​​other water bodies within the aggregation zone x, S Other-water(i) represents the area of ​​the i-th other water body, num Other-water (x) represents the number of receiving land parcels within the region corresponding to cluster x, i = 1, 2, 3, ..., num. Other-water (x).

[0146] In one embodiment, rural domestic sewage treatment strategies mainly include: centralized treatment-pipeline connection, centralized treatment-standard discharge, decentralized treatment-resource utilization, and decentralized treatment-standard discharge models: ① Centralized treatment-pipeline connection model: This involves extending urban sewage treatment facilities and services to surrounding rural areas, gradually establishing an integrated urban and rural sewage collection and treatment mechanism where conditions permit; ② Centralized treatment-standard discharge model: This mainly involves constructing centralized sewage treatment facilities in environmentally sensitive and relatively densely populated settlements, implementing standard treatment, and discharging the sewage; ③ Decentralized treatment-resource utilization model: This involves constructing resource utilization facilities in sparsely populated settlements with sufficient environmental absorption capacity, no environmentally sensitive areas, and drought and water shortage, achieving agricultural or landscape utilization nearby; ④ Decentralized treatment-standard discharge model: This involves treating sparsely populated areas that lack the conditions for resource utilization through standard discharge.

[0147] This embodiment fully utilizes the spatial distribution characteristics of buildings. Based on building spatial data, it employs spatial analysis methods such as buffer analysis and kernel density analysis to determine clustering parameters and extract population clusters. Simultaneously, it utilizes population statistics and the strong correlation between population distribution and building distribution to design and calculate the average number of buildings per household coefficient, achieving the inversion and merging of population data within clusters. Based on the aforementioned population cluster and scale data, combined with topographic data, land classification data, river system data, and other data resources, it performs indicator calculations to complete the assessment of basic conditions for rural domestic sewage treatment and implements an automated matching process for sewage treatment strategies. Furthermore, it can accurately match the most suitable sewage treatment strategy for rural clusters of different sizes and characteristics.

[0148] Figure 4 This application provides a rural wastewater treatment strategy evaluation and matching system, the system comprising:

[0149] Data acquisition module 401: Acquires building data and population / household statistics for each township;

[0150] Calculation and integration module 402: Utilizes the building data and population statistics of each township to calculate and integrate the results of the average building patch coefficient per household at the township level;

[0151] Classification prediction module 403: Based on the list of average building patch coefficients per household at the township level and the preset population agglomeration area size calculation classifier, the classification results of each population agglomeration area are obtained;

[0152] Strategy determination module 404: Input the classification results of each population cluster into the preset rural cluster sewage treatment strategy applicability determination model for strategy matching, and obtain the rural cluster sewage treatment strategy corresponding to each cluster.

[0153] like Figure 5 As shown in the figure, this application embodiment provides a terminal, including a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions to execute some or all of the steps of any of the rural sewage treatment strategy evaluation and matching methods described in the above embodiments.

[0154] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the rural wastewater treatment strategy evaluation and matching methods described in the above method embodiments.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for evaluating and matching rural sewage treatment strategies, characterized in that, include: Obtain building data and population / household statistics for each township; Using the building data and population statistics of each township, calculate and integrate to obtain a list of average building patch coefficients per household at the township level; Based on the list of average building patch coefficients per household at the township level and the preset population agglomeration area size calculation classifier, the classification results of each population agglomeration area are obtained. The classification results of each population cluster area are input into a preset rural cluster area sewage treatment strategy applicability judgment model for strategy matching to obtain the rural cluster area sewage treatment strategy corresponding to each cluster area. The list of results obtained by calculating and integrating building data and population statistics of each township to obtain the average building patch coefficient at the township level includes: Obtain the total number of households in each administrative village within each township from the aforementioned population and household statistics; Obtain the surface data of each building from the building data of each township; Buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area; The spatial graphic data of each cluster area are divided into administrative regions to obtain the total number of building patches in each administrative village within each township; Based on the total number of households in each administrative village within each township, the total number of building plots in each administrative village within each township, and the total number of administrative villages within each township, the coefficient of the average number of building plots per household in each township is calculated. The average number of building patches per household in each township is integrated to obtain a list of average building patch coefficients at each township level. The buffer analysis of the surface data of each building yields spatial graphic data of each cluster area, including: The spatial distribution characteristics of the surface data of each building were analyzed using the weighted kernel density method to obtain the buffer distance parameter; Based on the buffer distance parameter, buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area; The coefficient for the average number of building patches per household in each township is calculated based on the total number of households in each administrative village within each township, the total number of building patches in each administrative village within each township, and the total number of administrative villages within each township. This includes: The total number of building patches in each administrative village within each township and the corresponding population of each administrative village within each township are used to calculate the total number of building patches in each administrative village within each township. The average number of building plots per household in each administrative village within each township is obtained by calculating the ratio of the total number of households. The average number of building plots per household in each administrative village within each township is added together to obtain the total average number of building plots per household in each administrative village within each township. The coefficient of the average number of building plots per household in each township is obtained by calculating the ratio of the sum of the average number of building plots per household in each administrative village to the total number of administrative villages in each township.

2. The method for evaluating and matching rural sewage treatment strategies according to claim 1, characterized in that, The list of average building patch coefficients per household based on the township granularity and the preset population agglomeration area size calculation classifier are used to obtain the classification results of each population agglomeration area. The preset population agglomeration area size calculation classifier includes: a population agglomeration area size calculation module and a population agglomeration area size classification module. The list of average building patch coefficients per household based on the township granularity, along with a preset population agglomeration area size calculation classifier, yields classification results for each population agglomeration area, including: The population agglomeration area scale calculation module obtains the number of building patches in each population agglomeration area, and obtains the average building patch coefficient of each agglomeration area from the list of average building patch coefficients per household at the township level according to the name of the township to which each agglomeration area belongs. The number of building patches in each population cluster area is calculated by ratioing the number of building patches per household to the corresponding average building patch coefficient, and the number of households in each population cluster area is obtained. The population cluster size classification module matches the number of households in each population cluster with a preset population cluster grading system to obtain the classification results of each population cluster.

3. The method for evaluating and matching rural sewage treatment strategies according to claim 1, characterized in that, The classification results of each population cluster area are input into a preset rural cluster area sewage treatment strategy applicability judgment model for strategy matching to obtain the rural cluster area sewage treatment strategy corresponding to each cluster area. The preset rural cluster area sewage treatment strategy applicability judgment model includes: an indicator judgment module and a strategy evaluation and matching module. The step of inputting the classification results of each population cluster into a preset rural cluster wastewater treatment strategy applicability judgment model to obtain the rural cluster wastewater treatment strategy corresponding to each cluster includes: The index determination module obtains the cluster size factor of each population cluster classification result, and determines the type of the current cluster based on the cluster size factor of each population cluster classification result. If the current cluster belongs to the first cluster type, then the governance strategy matched by the strategy evaluation and matching module is the centralized treatment-compliant emission mode. If the current cluster belongs to the second cluster type, then the spatial distance factor and terrain factor of the current cluster are obtained. The indicator determination module determines whether the current cluster is located within 3km of an urban built-up area or a cluster with existing sewage treatment facilities, and whether it meets the pipeline construction cost requirements, based on the spatial distance factor and terrain factor. If the judgment result is False, then the matching strategy is determined by the strategy evaluation matching module and the matching governance strategy is the centralized treatment-compliant emission mode. If the judgment result is True, then the matching module will evaluate the strategy and match the governance strategy as the centralized processing-management mode. If the current cluster belongs to the third cluster type, then the land resource factor of the current cluster is obtained. Based on the land resource factor, the indicator determination module determines whether the current cluster meets the requirements of per capita land return area greater than 0.3 mu and the area of ​​other water body types in the region other than receiving water bodies is 0. If the judgment result is True, then the matching strategy is matched as a decentralized processing-resource utilization mode through the strategy evaluation matching module. If the judgment result is False, then the matching strategy is determined by the strategy evaluation matching module and the matching governance strategy is the decentralized treatment-compliant emission mode.

4. The method for evaluating and matching rural sewage treatment strategies according to claim 3, characterized in that, The number of households in the first cluster area belongs to a cluster type greater than 200; The number of households in the second cluster type is greater than 20 and less than or equal to 200. The number of households in the third cluster area is less than 20.

5. A rural sewage treatment strategy evaluation and matching system, characterized in that, The system includes: Data acquisition module: Acquires building data and population / household statistics for each township; Calculation and integration module: Utilizes the building data and population statistics of each township to calculate and integrate the results to obtain a list of average building patch coefficients per household at the township level; Classification and prediction module: Based on the list of average building patch coefficients per household at the township level and the preset population agglomeration area size calculation classifier, the classification results of each population agglomeration area are obtained; Strategy determination module: Input the classification results of each population cluster area into the preset rural cluster area sewage treatment strategy applicability determination model for strategy matching, and obtain the rural cluster area sewage treatment strategy corresponding to each cluster area; The list of results obtained by calculating and integrating building data and population statistics of each township to obtain the average building patch coefficient at the township level includes: Obtain the total number of households in each administrative village within each township from the aforementioned population and household statistics; Obtain the surface data of each building from the building data of each township; Buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area; The spatial graphic data of each cluster area are divided into administrative regions to obtain the total number of building patches in each administrative village within each township; Based on the total number of households in each administrative village within each township, the total number of building plots in each administrative village within each township, and the total number of administrative villages within each township, the coefficient of the average number of building plots per household in each township is calculated. The average number of building patches per household in each township is integrated to obtain a list of average building patch coefficients at each township level. The buffer analysis of the surface data of each building yields spatial graphic data of each cluster area, including: The spatial distribution characteristics of the surface data of each building were analyzed using the weighted kernel density method to obtain the buffer distance parameter; Based on the buffer distance parameter, buffer analysis is performed on the surface data of each building to obtain spatial graphic data of each cluster area; The coefficient for the average number of building patches per household in each township is calculated based on the total number of households in each administrative village within each township, the total number of building patches in each administrative village within each township, and the total number of administrative villages within each township. This includes: The total number of building patches in each administrative village within each township and the corresponding population of each administrative village within each township are used to calculate the total number of building patches in each administrative village within each township. The average number of building plots per household in each administrative village within each township is obtained by calculating the ratio of the total number of households. The average number of building plots per household in each administrative village within each township is added together to obtain the total average number of building plots per household in each administrative village within each township. The coefficient of the average number of building plots per household in each township is obtained by calculating the ratio of the sum of the average number of building plots per household in each administrative village to the total number of administrative villages in each township.

6. A terminal, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to invoke the program instructions to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-4.

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