A method for optimizing the layout of tourist toilets in scenic spots

Abstract

The invention discloses a method for optimizing the layout of tourist toilets in scenic spots, the method comprising obtaining relevant information of tourist toilets in scenic spots; based on the relevant information, under the condition of a given number of newly added tourist toilets, taking the average shortest distance from tourists to toilets in 24 hours as the target, and taking the squatting service capacity as the constraint, constructing a tourist toilet layout optimization model in scenic spots; solving the model using a simulated annealing algorithm and a nested loop judgment algorithm; repeatedly solving the model to finally obtain a global optimal solution; and outputting the location of newly added tourist toilets and the number of squatting positions. The method can be used in scenic spots with a large number of tourist toilets to check for omissions and fill gaps in individual areas and optimize the overall layout, which can not only meet the needs of tourists for close-range toilet use, but also reduce the capital investment in newly added tourist toilets.

Description

Technical Field

[0001] The invention relates to the technical field of geographic space data application, and in particular to a method for optimizing the layout of tourist toilets in a scenic area. Background Art

[0002] In recent years, with the sustained development of my country's national economy and the continuous improvement of people's living standards, my country's tourism industry has developed rapidly. In order to provide better services to tourists, scenic spots in various places have actively improved service facilities and service levels. However, due to various reasons, tourist toilets are still a shortcoming of scenic spot services. Therefore, the construction of tourist toilets has become the focus of cultural and tourism departments.

[0003] The layout of tourist toilets in scenic spots is the key content of tourist toilet construction. Reasonable layout of tourist toilets can not only improve the quality of scenic spots, but also reflect the humanistic care of scenic spots for tourists. At present, there are three main methods for the layout of tourist toilets in scenic spots: (1) Expert layout method, experts make layouts according to the situation of scenic spots, according to the principle of focusing on the main nodes and appropriately arranging the secondary nodes; (2) Crowd density prediction method, based on the crowd density prediction results, using GIS technology to analyze the rationality of tourist toilet layout and put forward optimization suggestions; (3) Population distribution method, based on population distribution data (mainly statistical data, without considering floating population) using relevant national standards and specifications to analyze the rationality of public toilet layout.

[0004] In the context of the large-scale construction of two rounds of tourist toilets, the next step of tourist toilet construction should focus on filling gaps in individual areas and optimizing the overall layout. Taking into account the temporal and spatial distribution characteristics of tourists in scenic spots, waiting time limits, toilet squat capacity and other factors, a reasonable layout optimization plan for tourist toilets is provided, so that the layout of tourist toilets and the number of squats are adapted to the actual needs of tourists. Obviously, the expert layout method, crowd density prediction method, and population distribution method cannot provide a layout optimization plan that meets this requirement. Summary of the invention

[0005] The purpose of the present invention is to provide a method for optimizing the layout of tourist toilets in a scenic area to solve the problems encountered in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing the layout of tourist toilets in a scenic area, comprising the following steps:

[0007] Step S1, obtaining relevant information about tourist toilets in the scenic area, the relevant information including: the current location and number of toilets in the scenic area, the number of tourists in the scenic area grid (50 meters × 50 meters) in 24 hours during the peak tourist season, and the land use of the scenic area.

[0008] Step S2, based on the relevant information, under the condition of a given number of newly added tourist toilets, taking the average shortest distance from tourists to toilets in 24 hours as the goal, and taking the squatting service capacity as the constraint, construct a tourist toilet layout optimization model in the scenic area as follows:

[0009]

[0010]

[0011]

[0012] Among them, m represents the number of tourist grids, n represents the number of tourist toilets, and p it represents the number of tourists in the ith grid at time t, x ijt ∈{0,1}, when x ijt =1 means that the tourist in the i-th grid goes to the j-th tourist toilet at time t, otherwise it is 0; d ijt represents the shortest actual distance from the i-th tourist grid to the j-th tourist toilet at time t; q j represents the number of squatting positions in the jth tourist toilet;

[0013] The objective function of formula (1) represents the average distance from tourists to toilets.

[0014] The constraint condition in formula (2) indicates that the toilet needs of all grid tourists are met.

[0015] The constraint condition in formula (3) indicates the upper limit of the number of people that can be served by each tourist toilet stall;

[0016] Step S3, using the simulated annealing algorithm nested loop judgment algorithm to solve the model in step S2, the solving steps are as follows:

[0017] Step S31, using the outer layer simulated annealing algorithm to optimize the location of new tourist toilets to meet the needs of tourists to use toilets at close range;

[0018] Step S32, using a loop judgment algorithm to determine the number of new tourist toilets, and substituting the determined result into the outer simulated annealing algorithm of step S31 to calculate the objective function;

[0019] Step S4, repeating step S3, and finally obtaining the global optimal solution;

[0020] Step S5, output the location of the newly added tourist toilets and the number of squatting seats.

[0021] Furthermore, the number of tourist grids in the scenic spot in the peak tourist season within 24 hours in step S1 is obtained by collecting the number of 50m×50m grids of Baidu Huiyan in July and August within 24 hours and averaging the weighted calculation.

[0022] Furthermore, the scenic area tourist toilet layout optimization model in step S2 meets the following preset conditions: tourist toilets give priority to serving the nearest tourists, and when the average number of people served by a squatting position in a tourist toilet is 3 times the average number of people served by squatting positions in all tourist toilets in the scenic area, tourists who are far away from the tourist toilet will be allocated to other tourist toilets.

[0023] Furthermore, the shortest actual distance value from the i-th tourist grid to the j-th tourist toilet in step S2 is the walking distance value of the j-th tourist toilet closest to the i-th grid that meets the squatting service capacity constraint through the Baidu Map or Amap walking navigation service traversal.

[0024] Furthermore, the step S31 specifically includes the following steps:

[0025] S311, initialize the outer layer simulated annealing algorithm and set the parameters: initial temperature T0, end temperature T e , temperature attenuation coefficient b, number of iterations Q, and set the current temperature T as the initial temperature, where the number of iterations Q is the square of the number of newly added tourist toilets;

[0026] S312, randomly generate an initial layout S of newly added tourist toilets, each tourist toilet in the initial layout S should be located in the scenic area and not in the water area, bring S into the outer simulated annealing algorithm to obtain the initial value f(S) of the objective function, and set the optimal solution as the initial value of the objective function;

[0027] S313, generating disturbance to generate a new layout S′ of newly added tourist toilets, each tourist toilet in the new layout S′ should be located in the scenic area and not in the water area, and calculating the objective function value f(S′) of the new layout;

[0028] S314, if f(S′) is less than the initial value f(S), the optimal solution is set as the new layout objective function value, otherwise it is determined whether to accept the new layout objective function value according to the Metropolis criterion;

[0029] S315, when the number of iterations Q is reached, proceed to the next step, otherwise repeat step S314;

[0030] S316, the current temperature T drops to the end temperature T e , then the global optimal solution is obtained, otherwise the temperature is lowered, the current temperature is set to b×T, and the process returns to step S314.

[0031] Furthermore, the step S32 specifically includes the following steps:

[0032] S321, initialize the inner loop judgment algorithm and set the parameters: the minimum number of squatting positions in the current tourist toilets in the scenic area R min , the maximum number R max , and the number of new tourist toilets is R kSet to R min ;

[0033] S322, when the average number of people served by new tourist toilets is W k Less than the average number of people served by all tourist toilets in the scenic area W avg If the number of hours Y exceeds twenty hours, then output R k And jump to step S313, otherwise the number of squatting positions of the new tourist toilet is set to R k +1;

[0034] S323, when the number of new tourist toilets reaches R max , then the output R max , otherwise return to step S322.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention proposes a method for optimizing the layout of tourist toilets in scenic spots, which comprehensively considers the 24-hour spatiotemporal distribution characteristics of tourists, the current locations of tourist toilets and the number of toilets, the land use of scenic spots, and the constraints on the service capacity of toilets. Based on a nested algorithm combining a simulated annealing algorithm and a cyclic judgment method, an optimization plan for the layout of tourist toilets in scenic spots is automatically generated.

[0037] The present invention can be used in scenic spots with a large number of tourist toilets to check for omissions and fill gaps in individual areas and optimize the overall layout, which can not only meet the tourists' needs for close-range toilet use, but also reduce the capital investment in new tourist toilets. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of the flow chart of the method for optimizing the layout of tourist toilets in a scenic area provided by the present invention;

[0039] Figure 2 The algorithm flow chart of the method for optimizing the layout of tourist toilets in a scenic area provided by the present invention;

[0040] Figure 3 A diagram showing the current location and number of squatting toilets in a scenic spot in an embodiment of the present invention;

[0041] Figure 4 The spatial and temporal distribution diagram of tourists in the implementation case of the present invention, (a) 9 o'clock, (b) 12 o'clock, (c) 16 o'clock, (d) 20 o'clock;

[0042] Figure 5 The land use map of the scenic area in the embodiment of the present invention;

[0043] Figure 6 It is a diagram showing the change process of the objective function in an embodiment of the present invention;

[0044] Figure 7This is a schematic diagram of the location and number of squatting seats of newly added tourist toilets in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] This embodiment takes the Wanpingkou Tourist Area in Rizhao City as an example to illustrate the specific implementation of the present invention. The specific implementation steps of the present invention for optimizing the layout of tourist toilets in scenic spots will be specifically described below in conjunction with this example:

[0047] S1, obtain relevant information about tourist toilets in the scenic area. The current location and number of toilets in the scenic area, the number of tourists in the scenic area in 24 hours during the peak tourist season (50 meters × 50 meters), and the land use of the scenic area.

[0048] Current location of tourist toilets and number of squatting toilets in the scenic area: A survey was conducted in Wanpingkou Tourist Area, and the locations of 61 current tourist toilets and the number of squatting toilets were collected and saved in Excel (.xls) file format. The file contains four columns. The first column is the serial number of the tourist toilet, the second and third columns are longitude and latitude respectively, and the fourth column is the number of squatting toilets in the tourist toilets. The Excel to shp tool in ArcGIS software was used to generate shp data, which was then converted to the CGCS2000 coordinate system to generate the current location of tourist toilets and the number of squatting toilets in the scenic area ( Figure 3 ).

[0049] 24-hour grid number of tourists in scenic spots during peak tourist season: The number of tourists in the 50m×50m grid of Wanpingkou Tourist Area in 24 hours was collected from Baidu Huiyan for 62 days in July and August 2021. Since the coordinate system of this data is the Mars coordinate system, it is first converted to the WGS84 coordinate system and then to the CGCS2000 coordinate system. The number of tourists in the same grid for 62 days and the same time period (for example, 16:00) is weighted averagely to obtain the 24-hour grid number of tourists in scenic spots during peak tourist season ( Figure 4 ).

[0050] Scenic area land use: The land use information of Wanpingkou tourist area was extracted from the third national land survey data of Rizhao City ( Figure 5 ), which combines river surfaces, pond surfaces, coastal mudflats, etc. into water areas.

[0051] Calculate the 10 tourist toilets closest to the tourist grid: In order to improve the efficiency of subsequent solutions, first extract the 10 tourist toilets closest to each tourist grid. In Excel, calculate the Euclidean distance from each tourist grid to the 61 tourist toilets in turn, and then use the large function to extract the 10 tourist toilets closest to each tourist grid.

[0052] Step S2, constructing a layout optimization model for tourist toilets in scenic spots. Taking the average shortest distance from tourists to toilets in 24 hours as the target and squatting service capacity as the constraint, the layout optimization model for tourist toilets in scenic spots is constructed as follows:

[0053] Constraints:

[0054]

[0055]

[0056] Among them, m represents the number of tourist grids, n represents the number of tourist toilets, and p it represents the number of tourists in the ith grid at time t, x ijt ∈{0,1}, when x ijt =1 means that the tourist in the i-th grid goes to the j-th tourist toilet at time t, otherwise it is 0; d ijt represents the shortest actual distance from the i-th tourist grid to the j-th tourist toilet at time t; q j Indicates the number of squatting digits in the j-th tourist toilet.

[0057] Formula (1) represents the average distance from tourists to toilets; Formula (2) represents that the toilet needs of all grid tourists are met; Formula (3) represents the upper limit of the number of people that each tourist toilet can serve.

[0058] S3, using the simulated annealing algorithm and nested loop judgment algorithm to solve the model in S2. The number of new tourist toilets is set to 3, Figure 2 This is the flow chart of the nested loop judgment algorithm of the simulated annealing algorithm. Figure 6 This is a diagram of the change process of the objective function of the simulated annealing algorithm nested loop judgment algorithm, where the horizontal axis is the iteration process and the vertical axis is the objective function value (average distance). The solution steps are as follows:

[0059] The parameter settings of the simulated annealing algorithm are shown in Table 1.

[0060] Table 1 Simulated annealing parameters

[0061]

[0062]

[0063] S31, using the outer layer simulated annealing algorithm to optimize the location of the newly added tourist toilets to meet the needs of tourists to use the toilets at close range. The specific steps of solving the nested loop judgment algorithm of the simulated annealing algorithm are as follows:

[0064] S311, algorithm initialization, setting simulated annealing algorithm parameters. Initial temperature T0, end temperature T e , temperature attenuation coefficient b, number of iterations Q, and set the current temperature T as the initial temperature, where the number of iterations Q is the square of the number of newly added tourist toilets;

[0065] S312, randomly generate an initial layout S of newly added tourist toilets, bring S into the outer simulated annealing algorithm to obtain the initial value of the objective function f(S), and set the optimal solution as the initial value of the objective function. The initial layout is the location of the newly added tourist toilets. Each tourist toilet should be located in the scenic area and not in the water area.

[0066] S313, generate disturbance to generate a new layout S' of newly added tourist toilets, and calculate the new layout objective function value f(S'). The new layout is the updated location of the newly added tourist toilets, and each tourist toilet should be located in the scenic area and not in the water area;

[0067] S314, if f(S′) is less than the initial value f(S), the optimal solution is set as the new layout objective function value, otherwise it is determined whether to accept the new layout objective function value according to the Metropolis criterion;

[0068] S315, when the number of iterations Q is reached, proceed to the next step, otherwise repeat S314;

[0069] S316, the current temperature T drops to the end temperature T e , then the global optimal solution is obtained, otherwise the temperature is lowered, the current temperature is set to b×T, and the process returns to S314.

[0070] Generation of new layout: For each tourist toilet Z in the initial layout, generate a vector h with a length of random(0,L) and a direction of random(0,360)°, and replace Z with Z+h to generate a new layout. The initial L is set to 3 kilometers, and it decays as the algorithm iterates, and the decay coefficient is set to 0.99.

[0071] S32, using a cyclic judgment algorithm to determine the number of new tourist toilets, and substituting the determined result into the outer simulated annealing algorithm of step S31 to calculate the objective function.

[0072] S321, initialize the inner loop judgment algorithm and set the parameters: the minimum number of squatting positions in the Wanpingkou tourist area toilets R min is 6, the maximum number R max 12, adding the number of squatting toilets for tourist use R k Set to 6;

[0073] S322, when the average number of people served by new tourist toilets is W k Less than the average number of people served by all tourist toilets in the scenic area W avg If the number of hours Y exceeds twenty hours, then output R k And jump to step S313, otherwise the number of squatting positions of the new tourist toilet is set to R k +1;

[0074] S3221, using Baidu Map API walking navigation service (BMapGL.WalkingRoute) to calculate the walking distance value from each tourist grid to the nearest 10 tourist toilets in turn;

[0075] S3222, assign the number of tourists in the grid to the tourist toilets closest to them. Count the number of people served by the 61 tourist toilets at time t, and calculate the average number of people served by the 61 tourist toilets at time t and the average number of people served by all tourist toilets in the scenic area based on the number of squatting positions. avg When the average number of people served by a squatting toilet is 3 times greater than the average number of people served by all the squatting toilets in the scenic area, tourists who are far away from the toilet will be assigned to the second closest toilet, and so on, until the squatting toilet service capacity requirements are met;

[0076] S3223, statistics on the average number of people served by newly added tourist toilets W k Less than the average number of people served by all tourist toilets in the scenic area W avg If Y exceeds twenty hours, output R k And jump out of the loop judgment algorithm, otherwise the number of new tourist toilets will be increased by 1;

[0077] S323, if the number of newly added tourist toilets reaches 12, output 12, otherwise return to S322;

[0078] S4, repeat step S3, and finally get the global optimal solution. The global optimal solution can make the average distance from tourists to tourist toilets the shortest, and the capital investment is the least under the condition of a given number of new tourist toilets;

[0079] S5, output the location and number of squatting toilets added to the tourist toilets ( Figure 7 ).

[0080] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the layout of tourist toilets in a scenic area, characterized in that: The steps include: Step S1, obtaining information related to tourist toilets in the scenic area, the relevant information including: the location and number of toilets in the scenic area, the number of tourists in the scenic area in 24 hours during the peak tourist season, and the land use of the scenic area; Step S2, based on the relevant information, under the condition of a given number of newly added tourist toilets, taking the average shortest distance from tourists to toilets in 24 hours as the goal, and taking the squatting service capacity as the constraint, construct a tourist toilet layout optimization model in the scenic area as follows: Among them, m represents the number of tourist grids, n represents the number of tourist toilets, and p it represents the number of tourists in the ith grid at time t, x ijt ∈{0,1}, when x ijt =1 means that the tourist in the i-th grid goes to the j-th tourist toilet at time t, otherwise it is 0; d ijt represents the shortest actual distance from the i-th tourist grid to the j-th tourist toilet at time t; q j represents the number of squatting positions in the jth tourist toilet; The objective function of formula (1) represents the average distance from tourists to toilets. The constraint condition in formula (2) indicates that the toilet needs of all grid tourists are met. The constraint condition in formula (3) indicates the upper limit of the number of people that can be served by each tourist toilet stall; Step S3, using the simulated annealing algorithm nested loop judgment algorithm to solve the model in step S2, the solving steps are as follows: Step S31, using the outer layer simulated annealing algorithm to optimize the location of new tourist toilets to meet the needs of tourists to use toilets at close range; Step S32, using a loop judgment algorithm to determine the number of new tourist toilets, and substituting the determined result into the outer simulated annealing algorithm of step S31 to calculate the objective function; Step S4, repeating step S3, and finally obtaining the global optimal solution; Step S5, outputting the location and number of squatting seats of newly added tourist toilets; The step S31 specifically includes the following steps: S311, initialize the outer layer simulated annealing algorithm and set the parameters: initial temperature T0, end temperature T e , temperature attenuation coefficient b, number of iterations Q, and set the current temperature T as the initial temperature, where the number of iterations Q is the square of the number of newly added tourist toilets; S312, randomly generate an initial layout S of newly added tourist toilets, each tourist toilet in the initial layout S should be located in the scenic area and not in the water area, bring S into the outer simulated annealing algorithm to obtain the initial value f(S) of the objective function, and set the optimal solution as the initial value of the objective function; S313, generating disturbance to generate a new layout S′ of newly added tourist toilets, each tourist toilet in the new layout S′ should be located in the scenic area and not in the water area, and calculating the objective function value f(S′) of the new layout; S314, if f(S′) is less than the initial value f(S), the optimal solution is set as the new layout objective function value, otherwise it is determined whether to accept the new layout objective function value according to the Metropolis criterion; S315, if the number of iterations Q is reached, proceed to the next step, otherwise repeat step S314; S316, the current temperature T drops to the end temperature T e , then the global optimal solution is obtained, otherwise the temperature is lowered, the current temperature is set to b×T, and the process returns to step S314; The step S32 specifically includes the following steps: S321, initialize the inner loop judgment algorithm and set the parameters: the minimum number of squatting positions in the current tourist toilets in the scenic area R min , the maximum number R max , and the number of squatting toilets added to tourist toilets R k Set to R min ; S322, when the average number of people served by new tourist toilets is W k Less than the average number of people served by all tourist toilets in the scenic area W avg If the number of hours Y exceeds twenty hours, then output R k And jump to step S313, otherwise the number of squatting positions of the new tourist toilet is set to R k +1; S323, when the number of new tourist toilets reaches R max , then the output R max , otherwise return to step S322.

2. A method for optimizing the layout of tourist toilets in a scenic area according to claim 1, characterized in that: The number of tourists in the scenic spot grid in 24 hours during the peak tourist season in step S1 is obtained by collecting the number of people in the 50m×50m grid of Baidu Huiyan in July and August and calculating the average weighted number.

3. A method for optimizing the layout of tourist toilets in a scenic area according to claim 1, characterized in that: The scenic area tourist toilet layout optimization model in step S2 meets the following preset conditions: tourist toilets give priority to serving the nearest tourists, and when the average number of people served by a tourist toilet squatting position is greater than 3 times the average number of people served by all tourist toilets in the scenic area, tourists who are far away from the tourist toilet will be allocated to other tourist toilets.

4. A method for optimizing the layout of tourist toilets in a scenic area according to claim 1, characterized in that: In step S2, the shortest actual distance from the i-th tourist grid to the j-th tourist toilet is the walking distance value of the j-th tourist toilet closest to the i-th grid that meets the squatting service capacity constraint through the Baidu map or Amap walking navigation service traversal.

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