Rural sewage treatment strategy evaluation matching method and system, terminal and medium

Abstract

The invention provides a rural sewage treatment strategy evaluation matching method and system, a terminal and a medium, and belongs to the technical field of rural sewage treatment, and the method comprises the steps: obtaining building data and population statistics data of all villages and towns; by utilizing the building data and the population household number statistical data of each town, calculating and integrating to obtain a per-household building pattern spot coefficient result list of the town granularity, and based on the per-household building pattern spot coefficient result list of the town granularity and a preset population accumulation area scale calculation classifier, obtaining a classification result of each population accumulation area; and inputting the classification result of each population accumulation area into a preset rural accumulation area sewage treatment strategy applicability judgment model for strategy matching to obtain a rural accumulation area sewage treatment strategy corresponding to each accumulation area. According to the invention, the most suitable sewage treatment strategy can be accurately matched for rural accumulation areas with different scales and characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of rural sewage treatment, and in particular to a rural sewage treatment strategy evaluation and matching method, system, terminal and medium. Background Art

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

[0003] However, my country's long-term urban-rural dual structure has led to a significant gap between rural and urban sewage treatment. Urban areas have a complete legal system, standard system and sewage treatment facilities, while rural areas generally lack drainage channels and sewage treatment systems. A large amount of untreated domestic sewage is directly discharged, which seriously pollutes water sources and causes ecological and environmental problems such as blue algae blooms. In addition, rural domestic pollution sources are scattered and difficult to concentrate, residents have weak environmental awareness, and the economic level is relatively backward. These factors together constitute a huge challenge for rural domestic sewage treatment.

[0004] At present, there are prominent problems in the process of promoting rural domestic sewage treatment: first, rural areas are vast and the population is dispersed. The top-down manual reporting mechanism of population gathering areas cannot accurately determine the boundaries of the gathering areas, and the accuracy of data is difficult to guarantee. Large-scale field surveys will lead to huge work costs; second, the granularity of population data in rural areas is limited, the basic spatial data resources are weak, and the spatialization of population data is difficult, which makes it difficult to meet the needs of rural domestic sewage treatment for rural population gathering situation analysis; third, there is insufficient research on quantitative analysis methods for the formulation of sewage treatment plans in rural areas, and there is a lack of automated evaluation and matching processes to support decision-making. This makes it difficult to implement precise policies and scientific governance in the planning, design, and implementation of sewage treatment projects, which affects the governance effect.

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

[0006] In view of the deficiencies in the prior art, the present invention proposes a rural sewage treatment strategy evaluation and matching method, system, terminal and medium to solve the technical problems existing in the prior art.

[0007] The technical solution adopted by the present invention is:

[0008] A first aspect of an embodiment of the present application provides a rural sewage treatment strategy evaluation and matching method, comprising:

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

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

[0011] Based on the result list of average household building pattern coefficients at the township granularity and the preset population agglomeration area scale estimation classifier, the classification results of each population agglomeration area are obtained;

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

[0013] Furthermore, the building data and population statistics of each township are used to calculate and integrate the average household building pattern coefficient results list at the township granularity, including:

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

[0015] Acquire the surface data of each building from the building data of each town;

[0016] Performing buffer zone analysis on the surface data of each building to obtain spatial graphic data of each gathering area;

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

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

[0019] The average household building pattern number coefficients of each township are integrated to obtain a list of average household building pattern coefficient results for each township granularity.

[0020] Furthermore, the buffer zone analysis is performed on the surface data of each building to obtain spatial graphic data of each gathering area, including:

[0021] The weighted kernel density method is used to analyze the spatial distribution characteristics of the surface data of each building to obtain a buffer distance parameter;

[0022] Based on the buffer distance parameters, a buffer zone analysis is performed on the surface data of each building to obtain spatial graphic data of each gathering area.

[0023] Furthermore, the coefficient of the number of building spots per household in each township is calculated based on the total number of households in each administrative village in each township, the total number of building spots in each administrative village in each township, and the total number of administrative villages in each township, including:

[0024] The total number of building spots in each administrative village in each township is calculated by the ratio of the total number of households in each administrative village in each township to obtain the average number of building spots per household in each administrative village in each township;

[0025] The average number of building spots per household in each administrative village in each township is added up to obtain the total number of building spots per household in each administrative village in each township;

[0026] The ratio of the sum of the average number of building patches per household in the administrative villages in each township to the total number of administrative villages in the corresponding township is calculated to obtain the average number of building patches per household in each township.

[0027] Furthermore, the result list of average household building pattern coefficients based on the township granularity and the preset population agglomeration area scale calculation classifier are used to obtain the classification results of each population agglomeration area, wherein the preset population agglomeration area scale calculation classifier includes: a population agglomeration area scale calculation module and a population agglomeration area scale classification module;

[0028] The result list of average household building pattern coefficient based on the township granularity and the preset population concentration area scale calculation classifier are used to obtain the classification results of each population concentration area, including:

[0029] The number of building patches in each population gathering area is obtained through the population gathering area scale estimation module, and the average household building patch coefficient corresponding to each gathering area is obtained from the result list of average household building patch coefficient at the township granularity according to the township name to which each gathering area belongs;

[0030] Calculate the ratio of the number of building patches in each population gathering area to the corresponding average building patch coefficient per household to obtain the number of households in each population gathering area;

[0031] The population concentration area scale classification module uses the household number results of each population concentration area to match with the preset population concentration area classification system to obtain the classification results of each population concentration area.

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

[0033] The classification results of each population gathering area are input into a preset rural gathering area sewage treatment strategy applicability determination model to obtain the rural gathering area sewage treatment strategy corresponding to each gathering area, including:

[0034] Obtaining the cluster area scale factor of each population cluster area classification result through the indicator determination module, and judging the type of the current cluster area according to the cluster area scale factor of each population cluster area classification result;

[0035] If the current cluster area belonging type is the first cluster area belonging type, then through the strategy evaluation matching module, the matching governance strategy is the centralized treatment-standard emission mode;

[0036] If the current clustering area belongs to the second clustering area, the spatial distance factor and terrain factor of the current clustering area are obtained. The indicator determination module determines whether the current clustering area is located within 3km of the urban built-up area or the clustering area where sewage treatment facilities have been built and meets the pipeline construction cost requirements according to the spatial distance factor and terrain factor.

[0037] If the judgment result is False, the matching governance strategy is the centralized treatment-standard emission mode through the strategy evaluation matching module;

[0038] If the judgment result is True, the matching governance strategy is the centralized processing-management mode through the policy evaluation matching module;

[0039] If the current cluster area belongs to the third cluster area, the land resource factor of the current cluster area is obtained. The indicator determination module determines whether the current cluster area meets the requirement that the per capita farmland area is greater than 0.3 mu and the area of ​​other water bodies in the region except the receiving water body is 0 according to the land resource factor.

[0040] If the judgment result is True, the matching governance strategy is a decentralized processing-resource utilization mode through the strategy evaluation matching module;

[0041] If the judgment result is False, the matching governance strategy is the decentralized processing-standard emission mode through the strategy evaluation matching module.

[0042] Furthermore, the number of households in the cluster of the first cluster type is greater than 200;

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

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

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

[0046] Data collection module: obtain building data and population statistics of each township;

[0047] Calculation and integration module: using the building data and population statistics of each township, calculate and integrate to obtain a list of results of average household building pattern coefficients at the township granularity;

[0048] Classification prediction module: based on the result list of average household building pattern coefficients at the township granularity and the preset population cluster scale calculation classifier, the classification results of each population cluster are obtained;

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

[0050] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions and execute the step instructions as in the first aspect of the embodiment of the present application.

[0051] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the step instructions in the first aspect of the embodiments of the present application.

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

[0053] 1. The present invention can accurately match the most suitable sewage treatment strategy for rural agglomerations of different sizes and characteristics by comprehensively considering multiple factors such as building distribution, population, spatial distance, terrain and land resources in each township.

[0054] 2. The present invention can promote the sustainable development of rural areas by scientifically and rationally formulating sewage treatment strategies, help protect rural water resources, improve water resource utilization efficiency, promote rural ecological civilization construction, and help achieve green development of the rural economy and a virtuous cycle of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the prior art description. 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 according to the actual scale.

[0056] Figure 1 It is a flowchart of the technical process route of the embodiment of the present invention;

[0057] Figure 2 This is a working framework diagram of a population cluster area scale estimation classifier according to an embodiment of the present invention;

[0058] Figure 3 This is a working framework diagram of a model for determining the suitability of sewage treatment strategies in rural areas 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 Schematic diagram of a terminal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0062] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0063] Example

[0064] The working principle of the embodiment is described in detail below:

[0065] Buildings are the carriers of human production and life. The distribution of population clusters is highly correlated with the spatial distribution of buildings. By using building spatial data and fully combining the spatial distribution characteristics of buildings with demographic data, we can quickly extract population clusters and improve the accuracy and refinement of scale classification. The discharge of rural domestic sewage has a positive correlation with the number of households and building scale in the settlement. The scale of sewage discharge, facility construction and management costs in population clusters affect the selection of measures for rural domestic sewage treatment. Scientific and reasonable decision-making and implementation of rural domestic sewage treatment measures require precise and reliable information on population clusters.

[0066] like Figure 1 As shown, in one embodiment, a technical process of a rural sewage treatment strategy evaluation and matching method is provided, including:

[0067] Preparation of basic data, including preparation of building data, population statistics, terrain data (digital elevation model), land use classification data, sewage treatment facility status data and other data.

[0068] Agglomeration area extraction and scale back calculation: first, the agglomeration area extraction method based on building characteristics is constructed, and a method based on the fusion of building surface data buffer is designed and constructed. At the same time, the weighted kernel density analysis of building scale is used to determine the buffer parameters, and then the building agglomeration area results data is obtained; second, the average household building patch number coefficient is designed and calculated. The average household building patch number coefficient is designed, and the building data and population household statistical data are used to calculate and integrate the average household building patch number coefficient results list at the township granularity; third, the population agglomeration area scale calculation classifier is constructed. According to the name of the township to which the agglomeration area belongs, the coefficient is obtained using the average household building patch number coefficient results list, and the population agglomeration area household number calculation is completed. Classification rules are designed to perform scale classification and complete the construction of the population agglomeration area scale calculation classifier.

[0069] Construction of a model for determining the practicality of treatment strategies: First, an index system for factors influencing sewage treatment decisions is constructed. The key considerations of various sewage treatment strategies are fully analyzed to construct a determination index system that includes four aspects: cluster size factor, terrain factor, spatial distance factor, and land resource factor. Second, based on the above index system, a decision tree model is used to construct a model for determining the applicability of sewage treatment strategies in rural clusters.

[0070] Form decision-making support, bring basic data into the overall process, form the selection of rural agglomeration sewage treatment strategies corresponding to each agglomeration, and complete the evaluation and matching of treatment strategies.

[0071] In one embodiment, a rural sewage treatment strategy evaluation and matching method specifically includes:

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

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

[0074] Step 3: Based on the result list of average household building pattern coefficients at the township granularity and the preset population agglomeration area scale estimation classifier, the classification results of each population agglomeration area are obtained;

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

[0076] Specifically, in step 1, basic data preparation is required, including building data, population statistics, terrain data (digital elevation model), land use classification data, sewage treatment facility status data, etc.

[0077] In step 2, using the building data and population statistics of each township, calculate and integrate to obtain a list of results of average household building pattern coefficients at the township granularity;

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

[0079] Acquire the surface data of each building from the building data of each town;

[0080] Performing buffer zone analysis on the surface data of each building to obtain spatial graphic data of each gathering area;

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

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

[0083] The average household building pattern number coefficients of each township are integrated to obtain a list of average household building pattern coefficient results for each township granularity.

[0084] Specifically, the surface data of each building is subjected to buffer zone analysis to obtain spatial graphic data of each gathering area, including:

[0085] The weighted kernel density method is used to analyze the spatial distribution characteristics of the surface data of each building to obtain a buffer distance parameter;

[0086] Based on the buffer distance parameters, a buffer zone analysis is performed on the surface data of each building to obtain spatial graphic data of each gathering area.

[0087] In one embodiment, in terms of the method for extracting the cluster area, the core method is buffer analysis. Buffer analysis refers to a spatial analysis method that automatically establishes a buffer polygon layer within a certain width range around a point, line or surface spatial graphic based on it, and uses the formed polygon range to carry out analysis. It is one of the spatial analysis tools used to solve the proximity problem. The proximity displayed by the buffer can be used to describe the degree of proximity between two objects in the geographic space. Buffer distance (Buffer_distance) is the core parameter in buffer analysis. On the basis of the buffer results constructed according to the buffer distance, the buffers with intersecting relationships are merged and merged, and the buffers without intersecting relationships retain their original shapes. Then, the merging of multiple buffers can be realized to form a result that conforms to the spatial distribution law of objects and can fully reflect the proximity relationship of objects. Building data is surface data, and the spatial graphics are mostly polygonal in form. Its minimum outer bounding rectangle has the characteristic of different axis lengths. Using building surface data for buffer analysis can more accurately express the spatial morphological characteristics of the cluster area, and can more accurately determine and accurately divide the boundaries of the cluster area, and improve the extraction accuracy of the cluster area.

[0088] Buffer distance is an important parameter in the process of cluster extraction through buffer zone analysis. In order 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, and the optimal solution for the buffer distance parameter value is determined by combining the results of kernel density analysis. In this embodiment, the weighted kernel density model: the distribution characteristics of the cluster area are nonlinear and cannot be analyzed using linear equations, that is, there is no prior knowledge to determine the probability density function of each factor, and a non-parametric estimation method is required for distribution analysis. Let X 1 , X 2 , …, X n is an independent and identically distributed sample of the unit variable X, then the kernel density estimate of the probability density function obeyed by X is:

[0089]

[0090] Where: k(x) is the kernel function, and k(x)≥0, h n is the window width, referred to as window width or bandwidth, n is the total number of independent and identically distributed samples of the unit variable X, and the independent and identically distributed samples of the unit variable X are X 1 , X 2 , ..., X n ;

[0091] 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] Where: i = 1, 2, ..., n is the input point data, pop i is the population field value of point i, which can also be understood as a weighted field, dist i is the distance between point i and the (x,y) position, and radius is the search radius.

[0095] Based on the building data, it is implemented through the KernelDensity function. The building space data is used as the input data, the "total building area" is selected as the weighted weight, and the search radius is set to obtain the building layout kernel density index. At the same time, the natural breakpoint method is used to segment the data. The segmented data is compared with the buffer results 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, the building surface graphic data is used as input data, the output path of the results is set, the buffer distance parameter is 30m, the buffer direction, buffer endpoint type, fusion method and other parameters are set, and the function is executed to complete the extraction of 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 the number of households. The number of households is an important statistic often used to characterize the population in sewage treatment decisions. According to the seventh census data of a certain city, there is a conversion coefficient of 2.5 between the number of households and the number of population, that is, the average number of people per household is 2.5. In terms of the correspondence between buildings and households, there are significant differences between buildings in rural areas and buildings in urban areas: (1) The relationship between building patches and 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 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 respectively. The two show a completely opposite correspondence. Since the present invention focuses on rural agglomeration areas, in order to exclude the interference of urban buildings on the coefficient of average building patch volume per household as much as possible, the building volume and population data in the community range are eliminated by comparing the relationship between urban areas and urban management units;

[0098] In one embodiment, the coefficient of the number of building spots per household in each township is calculated based on the total number of households in each administrative village in each township, the total number of building spots in each administrative village in each township, and the total number of administrative villages in each township, including:

[0099] The total number of building spots in each administrative village in each township is calculated by the ratio of the total number of households in each administrative village in each township to obtain the average number of building spots per household in each administrative village in each township;

[0100] The average number of building spots per household in each administrative village in each township is added up to obtain the total number of building spots per household in each administrative village in each township;

[0101] The ratio of the sum of the average number of building patches per household in the administrative villages in each township to the total number of administrative villages in the corresponding township is calculated to obtain the average number of building patches per household in each township.

[0102] In one embodiment, based on the total number of households in each administrative village in each township, the total number of building spots in each administrative village in each township, and the total number of administrative villages in each township, the coefficient of the number of building spots per household in each township is calculated and expressed by a specific formula:

[0103]

[0104] Where: I Per_household_buildingblock (x) is the calculation function of the coefficient of the average number of building patches per household in township x, x is the xth township in the range, Num(x) is the total number of administrative villages contained in township x, n is the nth administrative village in the township n=1,2,3,…,Num(x), Count All_building_block (n) is the total number of building spots within the administrative village n, Count All_household (n) is the total number of households within the administrative village n.

[0105] In one embodiment, based on population household statistics, the population households of each level of administrative unit are obtained, and each township is used as an aggregation unit. The ratio of the total number of building patches in each administrative village in the township to the total number of households is added and divided by the total number of administrative villages in the township, and the average building patch number coefficient per household in each township is obtained. The result is stored in the average building patch number coefficient result list to complete the coefficient calculation.

[0106] In step 3, based on the result list of average household building pattern coefficients at the township granularity and the preset population agglomeration area scale estimation classifier, the classification results of each population agglomeration area are obtained, including:

[0107] The number of building patches in each population gathering area is obtained through the population gathering area scale estimation module, and the average household building patch coefficient corresponding to each gathering area is obtained from the list of average household building patch coefficient results at the township granularity according to the township name to which each gathering area belongs;

[0108] Calculate the ratio of the number of building patches in each population gathering area to the corresponding average building patch coefficient per household to obtain the number of households in each population gathering area;

[0109] The population concentration area scale classification module uses the household number results of each population concentration area to match with the preset population concentration area classification system to obtain the classification results of each population concentration area.

[0110] Specifically, the size of the population agglomeration area directly affects the amount of sewage discharge. Therefore, a population agglomeration area size estimation and classifier is constructed to estimate and classify the population size characteristics of the population agglomeration area. The population agglomeration area size estimation and classifier includes a "population agglomeration area size estimation module" and a "population agglomeration area size classification module". The working framework of the population agglomeration area size estimation and classifier is shown in the figure below. Figure 2 shown.

[0111] The population gathering area scale calculation module obtains the number of building spots in each population gathering area, and obtains the average building spot coefficient corresponding to each gathering area according to the name of the township to which each gathering area belongs. In one embodiment, the population gathering area scale calculation module is based on the building spatial data, and uses the FeatureToPoint_management function in the arcpy site package to obtain the center point of the building surface spot, and converts the building surface spot element into a point element for easy counting and statistics. Then, the population gathering area spatial range data is used to perform spatial association and regional statistics through the SpatialJoin function to obtain the number of building spots in each population gathering area. Based on the statistical results of the number of building spots in each population gathering area, the gathering area is traversed, the township attribute information of the gathering area is read, and the average building spot coefficient corresponding to the gathering area is obtained according to the township name information from the average building spot coefficient result list. The ratio of the number of building spots in the population gathering area to the average building spot coefficient per household is calculated to obtain the number of households, thereby completing the population gathering area scale calculation.

[0112] The population agglomeration area scale classification module matches the household number results of each population agglomeration area with the preset population agglomeration area classification system to obtain the classification results of each population agglomeration area. In one embodiment, the population agglomeration area scale classification module matches the population agglomeration area scale measurement 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 matched in combination with the rural sewage treatment regular user number scale classification system, with 20 households and 200 households as the interval division values, to construct three classification systems: ① population agglomeration areas with more than 200 households; ② population agglomeration areas with 20 to 200 households; ③ population agglomeration areas with less than 20 households.

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

[0114] In step 4, the classification results of each population gathering area are input into the preset rural gathering area sewage treatment strategy applicability judgment model for strategy matching, and the rural gathering area sewage treatment strategy corresponding to each gathering area is obtained, such as Figure 3 shown.

[0115] The classification results of each population gathering area are input into a preset rural gathering area sewage treatment strategy applicability determination model to obtain the rural gathering area sewage treatment strategy corresponding to each gathering area, including:

[0116] Obtaining the cluster area scale factor of each population cluster area classification result through the indicator determination module, and judging the type of the current cluster area according to the cluster area scale factor of each population cluster area classification result;

[0117] In one embodiment, the scale factor of the cluster area is classified into high-level, middle-level and low-level according to the commonly used scale classification system of rural domestic sewage treatment. The classification is determined as follows:

[0118]

[0119] Where: x is the variable of cluster area x, Scale(x) is the function for calculating the number of households in cluster area x, Level Scale (x) is the calculation function of the scale type of cluster x.

[0120] If the current cluster area belonging type is the first cluster area belonging type High-level, then through the strategy evaluation matching module, the matching governance strategy is the centralized treatment-standard emission mode;

[0121] If the current clustering area belongs to the second clustering area belonging to the Middle-level type, the spatial distance factor and terrain factor of the current clustering area are obtained. The indicator determination module determines whether the current clustering area is located within 3km of the urban built-up area or the clustering area where sewage treatment facilities have been built and meets the pipeline construction cost requirements according to the spatial distance factor and terrain factor.

[0122] If the judgment result is False, the matching governance strategy is the centralized treatment-standard emission mode through the strategy evaluation matching module;

[0123] If the judgment result is True, the matching governance strategy is the centralized processing-management mode through the policy evaluation matching module;

[0124] In one embodiment, the spatial distance factor: The spatial distance factor in the domestic sewage treatment process refers to the three-dimensional distance index of the cluster area with the participation of elevation information, which is used to measure 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] Where i is a population gathering area, j is a population gathering area or a built-up urban area, Point(i) and Point(j) are the center point information of each area, X, Y, and Z are the x-axis coordinate values, y-axis coordinate values, and z-axis coordinate values, respectively. Dis 3D (i,j) is the three-dimensional space distance measurement function between the center point of area i and the center point of area j.

[0127] In one embodiment, terrain factors: rural areas often have more complex terrain surface features than urban areas. The terrain slope index and the surface roughness index directly affect the difficulty of pipeline laying and directly affect the cost accounting of sewage treatment measures. The terrain slope index is the slope measurement between points, that is, the slope value between the center point of the gathering area A and the center point of the gathering area B or the slope value between the center point of the gathering area A and the center point of the urban built-up area C. The calculation formula is as follows:

[0128]

[0129] Where i is a population gathering area, j is a population gathering area or a built-up urban area, Point(i) and Point(j) are the center point information of each area, X, Y, and Z are the x-axis coordinate values, y-axis coordinate values, and z-axis coordinate values, respectively. Slope point (i,j) is the slope value calculation function between the center point of area i and the center point of area j.

[0130] Surface roughness refers to the ratio of the earth's surface area to its projected area in a specific area. The surface roughness index is an indicator that reflects the surface undulation and erosion degree. Roughness is a macroscopic terrain factor that can reflect the undulation and erosion degree of the terrain. It is an important quantitative indicator for measuring the degree of surface erosion. The surface roughness is calculated using digital elevation model (DEM) data. First, the DEM data is segmented by the minimum enclosing rectangle formed by cluster area A and cluster area B or the minimum enclosing rectangle formed by cluster area A and urban built-up area C to form input variables. Then, the surface roughness calculation results are averaged. The calculation formula is as follows:

[0131]

[0132] Where i is a population gathering area, j is a population gathering area or a built-up urban area, MBR(i,j) is the minimum enclosing rectangle construction function of the two areas, and DEM slope (a) is the slope data within the range of a formed by clipping the slope results generated based on DEM data using the minimum enclosing rectangle a. Average (b) is the average value calculation function of b. I Surface-roughness (i,j) is the surface roughness index corresponding to the minimum enclosing rectangle of region i and region j.

[0133] If the current cluster area belongs to the third cluster area belonging to the Low-level type, the land resource factor of the current cluster area is obtained, and the indicator determination module determines whether the current cluster area meets the requirement that the per capita farmland area is greater than 0.3 mu and the area of ​​other water bodies in the region except the receiving water body is 0 according to the land resource factor.

[0134] If the judgment result is True, the matching governance strategy is a decentralized processing-resource utilization mode through the strategy evaluation matching module;

[0135] If the judgment result is False, the matching governance strategy is the decentralized processing-standard emission mode through the strategy evaluation matching module.

[0136] In one embodiment, land resource factors: land resource factors include the total absorption area of ​​receiving bodies in the region, the per capita farmland area and the area of ​​other water bodies in the region, which are used to evaluate whether the current region has the basic conditions for resource utilization of rural domestic sewage. Receiving bodies for resource utilization of rural domestic sewage include cultivated land, economic and fruit forest land, grassland, woodland, oxidation ponds, ecological ditches, ecological wetlands, hydroponic systems, etc. Combined with the land data of the national land change survey and the ecological water body data, the receiving bodies are classified into two categories: receiving land and receiving water bodies. At the same time, all kinds of water bodies except receiving water bodies are classified into other water body types.

[0137] The total absorption area of ​​the receiving bodies in the region is obtained through spatial superposition analysis, and the receiving land and receiving water bodies that have a spatial overlap relationship with the current gathering area are obtained to form a set of receiving bodies corresponding to the current gathering area, and their total area is summed up. The receiving land and receiving water bodies are counted according to their own land area. The calculation formula for the total absorption area of ​​the receiving bodies in the region is as follows:

[0138]

[0139] Where: S Totall-acceptor (x) is the calculation function of the total absorption area of ​​the receiving bodies in the region corresponding to the aggregation area x, S Acceptor-soil (i) is the area of ​​the i-th receiving land, num Acceptor-soil (x) is the number of receiving land patches in the area corresponding to the cluster x, i = 1, 2, 3, ..., num Acceptor-soil (x), S Acceptor-water (j) is the area of ​​the jth receiving water body, num Acceptor-water (x) is the number of receiving water body patches in the area corresponding to the cluster area x, j = 1, 2, 3, ..., num Acceptor-water (x).

[0140] The per capita land area returned to farmland is calculated based on the overall consumption area of ​​the receiving bodies in the region and the population data of the agglomeration area. The per capita land area returned to farmland is an important quantitative indicator for determining whether the current agglomeration area can be used as a resource. The calculation formula is as follows:

[0141]

[0142] Where: S Per-capita-acceptor (x) is the calculation function of the per capita farmland area in the cluster area x, S Totall-acceptor (x) is the total absorption area of ​​the receiving body in the area corresponding to the cluster x, num household (x) is the total number of households within the cluster area x, and k is the average household population coefficient.

[0143] When the agglomeration area has a spatial overlap with other water types other than oxidation ponds, ecological ditches, ecological wetlands, and hydroponic systems, if the rural domestic sewage treatment in the area is carried out in a resource-based manner, it may cause pollution of other water bodies. Therefore, the area of ​​other water bodies in the area is a veto indicator for the current agglomeration area to be able to be utilized as a resource. The calculation formula is as follows:

[0144]

[0145] Where: S Other-water (x) is the calculation function of the area of ​​other water bodies in the gathering area x, S Other-water(i) is the area of ​​the i-th other water body, num Other-water (x) is the number of receiving land patches in the area corresponding to the cluster x, i = 1, 2, 3, ..., num Other-water (x).

[0146] In one embodiment, the rural domestic sewage treatment strategy mainly includes: centralized treatment-management, centralized treatment-standard discharge, decentralized treatment-resource utilization, decentralized treatment-standard discharge and other modes: ① centralized treatment-management mode: urban sewage treatment facilities and services are extended to surrounding rural areas, and an integrated urban and rural sewage collection and treatment mechanism is gradually established in places with conditions; ② centralized treatment-standard discharge mode: mainly for environmentally sensitive and relatively concentrated population settlements to build centralized sewage treatment facilities, implement standard treatment and discharge. ③ decentralized treatment-resource utilization mode: for settlements with dispersed populations, sufficient environmental absorption capacity, no environmental sensitive points, drought and water shortage, resource utilization facilities are built, and agricultural or landscape utilization is realized nearby; ④ decentralized treatment-standard discharge mode: for those with dispersed populations but no resource utilization conditions, treatment is carried out by standard discharge.

[0147] This embodiment makes full use of the spatial distribution characteristics of buildings, based on the building spatial data, using buffer analysis, kernel density analysis and other spatial analysis methods to determine clustering parameters and extract population clusters. At the same time, it uses demographic data and the strong correlation between population distribution and building distribution to design and calculate the coefficient of the number of buildings per household, and realize the inversion and merging of population data in clusters. Based on the above population clusters and scale data results, combined with terrain data, land classification data, river system data and other data resources, index calculations are performed to complete the basic conditions assessment of rural domestic sewage treatment, and realize the automated matching process of sewage treatment strategies, and can accurately match the most suitable sewage treatment strategies for rural clusters of different sizes and characteristics.

[0148] Figure 4 A rural sewage treatment strategy evaluation and matching system is provided for an embodiment of the present application, and the system includes:

[0149] Data collection module 401: obtain building data and population statistics of each township;

[0150] Calculation and integration module 402: using the building data and population statistics of each town, calculate and integrate to obtain a list of results of average household building pattern coefficients at the town level;

[0151] Classification prediction module 403: based on the result list of average household building pattern coefficients at the township granularity and the preset population concentration area scale calculation classifier, obtain the classification results of each population concentration area;

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

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

[0154] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any rural sewage treatment strategy evaluation and matching method recorded 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, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A rural sewage treatment strategy evaluation and matching method, characterized in that: include: Obtain building data and population statistics for each township; Using the building data and population statistics of each township, calculate and integrate to obtain a list of results of average household building pattern coefficients at the township granularity; Based on the result list of average household building pattern coefficients at the township granularity and the preset population agglomeration area scale estimation classifier, the classification results of each population agglomeration area are obtained; The classification results of each population gathering area are input into the preset rural gathering area sewage treatment strategy applicability judgment model for strategy matching to obtain the rural gathering area sewage treatment strategy corresponding to each gathering area.

2. A rural sewage treatment strategy evaluation and matching method according to claim 1, characterized in that: The above-mentioned use of building data and population statistics of each township to calculate and integrate the average household building pattern coefficient results list of township granularity includes: Obtain the total number of households in each administrative village in each township from the population household statistics; Acquire the surface data of each building from the building data of each town; Performing buffer zone analysis on the surface data of each building to obtain spatial graphic data of each gathering area; The spatial graphic data of each cluster area are divided into administrative areas to obtain the total number of building patches in each administrative village in each township; Based on the total number of households in each administrative village in each township, the total number of building patches in each administrative village in each township, and the total number of administrative villages in each township, the coefficient of the number of building patches per household in each township is calculated; The average household building pattern number coefficients of each township are integrated to obtain a list of average household building pattern coefficient results for each township granularity.

3. A rural sewage treatment strategy evaluation and matching method according to claim 2, characterized in that: The buffer zone analysis is performed on the surface data of each building to obtain spatial graphic data of each gathering area, including: The weighted kernel density method is used to analyze the spatial distribution characteristics of the surface data of each building to obtain a buffer distance parameter; Based on the buffer distance parameters, a buffer zone analysis is performed on the surface data of each building to obtain spatial graphic data of each gathering area.

4. A rural sewage treatment strategy evaluation and matching method according to any one of claims 2-3, characterized in that: The coefficient of the number of building spots per household in each township is calculated based on the total number of households in each administrative village in each township, the total number of building spots in each administrative village in each township, and the total number of administrative villages in each township, including: The total number of building spots in each administrative village in each township is calculated by the ratio of the total number of households in each administrative village in each township to obtain the average number of building spots per household in each administrative village in each township; The average number of building spots per household in each administrative village in each township is added up to obtain the total number of building spots per household in each administrative village in each township; The ratio of the sum of the average number of building patches per household in the administrative villages in each township to the total number of administrative villages in the corresponding township is calculated to obtain the average number of building patches per household in each township.

5. A rural sewage treatment strategy evaluation and matching method according to claim 1, characterized in that: The result list of average household building pattern coefficient based on the township granularity and the preset population agglomeration area scale calculation classifier are used to obtain the classification results of each population agglomeration area, wherein the preset population agglomeration area scale calculation classifier includes: a population agglomeration area scale calculation module and a population agglomeration area scale classification module; The result list of average household building pattern coefficient based on the township granularity and the preset population concentration area scale calculation classifier are used to obtain the classification results of each population concentration area, including: The number of building patches in each population gathering area is obtained through the population gathering area scale estimation module, and the average household building patch coefficient corresponding to each gathering area is obtained from the result list of average household building patch coefficient at the township granularity according to the township name to which each gathering area belongs; Calculate the ratio of the number of building patches in each population gathering area to the corresponding average building patch coefficient per household to obtain the number of households in each population gathering area; The population concentration area scale classification module uses the household number results of each population concentration area to match with the preset population concentration area classification system to obtain the classification results of each population concentration area.

6. A rural sewage treatment strategy evaluation and matching method according to claim 1, characterized in that: The classification results of each population gathering area are input into a preset rural gathering area sewage treatment strategy applicability determination model for strategy matching to obtain the rural gathering area sewage treatment strategy corresponding to each gathering area, wherein the preset rural gathering area sewage treatment strategy applicability determination model includes: an indicator determination module and a strategy evaluation matching module; The classification results of each population gathering area are input into a preset rural gathering area sewage treatment strategy applicability determination model to obtain the rural gathering area sewage treatment strategy corresponding to each gathering area, including: Obtaining the cluster area scale factor of each population cluster area classification result through the indicator determination module, and judging the type of the current cluster area according to the cluster area scale factor of each population cluster area classification result; If the current cluster area belonging type is the first cluster area belonging type, then through the strategy evaluation matching module, the matching governance strategy is the centralized treatment-standard emission mode; If the current clustering area belongs to the second clustering area, the spatial distance factor and terrain factor of the current clustering area are obtained. The indicator determination module determines whether the current clustering area is located within 3km of the urban built-up area or the clustering area where sewage treatment facilities have been built and meets the pipeline construction cost requirements according to the spatial distance factor and terrain factor. If the judgment result is False, the matching governance strategy is the centralized treatment-standard emission mode through the strategy evaluation matching module; If the judgment result is True, the matching governance strategy is the centralized processing-management mode through the policy evaluation matching module; If the current cluster area belongs to the third cluster area, the land resource factor of the current cluster area is obtained. The indicator determination module determines whether the current cluster area meets the requirement that the per capita farmland area is greater than 0.3 mu and the area of ​​other water bodies in the region except the receiving water body is 0 according to the land resource factor. If the judgment result is True, the matching governance strategy is a decentralized processing-resource utilization mode through the strategy evaluation matching module; If the judgment result is False, the matching governance strategy is the decentralized processing-standard emission mode through the strategy evaluation matching module.

7. A rural sewage treatment strategy evaluation and matching method according to claim 6, characterized in that: The number of households in the cluster of the first cluster type is greater than 200; The number of households in the cluster of the second cluster type is greater than 20 and less than or equal to 200; The number of households in the cluster of the third cluster type is less than 20.

8. A rural sewage treatment strategy evaluation and matching system, characterized in that: The system comprises: Data collection module: obtain building data and population statistics of each township; Calculation and integration module: using the building data and population statistics of each township, calculate and integrate to obtain a list of results of average household building pattern coefficients at the township granularity; Classification prediction module: based on the result list of average household building pattern coefficients at the township granularity and the preset population cluster scale calculation classifier, the classification results of each population cluster are obtained; Strategy determination module: input the classification results of each population gathering area into the preset rural gathering area sewage treatment strategy applicability determination model for strategy matching, and obtain the rural gathering area sewage treatment strategy corresponding to each gathering area.

9. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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