Ardealite supply and demand allocation and transportation path optimization method for filling in region

Abstract

The invention discloses a supply and demand allocation and transportation path optimization method for ardealite for filling in a region, which comprises the following steps: acquiring a to-be-allocated region of ardealite for filling, and establishing an ardealite supply and demand network directed graph in the region; on the basis of the network directed graph, constructing a supply and demand allocation and transportation path optimization model of ardealite for filling in the region; the optimization model comprises minimum transportation cost and maximum transportation flow; and iteratively searching the augmented chain by adopting a Ford-Fulkerson algorithm, searching an optimal path meeting the maximum transportation flow and the minimum transportation cost, and obtaining supply and demand allocation and transportation path optimization results of the ardealite for filling in the region. According to the method, the supply amount, the demand amount, the transportation distance, the transportation cost and the environmental constraint conditions of the ardealite are comprehensively considered, and an optimal allocation scheme and an optimal transportation path are generated by constructing a transportation allocation optimization model of the ardealite for filling.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation, and more particularly to a method for allocating supply and demand of phosphogypsum for filling in a region and optimizing the transportation route. Background Art

[0002] Currently, phosphogypsum is primarily stored on the surface in some areas, which not only consumes a significant amount of land resources but also poses significant safety risks due to instability and damage. Backfilling mined-out areas with phosphogypsum is the most effective way to dispose of phosphogypsum on a large scale and will be the primary method for its future large-scale disposal.

[0003] Although phosphogypsum filling technology has broad application prospects, its large-scale promotion still faces many challenges, one of which is the transportation and allocation of phosphogypsum. Traditional phosphogypsum transportation and allocation methods are usually based on experience and the principle of distribution based on proximity, without considering the supply and demand relationship of phosphogypsum in the entire region. This method often leads to high transportation costs, low efficiency, and even problems such as supply and demand mismatch and waste of resources. Some phosphogypsum depots have an excess supply but cannot be allocated to the demand points of phosphate mines in a timely manner, while other phosphogypsum depots cannot meet the filling needs of phosphate mines due to insufficient supply.

[0004] Therefore, providing a scientific method for optimizing the transportation and allocation of phosphogypsum to solve problems such as supply and demand mismatch and waste of resources is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method for supply and demand allocation and transportation route optimization of phosphogypsum for filling in a region, which can reduce costs while meeting the maximum transportation demand, improve the transportation efficiency of phosphogypsum, realize the combination of resource scheduling and waste reduction in the entire region, and reduce the adverse impact of phosphogypsum storage on the environment.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention provides a method for adjusting the supply and demand of phosphogypsum for filling in a region and optimizing the transportation route, comprising the following steps:

[0008] S1. Obtaining an area where phosphogypsum for filling is to be prepared, and establishing a directed graph of the phosphogypsum supply and demand network in the area;

[0009] S2. Based on the network directed graph, construct a supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region; the optimization model includes minimizing transportation costs and maximizing transportation flow;

[0010] S3. Use the Ford-Fulkerson algorithm to iteratively search the augmented chain to find the optimal path that meets the target conditions of maximum transportation flow and minimum transportation cost, and obtain the supply and demand allocation and transportation route optimization results of phosphogypsum for filling in the region.

[0011] Furthermore, step S1 specifically includes:

[0012] Obtaining an area where phosphogypsum for filling is to be prepared, wherein the area includes multiple demand points and multiple supply points;

[0013] Use spatial clustering algorithms to reduce the number of multiple demand points;

[0014] Establish connecting arcs between the reduced demand points and supply points;

[0015] Define virtual super source points and virtual super sink points as the starting point and end point of all connection arcs respectively, connect all connection arcs, and transform the multi-source and multi-sink problem into a single-source and single-sink problem;

[0016] Among them, the supply on each connecting arc is greater than zero and less than the maximum supply of the supply point; the supply of all supply points is greater than the demand of the demand point, and the demand is greater than zero.

[0017] Furthermore, the spatial clustering algorithm is used to reduce the number of multiple demand points, specifically including:

[0018] Obtain the latitude and longitude coordinates of multiple demand points, and perform standardization and coordinate adjustment;

[0019] Determine the optimal number of clusters, select an initial center point and ensure that the intra-class distance is minimized and the inter-class separation is maximized; continuously select cluster centers for iteration, repeatedly adjust cluster centers and assign data points until convergence;

[0020] The silhouette coefficient was used to evaluate the clustering effect and adjust the clustering results.

[0021] Furthermore, the directed graph of the phosphogypsum supply and demand network in the region is expressed as follows:

[0022] D=(V,A,C)

[0023] V={S,T}∪v i ∪v j

[0024] (v i ,v j )∈A

[0025] C=|c ij |

[0026] 0≤f ij ≤cij

[0027] Where D represents the directed graph of the supply and demand network, V represents the node set of the directed graph of the supply and demand network, A represents the node connection arc set, C represents the maximum flow set of the supply quantity constraints on each connection arc; S is the virtual super source point, T is the virtual super sink point, v i For each source node, v j is each sink node; c ij represents the maximum supply on the connecting arc; f ij It represents the feasible demand of each node.

[0028] Furthermore, the feasible demand of each node f ij , capacity constraint conditions and supply and demand balance conditions need to be met;

[0029] The capacity limitation condition is that the supply of phosphogypsum for filling in the region is much greater than the demand; the formula is:

[0030] 0≤f ij ≤c ij

[0031] The supply and demand balance condition includes the balance between the virtual super source and the virtual super sink. The net outflow of the virtual super source or the net inflow of the virtual super sink represents the total flow of the transportation network. The formula is:

[0032]

[0033] Where v(f) represents the total flow of the transportation network; f ij represents the forward flow from the source to the sink; f ji Indicates the reverse flow from the sink to the source.

[0034] Furthermore, maximizing the transport flow includes:

[0035] By solving the maximum flow rate of the supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region, the supply and demand of phosphogypsum can be matched;

[0036] The formula is:

[0037]

[0038] Where v(F) is the maximum flow that satisfies the constraints; i is the node type; s is the total number of supply points, t is the total number of demand points; F ij represents the dump truck flow from source to sink, F ji Represents the flow of dump trucks from a sink to a source.

[0039] Furthermore, minimizing transportation costs includes:

[0040] Under the constraint of maximizing transportation flow, the minimum transportation cost of the supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region is solved;

[0041] The formula is:

[0042]

[0043] Among them, b ij Indicates the transportation cost of a dump truck on a specific dispatch route; d i represents a feasible flow, v(F max ) represents the maximum flow value obtained by maximizing the transport flow, and the positive and negative signs indicate the direction.

[0044] Furthermore, step S3 specifically includes:

[0045] S31, constructing a residual network corresponding to the network directed graph and the original network; initializing each connection arc of the original network to zero, so that the remaining supply of each connection arc in the residual network is the maximum supply of the corresponding connection arc;

[0046] S32. Starting from the virtual super source point in the residual network, using a breadth-first search or a depth-first search method, in the form of a path of "virtual super source point - supply point - demand point - virtual super sink point", find a path to the virtual super sink point as an augmenting chain;

[0047] S33. After finding the augmented chain, add the supply to the corresponding connection arc according to the minimum residual capacity on the path, and update the residual network;

[0048] S34. Iteratively search for augmenting chains according to steps S31-S33 until no new augmenting chain is found; and obtain the optimal path with maximum transport flow and minimum transport cost in the original network.

[0049] Furthermore, the minimum residual capacity is represented by the maximum additional flow that the corresponding path can bear, and is expressed by the formula:

[0050]

[0051] Where μ represents the minimum residual capacity of the corresponding path.

[0052] Through the above technical solution, it can be seen that compared with the existing technology, the present invention discloses a method for adjusting the supply and demand of phosphogypsum for filling in a region and optimizing the transportation route, which has the following beneficial effects compared with the existing technology:

[0053] The present invention reduces the number of demand points and redundant nodes based on a spatial clustering algorithm, and combines it with the Ford-Fulkerson algorithm to dynamically search for the shortest path. While ensuring supply and demand matching, it improves the algorithm's computational efficiency and is suitable for rapid optimization of large-scale transportation networks.

[0054] The present invention adopts a dual-objective priority network flow model, prioritizes maximum flow and then optimizes transportation costs, thereby achieving global minimization and dynamic fair distribution of transportation costs.

[0055] At the same time, the method of the present invention is used to allocate costs based on the weight of the phosphogypsum demand of each phosphate mining enterprise, eliminating the unfairness caused by differences in transportation volume and transportation distance, directly linking cost allocation with demand, ensuring corporate cooperation, and contributing to the feasibility of the optimization plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0057] Figure 1 A flow chart of a method for adjusting the supply and demand of phosphogypsum for filling in a region and optimizing the transportation route is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0059] The embodiment of the present invention discloses a method for optimizing the supply and demand of phosphogypsum for filling in a region and the transportation route, referring to Figure 1 As shown, the following steps are included:

[0060] S1. Obtaining the area where phosphogypsum for filling is to be prepared and establishing a directed graph of the phosphogypsum supply and demand network in the area;

[0061] S2. Based on a directed network graph, a supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region is constructed; the optimization model includes minimizing transportation costs and maximizing transportation flow;

[0062] S3. Use the Ford-Fulkerson algorithm to iteratively search the augmented chain to find the optimal path that meets the target conditions of maximum transportation flow and minimum transportation cost, and obtain the supply and demand allocation and transportation route optimization results of phosphogypsum for filling in the region.

[0063] This embodiment takes the allocation of the total demand for phosphogypsum for filling in the phosphate mines of Yichang City and the total stockpile of phosphogypsum storage as the task, and adopts the phosphogypsum transportation allocation optimization scheme of the present invention to achieve the maximum transportation flow and minimum transportation cost of phosphogypsum allocation. This embodiment first establishes a directed graph of the phosphogypsum supply and demand network in the phosphate mines and phosphogypsum storage areas of Yichang City; defines the directed graph of the phosphogypsum supply and demand network and the feasible flow function in the area. Secondly, it constructs an optimization model for the supply and demand allocation and transportation path of phosphogypsum for filling in the area, configures the parameters of the network directed graph in the area, and initializes the residual network; sets the arc capacity from the virtual super source point to the source point as the flow on the corresponding source point; sets the arc capacity from the sink point to the virtual super sink point as the flow on the corresponding sink point; sets the initial flow of each arc to 0; adopts the Ford-Fulkerson algorithm to iteratively search the augmented chain, and finds the optimal path in the residual network that meets the target conditions of maximum flow and minimum total cost while conforming to a specific path form. The path form is "virtual super source point-supply point-demand point-virtual super sink point". Finally, after obtaining the optimal path, this embodiment further calculates the total transportation cost of phosphogypsum for filling in the region, and amortizes the phosphogypsum transportation cost based on the dynamic weight of the phosphogypsum demand of each phosphate mining enterprise.

[0064] This embodiment comprehensively considers the supply and demand of phosphogypsum, transportation distance, transportation costs, and environmental constraints. By constructing an optimization model for the transportation and allocation of phosphogypsum for filling, it generates an optimal allocation plan and transportation route. Furthermore, this embodiment is versatile, capable of generating optimized plans for different regions based on specific supply and demand points and different transportation networks, ensuring efficient and reliable transportation, improving transportation efficiency, and reducing transportation costs.

[0065] The specific steps for adjusting the supply and demand of phosphogypsum and optimizing the transportation route in this embodiment are described in detail below.

[0066] Step S1, establishing a directed graph D=(V, A, C) of the phosphogypsum supply and demand network in the phosphate mines and phosphogypsum storage areas of Yichang City; wherein D represents the directed graph of the supply and demand network.

[0067] V represents the node set of the directed graph of the supply and demand network, including the virtual super source S, the virtual super sink T, the supply point v i and demand point v j ; The formula is: V = {S, T} ∪ v i ∪v j .

[0068] A represents the node connection arc set. In this embodiment, the connection arcs between the supply and demand points are sorted based on the principle of proximity, including the connection arcs between the virtual super source point and each source point, which are represented as S→v i , the connection arcs between each source point and each sink point are represented by v i →v j , the connection arc from each sink to the virtual super sink is represented by v j →T.

[0069] C represents the maximum flow set of supply constraints on each connection arc, C = |c ij |.

[0070] In this embodiment, the virtual super source points and virtual super sink points added to the network directed graph convert the multi-source and multi-sink problem into a single-source and single-sink problem; the network directed graph also needs to consider the feasible flow problem on the path, that is, the transportation flow problem from the virtual super source point to the virtual super sink point, specifically the transportation flow problem on a single connection arc.

[0071] For the feasible demand f on a single connecting arc ij , capacity constraint conditions and supply and demand balance conditions need to be met;

[0072] Among them, the capacity limit is that the supply of phosphogypsum for filling in the region is much greater than the demand, and the supply on each connecting arc is greater than 0 and less than the maximum capacity of the supply point (i.e., the maximum supply), which is expressed as (v i ,v j )∈A,0≤f ij ≤c ij ;

[0073] The equilibrium condition is that for all supply points and demand points in the middle, the supply is always greater than the demand and the demand is greater than zero, that is, for the middle nodes:

[0074]

[0075] For the virtual super source S, we have:

[0076]

[0077] For the virtual super sink T, we have:

[0078]

[0079] Where v(f) represents the total flow of the transportation network; f ij represents the forward flow from the source to the sink; f ji It represents the reverse flow from the sink to the source. The positive and negative signs only indicate the direction of the feasible demand, and the feasible demand at the source always exists.

[0080] When calculating, when i is a virtual super source S, the net outflow of the super source is equal to the total flow v(f) of the entire network; when j is a virtual super sink T, the net inflow of the super sink is equal to the total flow v(f) of the entire network; i is the starting point of the flow; j is the end point of the flow; f ij represents the forward flow from i to j; f ji Indicates the reverse flow from j to i. In actual transportation, there is no case of phosphogypsum flowing back to the supply point. If the flow starting point is the demand point, the value is 0 in actual transportation; v i is the source point; v j is the sink; v(f) represents the total flow of the transportation network. Its positive or negative value only indicates the direction of the feasible demand, and the feasible demand at the source point always exists.

[0081] Before constructing or calculating a directed network graph, if the number of nodes is too large, spatial clustering can be used to reduce the number of nodes. This embodiment uses a spatial clustering algorithm to reduce the number of demand points.

[0082] The spatial clustering operation method of this embodiment includes the following steps:

[0083] The first step is to obtain the actual latitude and longitude coordinates of multiple demand points and perform Z-Score standardization in SPSS software.

[0084] The second step is to construct a spatial dataset L = {(lng k ,lat k )}, adjust the standardized coordinates and use the formula:

[0085]

[0086] Among them, lng' k is the adjusted longitude, lat' k is the adjusted latitude, lng k is longitude, lat k is the latitude, k is the number of demand points, μ lng and μ lat are the means of longitude and latitude respectively, σ lng and σ lat are the standard deviations of longitude and latitude, respectively.

[0087] The third step is to perform spatial clustering calculation on the latitude and longitude of the nodes.

[0088] First, determine the optimal number of clusters, K. This is determined based on experience and data distribution. Next, select initial centers. Randomly select K data points as initial cluster centers. Then, perform iterative optimization. Assign data points to the cluster with the closest cluster center. Update the centers by calculating the average of all data points in each cluster and using that average as the new cluster center. Repeat these steps until the cluster centers no longer change or the maximum number of iterations is reached.

[0089] Finally, the clustering results are evaluated by calculating the silhouette coefficient S, which measures the effectiveness of clustering; a larger S value indicates a better clustering effect. Validity is determined. In this example, if the S value is greater than or equal to 0.6, the clustering result is considered valid. If the S value is less than 0.6, the initial center point is adjusted and clustering is repeated. Through these steps, similar phosphate mines and phosphogypsum depots can be grouped together, simplifying the network diagram, reducing the number of nodes, and improving computational efficiency.

[0090] This embodiment determines the optimal number of clusters K, selects an initial center point and ensures that the intra-class distance is minimized and the inter-class separation is maximized; initializes K cluster centers, and iteratively optimizes the mining point allocation until convergence; calculates the silhouette coefficient S, and if S ≥ 0.6, the clustering result is determined to be valid; otherwise, the initial center point is adjusted and clustering is performed again.

[0091] Step S2: construct a supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region, configure the network directed graph parameters in the region, and initialize the residual network.

[0092] When configuring the network directed graph parameters in the region, set the arc capacity from the super source to the source as the flow rate on the corresponding source; set the arc capacity from the sink to the super sink as the flow rate on the corresponding sink; assign weight d to the directed edge. ij , where d ij Represents the corresponding node v i and v j The transportation distance between them.

[0093] This embodiment establishes a transportation and allocation optimization model for phosphogypsum for filling based on multi-source data to solve the two major problems of supply and demand matching and transportation route optimization, with the goal of minimizing transportation costs and maximizing transportation flow.

[0094] Among them, maximizing transportation flow includes solving the maximum flow of the phosphogypsum transportation allocation optimization model to ensure that the supply of phosphogypsum matches the demand;

[0095] The formula is:

[0096]

[0097] Where v(F) is the maximum flow that satisfies the constraints; i is the node type; s is the total number of supply points, t is the total number of demand points; F ij represents the dump truck flow from source to sink, F ji Represents the flow of dump trucks from a sink to a source.

[0098] Minimizing transportation costs is to find the minimum cost of the phosphogypsum transportation and allocation optimization model under the maximum flow constraint.

[0099] The formula is:

[0100]

[0101]

[0102] Among them, b ij The transportation cost of a dump truck on a specific dispatching route is expressed as the transportation unit price p and the transportation distance d. ij The product calculation of d i represents a feasible flow, and a feasible flow for all source points always exists; v(F max ) represents the maximum flow value obtained by maximizing the transport flow, and the positive and negative signs indicate the direction.

[0103] Get the minimum transportation cost b under the maximum flow v(F) that meets the constraints ij , and then the minimum transport distance d is obtained ij .

[0104] Step S3, based on the demand for phosphogypsum for filling in each actual phosphate mine, uses the Ford-Fulkerson algorithm to iteratively search the augmented chain method to solve the supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region constructed in S2, and obtains the supply and demand allocation and transportation route optimization results for phosphogypsum for filling in the region.

[0105] Step S3 specifically includes:

[0106] S31, set the initial flow, set the flow of all arcs to 0; build a residual network corresponding to the original network; the residual network is a copy of the original network, in which the capacity of each arc is the remaining capacity of the corresponding arc in the original network.

[0107] In step S32, a breadth-first search is performed in the network directed graph, starting from the virtual super source S and searching for an augmenting path from the source to the sink along the forward or reverse edges in the residual network. If no augmenting path can be reached to the virtual super sink T, the current flow rate F is output. If such a path exists, the algorithm proceeds to step S33; otherwise, the algorithm terminates and the current total flow rate is the maximum flow rate.

[0108] S33, find the minimum residual capacity μ on the augmenting path, that is, the maximum additional flow that the path can withstand:

[0109]

[0110] Among them, μ is the minimum capacity value of all edges on this path, and this value is the flow that can be increased.

[0111] Along this augmenting path, update the edges in the residual network: for each edge v on the path j →v i Reduce its residual capacity while increasing the reverse side v i →v j The amount of reduction here is the minimum residual capacity found in step S32, that is, the increased flow value. For example, if the original edge is forward, then its residual capacity is reduced by the flow, while the residual capacity of the reverse edge is increased by the same flow.

[0112] S34: Update the total flow, add the flow value found in step S33 to the total flow, and then return to step S32 to continue looking for the next augmenting path.

[0113] Finally, based on the optimal path and calculation results, t = 16 and s = 11 represent the demand points as numbers 1-16 and the supply points as numbers 17-27, and the total specific data is shown in Table 1:

[0114] Table 1

[0115]

[0116]

[0117] In this example, the total transportation cost of phosphogypsum for filling within the region is calculated. The calculation formula is:

[0118] P=k×p×∑d ij ×F ij

[0119] Where P is the total cost of transporting phosphogypsum for filling in the region; k is the load factor; p is the unit price of transportation; d ij v within the area measured by the GIS system i to v j Actual transport distance; F ij is the number of dump trucks on the road section.

[0120] Generate the flow set ∑c of the phosphogypsum transportation plan in the region ij Calculate the minimum total transportation cost P based on the product of the transportation unit price and the flow set on the shortest path, and return the total transportation cost of each transportation path v i →vj The distribution flow and total transportation distance are minimized.

[0121] Finally, the transportation cost of phosphogypsum is allocated based on the phosphogypsum demand of each phosphate mining enterprise.

[0122] Calculate the proportion of each company's phosphogypsum demand in the total demand; multiply the company's respective proportion by the total transportation cost to obtain the new cost plan after the equalization strategy. Specifically including:

[0123] 1) Investigate and obtain the demand for phosphogypsum q of each phosphate mining enterprise i ;

[0124] 2) Calculate the dynamic weight of demand where ∑q i is the total demand of multiple companies;

[0125] 3) The total transportation cost P is calculated by α i Allocated to each enterprise, the average transportation cost of the phosphate mine enterprise is P i =α i ×P.

[0126] This case takes into account the acceptance of the optimized plan by enterprises to ensure its feasibility. It adopts an equalization strategy to balance the transportation costs borne by each enterprise, which can provide a good economic foundation and sustainable development prospects for the use of phosphogypsum as filling.

[0127] The present invention promotes the comprehensive utilization of phosphogypsum resources, provides a scientific transportation and allocation solution for the comprehensive utilization of phosphogypsum resources, and helps promote the large-scale consumption and application of phosphogypsum resources. By reducing transportation costs and improving transportation efficiency, the cost of comprehensive utilization of phosphogypsum resources can be reduced, thereby improving economic benefits.

[0128] By optimizing the supply and demand allocation plan, this invention ensures the effective utilization of phosphogypsum resources, avoiding resource waste and supply-demand mismatches. Through a cost-sharing mechanism, it promotes collaboration among enterprises and jointly promotes the comprehensive utilization of phosphogypsum resources, achieving rational resource allocation. By finding the optimal path, it avoids congestion and detours during transportation, improving transportation efficiency. By simplifying the network diagram through a spatial clustering algorithm, it reduces the number of nodes and improves the algorithm's computational efficiency.

[0129] At the same time, by reducing the stockpiling of phosphogypsum, the occupation of land resources and environmental pollution can be reduced. By using phosphogypsum to fill goaf, the stability of goaf can be improved and the occurrence of geological disasters can be reduced. In summary, this application provides a scientific, efficient and fair phosphogypsum transportation and allocation plan, which is of great significance for promoting the comprehensive utilization of phosphogypsum resources, reducing transportation costs, improving transportation efficiency, achieving rational resource allocation and reducing environmental pollution.

[0130] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0131] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for adjusting the supply and demand of phosphogypsum for filling in a region and optimizing the transportation route, characterized in that: The following steps are involved: S1. Obtaining an area where phosphogypsum for filling is to be prepared, and establishing a directed graph of the phosphogypsum supply and demand network in the area; S2. Based on the network directed graph, construct a supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region; the optimization model includes minimizing transportation costs and maximizing transportation flow; S3. Use the Ford-Fulkerson algorithm to iteratively search the augmented chain to find the optimal path that meets the maximum transportation flow and minimum transportation cost, and obtain the supply and demand allocation and transportation route optimization results of phosphogypsum for filling in the region.

2. The method for optimizing the supply and demand allocation and transportation route of phosphogypsum for filling in a region according to claim 1, characterized in that: Step S1 specifically includes: Obtaining an area where phosphogypsum for filling is to be prepared, wherein the area includes multiple demand points and multiple supply points; Use spatial clustering algorithms to reduce the number of multiple demand points; Establish connecting arcs between the reduced demand points and supply points; Define virtual super source points and virtual super sink points as the starting point and end point of all connection arcs respectively, connect all connection arcs, and transform the multi-source and multi-sink problem into a single-source and single-sink problem; Among them, the supply on each connecting arc is greater than zero and less than the maximum supply of the supply point; the supply of all supply points is greater than the demand of the demand point, and the demand is greater than zero.

3. The method for optimizing the supply and demand allocation and transportation route of phosphogypsum for filling in a region according to claim 2, characterized in that: The spatial clustering algorithm is used to reduce the number of multiple demand points, specifically including: Obtain the latitude and longitude coordinates of multiple demand points, and perform standardization and coordinate adjustment; Determine the optimal number of clusters, select an initial center point and ensure that the intra-class distance is minimized and the inter-class separation is maximized; continuously select cluster centers for iteration, repeatedly adjust cluster centers and assign data points until convergence; The silhouette coefficient was used to evaluate the clustering effect and adjust the clustering results.

4. The method for optimizing the supply and demand allocation and transportation route of phosphogypsum for filling in a region according to claim 1, characterized in that: The directed graph of the phosphogypsum supply and demand network in the region is expressed as follows: D=(V,A,C) V={S,T}∪v i ∪v j (v i ,v j )∈A C=|c ij | 0≤f ij ≤c ij Where D represents the directed graph of the supply and demand network, V represents the node set of the directed graph of the supply and demand network, A represents the node connection arc set, C represents the maximum flow set of the supply quantity constraints on each connection arc; S is the virtual super source point, T is the virtual super sink point, v i For each source node, v j is each sink node; c ij represents the maximum supply on the connecting arc; f ij It represents the feasible demand of each node.

5. The method for optimizing the supply and demand allocation and transportation route of phosphogypsum for filling in a region according to claim 4, characterized in that: The feasible demand f of each node ij , capacity constraint conditions and supply and demand balance conditions need to be met; The capacity limitation condition is that the supply of phosphogypsum for filling in the region is much greater than the demand; the formula is: 0≤f ij ≤c ij The supply and demand balance condition includes the balance between the virtual super source and the virtual super sink. The net outflow of the virtual super source or the net inflow of the virtual super sink represents the total flow of the transportation network. The formula is: Where v(f) represents the total flow of the transportation network; f ij represents the forward flow from the source to the sink; f ji Indicates the reverse flow from the sink to the source.

6. The method for optimizing the supply and demand allocation and transportation route of phosphogypsum for filling in a region according to claim 1, characterized in that: The maximization of transport flow includes: By solving the maximum flow rate of the supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region, the supply and demand of phosphogypsum can be matched; The formula is: Where v(F) is the maximum flow that satisfies the constraints; i is the node type; s is the total number of supply points, t is the total number of demand points; F ij represents the dump truck flow from source to sink, F ji Represents the flow of dump trucks from a sink to a source.

7. A method for adjusting supply and demand of phosphogypsum for filling in a region and optimizing transportation routes according to claim 6, characterized in that: The minimization of transportation costs includes: Under the constraint of maximizing transportation flow, the minimum transportation cost of the supply and demand allocation and transportation route optimization model for phosphogypsum for filling in the region is solved; The formula is: Among them, b ij Indicates the transportation cost of a dump truck on a specific dispatch route; d i represents a feasible flow, v(F max ) represents the maximum flow value obtained by maximizing the transport flow, and the positive and negative signs indicate the direction.

8. The method for optimizing the supply and demand allocation and transportation route of phosphogypsum for filling in a region according to claim 1, characterized in that: Step S3 specifically includes: S31, constructing a residual network corresponding to the network directed graph and the original network; initializing each connection arc of the original network to zero, so that the remaining supply of each connection arc in the residual network is the maximum supply of the corresponding connection arc; S32. Starting from the virtual super source point in the residual network, using a breadth-first search or a depth-first search method, find a path to the virtual super sink point in the form of "virtual super source point - supply point - demand point - virtual super sink point" as an augmenting chain; S33. After finding the augmented chain, add the supply to the corresponding connection arc according to the minimum residual capacity on the path, and update the residual network; S34. Iteratively search for augmenting chains according to steps S31-S33 until no new augmenting chain is found; and obtain the optimal path with maximum transport flow and minimum transport cost in the original network.

9. The method for adjusting supply and demand of phosphogypsum for filling in a region and optimizing transportation routes according to claim 8, characterized in that: The minimum residual capacity is expressed as the maximum additional flow that the corresponding path can bear, and is expressed as follows: Where μ represents the minimum residual capacity of the corresponding path.

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