Automatic station planning method for multiple portal cranes and hoppers and storage medium

Abstract

The invention provides a multi-portal crane and hopper automatic station planning method and a storage medium, and belongs to the technical field of port portal crane automatic collaboration, and the method comprises the following steps: S1, obtaining information of portal cranes and hoppers through data acquisition equipment; s2, the material height of each cabin of the cargo ship is obtained through radar scanning, and distribution information of cabin materials is collected; s3, constructing an assignment problem model, and obtaining an optimal scheduling relationship between the portal crane and the cabin; and S4, considering adjacent portal crane station constraints and portal crane boom amplitude variation limitation, and by taking minimization of transportation time as a target, constructing an optimization model to calculate the optimal station of the portal crane and the hopper. And the optimal stations of the portal crane and the hopper are solved by constructing and assigning a problem machine model and a Gurobi optimization model.

Description

Technical Field

[0001] The present invention relates to the technical field of port portal crane automation collaboration, and particularly to a multi-portal crane and hopper automatic station planning method and storage medium. Background Art

[0002] Portal cranes are important shore equipment in ports. With the continuous development of the economy, the cargo flow is also increasing continuously, and the rapid growth of business has also put forward new requirements for the development of modern ports. The automation of portal cranes has become the primary task of port informatization.

[0003] In the process of port material transportation, the positions of portal cranes and their hoppers are crucial for improving transportation efficiency and reducing transportation time. Traditional portal crane and hopper positioning methods often rely on experience or fixed patterns, and do not fully consider the distribution of materials in the cargo hold, the complexity of the transportation route, and the operating efficiency of portal cranes and hoppers. Therefore, there are problems such as long transportation time and low efficiency.

[0004] Therefore, it is very necessary to provide a multi-portal crane and hopper automatic station planning method to obtain the portal crane track, the size of each mechanism of the portal crane, and the station range, construct the scheduling planning relationship between the portal crane and the cargo hold, minimize the transportation time, and improve the loading efficiency. Summary of the Invention

[0005] In view of this, the present invention proposes a multi-portal crane and hopper automatic station planning method and storage medium that, after obtaining information on multiple portal cranes and cargo hold materials, reasonably constructs a problem model according to the material distribution and the positions of the portal cranes, and considers the safety requirements and luffing limitations of adjacent portal crane operations to improve the material transportation efficiency.

[0006] The technical solution of the present invention is implemented as follows:

[0007] On the one hand, the present invention provides a multi-portal crane and hopper automatic station planning method, including the following steps:

[0008] S1: Obtain information on portal cranes and hoppers through data acquisition devices;

[0009] S2: Obtain the material height of each cargo hold of the cargo ship through radar scanning, and collect the distribution information of the cargo hold materials;

[0010] S3: Construct an assignment problem model to obtain the optimal scheduling relationship between portal cranes and cargo holds;

[0011] S4: Consider the adjacent portal crane station constraints and the luffing limitations of the portal crane boom, and construct an optimization model with the goal of minimizing the transportation time to calculate the optimal positions of portal cranes and hoppers.

[0012] On the basis of the above technical solutions, preferably, the content of step S1 is to obtain information including the gantry crane name, central coordinates, gantry crane track range, sizes of various mechanisms of the gantry crane, hopper size, and hopper station range by setting corresponding data acquisition devices on the gantry crane.

[0013] Preferably, the specific content of step S3 is: Let n be the number of alternative gantry cranes, and r i be the maximum luffing value of the i-th gantry crane, i = 1, 2,..., n; m represents the number of holds to be unloaded; q j represents the unloading volume obtained according to the material height of the j-th hold, j = 1, 2,....m; d ij represents the Euclidean distance between the gantry crane and the center of the hold when the i-th gantry crane unloads the material of the j-th hold; p represents the required number of gantry cranes to be determined, p ∈ {m,..., n}; x ij = 1 indicates that the j-th hold is operated by the i-th gantry crane, x ij ∈ {0, 1}; y i = 1 indicates that the i-th gantry crane is enabled, y i ∈ {0, 1};

[0014] When obtaining the optimal scheduling relationship between the gantry crane and the hold, the following first constraint conditions are satisfied:

[0015] 1) Assume that in the initial state, the gantry cranes are arranged in sequence on the gantry crane track;

[0016] 2) Each hold uses one or two gantry cranes for unloading, or does not unload, that is and When, it satisfies x ij + x kj ≤ 1 + |i - k|, k = 1, 2,..., n, k ≠ i, i and k represent the i-th gantry crane and the k-th gantry crane;

[0017] 3) Select p gantry cranes,

[0018] 4) Ensure that the gantry cranes that do not operate are not assigned to any holds, x ij ≤ y i ;

[0019] 5) The gantry crane luffing constraint is satisfied as d ij x ij ≤ r i ;

[0020] 6) The goal of the scheduling relationship between the gantry crane and the hold is:

[0021] Further preferably, in step S3, a tabu search algorithm is used to solve the optimal scheduling relationship between the gantry crane and the hold, which specifically includes:

[0022] (a) Given the parameters of the tabu search algorithm, randomly generate an initial solution and set the tabu list to be empty;

[0023] (b) Use the neighborhood function of the initial solution to generate all neighborhood solutions and determine several candidate solutions from the neighborhood solutions;

[0024] (c) Select the optimal candidate solution from the candidate solutions, then compare the optimal state caused by this optimal solution with the optimal state caused by the tabu solution to select the optimal solution, and use the optimal solution as the new initial solution, and finally add it to the tabu list; if the optimal solution is a tabu solution, then update the tabu tenure;

[0025] (d) If all solutions are tabu, directly select the best value among the tabu solutions as the new initial solution, and update the tabu list after selecting the new initial solution;

[0026] (e) If the tabu list is empty, directly take the optimal value among several candidate solutions as the new initial solution, and update the tabu list after selecting the new initial solution;

[0027] (f) Compare the state corresponding to the optimal solution with the current optimal solution state. If the state corresponding to the optimal solution is better than the current optimal solution state, select the state corresponding to the optimal solution as the new current optimal state;

[0028] (g) Judge whether the algorithm termination condition is satisfied according to the first constraint condition: if so, end the algorithm and output the optimization result; otherwise, go to step (b).

[0029] Further preferably, according to the distribution of the cabin materials, one or two portal cranes are used for simultaneous operation in one cabin.

[0030] Further preferably, the rotation directions of the portal cranes are divided into two types: clockwise and counterclockwise.

[0031] Even more preferably, the specific content of step S4 is: let c i be the abscissa of the center of the i-th portal crane, rot i represent the rotation direction of the i-th portal crane, rot i =0 represents clockwise rotation, rot i =1 represents counterclockwise rotation; r_min i 、r_max i 、r_tail i respectively represent the minimum luffing value, the maximum luffing value and the tail radius of the i-th portal crane, hx i 、hy i respectively represent the abscissa and ordinate of the center of the hopper corresponding to the i-th portal crane, yc represents the ordinate value of the center of the portal crane track, holdx j 、holdy jThey respectively represent the abscissa and ordinate of the center of the j-th cabin. The adjacent portal crane is marked as the (i - 1)-th portal crane; SAFE_D represents the safety redundancy between adjacent portal cranes i and i - 1. By adding the second constraint condition and using the Gurobi model, the optimal positions of the portal cranes and hoppers are calculated.

[0032] More preferably, the second constraint condition is:

[0033] 1) Collision between adjacent portal cranes should be avoided, satisfying:

[0034] c i -c i-1 >rot i ×(r_tail i-1 -r_min i-1 )+r_min i-1 +(1 - rot i )(r_tail i -r_min i )+r_min i +SAFE_D;

[0035] Where c i-1 is the abscissa of the center of the i-th portal crane, r_tail i-1 is the tail radius of the (i - 1)-th portal crane, and r_min i-1 is the minimum luffing value of the (i - 1)-th portal crane;

[0036] 2) The hopper moves within the luffing range of the portal crane, satisfying: (hy i -yc) 2 +(hx i -c i ) 2 ≤r_max i , (hy i -yc) 2 +(hx i -c i ) 2 ≥r_min i ;

[0037] 3) The specified cabin is within the luffing range of the portal crane: [(holdy j -yc) 2 +(holdx j -c i ) 2 x ij ≤r_max i ;

[0038] 4) The rewrite of d in the optimization objective ij is:

[0039]

[0040] Further preferably, by using the Gurobi model, the optimal positions of the gantry crane and the hopper are calculated, including the following steps:

[0041] 1) Create a Gurobi model;

[0042] 2) Add continuous variables c i , hx i and hy i ; state variable rot i ;

[0043] 3) Define the objective function

[0044] 4) Add the second constraint condition;

[0045] 5) Solve the model and output the solution results, including the objective value of the optimal solution, the optimal values of the continuous variables and the state variables, the solution status, the solution time, and the intermediate results during the solution process.

[0046] On the other hand, the present invention also provides a computer-readable storage medium for storing a computer program, characterized in that when the stored computer program is executed, the above multi-gantry crane and hopper automatic positioning planning method is implemented.

[0047] The multi-gantry crane and hopper automatic positioning planning method and storage medium provided by the present invention have the following beneficial effects compared with the prior art:

[0048] (1) By forming an assignment problem model for the gantry crane track range, the gantry crane center position, the sizes of each mechanism of the gantry crane, the hopper size, the hopper positioning range, and the distribution of the hold materials, the present invention selects p gantry cranes from n gantry cranes, specifies the corresponding scheduling relationship between each gantry crane and each hold, and through the first constraint condition, combines the tabu search algorithm to find the optimal scheduling relationship between the gantry crane and the hold, and uses the better solution not in the tabu list as the initial solution for the next iteration. With the continuous update of the tabu list during the continuous iteration process, it prevents the search from falling into a dead loop and avoids local optimal solutions;

[0049] (2) Further using the mathematical programming model Gurobi, by performing the optimization process, the optimal solutions of the luffing range, the rotation angle range, and the rotation direction of each gantry crane corresponding to the positions of the gantry crane / hopper are solved under the second constraint condition of safe operation, and the usage method of the model is given to meet the objective requirement of minimizing the transportation time, thereby improving the loading efficiency of the port hold. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0051] Figure 1 It is a working flowchart of a multi-gantry and hopper automatic station planning method and storage medium of the present invention;

[0052] Figure 2 It is a schematic diagram of a solution process of a multi-gantry and hopper automatic station planning method and storage medium of the present invention;

[0053] Figure 3 It is a solution flowchart of the Gurobi model of a multi-gantry and hopper automatic station planning method and storage medium of the present invention. Specific embodiments

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in combination with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0055] The station positions of the gantry and its hopper are crucial for improving transportation efficiency and reducing transportation time. Traditional gantry and hopper station position methods often rely on experience or fixed patterns, and do not fully consider the distribution of cabin materials, the complexity of transportation routes, and the operating efficiency of the gantry and hopper, resulting in long material transportation time and low transportation efficiency. In view of this, as Figure 1 shown, on the one hand, the present invention provides a multi-gantry and hopper automatic station planning method, including the following steps:

[0056] S1: Obtain information about the gantry and hopper through data acquisition devices;

[0057] Specifically, by setting corresponding data acquisition devices on the gantry, obtain information including the gantry name, central coordinates, gantry track range, sizes of each mechanism of the gantry, hopper size, and hopper station position range.

[0058] Here, the data acquisition devices can use sensing detection devices such as pitch angle measurement sensors, displacement sensors, jib swing sensors, and gantry rotation angle sensors set on each gantry to obtain the sizes of the gantry and its hopper or the maximum adjustment range relative to the initial position.

[0059] S2: Obtain the material height of each cargo hold of the cargo ship through radar scanning, and collect the distribution information of the cargo hold materials;

[0060] The material in the cargo hold can be scanned by lidar at the top of the cargo hold to obtain the distance from the material surface to the lidar, and further converted into the height of the material in the cargo hold to obtain the volume of the material.

[0061] S3: Construct an assignment problem model to obtain the optimal scheduling relationship between the portal crane and the cargo hold;

[0062] The specific content of step S3 is as follows:

[0063] S31: Let n be the number of alternative portal cranes, and r i be the maximum luffing value of the i-th portal crane, i = 1, 2,..., n; m represents the number of cargo holds to be unloaded; q j represents the unloading volume of the j-th cargo hold obtained according to the material height, j = 1, 2,....m; d ij represents the Euclidean distance between the portal crane and the center of the j-th cargo hold when the i-th portal crane unloads the material of the j-th cargo hold; p represents the number of portal cranes required to be determined, p ∈ {m,..., n}; x ij = 1 means that the j-th cargo hold is operated by the i-th portal crane, x ij ∈ {0, 1}; y i = 1 means that the i-th portal crane is enabled, y i ∈ {0, 1};

[0064] S32: When obtaining the optimal scheduling relationship between the portal crane and the cargo hold, the following first constraint conditions are satisfied:

[0065] 1) Assume that in the initial state, the portal cranes are arranged in sequence on the portal crane track;

[0066] 2) Each cargo hold uses one or two portal cranes for unloading, or does not unload, that is and when, it satisfies x ij + x kj ≤ 1 + |i - k|, k = 1, 2,..., n, k ≠ i, i and k represent the i-th portal crane and the k-th portal crane;

[0067] According to the distribution of the cargo hold materials, each cargo hold uses one or two portal cranes to operate simultaneously according to the volume of the materials, or does not use a portal crane when there is no material. The rotation direction of the portal crane is divided into two types: clockwise and counterclockwise;

[0068] 3) Select p portal cranes,

[0069] 4) Ensure that the non-operating portal cranes are not assigned to any cargo hold, xij ≤y i ;

[0070] 5) The luffing constraint of the portal machine is satisfied as d ij x ij ≤r i ;

[0071] 6) The goal of the scheduling relationship between the portal machine and the cabin is:

[0072] S33: Use the tabu search algorithm to solve the optimal scheduling relationship between the portal machine and the cabin, specifically including:

[0073] (a) Given the parameters of the tabu search algorithm, randomly generate an initial solution and set the tabu list to be empty;

[0074] (b) Use the neighborhood function of the initial solution to generate all neighborhood solutions and determine several candidate solutions from the neighborhood solutions;

[0075] (c) Select the optimal candidate solution from the candidate solutions, then compare the optimal state caused by this optimal solution with the optimal state caused by the tabu solution to select the optimal solution, and use the optimal solution as the new initial solution, and finally add it to the tabu list; if the optimal solution is a tabu solution, then update the tabu tenure;

[0076] (d) If all solutions are tabu, directly select the best value in the tabu solutions as the new initial solution, and update the tabu list after selecting the new initial solution;

[0077] (e) If the tabu list is empty, directly take the optimal value among several candidate solutions as the new initial solution, and update the tabu list after selecting the new initial solution;

[0078] (f) Compare the state corresponding to the optimal solution with the current optimal solution state. If the state corresponding to the optimal solution is better than the current optimal solution state, select the state corresponding to the optimal solution as the new current optimal state;

[0079] (g) Judge whether the algorithm termination condition is satisfied according to the first constraint condition: if so, end the algorithm and output the optimization result; otherwise, go to step (b).

[0080] The taboo search algorithm adopts a neighborhood optimization search method. In order to escape the local optimal solution, the algorithm must accept inferior solutions, that is, each solution obtained is not necessarily better than the original solution. However, once an inferior solution is accepted, the algorithm iteration may fall into a loop. In order to avoid the loop, the algorithm puts some recently accepted neighborhood moves in the taboo table and prohibits them in subsequent iterations. That is, only the better solution that is not in the taboo table can be used as the initial solution for the next iteration. As the iteration proceeds, the taboo table is continuously updated. After a certain number of iterations, the neighborhood move that entered the taboo table earliest can be unlocked and exited, which is the taboo period mentioned above. The taboo table is used to prevent the occurrence of search dead loops, thereby avoiding the problem of local optimality.

[0081] like Figure 2 As shown, the larger rectangle at the top of the figure is the cabin filled with materials; the smaller rectangle at the bottom of the figure is the hopper, among which the black rectangle is the immovable gallery hopper, and the hollow small rectangle is the movable hopper; the circle is the gantry crane, and the arc with an arrow is the rotation direction of the gantry crane boom.

[0082] S4: Considering the adjacent gantry crane position constraints and the gantry crane boom amplitude limitation, with the goal of minimizing the transportation time, an optimization model is constructed to calculate the optimal position of the gantry crane and hopper.

[0083] The specific content is: Let c i is the central horizontal coordinate of the i-th gantry crane, rot i Indicates the rotation direction of the i-th gantry crane, rot i =0 means clockwise rotation, rot i =1 means counterclockwise rotation; r_min i 、r_max i 、r_tail i They represent the minimum amplitude change value, maximum amplitude change value and tail radius of the i-th gantry crane, respectively, and hx i ,hy i They represent the horizontal and vertical coordinates of the hopper center corresponding to the i-th gantry crane, yc represents the vertical coordinate value of the gantry crane track center, holdx j 、holdy j They represent the horizontal and vertical coordinates of the center of the j-th cabin respectively. The adjacent gantry crane is marked as the i-1th gantry crane. SAFE_D represents the safety redundancy between adjacent gantry cranes i and i-1. The second constraint is added, and the optimal positions of the gantry crane and the hopper are calculated by using the Gurobi model.

[0084] Among them, the optimal positions of the gate crane and hopper are calculated by using the Gurobi model. Figure 3 , including the following steps:

[0085] 1) Create a Gurobi model.

[0086] 2) Add continuous variable c i , hx i and hy i ; State variable rot i .

[0087] 3) Define the objective function The objective function has been given in step S3.

[0088] 4) Add the second constraint condition;

[0089] In the above content, the added second constraint condition is:

[0090] 1) Avoid collisions between adjacent portal cranes, satisfying:

[0091] c i -c i-1 > rot i ×(r_tail i-1 -r_min i-1 ) + r_min i-1 +(1 - rot i )(r_tail i -r_min i ) + r_min i + SAFE_D;

[0092] Where c i-1 is the central abscissa of the i-th portal crane, r_tail i-1 is the tail radius of the (i - 1)-th portal crane, and r_min i-1 is the minimum luffing value of the (i - 1)-th portal crane;

[0093] 2) The hopper moves within the luffing range of the portal crane, satisfying: (hy i -yc) 2 +(hx i -c i ) 2 ≤ r_max i , (hy i -yc) 2 +(hx i -c i ) 2 ≥ r_min i ;

[0094] 3) The specified cabin is within the luffing range of the portal crane: [(holdy j -yc) 2 +(holdx j -c i ) 2 x ij≤ r_max i ;

[0095] 4) Optimization objective In d ij is rewritten as:

[0096]

[0097] 5) Solve the model and output the solution results, including the objective value of the optimal solution, the optimal values of continuous variables and state variables, the solution status, the solution time, and the intermediate results during the solution process.

[0098] The objective value of the optimal solution is the minimum value of the luffing and rotation angle changes of the portal machine;

[0099] The optimal values of continuous changes and state variables are used to determine the best positions of each portal machine and hopper;

[0100] The solution status includes whether the Gurobi model is solved successfully, whether there is a feasible solution, whether the running time requirement is met, etc.;

[0101] The solution time includes the time required for the Gurobi model to find the optimal solution;

[0102] The intermediate results during the solution process include information such as the relaxation solution, the number of nodes, and the cutting plane.

[0103] If there is no solution, the Gurobi model will also return the corresponding solution process and intermediate results.

[0104] On the other hand, the present invention also provides a computer-readable storage medium for storing a computer program, characterized in that when the stored computer program is executed, the above multi-portal machine and hopper automatic positioning planning method is implemented.

[0105] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for automatic station planning of multi-door cranes and hoppers, characterized in that: The steps include: S1: Obtain the information of the gantry crane and hopper through the data acquisition equipment; S2: Obtain the material height of each hold of the cargo ship through radar scanning and collect the distribution information of the hold materials; S3: Construct an assignment problem model to obtain the optimal scheduling relationship between the gantry crane and the cabin; S4: Considering the adjacent gantry crane position constraints and the gantry crane boom amplitude limitation, with the goal of minimizing the transportation time, an optimization model is constructed to calculate the optimal position of the gantry crane and hopper.

2. A method for automatic site planning of multi-door cranes and hoppers according to claim 1, characterized in that: The content of step S1 is to obtain information including the gantry crane name, center coordinates, gantry crane track range, sizes of various gantry crane mechanisms, hopper size and hopper station range by setting corresponding data acquisition equipment on the gantry crane.

3. A method for automatic station planning of multi-door cranes and hoppers according to claim 2, characterized in that: The specific content of step S3 is: let n be the number of candidate door cranes, r i is the maximum amplitude variation of the i-th gantry crane, i = 1, 2, ..., n; m represents the number of cabins that need to be unloaded; q j represents the discharge amount of the jth hold according to the material height, j = 1, 2, ... m; d ij represents the Euclidean distance between the i-th gantry crane and the center of the cabin when the i-th gantry crane unloads the material in the j-th cabin; p represents the number of gantry cranes to be decided, p∈{m,…,n}; x ij =1 means the jth cabin is operated by the ith gantry crane, x ij ∈{0,1};y i =1 means the i-th door crane is enabled, y i ∈{0,1}; When obtaining the optimal scheduling relationship between the gantry crane and the cabin, the following first constraint is met: 1) Assume that in the initial state, the door cranes are arranged in sequence on the door crane track; 2) Each cabin uses one or two gantry cranes to unload, or no unloading, that is and When x ij +x kj ≤1+|ik|, k=1,2,...,n, k≠i, i and k represent the i-th door crane and the k-th door crane; 3) Select p gantry cranes, 4) Ensure that the non-operating gantry crane is not assigned to any cabin, x ij ≤y i ; 5) The gantry crane amplitude constraint satisfies d ij x ij ≤r i ; 6) The objectives of the scheduling relationship between the gantry crane and the cabin are:

4. A method for automatic site planning of multi-door cranes and hoppers according to claim 3, characterized in that: In step S3, the taboo search algorithm is used to solve the optimal scheduling relationship between the gantry crane and the cabin, which specifically includes: (a) Given the parameters of the taboo search algorithm, randomly generate an initial solution and set the taboo table to empty; (b) using the neighborhood function of the initial solution to generate all neighborhood solutions, and determining several candidate solutions from the neighborhood solutions; (c) Select the best candidate solution from the candidate solutions, compare the optimal state caused by the best solution with the optimal state caused by the forbidden solution to select the best solution, and use the best solution as the new initial solution, and finally add it to the taboo table; if the best solution is a taboo solution, update the taboo period; (d) If all solutions are taboo, directly select the best value among the taboo solutions as the new initial solution, and then update the taboo table after selecting the new initial solution; (e) If the taboo table is empty, directly take the best value among several candidate solutions as the new initial solution, and then update the taboo table after selecting the new initial solution; (f) Compare the state corresponding to the optimal solution with the current optimal solution state. If the state corresponding to the optimal solution is better than the current optimal solution state, select the state corresponding to the optimal solution as the new current optimal state. (g) Determine whether the algorithm termination condition is met based on the first constraint condition: if so, terminate the algorithm and output the optimization result; otherwise, go to step (b).

5. A method for automatic site planning of multi-door cranes and hoppers according to claim 3, characterized in that: According to the distribution of materials in the hold, one or two gantry cranes are used to operate simultaneously in one hold.

6. A method for automatic site planning of multi-door cranes and hoppers according to claim 3, characterized in that: The door machine's rotation direction is divided into clockwise and counterclockwise.

7. A method for automatic site planning of multi-door cranes and hoppers according to claim 4, characterized in that: The specific content of step S4 is: let c i is the central horizontal coordinate of the i-th gantry crane, rot i Indicates the rotation direction of the i-th gantry crane, rot i =0 means clockwise rotation, rot i =1 means counterclockwise rotation; r_min i 、r_max i 、r_tail i They represent the minimum amplitude change value, maximum amplitude change value and tail radius of the i-th gantry crane, respectively, and hx i ,hy i They represent the horizontal and vertical coordinates of the hopper center corresponding to the i-th gantry crane, yc represents the vertical coordinate value of the gantry crane track center, holdx j 、holdy j They represent the horizontal and vertical coordinates of the center of the j-th cabin respectively. The adjacent gantry crane is marked as the i-1th gantry crane. SAFE_D represents the safety redundancy between adjacent gantry cranes i and i-1. The second constraint is added, and the optimal positions of the gantry crane and the hopper are calculated by using the Gurobi model.

8. A method for automatic site planning of multi-door cranes and hoppers according to claim 7, characterized in that: The second constraint is: 1) Avoid collision between adjacent door machines and meet the following requirements: c i -c i-1 >rot i ×(r_tail i-1 -r_min i-1 )+r_min i-1 +(1-rot i )(r_tail i -r_min i )+r_min i +SAFE_D; where c i-1 is the central horizontal coordinate of the i-th gantry crane, r_tail i-1 is the tail radius of the i-1th gantry crane, r_min i-1 is the minimum amplitude variation value of the i-1th gantry crane; 2) The hopper moves within the variable amplitude range of the gantry crane, satisfying: (hy i -yc) 2 +(hx i -c i ) 2 ≤r_max i ,(hy i -yc) 2 +(hx i -c i ) 2 ≥r_min i ; 3) The designated cabin is within the range of the gantry crane: j -yc) 2 +(holdx j -c i ) 2 ]x ij ≤r_max i ; 4) Optimization goals Medium ij is rewritten as:

9. A method for automatic site planning of multi-door cranes and hoppers according to claim 8, characterized in that: By using the Gurobi model, the optimal positions of the gate crane and hopper are calculated, including the following steps: 1) Create a Gurobi model; 2) Add continuous variable c i 、hx i andhy i ; State variable rot i ; 3) Define the objective function 4) Add a second constraint; 5) Solve the model and output the solution results, including the target value of the optimal solution, the optimal values ​​of continuous variables and state variables, the solution status, the solution time and the intermediate results during the solution process.

10. A computer-readable storage medium for storing a computer program, characterized in that: When the stored computer program is executed, the method for automatic station planning of a multi-door crane and a hopper as claimed in any one of claims 1 to 9 is implemented.

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