New construction waste industrial park site selection method based on grid analysis

Abstract

The invention relates to the technical field of site selection of recycling facilities, in particular to a new construction waste industrial park site selection method based on grid analysis. Comprising the following steps: determining a site selection evaluation area, an industrial park processing scale and grid unit parameters; converting the rigidity and flexibility evaluation indexes into a rasterized thematic map layer; building waste generation amount estimation, space distribution and building waste generation distribution map layer generation; performing Boolean overlay analysis on the hard indexes to obtain a primary selection range; the weight of the flexible index is determined by adopting a CRITIC method, and an alternative site selection scheme is obtained after weighted superposition analysis; and constructing a multi-objective optimization model of the newly-built construction waste industrial park, and obtaining a Pareto optimal solution set through an NSGA-II algorithm. According to the method, the generation space distribution of the construction waste and the objectivity of the index weight are comprehensively considered, the scientificity and systematicness of site selection of the construction waste industrial park are improved, and the method has good universality and popularization value.

Description

Technical Field

[0001] This invention relates to the field of site selection technology for recycling facilities, and specifically to a site selection method for new construction waste industrial parks based on grid analysis. Background Technology

[0002] The site selection of construction waste treatment facilities is a key factor restricting resource utilization efficiency. Reasonable site selection not only affects the coverage efficiency of the facility's service capacity but also directly impacts transportation costs, NIMBY (Not In My Backyard) conflicts, and ecological environmental impacts. In recent years, research on the site selection of construction waste treatment facilities has largely relied on Geographic Information Systems (GIS), using methods such as the Analytic Hierarchy Process (AHP) and fuzzy AHP to determine weights through overlay analysis based on expert weighting or experience. However, existing technologies have the following shortcomings:

[0003] Existing methods typically treat construction waste generation as a uniformly distributed or simple regional statistic, failing to consider the spatial heterogeneity of waste generation caused by characteristic parameters such as building height, type, age, and structure. This leads to insufficient matching between site selection results and actual construction waste sources, reducing transportation efficiency and facility service capacity. Relying on expert weighting methods to determine the weights of flexible indicators is prone to introducing human bias, resulting in a lack of objectivity and repeatability in the evaluation results. Especially for dynamically changing regional characteristics, subjective weighting is difficult to adaptively adjust the weight allocation.

[0004] The aforementioned shortcomings result in insufficient quantification and weak adaptability of existing site selection methods, hindering the scientific nature of construction waste treatment facility layout. Therefore, there is an urgent need for a site selection method that integrates refined spatial distribution of construction waste, road network transport distance modeling, and objective weight allocation to improve the accuracy of supply and demand matching. Summary of the Invention

[0005] The main purpose of this invention is to provide a site selection method for new construction waste industrial parks based on grid analysis, which improves the matching degree of supply and demand of construction waste and the objectivity of the evaluation system, and ensures the quality of planning and site selection for construction waste industrial parks.

[0006] To achieve the above objectives, the present invention includes the following steps:

[0007] S1: Define the processing scale of the site selection assessment area and the new construction waste industrial park, and determine the grid unit parameters corresponding to the regional space;

[0008] S2: Collect rigid and flexible evaluation index data related to site selection, and convert the indexes into corresponding rasterized thematic layers in a geographic information system (GIS);

[0009] S3: Estimate the amount of construction waste generated in the assessment area and spatially allocate the grid cells to obtain a construction waste generation distribution layer;

[0010] S4: For hard indicators, perform Boolean overlay analysis on the rasterized thematic layer, remove raster cells that do not meet the conditions, and obtain the initial selection range;

[0011] S5: For the preliminary site selection range of flexibility index and membrane rigidity index, the CRITIC method is used to determine the weights, and after weighted overlay analysis, a comprehensive evaluation index layer is obtained to obtain alternative site selection schemes.

[0012] S6: Construct a multi-objective optimization model for the new construction waste industrial park, and obtain the Pareto optimal solution set for the site selection of the new construction waste industrial park through the non-dominated sorting genetic algorithm (NSGA-II).

[0013] Furthermore, in step S1, the raster unit parameters include raster size and range. All rasterized thematic layers use the same raster unit parameters. The raster size is preferably 50-100m, and the range is a rectangular range covering the evaluation area.

[0014] Furthermore, in step S2, the site selection indicators include: hard indicators, used to exclude unsuitable areas, including but not limited to urban planning, air protection, water protection, and NIMBY conditions; and flexible indicators, used to select suitable areas, including but not limited to regional land prices, comprehensive transportation distance for construction waste, distance between adjacent similar facilities, and road traffic conditions.

[0015] Furthermore, in step S2, the directional influence index, constrained by distance and direction, is implemented by constructing a directional buffer layer, including the following steps:

[0016] 1) Centered on the origin (0,0), construct a standard rasterized orientation buffer A(x) based on the constraints of direction θ, included angle α, and distance r. i ,y i );

[0017]

[0018] 2) Export the rasterized index source point Boolean layer B(x) j ,y j ), and each of its source points (x) j ,y j ) according to the coordinates (x) of each point in the standard orientation buffer i ,y i Translate to coordinate (x) i +x j ,y i +y j Generate source point translation layer B`;

[0019] B'(x i +x j ,yi +y j )=A(x i ,y i )×B(x j ,y j )

[0020] 3) Overlay the source point translation layer, and obtain the rasterized thematic layer by taking the maximum / minimum value according to the index type.

[0021]

[0022] Furthermore, in step S3, the estimated amount of construction waste generated in the area is assessed, calculated using the following formula:

[0023] Q = k U ×R×m

[0024] In the formula: Q represents the amount of construction waste generated in the region; k U , where is the correction coefficient for different urbanization rate ranges; R is the number of permanent residents in the region; m is the base amount of construction waste generated per unit population.

[0025] Furthermore, in step S3, the calculation method for spatial allocation of construction waste generation to grid cells is as follows:

[0026]

[0027] c i =k b ×k h ×k t ×k c ×k type

[0028] In the formula: q i c represents the amount of construction waste generated by the i-th grid cell; i k is the coefficient for construction waste generation per grid unit. b A judgment coefficient is generated for construction waste; it is 1 if the grid is a demolishable area of ​​the building, and 0 otherwise; k h k is the building height correction factor. h The building height h is calculated by dividing the building's unit floor height by the building's height h, which is obtained by subtracting the digital elevation model (DEM) from the digital surface model (DSM); k t This is a correction factor for the building's age, ranging from 1 to 0 based on the building's construction date, from oldest to most recent; k c This is a structural correction factor, categorized into reinforced concrete, brick-concrete, brick-timber, and steel structures; k type This is a building type correction factor, categorized into residential, industrial, commercial, and public buildings.

[0029] Furthermore, in step S4, the rigidity index is used to calculate the initially selected region through Boolean overlay analysis, which conforms to the following logical relationship:

[0030]

[0031] In the formula: R i The rigidity index is determined for the i-th grid cell. Let be the condition for whether the j-th rigid index is satisfied at the i-th grid; k is the number of rigid indices.

[0032] Furthermore, in step S5, the flexibility index is normalized, the weights are calculated using the CRITIC method, and a comprehensive evaluation index is obtained by overlaying the flexibility indexes according to the weights, as shown below:

[0033]

[0034] Among them, w i j ` is the normalized value of the j-th flexibility index at the i-th grid; w i j The value of the j-th flexibility index at the i-th grid; max / min w j θ represents the maximum / minimum value of the j-th flexibility index. j The objective weight of the j-th indicator; C j The amount of information contained in the j-th normalized flexibility index; r ij The correlation coefficient between normalized flexibility indices i and j is calculated using the flexibility index value of each grid cell within the initial selection range; δ j Let be the standard deviation of the j-th indicator.

[0035] Further, in step S5, the selection method for alternative site selection schemes is as follows: raster data with a comprehensive evaluation index greater than the 75th percentile is selected; based on the GIS platform, the above raster data is spatially aggregated using the "raster-to-polygon" method to obtain several continuous spatial areas; areas with an area greater than the preset lower limit of the construction waste industrial park land area are reserved as alternative site selection schemes. If the number of alternative site selection schemes is less than 5, the comprehensive evaluation index quantile is lowered until the quantity requirement is met.

[0036] Furthermore, in step S6, the objective function of the multi-objective optimization model includes the following three objective functions:

[0037] Minimize the overall transport distance of the road network min f1=D m

[0038] Maximize overall economic benefits: f2 = E = e s +e r -c g -ct -c d

[0039] Maximize the straight-line distance of sensitive land use: f3 = Ls = min(Ls) i )

[0040] In the formula, D m E represents the comprehensive transport distance of the road network; E represents the comprehensive economic benefits; e s For the economic benefits of construction waste reception; e r For the economic benefits of resource utilization; c g For land use costs; c t For transportation costs; c d The processing cost includes depreciation of plant and equipment; Ls is the straight-line distance to the nearest sensitive land use; Ls i Let be the distance to the i-th nearby sensitive land use site.

[0041] The multi-objective optimization model is solved using the non-dominated sorting genetic algorithm (NSGA-II), without setting pre-defined weights, to obtain the Pareto optimal solution set.

[0042] Furthermore, in steps S5 and S6, the comprehensive transportation distance of construction waste is calculated using a production-weighted method, which calculates the distance from each grid cell to the candidate facility point based on the transportation distance l. ij Sort in ascending order, and select the first k grid cells in sequence to maximize their construction waste generation q. i The sum of these quantities satisfies the processing capacity S of the newly built construction waste industrial park, with a comprehensive transportation distance L. i The calculation method is as follows:

[0043]

[0044] In the formula: L i The comprehensive transportation distance of construction waste for each grid point; ij The distance between the construction waste generation point and the site selection point is either Euclidean or road network distance. Euclidean distance is used in step S5, and road network distance is used in step S6; S is the processing scale of the construction waste industrial park.

[0045] Furthermore, the economic benefits of resource utilization e r The unit revenue of different construction waste components is estimated. The construction waste components are corrected based on the building characteristic parameters of the construction waste nodes in the comprehensive transportation distance calculation. The calculation method is shown in the following formula:

[0046]

[0047] In the formula: qc i R represents the collection volume of the i-th component of construction waste; i V represents the resource utilization rate of the i-th type of construction waste component;i The economic benefits of resource utilization per unit mass of the i-th type of construction waste component; c i kc represents the proportion of the i-th type of construction waste component generated. t,i 、kc c,i and kc type,i These are the correction coefficients for the construction age, structure, and type of the i-th type of construction waste component.

[0048] Furthermore, the method for calculating the distance of the construction waste road network is as follows: the road network shapefile data is converted into a network dataset using the osmnx library, the road node closest to the candidate solution is found, and the shortest path length between the road network node and each construction waste generation point is calculated using the shortest_path_length() method of the networkx library. The construction waste road network distance is the sum of the shortest path length and the straight-line distance from the center of the candidate solution to the nearest road node.

[0049] Correspondingly, the present invention also provides a site selection device for a new construction waste industrial park based on grid analysis, including a memory and a processor; the memory is used to store programs and data, including various index data and multi-objective optimization model data; the processor is used to execute the program to implement the method described.

[0050] Correspondingly, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program and data, wherein the computer program, when executed by a processor, implements the method described thereon.

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] This method improves the accuracy of predicting the spatial distribution of construction waste by using raster modeling and combining it with building feature parameters. It more closely reflects the actual generation of demolition waste and renovation waste, which are highly correlated with building features, and provides a more reliable waste supply data foundation for subsequent site selection.

[0053] In the evaluation of flexibility indicators, the CRITIC method is used to objectively determine the weights of each indicator. This method automatically calculates weights based on the conflict and contrast strength between indicators, avoiding human interference and bias caused by subjective weighting methods such as the analytic hierarchy process (AHP) and fuzzy AHP, which rely on expert scoring. For direction-sensitive rigid constraint indicators, a directional buffer construction technique is applied. This technique efficiently generates a rasterized constraint layer that accurately expresses directional constraints by constructing a standard fan-shaped buffer template and overlaying the source point through translation. This solves the problem that traditional Euclidean buffers cannot reflect directional constraints, making spatial exclusion more precise.

[0054] A multi-objective optimization model for new construction waste industrial parks was constructed, encompassing three key objectives: minimizing the comprehensive transportation distance of construction waste based on a real road network, maximizing comprehensive economic benefits, and maximizing the straight-line distance to sensitive land. The NSGA-II algorithm was used to solve the model without pre-setting weights, directly obtaining the Pareto optimal solution set. This provides decision-makers with a series of site selection schemes that achieve the best balance among different objectives, enhancing the flexibility and scientific nature of decision-making.

[0055] This invention is applicable to the site selection of newly built construction waste industrial parks in urban areas, especially to demolition waste and decoration waste whose generation volume distribution is highly correlated with building characteristics; it can also be applied to other scenarios with high construction waste generation intensity, potential for resource utilization, and clearly defined generation areas, depending on actual needs. Attached Figure Description

[0056] Figure 1 A flowchart illustrating a method for site selection of a new construction waste industrial park based on raster analysis, provided by this invention;

[0057] Figure 2 This is a directional buffer rasterized thematic layer for atmospheric protection indicators in this embodiment of the invention;

[0058] Figure 3 This is a distribution layer for construction waste generated according to an embodiment of the present invention;

[0059] Figure 4 This is the result of Boolean superposition analysis of rigid indices in an embodiment of the present invention;

[0060] Figure 5 The flexibility index of this invention is based on the results of CRITIC weighted overlap analysis. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for explaining the invention only and are not intended to limit the invention.

[0062] See Figure 1 As shown in the preferred embodiment, the site selection method for new construction waste industrial parks based on grid analysis of the present invention includes the following steps:

[0063] S1: Define the processing scale of the site selection assessment area and the newly built construction waste industrial park, and determine the raster unit parameters corresponding to the regional space. The raster unit parameters include the raster size and the extent. All rasterized thematic layers use the same raster unit parameters. The raster size is preferably 50-100m, and the extent is a rectangular area covering the assessment area.

[0064] S2: Collect data on rigid and flexible evaluation indicators related to site selection, and convert the indicators into corresponding rasterized thematic layers in a geographic information system (GIS).

[0065] Site selection criteria include: hard criteria, used to exclude unsuitable areas, including but not limited to urban planning, air protection, water protection, and NIMBY (Not In My Backyard) conditions; and flexible criteria, used to select suitable areas, including but not limited to regional land prices, comprehensive transportation distance for construction waste, distance between adjacent similar facilities, and road traffic conditions. Among these, directional impact criteria, such as air protection, which are limited by distance and direction, are implemented by constructing a directional buffer layer.

[0066] The implementation of the directional buffer layer includes the following steps:

[0067] 1) Centered on the origin (0,0), construct a standard rasterized orientation buffer A(x) based on the constraints of direction θ, included angle α, and distance r. i ,y i );

[0068]

[0069] 2) Export the rasterized index source point Boolean layer B(x) j ,y j ), and each of its source points (x) j ,y j ) according to the coordinates (x) of each point in the standard orientation buffer i ,y i Translate to coordinate (x) i +x j ,y i +y j Generate source point translation layer B`;

[0070] B'(x i +x j ,y i +y j )=A(x i ,y i )×B(x j ,y j )

[0071] 3) Overlay the source point translation layer, and obtain the rasterized thematic layer by taking the maximum / minimum value according to the index type.

[0072]

[0073] The atmospheric protection index is an adverse impact index. The maximum value indicates whether the grid cell is within the atmospheric protection range.

[0074] S3: Estimate the amount of construction waste generated in the assessment area and spatially allocate the grid cells to obtain a construction waste generation distribution layer.

[0075] The estimated amount of construction waste generated in the assessment area is estimated based on the area's permanent resident population and urbanization rate. Demolition waste and renovation waste can be estimated separately. Then, based on the height, type, structure, and year of construction of buildings in the area, the amount of construction waste generated is allocated to each grid, resulting in a construction waste generation distribution layer.

[0076] The formula for calculating the estimated amount of construction waste generated within the assessment area is as follows:

[0077] Q = k U ×R×m

[0078] In the formula: Q represents the amount of construction waste generated in the region; k U , where is the correction coefficient for different urbanization rate ranges; R is the number of permanent residents in the region; m is the base amount of construction waste generated per unit population.

[0079] The calculation method for spatial allocation of grid units based on the amount of construction waste generated is as follows:

[0080]

[0081] c i =k b ×k h ×k t ×k c ×k type

[0082] In the formula: q i c represents the amount of construction waste generated by the i-th grid cell; i k is the coefficient for construction waste generation per grid unit. b A judgment coefficient is generated for construction waste; it is 1 if the grid is a demolishable area of ​​the building, and 0 otherwise; k h k is the building height correction factor. h The building height h is calculated by dividing the building's unit floor height by the building's height h, which is obtained by subtracting the digital elevation model (DEM) from the digital surface model (DSM); k t This is a correction factor for the building's age, ranging from 1 to 0 based on the building's construction date, from oldest to most recent; k c This is a structural correction factor, categorized into reinforced concrete, brick-concrete, brick-timber, and steel structures; k type This is a building type correction factor, categorized into residential, industrial, commercial, and public buildings.

[0083] S4: For hard indicators, perform Boolean overlay analysis on the rasterized thematic layer, remove raster cells that do not meet the conditions, and obtain the initial selection range.

[0084] The rigidity index is used to calculate the initial selection area through Boolean superposition analysis, which conforms to the following logical relationship:

[0085]

[0086] In the formula: R i The rigidity index is determined for the i-th grid cell. Let be the condition for whether the j-th rigid index is satisfied at the i-th grid; k is the number of rigid indices.

[0087] S5: Normalize the flexibility index, and calculate the weights using the CRITIC method, as shown below:

[0088]

[0089] Among them, w i j ` is the normalized value of the j-th flexibility index at the i-th grid; w i j The value of the j-th flexibility index at the i-th grid; max / min w j θ represents the maximum / minimum value of the j-th flexibility index. j The objective weight of the j-th indicator; C j The amount of information contained in the j-th normalized flexibility index; r ij The correlation coefficient between normalized flexibility indices i and j is calculated using the flexibility index value of each grid cell within the initial selection range; δ j Let be the standard deviation of the j-th indicator.

[0090] When performing flexible index overlay analysis, the initial site selection range based on the rigidity index of the membrane is used to obtain a comprehensive evaluation index through flexible index overlay analysis according to weights. Raster data with a comprehensive evaluation index greater than the 75th percentile is selected. Based on a GIS platform, the above raster data is spatially aggregated using the "raster-to-surface" method to obtain several continuous spatial areas. Areas with an area larger than the preset lower limit of the construction waste industrial park land area are reserved as alternative site selection schemes. If the number of alternative site selection schemes is less than 5, the quantile of the comprehensive evaluation index is reduced until the quantity requirement is met.

[0091] Among them, the transportation distance for construction waste is calculated using Euclidean distance, and the comprehensive transportation distance for site selection is calculated based on a weighted average of the amount of construction waste generated. The calculation method is as follows: each grid is calculated according to its transportation distance to the candidate facility point l. ij Sort in ascending order, and select the first k grid cells in sequence to maximize their construction waste generation q. i The sum of these quantities satisfies the processing capacity S of the newly built construction waste industrial park, with a comprehensive transportation distance L. i The calculation method is as follows:

[0092]

[0093] In the formula: L i The comprehensive transportation distance of construction waste for each grid point; ij The distance between the construction waste generation point and the site selection point is either Euclidean or road network distance. Euclidean distance is used in step S5, and road network distance is used in step S6; S is the processing scale of the construction waste industrial park.

[0094] S6: Construct a multi-objective optimization model for the newly built construction waste industrial park, including:

[0095] Minimize the overall transport distance of the road network min f1=D m

[0096] Maximize overall economic benefits: f2 = E = e s +e r -c g -c t -c d

[0097] Maximize the straight-line distance of sensitive land use: f3 = Ls = min(Ls) i )

[0098] In the formula, D m E represents the comprehensive transport distance of the road network; E represents the comprehensive economic benefits; e s For the economic benefits of construction waste reception; e r For the economic benefits of resource utilization; c g For land use costs; c t For transportation costs; c d The processing cost includes depreciation of plant and equipment; Ls is the straight-line distance to the nearest sensitive land use; Ls i Let be the distance to the i-th nearby sensitive land use site.

[0099] The multi-objective optimization model obtains the Pareto optimal solution set for the site selection of a new construction waste industrial park by using the non-dominated sorting genetic algorithm (NSGA-II) without setting pre-weights.

[0100] Among them, the economic benefits of resource utilization e r The unit revenue of different construction waste components is estimated. The construction waste components are corrected based on the building characteristic parameters of the construction waste nodes in the comprehensive transportation distance calculation. The calculation method is shown in the following formula:

[0101]

[0102] In the formula: qc i R represents the collection volume of the i-th component of construction waste; i V represents the resource utilization rate of the i-th type of construction waste component; iThe economic benefits of resource utilization per unit mass of the i-th type of construction waste component; c i kc represents the proportion of the i-th type of construction waste component generated. t,i 、kc c,i and kc type,i These are the correction coefficients for the construction age, structure, and type of the i-th type of construction waste component.

[0103] The method for calculating the road network distance for construction waste is as follows: Convert the road network shapefile data into a network dataset using the osmnx library, find the road node closest to the candidate solution, and use the shortest_path_length() method of the networkx library to calculate the shortest path length between the road network node and each construction waste generation point. The road network distance for construction waste is the sum of the shortest path length and the straight-line distance from the center of the candidate solution to the nearest road node.

[0104] The implementation of the invention will now be described with reference to a specific embodiment:

[0105] This embodiment analyzes the site selection scheme for a newly built construction waste industrial park in a certain city. The scope of construction waste resource utilization includes demolition waste and decoration waste.

[0106] 1. The site selection assessment area boundary adopts the urban administrative boundary. The processing capacity of the newly built construction waste industrial park is S10,000 tons / year for demolition waste and S20,000 tons / year for renovation waste. Four grid cell sizes were selected: 30m, 50m, 100m, and 200m. The gridded range is represented by coordinates in the urban plane coordinate system, with the X range being X1~X2 and the Y range being Y1~Y2.

[0107] 2. The rigid and flexible indicators related to site selection and their processing instructions are shown in Table 1.

[0108] Table 1 Site Selection Indicators

[0109]

[0110]

[0111] Rigid indicators are divided into four types: Scope indicators, which define a specific area and are extracted into a GIS polygon layer and converted into a raster layer; these include indicators for urban planning, water protection, and cultural preservation. Distance indicators, which determine whether requirements are met based on distance from designated facilities; these are generated by creating a buffer zone on a GIS platform, which is then rasterized; these include indicators for NIMBY (Not In My Backyard) conditions. Directional distance indicators, created using the directional buffer calculation method of this patent, allow for local layer mapping. Figure 2As shown, the residential area range and the fan-shaped buffer zone starting from the residential area are visible; the lower limit of land use size is a special indicator, and the suitable area range needs to be based on the results of the flexible indicator overlay analysis, so it is implemented in step S4.

[0112] Flexible indicators are divided into three types: range-based indicators, whose values ​​are determined by range, and are converted into rasterized thematic layers after being created as surface layers and assigned values, including indicators such as regional land prices; distance-based indicators, whose values ​​are linearly related to Euclidean distance, and can be created as rasterized thematic layers through the Euclidean distance function of the GIS platform, including indicators such as the spacing between adjacent similar facilities and road traffic conditions; and construction waste transportation distance. Since this patent adopts a comprehensive transportation distance calculation method weighted by the amount of waste generated, its implementation on the GIS platform is relatively complex. In this embodiment, rasterized data of construction waste distribution is exported, processed by script according to the method of this patent, and then imported into a rasterized thematic layer.

[0113] During the thematic layer conversion process, it was found that when the raster cell size was 30m, the script took a long time to calculate the directional buffer, the distribution of construction waste, and the transportation distance; when the raster cell size was 200m, some indicator ranges were significantly distorted. Therefore, this patent preferably uses a raster cell size of 50-100m.

[0114] 3. Estimate the amount of demolition and renovation waste generated based on the permanent resident population within the assessment area. The distribution of demolition waste is adjusted for building height, construction year, type, and structure, while renovation waste is adjusted for building height and type. The unit generation amount and adjustment coefficient are calculated based on statistical results of construction waste from different cities. Separate generation and distribution layers for demolition and renovation waste are generated. A partial view of the demolition waste generation distribution layer is shown below. Figure 3 As shown.

[0115] 4. Perform Boolean overlay analysis on the rasterized thematic layers of all rigid indicators to obtain the initial selection range, such as... Figure 4 As shown.

[0116] 5. Based on the distribution layers of demolition waste and renovation waste, the weighted comprehensive transportation distance of construction waste generation is calculated using a script.

[0117] All thematic layers of flexible indicators were rasterized and normalized to a range of 0-1 through reclassification. The weights were calculated using the CRITIC method, and the calculation results are shown in Table 2.

[0118] Table 2. Weights of CRITIC Method Indicators

[0119] index Regional land prices Spacing between similar facilities Road traffic conditions Construction waste transportation distance Weight 0.183 0.292 0.265 0.260

[0120] The initial site selection range based on the rigidity index of the membrane coating is used to obtain a comprehensive evaluation layer by performing a weighted overlay analysis of the flexibility index, such as local areas. Figure 5As shown, by reclassifying, a new layer is created for the raster points whose comprehensive evaluation index is greater than the 75th percentile. The "raster to polygon" function is used to convert the raster into polygons. Based on the area in the polygon attribute table, 26 polygons with an area greater than the lower limit of the land use size are selected as alternative site selection schemes.

[0121] 6. Use the osmnx library to convert the shapefile data of the regional road network into a network dataset, find the road node closest to the candidate solution, and use the shortest_path_length() method of the networkx library to calculate the shortest path length between the road network node and each construction waste generation point. The construction waste road network distance is the sum of the shortest path length and the straight-line distance from the center of the candidate solution to the nearest road node.

[0122] The economic benefits of resource utilization are estimated by calculating the unit revenue of different construction waste components, and the overall economic benefits are calculated.

[0123] A multi-objective optimization model for the construction waste industrial park was constructed, which includes minimizing the comprehensive transportation distance of the road network, maximizing the comprehensive economic benefits, and maximizing the straight-line distance of sensitive land. The Pareto optimal solution set for the site selection of the construction waste industrial park was obtained by using the non-dominated sorting genetic algorithm (NSGA-II) without setting pre-weights. Some results are shown in Table 3.

[0124] Table 3 Pareto optimal solution set for multi-objective optimization

[0125] plan <![CDATA[Comprehensive transport distance f1 of road network]]> <![CDATA[Comprehensive economic benefit f2]]> <![CDATA[Straight-line distance of sensitive land f3]]> 1 15.28 129.18 1.05 2 13.79 103.03 1.69 3 17.74 94.34 3.82 4 15.48 102.57 2.56 5 17.40 121.91 2.07

[0126] Of the above options, options 1-3 offer the best overall economic benefits, comprehensive road network transport distance, and straight-line distance to sensitive land, respectively. However, they also have shortcomings in terms of straight-line distance to sensitive land, overall economic benefits, and comprehensive road network transport distance. These three options represent extreme optimizations in a single aspect. Options 4 and 5 represent balanced optimizations of all three objectives.

[0127] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0128] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0129] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for site selection of new construction waste industrial parks based on raster analysis, characterized in that, Includes the following steps: S1: Define the processing scale of the site selection assessment area and the new construction waste industrial park, and determine the grid unit parameters corresponding to the regional space; S2: Collect rigid and flexible evaluation index data related to site selection, and convert the indexes into corresponding rasterized thematic layers in a geographic information system (GIS); S3: Estimate the amount of construction waste generated in the assessment area and spatially allocate the grid cells to obtain a construction waste generation distribution layer; S4: For hard indicators, perform Boolean overlay analysis on the rasterized thematic layer, remove raster cells that do not meet the conditions, and obtain the initial selection range; S5: For the preliminary site selection range of flexibility index and membrane rigidity index, the CRITIC method is used to determine the weights, and after weighted overlay analysis, a comprehensive evaluation index layer is obtained to obtain alternative site selection schemes. S6: Construct a multi-objective optimization model for the new construction waste industrial park, and obtain the Pareto optimal solution set for the site selection of the new construction waste industrial park through the non-dominated sorting genetic algorithm (NSGA-II).

2. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S1, the raster unit parameters include raster size and range. All rasterized thematic layers use the same raster unit parameters, with a raster size of 50-100m and a range that is a rectangular area covering the evaluation region.

3. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S2, the site selection indicators include: hard indicators, used to exclude unsuitable areas, including but not limited to urban planning, air protection, water protection, and NIMBY conditions; and flexible indicators, used to select suitable areas, including but not limited to regional land prices, comprehensive transportation distance for construction waste, distance between adjacent similar facilities, and road traffic conditions.

4. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S2, the directional influence index, which is constrained by distance and direction, is implemented by constructing a directional buffer layer, including the following steps: 1) Centered on the origin (0,0), construct a standard rasterized orientation buffer A(x) based on the constraints of direction θ, included angle α, and distance r. i ,y i ); 2) Export the rasterized index source point Boolean layer B(x) j ,y j ), and each of its source points (x) j ,y j ) according to the coordinates (x) of each point in the standard orientation buffer i ,y i Translate to coordinate (x) i +x j ,y i +y j Generate source point translation layer B`; B'(x i +x j ,and i +and j )=A(x i ,and i )×B(x j ,and j ) 3) Overlay the source point translation layer, and obtain the rasterized thematic layer by taking the maximum / minimum value according to the index type.

5. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S3, the estimated amount of construction waste generated in the area is assessed, and the calculation formula is as follows: Q=k U ×R×m In the formula: Q represents the amount of construction waste generated in the region; k U , where is the correction coefficient for different urbanization rate ranges; R is the number of permanent residents in the region; m is the base amount of construction waste generated per unit population.

6. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S3, the calculation method for spatial allocation of grid units based on the amount of construction waste generated is as follows: c i =k b ×k h ×k t ×k c ×k type In the formula: q i c represents the amount of construction waste generated by the i-th grid cell; i k is the coefficient for construction waste generation per grid unit. b A judgment coefficient is assigned to determine the amount of construction waste; if the grid is a construction waste generation area, the coefficient is 1, otherwise it is 0; k h k is the building height correction factor. h The building height h is calculated by dividing the building unit floor height by the building height h, which is obtained by subtracting the digital elevation model (DEM) from the digital surface model (DSM); k t This is a correction factor for the building's age, ranging from 1 to 0 based on the building's construction date, from oldest to most recent; k c This is a structural correction factor, categorized into reinforced concrete, brick-concrete, brick-timber, and steel structures; k type This is a building type correction factor, categorized into residential, industrial, commercial, and public buildings.

7. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S4, the rigidity index is used to calculate the initial selected region through Boolean superposition analysis, which conforms to the following logical relationship: In the formula: R i The rigidity index is determined for the i-th grid cell. Let be the condition for whether the j-th rigid index is satisfied at the i-th grid; k is the number of rigid indices.

8. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S5, the flexibility index is normalized, the weights are calculated using the CRITIC method, and a comprehensive evaluation index is obtained by overlaying the flexibility indexes according to the weights, as shown below: in, Let be the normalized value of the j-th flexibility index at the i-th grid. The value of the j-th flexibility index at the i-th grid; max / min w j θ represents the maximum / minimum value of the j-th flexibility index. j The objective weight of the j-th indicator; C j The amount of information contained in the j-th normalized flexibility index; r ij The correlation coefficient between normalized flexibility indices i and j is calculated using the flexibility index value of each grid cell within the initial selection range; δ j Let be the standard deviation of the j-th indicator.

9. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S5, the selection method for alternative site selection schemes is as follows: raster data with a comprehensive evaluation index greater than the 75th percentile is selected; based on the GIS platform, the above raster data is spatially aggregated using the "raster-to-surface" method to obtain several continuous spatial areas; areas with an area greater than the preset lower limit of the construction waste industrial park land area are reserved as alternative site selection schemes. If the number of alternative site selection schemes is less than 5, the comprehensive evaluation index quantile is lowered until the quantity requirement is met.

10. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In step S6, the objective function of the multi-objective optimization model includes the following three objective functions: Minimize the overall transport distance of the road network min f1=D m Maximize overall economic benefits: f2 = E = e s +e r -c g -c t -c d Maximize the straight-line distance of sensitive land use: f3 = Ls = min(Ls) i ) In the formula, D m E represents the comprehensive transport distance of the road network; E represents the comprehensive economic benefits; e s For the economic benefits of construction waste reception; e r For the economic benefits of resource utilization; c g For land use costs; c t For transportation costs; c d The processing cost includes depreciation of plant and equipment; Ls is the straight-line distance to the nearest sensitive land use; Ls i Let be the distance to the i-th nearby sensitive land use site. The multi-objective optimization model is solved using the non-dominated sorting genetic algorithm (NSGA-II), without setting pre-defined weights, to obtain the Pareto optimal solution set.

11. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 1, characterized in that, In steps S5 and S6, the comprehensive transportation distance of construction waste is calculated using a production-weighted method, which calculates the distance from each grid cell to the candidate facility point based on the transportation distance l. ij Sort in ascending order, and select the first k grid cells in sequence to maximize their construction waste generation q. i The sum of these quantities satisfies the processing capacity S of the newly built construction waste industrial park, with a comprehensive transportation distance L. i The calculation method is as follows: In the formula: L i The comprehensive transportation distance of construction waste for each grid point; ij The distance between the construction waste generation point and the site selection point is either Euclidean or road network distance. Euclidean distance is used in step S5, and road network distance is used in step S6; S is the processing scale of the construction waste industrial park.

12. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 10, characterized in that, The economic benefits of resource utilization mentioned above e r The unit revenue of different construction waste components is estimated. The construction waste components are corrected based on the building characteristic parameters of the construction waste nodes in the comprehensive transportation distance calculation. The calculation method is shown in the following formula: In the formula: qc i R represents the collection volume of the i-th component of construction waste; i V represents the resource utilization rate of the i-th type of construction waste component; i The economic benefits of resource utilization per unit mass of the i-th type of construction waste component; c i kc represents the proportion of the i-th type of construction waste component generated. t,i 、kc c,i and kc type,i These are the correction coefficients for the construction age, structure, and type of the i-th type of construction waste component.

13. The method for site selection of new construction waste industrial parks based on raster analysis according to claim 11, characterized in that, The method for calculating the distance of the construction waste road network is as follows: the road network shapefile data is converted into a network dataset using the osmnx library, the road node closest to the candidate solution is found, and the shortest path length between the road network node and each construction waste generation point is calculated using the shortest_path_length() method of the networkx library. The construction waste road network distance is the sum of the shortest path length and the straight-line distance from the center of the candidate solution to the nearest road node.

14. A site selection device for new construction waste industrial parks based on grid analysis, characterized in that, Including memory and processor; The memory is used to store programs and data, including various indicator data and multi-objective optimization model data; The processor is used to execute the program to implement the method as described in any one of claims 1 to 13.

15. A non-transitory computer-readable storage medium having stored computer programs and data thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 13.

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