System and method for dividing work areas for recycling of renewable resources

Abstract

This invention relates to the field of waste sorting, and more particularly to a system and method for dividing work areas for recycling. The method first constructs a matrix coordinate system using information from all communities in the project. Then, within the matrix coordinate system, a locally optimal path L connecting all communities is obtained using the GA genetic path planning algorithm. Next, using points 1, 2, 3, ..., n as initial points in the optimal path L, all community points in the optimal path L are allocated into k zones, forming {S1, S2, ..., S...}. n Finally, evaluation indicators are calculated for each partitioning scheme to obtain the optimal scheme. This scheme scientifically divides the work areas of recyclable waste collectors, ensuring a balanced labor cost for project operation and fairness in labor allocation among collectors.

Description

Technical Field

[0001] This invention relates to the field of waste sorting, and in particular to a system and method for dividing work areas for recycling of recyclable resources. Background Technology

[0002] In the daily operation of waste sorting and recyclable waste management projects, the balance between the number of collectors and the weight of recyclable waste—that is, the supply and demand of transportation capacity—is a key factor for the healthy development of the overall project operation. Currently, in the recycling market, unreasonable daily staffing, unequal division of work areas among collectors, and unfair allocation of demand have led to persistently high operating costs and difficulties in personnel management, lacking long-term solutions. This involves not only balancing project operating labor costs but also ensuring fairness in labor allocation among collectors and improving the user experience of the equipment. A crucial issue here is how to create a scientific and applicable method for work area division across multiple scenarios. Therefore, researching a work area division method that incorporates collector productivity calculations is of great significance for promoting the healthy development of the overall project operation. Summary of the Invention

[0003] To address the aforementioned issues, the primary objective of this invention is to provide a method for dividing work areas in the recycling of recyclable resources. This method supports interactive capacity prediction and work area division, comprehensively considering the average daily delivery volume W of each community, the cycling distance and time spent between communities, the average collection efficiency per transaction, and the average time spent on cleaning, loading, and temporary storage. It scientifically divides the work areas of recyclable waste collectors, ensuring a balanced labor cost for project operations and fairness in the allocation of labor among collectors.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] The method for dividing the work area for recycling of renewable resources is characterized by the following steps:

[0006] Step 1. Obtain information on all communities included in the project, including the number of communities n, the latitude and longitude of each community's geographical location, and the average daily delivery volume W for each community; use map software to obtain the electric vehicle riding distance d between every two communities. ij Based on distance d ij Data constructs a matrix coordinate system;

[0007] Step 2. Within the matrix coordinate system constructed in Step 1, different sequences of cells are randomly generated according to the GA gene genetic path planning algorithm to form paths. Then, position mutation and path mutation are continuously performed. Based on fitness, the optimal path L is obtained through rapid iteration.

[0008] Step 3. Initial capacity calculation for the area; Based on the business collection data, calculate the average collection weight per trip as w0 (kg), the average collection efficiency per trip as e (kg / minute), and the average turnover speed of the tricycles between areas as v (km / h); Based on the actual project schedule, the total working time is T hours; The average total time for collectors to clean, load, and temporarily store after each collection is t minutes; Based on the distance matrix between areas and the optimal path L, calculate the average single turnover distance as d0 = avg(D L (km); the total initial human efficiency is calculated as

[0009] Step 4. For the optimal path L, take the first point of the path as the initial point A1 = {a1}. If the total weight of garbage in the area is W (kg), add new points (cells) according to the greedy rule. If the second point is within the constraints, then form a cell set A1 = {a1, a2}. Update the average collection weight w and average turnover distance d = avg(D) of the iteration set. A1 According to Formula 1, update the maximum efficiency of the set; repeatedly add points until the constraints are not met, then circuit breaker is applied to form a region, select the next point as the initial point, and repeat the process of adding points or circuit breaker; the constraints are:

[0010] ① Maximum human efficiency constraint: W <C

[0011] ② Distance constraints between adjacent cells: d ij <d

[0012] Step 5. Repeat step 4 until all cell points in the optimal path L are assigned to k areas. This area allocation scheme is S1. The final set of areas is {1,2,...,k}, and the path set is... Distance matrix set Human efficiency collection Demand weight set

[0013] Step 6. Based on the optimal path L described above, select the 2nd, 3rd, ..., nth points in sequence as initial points, and repeat steps 4 and 5; forming a set of solutions {S1, S2, ..., S...} n}, calculate the total number of people k and the total maximum efficiency ∑{C} for each plan. A1 C A2 ,...,C An}, and the standard deviation of regional demand δ=std({W A1 ,W A2 ,...,W An});

[0014] Step 6. Based on project requirements: ensuring relatively fair allocation of collectors' work, balancing collector manpower with project needs, and fully utilizing the efficiency of personnel in the designated area, scientifically transform these into solution evaluation indicators.

[0015] Step 7. Algorithm Optimization: The genetic algorithm is a heuristic algorithm. To prevent the local optimum of the path order in a single planning process from causing the region partitioning result, steps 2-6 are run m times, and the final region partitioning scheme is selected from n*m schemes.

[0016] This invention employs the aforementioned technical solution, which relates to a method for dividing the working area of ​​renewable resource recycling. This method first constructs a matrix coordinate system using information from all communities in the project. Then, within the matrix coordinate system, a locally optimal path L connecting all communities is obtained using the GA genetic path planning algorithm. Next, within the optimal path L, starting from points 1, 2, 3, ..., n, all community points in the optimal path L are allocated into k regions, forming {S1, S2, ..., S...} n Finally, an evaluation index is calculated for each partitioning scheme to obtain the optimal partitioning scheme.

[0017] The above-mentioned work area division method first uses a computer to select the optimal path L from all paths, and then calculates a division scheme for each community as the starting point on the optimal path L. After layers of selection, the optimal division scheme is obtained. The entire algorithm supports interactive capacity prediction and work area division, and comprehensively considers the average daily delivery volume W of each community, the cycling distance and time between communities, the average collection efficiency per trip, and the average time spent on cleaning, loading, and temporary storage. It scientifically divides the work areas of recyclable waste collectors, ensuring a balanced labor cost for project operation and fairness in labor allocation among collectors.

[0018] A second objective of this invention is to provide a work area division system for recycling renewable resources, which performs the method described in claim 1. Attached Figure Description

[0019] Figure 1 The accompanying figure illustrates the construction of the optimal path L in a specific implementation example of the work area division method based on the present invention.

[0020] Figure 2 for Figure 1 The optimal partitioning scheme formed on the optimal path L shown (based on point 36 in the figure as the initial point). Detailed Implementation

[0021] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more, unless explicitly defined otherwise.

[0024] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0025] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0026] Example 1:

[0027] The method for dividing the work area for recycling of renewable resources is characterized by the following steps:

[0028] Step 1. Obtain information on all communities included in the project, including the number of communities n, the latitude and longitude of each community's geographical location, and the average daily delivery volume W for each community; use map software to obtain the electric vehicle riding distance d between every two communities. ij Based on distance d ij Data constructs a matrix coordinate system;

[0029] Step 2. Within the matrix coordinate system constructed in Step 1, different sequences of cells are randomly generated according to the GA gene genetic path planning algorithm to form paths. Then, position mutation and path mutation are continuously performed. Based on fitness, the optimal path L is obtained through rapid iteration.

[0030] Step 3. Initial capacity calculation for the area; Based on the business collection data, calculate the average collection weight per trip as w0 (kg), the average collection efficiency per trip as e (kg / minute), and the average turnover speed of the tricycles between areas as v (km / h); Based on the actual project schedule, the total working time is T hours; The average total time for collectors to clean, load, and temporarily store after each collection is t minutes; Based on the distance matrix between areas and the optimal path L, calculate the average single turnover distance as d0 = avg(D L (km); the total initial human efficiency is calculated as

[0031] Step 4. For the optimal path L, take the first point of the path as the initial point A1 = {a1}. If the total weight of garbage in the area is W (kg), add new points (cells) according to the greedy rule. If the second point is within the constraints, then form a cell set A1 = {a1, a2}. Update the average collection weight w and average turnover distance d = avg(D) of the iteration set. A1 According to Formula 1, update the maximum efficiency of the set; repeatedly add points until the constraints are not met, then circuit breaker is applied to form a region, select the next point as the initial point, and repeat the process of adding points or circuit breaker; the constraints are:

[0032] ① Maximum human efficiency constraint: W <C

[0033] ② Distance constraints between adjacent cells: d ij <d;

[0034] Step 5. Repeat step 4 until all cell points in the optimal path L are assigned to k areas. This area allocation scheme is S1. The final set of areas is {1,2,...,k}, and the path set is... Distance matrix set Human efficiency collection Demand weight set

[0035] Step 6. Based on the optimal path L described above, select the 2nd, 3rd, ..., nth points in sequence as initial points, and repeat steps 4 and 5; forming a set of solutions {S1, S2, ..., S...} n}; Calculate the total number of people k and the total maximum efficiency ∑{C} for each plan. A1 C A2 ,...,C An}, and the standard deviation of regional demand δ=std({W A1 ,W A2 ,...,W An});

[0036] Step 6. Based on project requirements: ensuring relatively fair allocation of collectors' work, balancing collector manpower with project needs, and fully utilizing the efficiency of personnel in the designated area, scientifically transform these into solution evaluation indicators.

[0037] Step 7. Algorithm Optimization: The genetic algorithm is a heuristic algorithm. To prevent the local optimum of the path order in a single planning process from causing the region partitioning result, steps 2-6 are run m times, and the final region partitioning scheme is selected from n*m schemes.

[0038] This invention employs the aforementioned technical solution, which relates to a method for dividing the working area of ​​renewable resource recycling. This method first constructs a matrix coordinate system using information from all communities in the project. Then, within the matrix coordinate system, a locally optimal path L connecting all communities is obtained using the GA genetic path planning algorithm. Next, within the optimal path L, starting from points 1, 2, 3, ..., n, all community points in the optimal path L are allocated into k regions, forming {S1, S2, ..., S...} n Finally, an evaluation index is calculated for each partitioning scheme to obtain the optimal partitioning scheme.

[0039] The above-mentioned work area division method first uses a computer to select the optimal path L from all paths, and then calculates a division scheme for each community as the starting point on the optimal path L. After layers of selection, the optimal division scheme is obtained. The entire algorithm supports interactive capacity prediction and work area division, and comprehensively considers the average daily delivery volume W of each community, the cycling distance and time between communities, the average collection efficiency per trip, and the average time spent on cleaning, loading, and temporary storage. It scientifically divides the work areas of recyclable waste collectors, ensuring a balanced labor cost for project operation and fairness in labor allocation among collectors.

[0040] like Figure 1 and 2The diagram shows a specific implementation example of the above scheme. The first image is a matrix coordinate diagram constructed based on the information of all cells in the project, and the connecting lines in the diagram represent the shortest path (i.e., the optimal path L). In the... Figure 1 Based on the algorithm, the optimal solution is to start with point 36, add point 46, and check if the distance between 36 and 46 exceeds the constraint. If not, update the basic indicators in the point set: average weight collected per trip, average turnover distance per trip, and then update the maximum capacity according to the formula [the estimated capacity based on limited working time, limited work efficiency, riding speed, and time spent cleaning and packing temporary boxes after each collection]. Calculate the total daily delivery volume of points 36 and 46, and check if the maximum capacity is greater than the total daily delivery volume. If so, continue adding points 43... and updating the capacity of each point set, then comparing it with the total delivery volume until the total delivery volume is closest to the maximum capacity—producing the first red area. Figure 2 The central triangular area is [36, 46, 43, 7, ..., 47, 16, 22, 23, 38]; Then, starting from the next point 4, [4, 6, 45, ..., 5, 33, 1] is the blue area. Figure 2 The next rectangular block area; then the next one starting from point 11 [11, 20, 0, ..., 22, 30] is the orange area. Figure 2 (Middle cross-shaped area) [Note: No next point was added at 30 because the next point / small area delivery exceeded the maximum capacity]; then from 13 onwards [13, 15, 3, ..., 31, 8, 2] are green areas ( Figure 2 (Central Five-Pointed Star Area), all points have been assigned, end.

[0041] Example 2:

[0042] A second objective of the present invention is to provide a work area division system for recycling renewable resources, which performs the method described in Embodiment 1 above.

[0043] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0044] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A method for dividing work areas in the recycling of renewable resources, characterized in that: Includes the following steps: Step 1. Obtain information on all communities included in the project, including the number of communities n, the latitude and longitude of each community's geographical location, and the average daily delivery volume W for each community; use map software to obtain the electric vehicle riding distance d between every two communities. ij Based on distance d ij Data constructs a matrix coordinate system; Step 2. Within the matrix coordinate system constructed in Step 1, different sequences of cells are randomly generated according to the GA gene genetic path planning algorithm to form paths. Then, position mutation and path mutation are continuously performed. Based on fitness, the optimal path L is obtained through rapid iteration. Step 3. Initial capacity calculation for the area; based on the business collection data, calculate the average collection weight per trip as w0 (kg), the average collection efficiency per trip as e (kg / minute), and the average turnover speed of the tricycles between areas as v (km / h); based on the actual project schedule, the total working time is T hours; the average total time for collectors to clean, load, and temporarily store after each collection is t minutes; based on the distance matrix between areas and the optimal path L, calculate the average single turnover distance as d0 = avg(D L (km); the total initial human efficiency is calculated as Step 4. For the optimal path L, take the first point of the path as the initial point A1 = {a1}. If the total weight of garbage in the area is W (kg), add new points, i.e., sub-areas, according to the greedy rule. If the second point is within the constraints, then form a sub-area set A1 = {a1, a2}. Update the average collection weight w and average turnover distance d = avg(D) of the iterative set. A1 According to Formula 1, update the maximum efficiency of the set; repeatedly add points until the constraints are not met, then circuit breaker is applied to form a region, select the next point as the initial point, and repeat the process of adding points or circuit breaker; the constraints are: ① Maximum human efficiency constraint: W <C; ② Distance constraints between adjacent cells: d ij <d; Step 5. Repeat step 4 until all cell points in the optimal path L are assigned to k areas. This area allocation scheme is S1; ultimately, there is an area set {1,2,...,k} and a path set. Distance matrix set Human efficiency collection Demand weight set Step 6. Based on the optimal path L described above, sequentially select the 2nd, 3rd, ..., nth initial points, and repeat steps 4 and 5 to form a set of solutions {S1, S2, ..., Sn}. n }, calculate the total number of people k and the total maximum efficiency ∑{C} for each plan. A1 C A2 ,...,C An }, and the standard deviation of regional demand δ=std({W A1 W A2 ,...,W An }); Step 6. Based on project requirements: ensuring relatively fair allocation of collectors' work, balancing collector manpower with project needs, and fully utilizing the efficiency of personnel in the designated area, scientifically transform these into solution evaluation indicators. Step 7. Algorithm Optimization: The genetic algorithm is a heuristic algorithm. Steps 2-6 are run m times, and the final region partitioning scheme is selected from n*m schemes.

2. A work area division system for recycling renewable resources, characterized in that: Perform the method described in claim 1 above.

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