Optimal path planning for medical waste recycling scheduling system based on A* algorithm

Abstract

The present invention discloses an optimal path planning and scheduling system for medical waste recycling based on an A* algorithm. The system comprises: a sensor module disposed in a trash bin, for obtaining information on the current status, remaining capacity, and waste inflow rate of the medical waste recycling bin; a positioning module disposed on the trash bin and recycling vehicle, for obtaining real-time location information of the medical waste recycling bin and recycling vehicle; and a recycling scheduling module, for determining an optimal scheduling plan based on the current status information of the medical waste recycling bin and, when a waste bin in a waiting state that meets recycling conditions exists, controlling the medical waste recycling vehicle and the trash bin to perform medical waste recycling scheduling. The system of the present invention is capable of dynamic path planning, timely adjusting the scheduling plan for newly appearing medical waste bins that meet recycling conditions, and providing an optimal path after comprehensively considering multi-dimensional costs.

Description

Technical Field

[0001] The present invention relates to medical waste recycling technology, and in particular to an optimal path planning medical waste recycling scheduling system based on an A* algorithm. Background Art

[0002] Medical waste is a special type of hazardous waste. China lists it as No. 1 hazardous waste in its "List of Hazardous Wastes," demonstrating its hazard. The recycling and transportation of medical waste has become a global concern. Therefore, improving the medical waste recycling and transportation system is crucial for both preventing large-scale health and safety incidents and effectively mitigating environmental risks.

[0003] The recycling of medical waste has more special requirements due to the potential safety hazards it poses. If it cannot be recycled and transported in time, it is likely to cause secondary pollution. However, frequent recycling at fixed times and locations will lead to waste of resources. How to improve the medical waste recycling and scheduling system, improve the recycling and transportation capacity of medical waste, reduce energy consumption in the recycling process, and reduce recycling costs is an urgent problem that needs to be solved.

[0004] The existing garbage collection scheduling system based on path planning algorithm has the disadvantages of low execution efficiency, poor optimization effect, easy combinatorial explosion when the node scale is too large (such as the large number of medical waste recycling bins in this system), and inability to perform dynamic path planning through real-time information collection of each node. It is difficult to be put into real-time application scenarios and cannot achieve the effect of energy conservation and emission reduction. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a medical waste recycling scheduling system with optimal path planning based on the A* algorithm in response to the defects in the existing technology.

[0006] The technical solution adopted by the present invention to solve the technical problem is: an optimal path planning medical waste recycling scheduling system based on the A* algorithm, comprising:

[0007] A sensor module is provided in the trash can, for obtaining information on the current state, remaining capacity, and waste input rate of the medical waste recycling trash can; the current state of the medical waste recycling trash can includes a waiting state that meets recycling conditions and a non-recycling state;

[0008] Positioning modules installed on trash bins and recycling vehicles are used to obtain real-time location information of medical waste recycling bins and recycling vehicles;

[0009] The recycling scheduling module is used to control the medical waste recycling vehicles and garbage bins to schedule medical waste recycling based on the current status information of the medical waste recycling bins. When there are garbage bins in a waiting state that meet the recycling conditions, the optimized A* algorithm is used to obtain the optimal scheduling plan.

[0010] The optimized A* algorithm is used to obtain the optimal scheduling solution, which is as follows:

[0011] 1) A multi-objective topological mapping method was used to build a two-dimensional model of the working environment of the recycling vehicle, and a multi-objective topological map was obtained by mapping the real environment where the medical waste recycling vehicle is located;

[0012] 2) Record the geographical locations of the waste bins that meet the recycling conditions and are in a state of being recycled in the multi-target topology map and mark them as key nodes; set the medical waste recycling station as both the source node and the target node;

[0013] 3) Preprocess the multi-objective topology map, set prohibited sections, and remove prohibited sections from the multi-objective topology map to simplify the path scale;

[0014] 4) Marking common nodes in the pre-processed multi-target topology graph, where the common nodes are node locations with two or more selectable paths;

[0015] 5) Use the optimized A* algorithm to obtain the optimal scheduling solution.

[0016] According to the above scheme, the optimized A* algorithm is used in step 5 to obtain the optimal scheduling scheme, which is as follows:

[0017] 5.1) Set the total number of nodes in the topology graph to N. All nodes include key nodes, common nodes, source nodes, and target nodes. The source node is represented by n s , the target node is n g , the key node is n k1 、n k2 、n k3 ..., ordinary nodes are n1, n2, n3...;

[0018] 5.2) Let the total number of key nodes be k, introduce the concept of state, denoted as s, and use a k-bit binary array to represent the state of the key node, where the k-th bit of the state s is represented by s[k]. When the k-th bit of the binary array is 1, that is, s[k] = 1, it means that the k-th key node has been passed, that is, the k-th medical waste bin that meets the recycling conditions has been recycled; when the k-th bit of the binary array is 0, that is, s[k] = 0, it means that the k-th medical waste bin that meets the recycling conditions has not been recycled. The set U S Indicates the set of trash bins that have not been recycled in state s;

[0019] 5.3) Considering the multi-dimensional cost, construct the heuristic function p = price(n, s); the heuristic function p represents the process of starting from the current node n, passing through all the key nodes that have not been passed, and finally reaching the target node n. g The minimum cost p is that the medical waste recycling vehicle starts from the current location, collects all the garbage bins that meet the recycling conditions, and finally returns to the recycling station at the minimum cost p;

[0020] 5.4) For each node n, if the recycling state is s i , establish a set T n,s , set T n,s Used to store the cost vectors that are not dominated by all the determined paths from the starting node to the current node n during the entire calculation process;

[0021] Define the medical waste recycling truck starting from the recycling station and select the set T n,s The minimum cost of a certain path to reach the current recycling state is d;

[0022] Define each node-state-cost triplet (n, s i , d) is a group of nodes that also start from the starting state s Departure, the state is s when arriving at node n i A set of paths with a cost of d, that is, a set of paths that the medical waste recycling vehicle starts from the recycling station and reaches the current recycling state with a total cost of d;

[0023] 5.5) Set the estimated cost e = d + price (n, s),

[0024] price(n, s) is the minimum cost in the heuristic function, and the estimated cost e is used to estimate the minimum cost of the medical waste recycling truck starting from the recycling station and returning to the recycling station after collecting all the garbage bins that meet the recycling conditions according to the above path scheduling plan (n, s, d);

[0025] Perform iterative calculations on node n. In each iteration, select the node-state-cost triplet (n, s, d) with the minimum estimated cost e, and repeat this cycle until the target set T is obtained. ng,sg , and then reconstruct the path, and finally obtain the Pareto optimal solution, which is the optimal path for medical waste recycling scheduling.

[0026] According to the above scheme, in step 5.3), the multi-dimensional cost is comprehensively considered to construct a heuristic function p=price(n, s); the calculation method of the heuristic function p is as follows:

[0027] Let i represent the number of dimensions of the cost, dist[n1][n2][i] represent the minimum cost between node n1 and node n2 when only the i-th dimension cost is considered, and dist[n1][n2] represent the minimum cost between node n1 and node n2 when multiple dimensions of cost are considered;

[0028] Assume that the initial value of the ideal price is p0, that is, the cost of the garbage collection vehicle when it is ready to start from the current node is p0. The ideal price p = price(n, s), which is the initial value p0 plus the cost of starting from the current node n, passing through all the key nodes that have not been passed, and finally arriving at n. g The minimum cost is the minimum cost for the medical waste recycling vehicle to start from the current location, collect all the garbage bins that meet the recycling conditions, and finally return to the recycling station;

[0029] There are several situations:

[0030] If the set |Us|=0, that is, all the garbage bins that meet the recycling conditions have been recycled, the ideal cost is dist[n][n g ];

[0031] If |Us|=1, let the node in Us be u, that is, the uth trash can that meets the recycling conditions has not been recycled. At this time, the ideal cost is dist[n][u]+dist[u][n g ], that is, the minimum cost for the medical waste recycling vehicle to start from the current node, collect the uth trash can, and then return to the recycling station;

[0032] If |Us|=2, let the nodes in Us be u and v, that is, the u-th and v-th garbage bins that meet the recycling conditions have not been recycled. By comparing the only two possible paths, that is, the two routes where the medical waste recycling vehicle starts from the current node, collects the u-th and v-th garbage bins and then returns to the recycling station, the smaller of the two total costs is taken as the ideal cost;

[0033] If |Us|≥3, that is, the number of garbage bins that meet the recycling conditions but are not recycled is greater than 3, and the enumeration method is too complicated to calculate, then for each dimension i∈{1,...,M}, the minimum total cost of connecting all key nodes in Us is calculated by solving the minimum spanning tree MST problem. Finally, the ideal cost is the minimum cost of node n to MST, plus the target node n. g The minimum cost to the MST and the total cost within the MST.

[0034] According to the above scheme, the comprehensive consideration of multi-dimensional costs is to comprehensively consider the factors of four dimensions: path length, current average travel time, number of intersections and driver's familiarity with the path.

[0035] The beneficial effects produced by the present invention are:

[0036] 1) The optimized A* path planning algorithm can comprehensively consider multiple factors. The optimal path is no longer just the shortest path, but the path with the lowest sum of multiple costs.

[0037] 2) Dynamic path planning can update the optimal path for newly emerging medical waste bins that meet recycling conditions and adjust the scheduling plan in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0039] Figure 1 It is a structural diagram of an embodiment of the present invention;

[0040] Figure 2 It is a flow chart of a scheduling solution method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] like Figure 1 As shown, an optimal path planning medical waste recycling scheduling system based on the A* algorithm includes:

[0043] A sensor module is provided in the trash can, for obtaining information on the current state, remaining capacity, and waste input rate of the medical waste recycling trash can; the current state of the medical waste recycling trash can includes a waiting state that meets recycling conditions and a non-recycling state;

[0044] Positioning modules installed on trash bins and recycling vehicles are used to obtain real-time location information of medical waste recycling bins and recycling vehicles;

[0045] The recycling scheduling module is used to control the medical waste recycling vehicles and garbage bins to schedule medical waste recycling based on the current status information of the medical waste recycling bins. When there are garbage bins in a waiting state that meet the recycling conditions, the optimized A* algorithm is used to obtain the optimal scheduling plan.

[0046] The optimized A* algorithm is used to obtain the optimal scheduling solution, such as Figure 2 , as follows:

[0047] 1) A multi-objective topological mapping method was used to build a two-dimensional model of the working environment of the recycling vehicle, and a multi-objective topological map was obtained by mapping the real environment where the medical waste recycling vehicle is located;

[0048] 2) Record the geographical locations of the waste bins that meet the recycling conditions and are in a state of being recycled in the multi-target topology map and mark them as key nodes; set the medical waste recycling station as both the source node and the target node;

[0049] 3) Preprocess the multi-objective topology map, set prohibited sections, and remove prohibited sections from the multi-objective topology map to simplify the path scale;

[0050] 4) Marking common nodes in the pre-processed multi-target topology graph, where the common nodes are node locations with two or more selectable paths;

[0051] 5) Use the optimized A* algorithm to obtain the optimal scheduling solution, as follows:

[0052] 5.1) Set the total number of nodes in the topology graph to N. All nodes include key nodes, common nodes, source nodes, and target nodes. The source node is represented by n s , the target node is n g , the key node is n k1 、n k2 、n k3 ..., ordinary nodes are n1, n2, n3...;

[0053] 5.2) Let the total number of key nodes be k, introduce the concept of state, denoted as s, and use a k-bit binary array to represent the state of the key node, where the k-th bit of the state s is represented by s[k]. When the k-th bit of the binary array is 1, that is, s[k] = 1, it means that the k-th key node has been passed, that is, the k-th medical waste bin that meets the recycling conditions has been recycled; when the k-th bit of the binary array is 0, s[k] = 0, it means that the k-th medical waste bin that meets the recycling conditions has not been recycled. The set U S Indicates the set of trash bins that have not been recycled in state s;

[0054] 5.3) Considering multi-dimensional costs (such as path length, traffic congestion, number of intersections, etc.), construct a heuristic function p = price(n, s);

[0055] i represents the number of dimensions of the cost. For example, in the present invention, i = 4, considering the four dimensions of path length, current average travel time, number of intersections, and driver familiarity with the path. dist[n1][n2][i] represents the minimum cost between node n1 and node n2 when only the i-th dimension cost is considered. dist[n1][n2] represents the minimum cost between node n1 and node n2 when multiple dimensions of cost are considered.

[0056] The specific calculation method is as follows: Assume that the initial value p0 of the ideal price is 0, that is, the cost of the garbage collection vehicle when it is ready to start from the current node is set to 0. The ideal price p = price (n, s), which is the initial value p0 plus the cost of starting from the current node n, passing through all the key nodes that have not been passed, and finally reaching n g The minimum cost is the minimum cost for the medical waste recycling truck to start from the current location, collect all the garbage bins that meet the recycling conditions, and finally return to the recycling station. There are the following cases: If |Us|=0, |Us| represents the number of elements in the set Us, that is, all the garbage bins that meet the recycling conditions are collected, the ideal cost is dist[n][n g ]; If |Us|=1, let the node in Us be u, that is, the uth trash can that meets the recycling conditions has not been recycled. At this time, the ideal cost is dist[n][u]+dist[u][n g ], that is, the minimum cost for the medical waste recycling truck to start from the current node and collect the uth garbage bin and then return to the recycling station; if |Us| = 2, let the nodes in Us be u and v, that is, the uth and vth garbage bins that meet the recycling conditions have not been collected. By comparison, there are only two possible paths n→u→v→n g and n→v→u→n g , that is, the medical waste recycling truck starts from the current node, collects the uth and vth garbage bins, and then returns to the recycling station. The smaller of the two routes is taken as the ideal cost; if |Us|≥3, that is, the number of garbage bins that meet the recycling conditions but are not recycled is greater than 3, and the enumeration method is too complicated to calculate, then for each dimension i∈{1,...,M}, the minimum total cost connecting all key nodes in Us is calculated by solving the minimum spanning tree (MST) problem. Finally, the ideal cost is the minimum cost of node n to MST, plus the cost of the target node n. g The minimum cost to the MST and the total cost within the MST;

[0057] 5.4) For each node n, if the recycling state is s i , establish a set T n,s , set T n,sIt is used to store the cost vectors of all the determined paths from the starting node to the current node n that are not absolutely dominant during the entire operation. For this scheduling system, it is necessary to comprehensively consider multi-dimensional costs. If there is a recycling path L1 that is better than another recycling path L2 in every dimension that needs to be considered, then the cost vector of recycling path L2 is said to be absolutely dominant, that is, the cost vector of L1 is absolutely better than L2. Therefore, it is defined that the medical waste recycling truck starts from the recycling station and selects a determined path that meets the above requirements to achieve the minimum cost of the current recycling state is d;

[0058] Define each node-state-cost triplet (n, s i , d) is a group of nodes that also start from the starting state s Departure, the state is s when arriving at node n i A set of paths with a cumulative cost of d, i.e., a set of paths that a medical waste recycling vehicle takes from a recycling station to the current recycling state with a total cost of d. The cumulative cost includes the path length, travel time, fuel consumption of the recycling vehicle, emissions, etc. (which can be increased or decreased according to demand), accumulated in a certain proportion.

[0059] Set the estimated cost e=d+price(n,s),

[0060] price(n, s) is the minimum cost mentioned in the above heuristic function. This formula is used to estimate the minimum cost of a medical waste recycling truck starting from the recycling station and returning to the recycling station after collecting all eligible waste bins according to the above route scheduling plan (n, s, d);

[0061] Perform iterative calculations on node n. In each iteration, select the node-state-cost triplet (n, s, d) with the minimum estimated cost e, and repeat this cycle until the target set T is obtained. ng,sg , and then reconstruct the path, and finally obtain the Pareto optimal solution, which is the optimal path for medical waste recycling scheduling.

[0062] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. An optimal path planning medical waste recycling scheduling system based on A* algorithm, characterized by: include: A sensor module installed in the trash can is used to obtain information about the current status, remaining capacity, and waste input rate of the medical waste recycling trash can; The current status of the medical waste recycling bin includes a waiting-for-recycling status that meets recycling conditions and a non-recycling status; Positioning modules installed on trash bins and recycling vehicles are used to obtain real-time location information of medical waste recycling bins and recycling vehicles; The recycling scheduling module is used to control the medical waste recycling vehicles and garbage bins to schedule medical waste recycling based on the current status information of the medical waste recycling bins. When there are garbage bins in a waiting state that meet the recycling conditions, the optimized A* algorithm is used to obtain the optimal scheduling plan. The optimized A* algorithm is used to obtain the optimal scheduling solution, as follows: 1) A multi-objective topological mapping method was used to build a two-dimensional model of the working environment of the recycling vehicle, and a multi-objective topological map was obtained by mapping the real environment in which the medical waste recycling vehicle was located; 2) Record the geographical locations of the waste bins that meet the recycling conditions and are in a waiting state in the multi-target topology map and mark them as key nodes; set the medical waste recycling station as both the source node and the target node; 3) Preprocess the multi-objective topology map, set prohibited sections, and remove prohibited sections from the multi-objective topology map to simplify the path scale; 4) Marking common nodes in the pre-processed multi-objective topology graph, where the common nodes are node locations with two or more selectable paths; 5) Use the optimized A* algorithm to obtain the optimal scheduling solution.

2. The optimal path planning medical waste recycling scheduling system based on the A* algorithm according to claim 1 is characterized in that: In step 5), the optimized A* algorithm is used to obtain the optimal scheduling solution, as follows: 5.1) Let the total number of nodes in the topology be N. All nodes include key nodes, common nodes, source nodes, and target nodes. The source node is represented by n. s , the target node is n g , the key node is n k1 、n k2 、n k3 ..., ordinary nodes are n1, n2, n3...; 5.2) Let the total number of key nodes be k, introduce the concept of state, denoted as s, and use a k-bit binary array to represent the state of the key node, where the k-th bit of the state s is represented by s[k]. When the k-th bit of the binary array is 1, that is, s[k]=1, it means that the k-th key node has been passed, that is, the k-th medical waste bin that meets the recycling conditions has been recycled; when the k-th bit of the binary array is 0, s[k]=0, it means that the k-th medical waste bin that meets the recycling conditions has not been recycled. The set U S Represents the set of trash bins that have not been recycled in state s; 5.3) Considering the multi-dimensional cost, construct the heuristic function p = price (n, s); the heuristic function p represents the process of starting from the current node n, passing through all the key nodes that have not been passed, and finally reaching the target node. The minimum cost p is that the medical waste recycling vehicle starts from the current location, collects all the garbage bins that meet the recycling conditions, and finally returns to the recycling station at the minimum cost p; 5.4) For each node n, if the recycling state is s i , create a collection ,gather Used to store the cost vectors that are not dominated by all the determined paths from the starting node to the current node n during the entire calculation process; Define the medical waste recycling truck to start from the recycling station and select the collection The minimum cost of a certain path to reach the current recycling state is d; Define each node-state-cost triplet (n, s i , d) is a group of nodes that also start from the starting state Departure, the state is s when arriving at node n i A set of paths with a cost of d, that is, a set of paths that the medical waste recycling vehicle starts from the recycling station and reaches the current recycling state with a total cost of d; 5.5) Set the estimated cost e = d + price(n,s), Price (n, s) is the minimum cost in the heuristic function, and the estimated cost e is used to estimate the minimum cost of the medical waste recycling truck starting from the recycling station and returning to the recycling station after collecting all the garbage bins that meet the recycling conditions according to the above-mentioned (n, s, d) path scheduling plan; Perform iterative calculations on node n. In each iteration, select the node-state-cost triplet (n, s, d) with the minimum estimated cost e, and repeat this cycle until the target set is obtained. , and then reconstruct the path, and finally obtain the Pareto optimal solution, which is the optimal path for medical waste recycling scheduling.

3. The optimal path planning medical waste recycling scheduling system based on the A* algorithm according to claim 2 is characterized in that: In step 5.3), the multi-dimensional cost is comprehensively considered to construct the heuristic function p = price (n, s); the calculation method of the heuristic function p is as follows: Let i represent the number of dimensions of the cost, dist[n1][n2][i] represent the minimum cost between node n1 and node n2 when only the i-th dimension cost is considered, and dist[n1][n2] represent the minimum cost between node n1 and node n2 when multiple dimensions of cost are considered; Assume that the initial value of the ideal price is p0, that is, the cost of the garbage collection vehicle when it is ready to start from the current node is p0. The ideal price p = price (n, s), which is the initial value p0 plus the cost of starting from the current node n, passing through all the key nodes that have not been passed, and finally arriving at n. g The minimum cost is the minimum cost for the medical waste recycling vehicle to start from the current location, collect all the garbage bins that meet the recycling conditions, and finally return to the recycling station; There are several situations: If the set |Us|=0, that is, all garbage bins that meet the recycling conditions have been recycled, the ideal cost is dist[n][n g ]; If |Us|=1, let the node in Us be u, that is, the u-th garbage bin that meets the recycling conditions has not been recycled. At this time, the ideal cost is dist[n][u] +dist[u][n g ], that is, the minimum cost for the medical waste recycling vehicle to start from the current node, collect the uth trash can, and then return to the recycling station; If |Us|=2, let the nodes in Us be u and v, that is, the u-th and v-th garbage bins that meet the recycling conditions have not been recycled. By comparing the only two possible paths, that is, the two routes where the medical waste recycling vehicle starts from the current node, collects the u-th and v-th garbage bins and then returns to the recycling station, the smaller of the two total costs is taken as the ideal cost; If |Us|≥3, that is, the number of garbage bins that meet the recycling conditions but are not recycled is greater than 3, and the enumeration method is too complicated to calculate, then for each dimension i∈{1,...,M}, the minimum total cost of connecting all key nodes in Us is calculated by solving the minimum spanning tree MST problem. Finally, the ideal cost is the minimum cost of node n to MST, plus the target node n. g The minimum cost to the MST and the total cost within the MST.

4. The optimal path planning medical waste recycling scheduling system based on the A* algorithm according to claim 3 is characterized in that: Comprehensive consideration of multi-dimensional costs is to comprehensively consider factors in four dimensions: path length, current average travel time, number of intersections and driver's familiarity with the path.

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