CN114399520A - Intelligent optimization method for rural garbage can layout - Google Patents
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Abstract
The invention discloses an intelligent optimization method for rural garbage can layout, which comprises the following steps: marking single houses and outlines on an electronic map of a village whole border, and marking the orientation of a main entrance; searching potential feasible positions of the garbage can on an electronic map of the village whole border through a computer; selecting feasible position points by a computer through a honeycomb covering method to form a feasible position set; the size of each trash can in the set of feasible locations is determined. The method can quickly provide an optimization scheme of the layout position and scale of the garbage can, and has the advantages of high efficiency and reasonable layout. The position of the potential garbage can is found out firstly, and then the feasible position point is determined, so that the computer processing efficiency is improved. The scale of the garbage can is determined through D, P, C, and a balanced optimal scheme can be found; the next generation optimized scheme group is generated in batch by adopting a random hybridization and variation mode, and more than one output garbage can layout scheme is adopted, so that the subsequent comparison and selection are convenient.
Description
Technical Field
The invention relates to the fields of architecture and urban and rural planning, in particular to a rural garbage can layout method based on intelligent algorithm optimization.
Background
The rural garbage bin is an important rural public facility and needs to be carefully planned and located. Factors that affect the goodness of addressing are many, including population of services, accessibility, and impact on the environment. Manual planning by a human or architect is therefore difficult to compromise with this number of influencing factors.
Research on the site selection problem of rural public facilities is less, and most of the research focuses on theoretical research. For example, guiding suggestions are provided for the site selection problem of the public service facilities in the small towns from three aspects of culture, efficiency and function based on the life circle theory. There is also a village garbage site selection configuration method with balanced benefits of three parties. Although there are some public facility layout evaluation indexes and rationality evaluation indexes, most of the indexes are developed from the concept perspective, and the research on a mathematical model of the rural public service facility site selection problem is lacked, so that the conclusion is difficult to be given quantitatively.
Disclosure of Invention
The invention provides an intelligent optimization method for rural garbage can layout, which is used for solving the problems that the existing facility site selection and layout technology cannot give consideration to various factors, cannot give various optimized schemes quantitatively and is difficult to be quickly applied to wide rural areas lacking geographic bottom materials.
In order to solve the technical problems, the invention comprises the following technical scheme:
an intelligent optimization method for rural garbage can layout comprises the following steps:
step 1, identifying a house monomer and an outline on an electronic map of a village whole border, and identifying the orientation of a main entrance;
step 2, searching potential feasible positions of the garbage can on an electronic map of the village whole border through a computer;
step 3, optimizing feasible position points by a computer through a cellular coverage method to form a feasible position set, and displaying the feasible position points on the electronic map, wherein the number of the feasible position sets is marked as N;
and 4, determining the scale of each garbage can in the feasible position set, and displaying the scale of the garbage can at the feasible position on the electronic map.
Further, the step 1 specifically includes the following steps:
step 1.1, shooting the whole village border by using an unmanned aerial vehicle to obtain a whole border oblique photographic picture with altitude;
step 1.2, identifying a single house and the outline thereof by adopting an edge detection algorithm according to the position of a roof or a wall to obtain the coordinates of a central point and the coordinates of an outline control point of each rural house, and displaying the coordinates on an electronic map of the whole village border;
and 1.3, identifying the bottom gate of each house from the side image of the oblique photography, using the bottom gate as a main entrance orientation, and displaying the bottom gate on an electronic map of the village whole environment.
Further, the step 2 specifically includes the following steps:
dividing a village whole border into a plurality of grid units on an electronic map;
presetting a judgment condition of the position of the potential garbage can in the computer, searching each grid unit in sequence, and judging whether the grid unit is the position of the potential garbage can according to the judgment condition;
and displaying the grid cells meeting the judgment condition in an electronic map.
Further, the feasible determination condition of the position of the garbage can is that the following conditions are simultaneously satisfied:
A. a is larger than or equal to a from all the house edges, and a is a preset value;
B. a house exists in a radius range of b meters, and b is a preset value;
C. not on the primary road.
Further, the step 3 specifically includes the following steps:
dividing village whole border into hexagonal honeycombs in an electronic map, wherein the side length range of the hexagonal honeycombs is [ E ]1,E2]In which E1Greater than or equal to 5 times of the side length of the grid cells in the step 2, E2>E1；
If 2 or more feasible position points exist in the hexagonal honeycomb, randomly selecting 1 feasible position point, and displaying the feasible position points on the electronic map;
the feasible location points after selection constitute a preferred feasible location set, and the total number of feasible location points is recorded as N.
Further, E1＝10，E2＝30；
For a dense area of a house, the side length of the hexagon is 10 meters, the side length of the general area is 20 meters, and the side length of the sparse area of the house is 30 meters.
Further, in the step 4, three indexes, namely equivalent and D of the walking distance of the garbage delivery, equivalent and P of the environmental influence and total deviation C of the coverage efficiency are evaluated, and D, P, C is smaller as a preferred scheme;
wherein the equivalent of the walking distance for delivering the garbage and the expression D are as follows,
Dkrepresents the distance of the kth house from the nearest trash can; x is the number ofi、yiIs the coordinate of the garbage bin, xk、ykThe geometric center coordinates of the house; t isiThe scale of the ith garbage can is shown;
wherein the environmental impact equivalent and the P expression are,
Pkrepresents the distance of the kth house from the nearest trash can; x is the number ofi、yiIs the coordinate of the garbage bin, xk、ykThe geometric center coordinates of the house; t isiThe scale of the garbage can is shown; a. thekTo take into account the coefficients of the orientation angle effect, Ak＝max{，cos(αk)}，αkThe included angle between the garbage can and the kth house is formed;
wherein, the total coverage efficiency deviation C is expressed as,
wherein q isiThe number of people covered by the ith garbage bin, Q is the total number of people in the whole village,
for the total size of all the garbage cans, TiThe scale of the ith garbage can is shown.
Further, the size of each trash can in the feasible location set is determined by D, P, C, and the following method is adopted:
step 4.1. generating an initial scheme group, wherein the initial scheme group comprises X random initial schemes, and each scheme uses N-dimensional floating point number single-column vectors (T)1,…,Ti,…,TN) Is shown in which T isiThe scale of the ith garbage can is shown, i is 1, 2, … and N; t isiRepresented by a floating point number between 0 and 1, TiThe larger the value is, the larger the scale of the garbage can isiWhen the value is less than k, the garbage can is not laid out, and k is a preset value;
step 4.2, an iterative process is entered, and the next generation optimized scheme group X is generated through random hybridization and variation1；
Wherein, the hybridization operation is to randomly select 2 schemes in the scheme group, and calculate the mean value of corresponding elements in 2 sets to obtain a next generation scheme;
wherein, the mutation operation is to randomly select 1 scheme, and then randomly select an element in the set and add a random number between-0.2 and 0.2 as a next generation scheme;
step 4.3. grouping scheme X1Inputting the equivalent and D of the walking distance of garbage delivery, the equivalent and P of environmental influence and the index expression of the total deviation C of the coverage efficiency into the scheme, removing the absolutely non-optimal scheme, leaving the new scheme group, and repeating the step 4.2 for iteration; if any absolutely non-dominant scheme can not be removed after a certain iteration, the step 4.4 is carried out;
and 4.4, stopping iteration, finding a scheme with the predominance of at least two indexes in the D, P, C three indexes, and outputting the scheme as a final preferred scheme.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: according to the method, the individual houses, the outlines and the main entrance orientation are marked on the electronic map of the village whole border, then the potential feasible positions of the garbage cans are searched by using the computer, feasible position points are preferably selected by using the computer through a cellular coverage method to form a feasible position set, then the scale of each garbage can in the feasible position set is determined, the optimization scheme of the garbage can layout position and scale can be rapidly given, and the method has the advantages of being high in efficiency and reasonable in layout. In addition, the village full border is divided into a plurality of grid units, the position of a potential feasible garbage can is found out, then the village full border is divided into hexagonal honeycombs with larger areas, feasible position points are determined, the computer processing efficiency is improved, and the defect that the garbage can is arranged too densely is overcome. Moreover, the scale of each garbage can in the feasible position set is determined through D, P, C indexes, and a balanced optimal scheme can be found; the initial scheme group is randomly hybridized and mutated to generate a next generation optimized scheme group, a more scientific batch generation mode of the initial scheme is adopted, exhaustive search with extremely high complexity is avoided, and more than one output garbage can layout scheme has unique advantages, so that subsequent expert schemes can be conveniently selected.
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Fig. 1 is a flowchart of an intelligent optimization method for rural trash can layout in an embodiment of the present invention.
Detailed Description
The intelligent optimization method for rural trash can layout provided by the invention is further described in detail with reference to the attached drawings and specific embodiments. The advantages and features of the present invention will become more apparent in conjunction with the following description. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
As shown in fig. 1, the intelligent optimization method for rural trash can layout provided by this embodiment includes the following steps:
step 1, identifying a single house and an outline on an electronic map of the village whole border, and identifying the orientation of a main entrance.
It should be noted that, the house individual and the outline and the main entrance orientation are identified on the electronic map, and they may be manually measured and identified. The more preferred embodiment is to carry out rapid operation through unmanned aerial vehicle shooting and computer automatic identification technology, and specifically includes:
step 1.1, shooting the whole village border by using an unmanned aerial vehicle to obtain a whole border oblique photographic picture with altitude;
step 1.2, identifying a single house and the outline thereof by adopting an edge detection algorithm according to the position of a roof or a wall to obtain the coordinates of a central point and the coordinates of an outline control point of each rural house, and displaying the coordinates on an electronic map of the whole village border;
and 1.3, identifying the bottom gate of each house from the side image of the oblique photography, using the bottom gate as a main entrance orientation, and displaying the bottom gate on an electronic map of the village whole environment.
As an example, for a village in fujian, oblique photographs of about 1000 houses are collected. And then, identifying the single house and the outline thereof by adopting an edge detection algorithm according to the position of the roof or the wall to obtain the coordinates of the central point and the outline control point of each rural house. From the oblique photographed side images, the floor gate of each house is identified as the main entrance orientation. The house singles and outlines are then identified on an electronic map of the village's full-scale, and the main entrance orientation is identified.
And 2, searching potential feasible positions of the garbage can on the electronic map of the village whole border through the computer.
Dividing the village whole border into a plurality of grid units on an electronic map, such as a square grid of 1m multiplied by 1 m;
presetting a judgment condition of the position of the potential garbage can in the computer, searching each grid unit in sequence, and judging whether the grid unit is the position of the potential garbage can according to the judgment condition;
and displaying the grid cells meeting the judgment condition in an electronic map.
By way of example, the grid cell is determined to be a potentially viable trash can location if the following conditions are simultaneously satisfied: A. a is larger than or equal to a from all the house edges, wherein a is a preset value, for example, a is 2 m; B. a house exists in a radius range of b meters, and b is a preset value, for example, b is 10 m; C. not on the primary road. According to the set conditions, 570 potential garbage can positions are found on the electronic map of the whole village of a village in Fujian.
And 3, optimizing the feasible position points by using a computer through a cellular coverage method to form a feasible position set, and displaying the feasible position points on the electronic map, wherein the number of the feasible position sets is marked as N. The method specifically comprises the following steps:
dividing village whole border into hexagonal honeycombs in an electronic map, wherein the side length range of the hexagonal honeycombs is [ E ]1,E2]，E1、E2Is a preset value, wherein E1Greater than or equal to 5 times of the side length of the grid cells in the step 2, E2>E1. For example, the side length of the hexagonal honeycomb is 10-30 meters, the honeycomb is fully paved and is not overlapped as far as possible, if a certain house is dense, the side length of the hexagon is smaller, and otherwise, the side length of the hexagon is larger;
if 2 or more feasible position points exist in the hexagonal honeycomb, randomly selecting 1 feasible position point, and displaying the feasible position points on the electronic map;
the feasible location points after selection constitute a preferred feasible location set, and the total number of feasible location points is recorded as N.
As an example, for a village in Fujian, 570 potential garbage can positions are found through step 2, the number of feasible positions is too large, so that it is further preferable to reduce the number of feasible positions. The village full border is divided into hexagonal honeycombs with larger edges in an electronic map, and the situation that the village full border is fully paved and is not overlapped as far as possible is guaranteed. For the dense area of the house on the south side, the side length of the hexagon is 10 meters, the side length of the general area is 20 meters, and the side length of the sparse area on the north side is 30 meters. Then, if there are 2 and more feasible location points inside the hexagonal cell, 1 is randomly selected. And (3) selecting 18 feasible position points to form a preferable feasible position set, wherein N is determined to be 18, namely, after the step 3 is finished, the layout positions and the number of the garbage cans can be obtained.
And 4, determining the scale of each garbage can in the feasible position set, and displaying the scale of the garbage can at the feasible position on the electronic map.
It should be noted that the dimensioning of each trash can is estimated based on the number of covered population of each trash can. As a preferred embodiment, the embodiment provides a more reasonable evaluation method for the scale of the garbage can, and the evaluation is carried out by three indexes of garbage delivery walking distance equivalent and D, environmental influence equivalent and P and total coverage efficiency deviation C, wherein D, P, C is smaller as a preferred scheme.
Wherein the equivalent of the walking distance for delivering the garbage and the expression D are as follows,
Dkrepresents the distance of the kth house from the nearest trash can; x is the number ofi、yiIs the coordinate of the garbage bin, xk、ykThe geometric center coordinates of the house; t isiThe scale of the ith garbage can is shown;
wherein the environmental impact equivalent and the P expression are,
Pkrepresents the distance of the kth house from the nearest trash can; x is the number ofi、yiIs the coordinate of the garbage bin, xk、ykThe geometric center coordinates of the house; t isiThe scale of the garbage can is shown; a. thekTo take into account the coefficients of the orientation angle effect, Ak＝max{，cos(αk)}，αkThe included angle between the garbage can and the kth house is formed;
wherein, the total coverage efficiency deviation C is expressed as,
wherein q isiThe number of people covered by the ith garbage bin, Q is the total number of people in the whole village,
for the total size of all the garbage cans, TiThe scale of the ith garbage can is shown.
It should be noted that, the size of each trash can in the feasible location set is determined by D, P, C, and the following preferred modes can be adopted:
step 4.1. generating an initial scheme group, wherein the initial scheme group comprises X random initial schemes, and each scheme uses N-dimensional floating point number single-column vectors (T)1,…,Ti,…,TN) Is shown in which T isiThe scale of the ith garbage can is shown, i is 1, 2, … and N; t isiRepresented by a floating point number between 0 and 1, TiThe larger the value is, the larger the scale of the garbage can isiWhen the value is smaller than k, the garbage can is not laid out, and k is a preset value.
By way of example, the initial population of recipes includes 100 recipes; for example, in an initial scenario, d is (0.5, …,0.1, …,0), which indicates that the 1 st feasible location requires a trash can with a layout size of 0.5, which is a medium trash can; when the size of the smallest garbage can is 0.2, the floating point number is smaller than 0.2, the garbage can is not laid out, and a certain feasible position 0.1 in the middle indicates that the garbage can is not laid out. Further, X is 100 to 200.
Step 4.2, an iterative process is entered, and the next generation optimized scheme group X is generated through random hybridization and variation1。
Wherein, the hybridization operation is to randomly select 2 schemes in the scheme group, and calculate the mean value of corresponding elements in 2 sets to obtain a next generation scheme; e.g. d1＝(0.5,…,0.1,…,0)，d2(0.3, …,0.5, …,0.9), then their next generation is d10＝(0.4,…,0.3,…,0.45)。
And the mutation operation is to randomly select 1 scheme, and then randomly select one element in the set and add a random number between-0.2 and 0.2 to serve as a next generation scheme. E.g. d1When the average value is (0.5, …,0.1, …,0), the next generation has a certain probability of becoming d20＝(0.62,…,0.08,…,0.07)。
Step 4.3. grouping scheme X1Inputting the equivalent and D of the walking distance of garbage delivery, the equivalent and P of environmental influence and the index expression of the total deviation C of the coverage efficiency into the scheme, removing the absolutely non-optimal scheme, leaving the new scheme group, and repeating the step 4.2 for iteration; if any absolutely non-dominant scheme can not be removed after a certain iteration, the procedure goes to step 4.4. It should be noted that for a certain scheme d, if three indexes of any scheme in the scheme group are better than d, d is called as an absolutely-less-preferred scheme.
And 4.4, stopping iteration, finding a scheme with the predominance of at least two indexes in the D, P, C three indexes, and outputting the scheme as a final preferred scheme.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. An intelligent optimization method for rural garbage can layout is characterized by comprising the following steps:
step 1, identifying a house monomer and an outline on an electronic map of a village whole border, and identifying the orientation of a main entrance;
step 2, searching potential feasible positions of the garbage can on an electronic map of the village whole border through a computer;
step 3, optimizing feasible position points by a computer through a cellular coverage method to form a feasible position set, and displaying the feasible position points on the electronic map, wherein the number of the feasible position sets is marked as N;
and 4, determining the scale of each garbage can in the feasible position set, and displaying the scale of the garbage can at the feasible position on the electronic map.
2. The intelligent optimization method for rural trash can layout of claim 1, wherein the step 1 specifically comprises the following steps:
step 1.1, shooting the whole village border by using an unmanned aerial vehicle to obtain a whole border oblique photographic picture with altitude;
step 1.2, identifying a single house and the outline thereof by adopting an edge detection algorithm according to the position of a roof or a wall to obtain the coordinates of a central point and the coordinates of an outline control point of each rural house, and displaying the coordinates on an electronic map of the whole village border;
and 1.3, identifying the bottom gate of each house from the side image of the oblique photography, using the bottom gate as a main entrance orientation, and displaying the bottom gate on an electronic map of the village whole environment.
3. The intelligent optimization method for rural trash can layout of claim 1, wherein the step 2 specifically comprises the following steps:
dividing a village whole border into a plurality of grid units on an electronic map;
presetting a judgment condition of the position of the potential garbage can in the computer, searching each grid unit in sequence, and judging whether the grid unit is the position of the potential garbage can according to the judgment condition;
and displaying the grid cells meeting the judgment condition in an electronic map.
4. The intelligent optimization method for rural trash can layout of claim 3,
the feasible position judgment conditions of the garbage can are that the following conditions are simultaneously met:
A. a is larger than or equal to a from all the house edges, and a is a preset value;
B. a house exists in a radius range of b meters, and b is a preset value;
C. not on the primary road.
5. The intelligent rural garbage can layout optimization method according to claim 3 or 4, wherein the step 3 specifically comprises the following steps:
dividing village whole border into hexagonal honeycombs in an electronic map, wherein the side length range of the hexagonal honeycombs is [ E ]1,E2]In which E1Greater than or equal to 5 times of the side length of the grid cells in the step 2, E2>E1；
If 2 or more feasible position points exist in the hexagonal honeycomb, randomly selecting 1 feasible position point, and displaying the feasible position points on the electronic map;
the feasible location points after selection constitute a preferred feasible location set, and the total number of feasible location points is recorded as N.
6. The intelligent optimization method for rural trash can layout of claim 5,
E1＝10，E2＝30；
for a dense area of a house, the side length of the hexagon is 10 meters, the side length of the general area is 20 meters, and the side length of the sparse area of the house is 30 meters.
7. The intelligent optimization method for rural garbage can layout according to claim 1, characterized in that in step 4, evaluation is performed through three indexes of garbage delivery walking distance equivalent sum D, environmental impact equivalent sum P and total coverage efficiency deviation C, and D, P, C is smaller as a preferred scheme;
wherein the equivalent of the walking distance for delivering the garbage and the expression D are as follows,
Dkrepresents the distance of the kth house from the nearest trash can; x is the number ofi、yiIs the coordinate of the garbage bin, xk、ykThe geometric center coordinates of the house; t isiThe scale of the ith garbage can is shown;
wherein the environmental impact equivalent and the P expression are,
Pkrepresents the distance of the kth house from the nearest trash can; x is the number ofi、yiIs the coordinate of the garbage bin, xk、ykThe geometric center coordinates of the house; t isiThe scale of the garbage can is shown; a. thekTo take into account the coefficients of the orientation angle effect, Ak＝max{，cos(αk)}，αkThe included angle between the garbage can and the kth house is formed;
wherein, the total coverage efficiency deviation C is expressed as,
wherein q isiThe number of people covered by the ith garbage bin, Q is the total number of people in the whole village,
for the total size of all the garbage cans, TiThe scale of the ith garbage can is shown.
8. The intelligent optimization method for rural trash can layout of claim 7,
the size of each garbage can in the feasible position set is determined by D, P, C, and the following method is adopted:
step 4.1. generating an initial scheme group, wherein the initial scheme group comprises X random initial schemes, and each scheme uses N-dimensional floating point number single-column vectors (T)1,…,Ti,…,TN) Is shown in which T isiThe scale of the ith garbage can is shown, i is 1, 2, … and N; t isiRepresented by a floating point number between 0 and 1, TiThe larger the value is, the larger the scale of the garbage can isiWhen the value is less than k, the garbage can is not laid out, and k is a preset value;
step 4.2, an iterative process is entered, and the next generation optimized scheme group X is generated through random hybridization and variation1；
Wherein, the hybridization operation is to randomly select 2 schemes in the scheme group, and calculate the mean value of corresponding elements in 2 sets to obtain a next generation scheme;
wherein, the mutation operation is to randomly select 1 scheme, and then randomly select an element in the set and add a random number between-0.2 and 0.2 as a next generation scheme;
step 4.3. grouping scheme X1Inputting the equivalent and D of the walking distance of garbage delivery, the equivalent and P of environmental influence and the index expression of the total deviation C of the coverage efficiency into the scheme, removing the absolutely non-optimal scheme, leaving the new scheme group, and repeating the step 4.2 for iteration; if any absolutely non-dominant scheme can not be removed after a certain iteration, the step 4.4 is carried out;
and 4.4, stopping iteration, finding a scheme with the predominance of at least two indexes in the D, P, C three indexes, and outputting the scheme as a final preferred scheme.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002046806A (en) * | 2000-05-24 | 2002-02-12 | Mn Engineering Kk | Refuse collecting method and refuse collecting control system |
| CN103646132A (en) * | 2013-11-26 | 2014-03-19 | 华南理工大学 | Layout method of urban public bicycle leasing network |
| CN105760960A (en) * | 2016-02-29 | 2016-07-13 | 东南大学 | Park and ride facility optimal siting and capacity determining method based on rail transit |
| CN107491899A (en) * | 2017-09-07 | 2017-12-19 | 复旦大学 | A kind of public resource geography equilibrium allocation method based on grid-based management theory |
| JP2018120485A (en) * | 2017-01-26 | 2018-08-02 | 株式会社ピリカ | Distribution predicting device, distribution predicting method, and distribution predicting program |
| CN109165771A (en) * | 2018-07-19 | 2019-01-08 | 安徽建筑大学 | A kind of buried bucket layout optimization method in rural garbage based on GIS network analysis |
| CN109160159A (en) * | 2018-07-23 | 2019-01-08 | 成都理工大学 | A kind of residents in rural community classification method and its sorting device |
| CN110851548A (en) * | 2019-10-14 | 2020-02-28 | 上海市政工程设计研究总院（集团）有限公司 | Municipal facility site selection layout method based on ArcGIS analysis |
| CN110996259A (en) * | 2019-12-04 | 2020-04-10 | 苏州大学 | Intelligent garbage monitoring and clearing method and device based on edge calculation |
| CN111301896A (en) * | 2020-02-18 | 2020-06-19 | 东莞蜂巢智慧智能科技有限公司 | Efficient garbage collection method and system, computer and storage medium |
| CN111639805A (en) * | 2020-05-29 | 2020-09-08 | 河南大学 | GIS-based refuse landfill site selection method |
| CN111844072A (en) * | 2020-07-21 | 2020-10-30 | 上海高仙自动化科技发展有限公司 | Automatic garbage dumping method and device for intelligent robot, intelligent robot and medium |
| CN112862176A (en) * | 2021-02-01 | 2021-05-28 | 上海元卓信息科技有限公司 | Public service facility site selection method and device |
-
2021
- 2021-12-03 CN CN202111460669.5A patent/CN114399520A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002046806A (en) * | 2000-05-24 | 2002-02-12 | Mn Engineering Kk | Refuse collecting method and refuse collecting control system |
| CN103646132A (en) * | 2013-11-26 | 2014-03-19 | 华南理工大学 | Layout method of urban public bicycle leasing network |
| CN105760960A (en) * | 2016-02-29 | 2016-07-13 | 东南大学 | Park and ride facility optimal siting and capacity determining method based on rail transit |
| JP2018120485A (en) * | 2017-01-26 | 2018-08-02 | 株式会社ピリカ | Distribution predicting device, distribution predicting method, and distribution predicting program |
| CN107491899A (en) * | 2017-09-07 | 2017-12-19 | 复旦大学 | A kind of public resource geography equilibrium allocation method based on grid-based management theory |
| CN109165771A (en) * | 2018-07-19 | 2019-01-08 | 安徽建筑大学 | A kind of buried bucket layout optimization method in rural garbage based on GIS network analysis |
| CN109160159A (en) * | 2018-07-23 | 2019-01-08 | 成都理工大学 | A kind of residents in rural community classification method and its sorting device |
| CN110851548A (en) * | 2019-10-14 | 2020-02-28 | 上海市政工程设计研究总院（集团）有限公司 | Municipal facility site selection layout method based on ArcGIS analysis |
| CN110996259A (en) * | 2019-12-04 | 2020-04-10 | 苏州大学 | Intelligent garbage monitoring and clearing method and device based on edge calculation |
| CN111301896A (en) * | 2020-02-18 | 2020-06-19 | 东莞蜂巢智慧智能科技有限公司 | Efficient garbage collection method and system, computer and storage medium |
| CN111639805A (en) * | 2020-05-29 | 2020-09-08 | 河南大学 | GIS-based refuse landfill site selection method |
| CN111844072A (en) * | 2020-07-21 | 2020-10-30 | 上海高仙自动化科技发展有限公司 | Automatic garbage dumping method and device for intelligent robot, intelligent robot and medium |
| CN112862176A (en) * | 2021-02-01 | 2021-05-28 | 上海元卓信息科技有限公司 | Public service facility site selection method and device |
Non-Patent Citations (9)
| Title |
|---|
| J. RAMACHANDRAN等: "Application of geospatial technologies in optimal allocation of municipal solid waste collection BINS", ECOLOGY ENVIRONMENT & CONSERVATION, 22 December 2016 (2016-12-22) * |
| NASIM GHADAMI等: "Smartening the movement path of municipal garbage trucks using genetic algorithm with emphasis on economic-environmental indicators", ANNALS OF ENVIRONMENTAL SCIENCE AND TOXICOLOGY, 28 June 2021 (2021-06-28) * |
| YULONG CHEN等: "Optimizing locations of waste transfer stations in rural areas", PLOS ONE, 21 May 2021 (2021-05-21) * |
| 刘敏: "基于GIS的农村生活垃圾收集点选址研究——以东坪镇为例", 中国优秀硕士学位论文全文数据库, 15 September 2015 (2015-09-15), pages 16 - 34 * |
| 张艳粉;刘科问;陈伟强;: "基于AHP和GIS的中心村建设选址研究――以巩义市西村镇为例", 地域研究与开发, no. 03, 10 June 2013 (2013-06-10) * |
| 朱娇娇;陈荔;吴建俊;: "城市生活垃圾处理设施多目标优化选址研究", 科技和产业, no. 02, 25 February 2020 (2020-02-25) * |
| 李元忠: "村庄垃圾收集设施选址及配置研究-以平果雅朗屯为例", 中国优秀硕士学位论文全文数据库, 15 June 2017 (2017-06-15), pages 6 - 7 * |
| 杨梓煊，周后卿等: "农村公共场地垃圾箱摆放点及清运车运输线路的优化设计", 资源·环境, 30 October 2021 (2021-10-30) * |
| 栗娜，李珍萍: "垃圾站选址问题的数学模型及应用", 技术与方法, 15 December 2011 (2011-12-15) * |
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