A cable machine group intelligent scheduling method considering quality and safety double pre-control standards

Abstract

The present application relates to the technical field of construction machinery scheduling, and particularly relates to a cable crane group intelligent scheduling method considering quality and safety double pre-control standards, comprising: strip dividing a pouring bin according to a strip method to obtain a centroid coordinate of the strip; generating an ordered strip sequence according to the centroid coordinate of the strip according to a preset sorting rule; constructing a multi-objective optimization function, and establishing a multi-objective optimization model of the matching relationship between the cable crane and the strip; using a space squeezing method to determine the operation boundary for the cable crane group, and performing deterministic allocation of part of the strip to determine a strip sequence to be allocated; using a greedy-local search optimization algorithm to determine a final matching scheme for the strip sequence to be allocated; performing pouring quality inspection on the final matching scheme, and outputting a cable crane scheduling scheme meeting the pouring quality inspection. The technical scheme of the present application improves the utilization rate of the cable crane, reduces the total pouring time, and solves the problems of low utilization rate of the cable crane and long time consumption in the traditional cable crane scheduling.

Description

Technical Field

[0001] This invention relates to the field of construction machinery scheduling technology, and in particular to an intelligent scheduling method for cable crane groups that considers both quality and safety pre-control standards. Background Technology

[0002] High arch dams efficiently transfer water load to the mountainsides on both banks through the "arch effect," combining structural safety, material conservation, and terrain adaptability, making them one of the preferred dam types for high dam construction in high mountain and canyon areas. However, the steep terrain and narrow spaces in these areas make traditional transportation methods either difficult to implement, resulting in poor overall cost-effectiveness, or inefficient enough to meet pouring requirements. Cable cranes, due to their large span, high flexibility, and strong terrain adaptability, have become key equipment for the vertical transportation of concrete in high arch dams. Therefore, cable crane scheduling is one of the important tasks in high arch dam construction, closely related to the dam's pouring progress, quality, and safety. Currently, cable crane scheduling mainly relies on manual labor, on the one hand, using the experience of managers to determine the correspondence between the cable crane and the conveyor belt, and on the other hand, having on-site commanders assist operators in operating the cable crane. However, due to the subjectivity and limitations of manual scheduling, as well as the inherent volatility of cable crane efficiency, manual scheduling cannot precisely control the efficiency and coordination of parallel operation of cable crane groups in multi-compartment simultaneous pouring scenarios. This leads to increased quality risks such as exceeding the standard coverage time of concrete pouring layers and uneven strip rise. It may also cause the distance between cable cranes to fail to meet the minimum safety distance requirements, increasing the risk of collision.

[0003] To address the aforementioned issues, CN202011619128.8 provides a BIM-based cable crane layout and scheduling method. This method, based on BIM technology, establishes a 3D BIM model of the dam. According to the cable crane layout sequence and safety spacing requirements, and based on the real-time pouring progress of the dam and the location and shape characteristics of the units to be poured, the method dynamically arranges the cable cranes to determine the operating range and number of cable cranes. It adjusts the upstream and downstream layout positions and sequence of the cable cranes, calculates the coverage area and concrete volume within the range, and ensures that the layout scheme meets the requirements for equipment operation safety, layer coverage time, and pouring strength. This results in a multi-layered, staggered, and multi-compartment simultaneous pouring cable crane layout scheme under the coupling of multiple factors, including cable crane coverage range, unit shape characteristics, layer coverage time, and equipment safety collision prevention.

[0004] However, existing technologies still have the following technical shortcomings: First, existing technologies mainly focus on the static layout design of cable cranes, lacking refined optimization algorithms to support the dynamic matching relationship between cable cranes and specific pouring operation units, thus failing to achieve optimal allocation of cable crane resources; second, existing technologies lack systematic mathematical modeling methods when dealing with multi-objective constraint problems, making it difficult to find the optimal balance point among multiple objectives such as pouring efficiency, workload balance, and quality and safety; finally, existing technologies lack sufficient support for real-time scheduling decisions during the collaborative operation of cable crane groups, lacking intelligent solution mechanisms for complex constraint conditions, and failing to meet the high requirements for the accuracy and real-time performance of cable crane scheduling in the construction of high arch dams. Summary of the Invention

[0005] In view of this, in order to solve the above-mentioned problems in the background technology, the present invention proposes an intelligent scheduling method for cable crane groups that considers both quality and safety pre-control standards. The aim is to efficiently match cable cranes and strips while taking into account both quality and safety requirements, thereby improving construction efficiency and quality and ensuring construction safety.

[0006] The technical solution of this invention is implemented as follows: This invention provides an intelligent scheduling method for cable crane groups that considers both quality and safety pre-control standards, including the following steps:

[0007] S1. Divide the casting chamber into strips using the strip method and obtain the centroid coordinates of the strips;

[0008] S2. Generate an ordered strip sequence according to the centroid coordinates of the strips and a preset sorting rule;

[0009] S3. Construct a multi-objective optimization function with the objectives of minimizing the maximum pouring time of the cable crane group and minimizing the root mean square error of the pouring time of each cable crane, and establish a multi-objective optimization model for the matching relationship between the cable crane and the strip by combining multiple constraints.

[0010] S4. Based on the strip sequence, the working boundary of the cable crane group is delineated using a spatial clamping method based on safety distance, and a deterministic allocation of some strips is performed to determine the strip sequence to be allocated.

[0011] S5. For the strip sequence to be assigned, based on the multi-objective optimization model, a greedy-local search optimization algorithm is used to optimize it and determine the final cable car and strip matching scheme.

[0012] S6. Conduct a casting quality inspection on the final cable crane and strip matching scheme, and output a cable crane scheduling scheme that meets the casting quality inspection requirements.

[0013] Based on the above technical solutions, preferably, in step S2, the preset sorting rule satisfies:

[0014]

[0015] In the formula, O w Indicates the global sort position of stripe W; (x i ,y i (x) represents the projected coordinates of stripe i on the yox plane; W represents the total number of stripes; (x) k ,y k ) represents the projected coordinates of stripe k on the plane yox.

[0016] Based on the above technical solution, preferably, in step S3, the multiple constraints include:

[0017] (1) Each strip can only be poured by one cable machine;

[0018] (2) The cable machines are numbered sequentially from upstream to downstream according to their global sorting positions. The cable machines cannot cross each other. Adjacent strips can only be cast by the same cable machine or by two adjacent cable machines. When matching, the strip with the smaller global sorting position is cast by the cable machine with the smaller number, and the strip with the larger global sorting position is cast by the cable machine with the larger number.

[0019] (3) During the operation of the cable crane, the distance between two adjacent cable cranes shall be greater than or equal to the minimum safe distance between the two adjacent cable cranes.

[0020] (4) The total time for pouring each layer of the casting chamber shall not exceed the initial setting time of the material to be poured.

[0021] Based on the above technical solutions, preferably, in step S3, the expression of the multi-objective optimization function is:

[0022] T = min(max) 1≤j≤N T j )

[0023]

[0024] In the formula, T represents the total pouring time of the cable crane in the optimization objective. j The maximum value represents the total pouring time for cable crane j; N represents the total number of cable cranes; max represents the maximum pouring time for cable crane j. 1≤j≤N T j This indicates the longest pouring time among all cable cranes; σ represents the average pouring time for each cable crane; σ represents the standard deviation of the cable crane pouring time in the optimization objective.

[0025]

[0026] In the formula, Indicates the total time taken for cable crane j to pour the concrete strip i; v represents the speed of the cable crane trolley; X ijy represents the decision variable for assigning strip i to cable machine j; W represents the total number of strips; i The y-coordinate represents the centroid of strip i; i+1 This represents the centroid ordinate of strip i+1.

[0027] Based on the above technical solutions, preferably, step S4 specifically includes:

[0028] S41. Based on the ordered strip sequence and combined with the preset safety distance between cable machines, a two-way boundary advancement strategy is adopted to determine the initial working boundary for each cable machine without interference, starting from both ends of the strip sequence.

[0029] S42. Compare the centroid coordinates of each strip with the initial working boundary of each cable machine. Determinely assign strips that fall within the working boundary of a single cable machine to determine their cable machine affiliation. Identify all strips that have not been determined and combine them into a strip sequence to be assigned.

[0030] S43. Based on the initially allocated strip range, calculate the foundation pouring time and the root mean square deviation of the foundation pouring time for each cable machine.

[0031] Based on the above technical solutions, preferably, the initial operating boundary of the cable crane satisfies the following conditions:

[0032]

[0033] In the formula, This indicates the lower boundary of cable machine j; D represents the upper boundary of cable car j; safe This indicates the safe distance between two adjacent cable machines.

[0034] Based on the above technical solutions, preferably, step S5 specifically includes:

[0035] S51. A greedy initial allocation strategy is used to obtain the initial allocation result for the strip sequence to be allocated;

[0036] S52. After obtaining the greedy initial allocation result, recalculate the global total pouring time based on the multi-objective optimization model. and global mean squared error And select two adjacent strips for local search optimization, the two adjacent strips being the two strips of the adjacent cable machine casting boundary after the greedy initial allocation;

[0037] S53. Select either the upward or downward optimization direction for the first optimization, and recalculate the cable crane pouring time T after the first optimization based on the multi-objective optimization model. ′ The root mean square error σ of the cable machine pouring time ′ Compare with the result of the greedy initial allocation;

[0038] S54. Compare the cable crane pouring time T after the first optimization. ′ The root mean square error σ of the cable machine pouring time ′ Is it less than the initial greedy allocation result? If not, choose the heterogeneous optimization direction and perform the first round of optimization again; if yes, choose the same optimization direction and perform the second round of optimization.

[0039] S55. Enter the iterative optimization process. The optimization result of each round is compared with the optimization result of the previous round until the convergence condition is met.

[0040] S56. Output the best matching scheme as the final stripe allocation result.

[0041] Based on the above technical solutions, preferably, the expression for the greedy initial allocation result is:

[0042] For the strip sequence to be assigned, satisfying the relation The optimal cable machine selection expression for each strip i is:

[0043]

[0044] In the formula, j * (i) represents the optimal cable car for strip i, y i This represents the ordinate value of the centroid of strip i. This indicates the lower boundary of cable machine j. σ′ represents the upper boundary of cable machine j+1. j σ′ represents the standard deviation of the pouring time after strip i is assigned to cable machine j. j+1 σ° represents the mean squared error of the time after strip i is assigned to cable machine j+1, and σ° represents the mean squared error of the foundation pouring time.

[0045] Based on the above technical solutions, preferably, in step S55, the convergence conditions include: the total pouring time decreases by no more than 0.5 hours; and the standard deviation decreases by less than 1%.

[0046] When multiple rounds of optimization fail to further improve system performance, or when neither upward nor downward optimization is possible, the cyclic optimization process is terminated.

[0047] Based on the above technical solutions, preferably, the pouring quality inspection in step S6 specifically includes: the final cable crane and strip matching scheme must satisfy that the total pouring time of any cable crane under any slab surface is less than or equal to the initial setting time of the material to be poured; if a satisfactory solution that satisfies all constraints cannot be found in the optimization process of step S5, then the initial parameters or constraints are adjusted and the optimization process is re-executed.

[0048] The intelligent scheduling method for cable crane groups in this invention, which considers both quality and safety pre-control standards, has the following advantages over existing technologies:

[0049] This invention optimizes the matching problem between cable cranes and pouring strips. It considers multiple constraints between cable cranes and between cable cranes and pouring strips, constructs a multi-objective optimization model for cable crane-strip matching, and further obtains the foundation pouring time for each cable crane based on spatial squeeze. A greedy-local search optimization method is used to allocate cable cranes to the pouring strips, which further improves the utilization rate of cable cranes and reduces the total pouring time, solving the problems of low cable crane utilization and long pouring time in the traditional cable crane scheduling problem. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart of the intelligent scheduling method for cable crane groups according to the present invention;

[0052] Figure 2 This is a schematic diagram of the strip division of the present invention;

[0053] Figure 3 This is a schematic diagram of the cable machine-strip sequence and a yox plane projection coordinate diagram of the present invention;

[0054] Figure 4 Color scale diagram showing the distribution of cable crane casting efficiency at different casting elevations and dam sections according to the present invention;

[0055] Figure 5 A simplified schematic diagram of the "strip-cable machine" matching problem based on the spatial clamping method of the present invention, which is based on a safe distance.

[0056] Figure 6 This is a schematic diagram of the initial allocation and pouring area for the cable machine according to the present invention;

[0057] Figure 7 This is a flowchart of the greedy-local search optimization discrimination process of the present invention;

[0058] Figure 8 This is a schematic diagram of the greedy-local search upward optimization of the present invention;

[0059] Figure 9 This is a schematic diagram of the greedy-local search downward optimization of the present invention. Detailed Implementation

[0060] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0061] like Figure 1 As shown, this invention provides an intelligent scheduling method for cable crane groups that considers both quality and safety pre-control standards, comprising the following steps:

[0062] S1. Divide the casting chamber into strips using the strip method and obtain the centroid coordinates of the strips;

[0063] S2. Generate an ordered strip sequence according to the centroid coordinates of the strips and a preset sorting rule;

[0064] S3. Construct a multi-objective optimization function with the objectives of minimizing the maximum pouring time of the cable crane group and minimizing the root mean square error of the pouring time of each cable crane, and establish a multi-objective optimization model for the matching relationship between the cable crane and the strip by combining multiple constraints.

[0065] S4. Based on the strip sequence, the working boundary of the cable crane group is delineated using a spatial clamping method based on safety distance, and a deterministic allocation of some strips is performed to determine the strip sequence to be allocated.

[0066] S5. For the strip sequence to be assigned, based on the multi-objective optimization model, a greedy-local search optimization algorithm is used to optimize it and determine the final cable car and strip matching scheme.

[0067] S6. Conduct a casting quality inspection on the final cable crane and strip matching scheme, and output a cable crane scheduling scheme that meets the preset constraints.

[0068] Specifically, in this invention, firstly, the pouring bins are divided into strips using a strip method, the centroid coordinates of each strip are obtained, and the strips are sorted to complete system initialization. This initialization process also includes numbering and managing the cable cranes. Next, a multi-objective optimization model is constructed. To simplify the optimization solution process, a spatial squeeze method based on a safe distance is used to pre-divide the working boundaries of the cable crane group, and the affiliation of some strips is determined based on the divided boundary regions. Then, for the remaining strip sequences to be assigned, a greedy-local search hybrid optimization algorithm is used to intelligently match the cable cranes with the strips to obtain the globally optimal cable crane and strip matching scheme. Finally, the optimized matching scheme is inspected for pouring quality, and the matching schemes that meet the preset constraints are output to form the final intelligent cable crane scheduling scheme.

[0069] Further, step S1 specifically includes: dividing the dam section into casting chambers according to the structural dimensions, load distribution and construction conditions, layering the casting chambers according to the layer thickness, then dividing each layer into strips according to the width, and obtaining the projection coordinates of the centroid of all strips on the plane yox: taking the perpendicular to the cable crane track as the x-axis and the centerline of the cable crane feeding platform track as the y-axis, which are the centroid coordinates of the strips.

[0070] like Figure 2 As shown, in one embodiment, according to the provisions of the "Specifications for Construction of Hydraulic Concrete" (DL / T 5144-2015) regarding the continuous layered pouring of hydraulic concrete, each pouring section is divided into several pouring layers with a thickness of 50cm. Then, continuous strips are divided along the dam axis with a width of 5m. This size design comprehensively considers the 6-8m effective coverage radius of the cable crane's hoisting tank and the economic efficiency of formwork support, while ensuring that the volume of each strip is controlled within the allowable range of initial setting time. Finally, the projection coordinates of the centroids of all strips on the plane yox are obtained: with the x-axis perpendicular to the cable crane track as the x-axis and the centerline of the cable crane's supply platform track as the y-axis, these are the centroid coordinates of the strips.

[0071] Further, step S2 specifically includes: sorting the strips according to their centroid coordinates, following the sorting rule of "first from upstream to downstream (corresponding to centroid y-values), then from the near-end main tower to the far-end secondary tower (corresponding to centroid x-values)", to generate a strip sequence {Str1, Str2, ..., Str...}. W}

[0072] The preset sorting rules satisfy:

[0073]

[0074] In the formula, O W Indicates the global sort position of stripe W; (x i ,y i (x) represents the projected coordinates of stripe i on the yox plane; W represents the total number of stripes; (x) k ,y k ) represents the projected coordinates of stripe k on the plane yox.

[0075] like Figure 3 As shown in the figure, taking a hydropower station as an example, three cable cranes are allocated to two dam sections being poured at the same time but on different dam sections. Based on their global location, they are sequentially ordered from upstream to downstream as cable crane 1, cable crane 2, and cable crane 3. The strips are sorted according to their centroid coordinates, following the rule of "first from upstream to downstream (corresponding to the centroid y-value), then from the near-end main tower to the far-end auxiliary tower (corresponding to the centroid x-value)". This sorting process generates a strictly ordered strip sequence {Str1, Str2, ..., Str...}. W}

[0076] Furthermore, in step S3, a multi-objective optimization model is established, and the expression of the multi-objective optimization function is:

[0077] T = min(max) 1≤j≤N T j )

[0078]

[0079] In the formula, T represents the cable crane pouring time in the optimization objective. j The maximum value represents the total pouring time for cable crane j; N represents the total number of cable cranes; max represents the maximum pouring time for cable crane j. 1≤j≤N T j This indicates the longest pouring time among all cable cranes; σ represents the average pouring time for each cable crane; σ represents the standard deviation of the cable crane pouring time in the optimization objective.

[0080] The expression for the total pouring time of cable crane j is:

[0081]

[0082] In the formula, Indicates the total time taken for cable crane j to pour the concrete strip i; v represents the speed of the cable crane trolley; X ij X represents the decision variable for assigning strip i to cable machine j. ij ∈{0,1}, if strip i is poured by cable machine j, then X ij =1, otherwise X ij =0; W represents the total number of stripes; y i The y-coordinate represents the centroid of strip i; i+1 This represents the centroid ordinate of strip i+1.

[0083] A multi-objective optimization model is established based on multiple constraints to optimize the objective function. The objective function is optimized in two directions: minimizing the total pouring time of the cable crane group and achieving efficiency balance among the cable cranes. The multiple constraints include:

[0084] (1) Each strip can only be poured by one cable machine;

[0085]

[0086] (2) The cable machines are numbered sequentially from upstream to downstream according to their global sorting positions. The cable machines cannot cross each other. Adjacent strips can only be cast by the same cable machine or by two adjacent cable machines. When matching, the strip with the smaller global sorting position is cast by the cable machine with the smaller number, and the strip with the larger global sorting position is cast by the cable machine with the larger number.

[0087]

[0088] In the formula, Z ik Z represents the adjacent identifiers of stripe i and stripe k in the stripe sequence. ik ∈{0,1}, only if O k -O i The value is 1 when X = 1, otherwise it is 0; kj Let N represent the decision variable for strip k being poured by cable machine j; N represents the total number of cable machines.

[0089] (3) Safety requirements: The cable crane must meet the safety distance requirements at all times during operation;

[0090] D jp ≥D safe

[0091] In the formula, D jp D represents the distance between cable car j and cable car p; safe This indicates the minimum safe distance between two adjacent cable machines.

[0092] (4) Quality requirements: The total time for pouring each layer of concrete in the pouring bin shall not exceed the initial setting time of the material to be poured, that is, the total time for pouring each layer of concrete in the pouring bin shall not exceed the initial setting time of the concrete.

[0093]

[0094] In the formula, t0 represents the time taken for cable crane j to pour all strips belonging to pouring bin b; t0 represents the initial setting time of the concrete; M represents the set of pouring bin numbers; and N represents the total number of cable cranes.

[0095] The time taken for cable crane J to pour all the strips belonging to warehouse number b The expression is:

[0096]

[0097] In the formula, W b W represents the set of all stripes belonging to casting bin number b. b -1 represents the number of differences in the ordinates of the centroids of all strips belonging to warehouse b, which is the vertical displacement traversed when moving from strip i+1 to strip i.

[0098] Under all constraints, first calculate the time required for a single cable crane to pour a single strip. Then, based on this time, calculate the total pouring time for each cable crane. Finally, calculate the objective function value based on the pouring time for each cable crane. The expression for the total time required for cable crane j to pour strip i is:

[0099]

[0100] In the formula, t r This indicates the time required to complete the r-th round of pouring operations. This can be queried based on the cable crane pouring efficiency distribution map for different pouring elevations and dam sections, such as... Figure 4 As shown; R i This represents the total number of cycles required to cast strip i.

[0101] The total number of cycles R required for casting strip i i The expression is:

[0102]

[0103] In the formula, V i q represents the volume of concrete required to pour strip i; q represents the capacity of the hoisting tank.

[0104] By establishing the above multi-objective optimization function and constraints, a complete multi-objective optimization model for the matching relationship between the cable car and the strip is formed, providing a mathematical foundation for subsequent intelligent scheduling algorithms.

[0105] Furthermore, step S4 specifically includes:

[0106] S41. Based on the ordered strip sequence and combined with the preset safety distance between cable machines, a two-way boundary advancement strategy is adopted to determine the initial working boundary for each cable machine without interference, starting from both ends of the strip sequence.

[0107] Specifically, such as Figure 5 As shown, based on the spatial clamping principle using a safety distance, the upper and lower boundaries of all cable cranes are defined as the initial working boundaries of the cable cranes, and the areas where the cable cranes must be cast are determined. In the figure, the gray area represents the area where the cable cranes must be cast, and the orange area represents the area where the cable cranes need to be assigned cable crane strips.

[0108] The initial operating boundary of the cable crane satisfies the following conditions:

[0109]

[0110] In the formula, This indicates the lower boundary of cable machine j; D represents the upper boundary of cable car j; safe This indicates the safe distance between two adjacent cable machines.

[0111] According to the principle that strips with smaller position numbers should be cast by machine with smaller numbers, and strips with larger position numbers should be cast by machine with larger numbers, the first and last strips can only be cast by the machine with the smallest number and the machine with the largest number, respectively. Figure 5 As shown, the lowest pouring boundary of each cable machine can be determined from top to bottom according to the sequential strip sequence, and the highest pouring boundary of each cable machine can be determined from bottom to top according to the reverse strip sequence.

[0112] S42. Compare the centroid coordinates of each strip with the initial working boundary of each cable machine. Determinely assign the strips that fall within the working boundary of a single cable machine to determine their cable machine affiliation and obtain the range of foundation pouring strips. Identify all strips that have not been determined and combine them into a sequence of strips to be assigned.

[0113] S43. Based on the initially allocated strip range, calculate the foundation pouring time and the root mean square deviation of the foundation pouring time for each cable machine.

[0114] Based on spatial pinch-offs with a safety distance, when the centroid coordinates (x...) of strip i... i ,y i )satisfy Take X ij =1, meaning that the cable machine j must be matched for pouring to reduce the number of candidate strip variables. For example... Figure 5 As shown, the unassigned stripe sequence is reduced to {Str a …Str b Str c …Str d Str e …Str f}. Based on the initial allocation result {Str1…Str a-1}、{Str b+1 …Str c-1}、{Str d+1 …Str e-1}、{Str f+1 …Str w Calculate the foundation pouring time for each cable machine. The root mean square error σ° is the length of time required for foundation pouring.

[0115] The formula for calculating the root mean square error σ° of the foundation pouring time is as follows:

[0116]

[0117] Based on the above operations, the final determination of the foundation pouring strip range and the range of the strip to be allocated is as follows: Figure 6 As shown, the white area represents the strip area to be assigned, and the foundation pouring strip will not be included in the greedy-local search optimization process.

[0118] The aforementioned spatial squeezing method based on safe distance ensures the safety of cable crane group operations, reduces the computational complexity of subsequent optimization algorithms through deterministic allocation, and establishes basic pouring parameters for each cable crane, providing a data foundation for subsequent intelligent scheduling optimization.

[0119] Furthermore, such as Figure 7 As shown, step S5 specifically includes:

[0120] S51. A greedy initial allocation strategy is used to obtain the initial allocation result for the strip sequence to be allocated;

[0121] S52. After obtaining the greedy initial allocation result, recalculate the global total pouring time based on the multi-objective optimization model. and global mean squared error And select any two adjacent stripes for local search optimization;

[0122] S53. Select either the upward or downward optimization direction for the first optimization, and recalculate the cable crane pouring time T after the first optimization based on the multi-objective optimization model. ′ The root mean square error σ of the cable machine pouring time ′ Compare with the result of the greedy initial allocation;

[0123] S54. Compare the cable crane pouring time T after the first optimization. ′ The root mean square error σ of the cable machine pouring time ′ Is it less than the initial greedy allocation result? If not, choose the heterogeneous optimization direction and perform the first round of optimization again; if yes, choose the same optimization direction and perform the second round of optimization.

[0124] S55. Enter the iterative optimization process. The optimization result of each round is compared with the optimization result of the previous round until the convergence condition is met.

[0125] S56. Output the best matching scheme as the final stripe allocation result.

[0126] Specifically, the strip sequence to be assigned A greedy initial allocation is used, where each strip in the sequence to be allocated satisfies the following relation: For the candidate cable crane sequence {j,j+1}, calculate the pouring time of each cable crane under the two allocation schemes. and ) and the root mean square error of the cable crane pouring time (σ′) j and σ′ j+1 ), and calculate the difference between the mean square error σ° of the foundation pouring time, and select the cable crane allocation scheme that minimizes the increase in mean square error, i.e., the greedy initial allocation result.

[0127] The expression for the greedy initial allocation result is:

[0128]

[0129] In the formula, j * (i) represents the optimal cable car for strip i, y i This represents the ordinate value of the centroid of strip i. This indicates the lower boundary of cable machine j. σ′ represents the upper boundary of cable machine j+1. j σ′ represents the standard deviation of the pouring time after strip i is assigned to cable machine j. j+1 σ° represents the mean squared error of the time after strip i is assigned to cable machine j+1, and σ° represents the mean squared error of the foundation pouring time.

[0130] After obtaining the initial allocation results, recalculate the total global pouring time after the greedy initial allocation. and the standard deviation of global pouring time Two adjacent strips, i and i+1, are selected for local search optimization. These two adjacent strips, i and i+1, represent the two strips on the boundary of adjacent cable cranes after the initial greedy allocation. There is only one unique pair of adjacent strips between any two cable cranes: strip i is cast by cable crane j, and strip i+1 is cast by cable crane j+1. Strips i and i+1 are adjacent, and cable crane j and cable crane j+1 must also be adjacent. In other words, adjacent strips i and i+1 must satisfy the following relationship:

[0131] (Z i,i+1 =1)∧(X ij =X i+1,j+1 =1)

[0132] In the formula, Z i,i+1 Indicates the adjacent identifiers of stripe i and stripe i+1 in the stripe sequence; X ij X represents the decision variable for assigning strip i to cable machine j; i+1,j+1 Let i+1 represent the decision variable for assigning strip i+1 to cable machine j+1.

[0133] like Figure 8 As shown, first, upward optimization is performed, keeping strip i+1 still cast by cable machine j+1, changing cable machine j of strip i to cable machine j+1, and then recalculating the cable machine casting time T′ and root mean square error σ′. If or This proves the optimization is feasible; retain the optimized solution and continue with the second round of optimization. and If the optimization scheme is not feasible, strips i and k will retain the original pouring allocation scheme. In the second round of optimization, strip i-1 is poured by cable crane j+1, the pouring time T″ and the root mean square error σ″ of the cable crane are recalculated, and compared with the results of the first round of optimization until the convergence condition is met.

[0134] If the cable crane pouring time and standard deviation obtained from the first round of upward optimization are both greater than the initial greedy allocation result, then reverse optimization is performed. Figure 9As shown, the cable machine j+1 of strip i+1 is changed to cable machine j. The cable machine pouring time and standard deviation under this allocation scheme are calculated and compared sequentially until no further optimization is possible. When neither upward nor downward optimization can be performed, the optimal allocation scheme is reached, and the output is the final strip matching result.

[0135] The convergence conditions include: (1) the total pouring time decreases by no more than 0.5h; (2) the mean square error decreases by less than 1%; when multiple rounds of optimization fail to further improve the system performance indicators, or when neither upward nor downward optimization can be performed, the cyclic optimization process is terminated.

[0136] Furthermore, the pouring quality inspection in step S6 specifically includes: the final determined matching scheme must satisfy that the total pouring time of any cable crane under any platform is less than or equal to the initial setting time of the material to be poured (i.e., concrete); if a satisfactory solution that satisfies all constraints cannot be found during the optimization process in step S5, the optimization process will be re-executed after adjusting the initial parameters or constraints.

[0137] Specifically, comparing the initial and final allocation schemes using a greedy algorithm, it can be seen that the final allocation result must satisfy the condition that all strips are poured by the cable cranes. The optimal matching scheme is then verified for pouring quality. The final optimal matching scheme must satisfy the condition that the pouring time of any cable crane under any span must be less than or equal to the initial setting time of the concrete. The expression for pouring quality verification is:

[0138]

[0139] In the formula, b represents the pouring bin number b, M represents the set of pouring bin numbers, j represents the cable crane j, and N represents the total number of cable cranes. t0 represents the time taken for cable crane j to pour all the strips belonging to pouring bin b, and t0 represents the initial setting time of the concrete.

[0140] If the final matching scheme does not meet the quality requirements, the casting quality requirements need to be verified after all strips are allocated in the greedy-local optimization search. Among the allocation schemes that meet the safety and quality requirements, the scheme with the shortest total casting time and the highest utilization rate of the cable crane group should be selected.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cable machine group intelligent scheduling method considering double pre-control standards of quality and safety, characterized in that, The method comprises the following steps: S1, the pouring bin is divided into strips according to the strip method, and the centroid coordinates of the strips are obtained; S2, according to the centroid coordinates of the strips, an ordered strip sequence is generated according to a preset ordering rule; S3, a multi-objective optimization function is constructed, which takes minimizing the maximum pouring time of the crane group and minimizing the mean square deviation of the pouring time of each crane as the target, and a multi-objective optimization model of the matching relationship between the crane and the strip is established combined with multiple constraint conditions; The multiple constraint conditions include: (1) each strip can only be poured by one crane; (2) the cranes are numbered from upstream to downstream according to the global sorting position, the cranes cannot cross each other, adjacent strips can only be poured by the same crane or by two adjacent cranes, and the matching satisfies that the strip with a smaller global sorting position is poured by a smaller numbered crane, and the strip with a larger global sorting position is poured by a larger numbered crane; (3) during the operation of the cranes, the distance between two adjacent cranes is greater than or equal to the minimum safety distance between two adjacent cranes; (4) the total pouring time of each base layer of the pouring bin does not exceed the initial setting time of the material to be poured; S4, according to the strip sequence, a space squeezing method based on the safety distance is used to determine the working boundary of the crane group, and the deterministic allocation of part of the strips is performed to determine the strip sequence to be allocated; S5, based on the multi-objective optimization model, a greedy-local search optimization algorithm is used to optimize the strip sequence to be allocated to determine the final matching scheme between the crane and the strip; S6, the final matching scheme between the crane and the strip is subjected to pouring quality inspection, and a crane scheduling scheme that meets the pouring quality inspection is output; Step S4 specifically includes: S41, according to the ordered strip sequence and combined with the preset safety distance between the cranes, a bidirectional boundary advancing strategy is used to determine the initial working boundary of each crane without interference from both ends of the strip sequence; S42, the centroid coordinates of each strip are compared with the initial working boundary of each crane, the strips falling only within the working boundary of a single crane are determined and allocated, the belonging crane is determined, and all strips not subjected to deterministic allocation are identified and combined into the strip sequence to be allocated; S43, according to the range of the initially allocated strips, the basic pouring time and the basic pouring time mean square deviation of each crane are calculated.

2. The cable machine group intelligent scheduling method considering both quality and safety double pre-control standards according to claim 1, wherein, In step S2, the preset ordering rule satisfies: ， wherein denotes the global ordering position of the slice W; denotes the slice the projection coordinates on the plane W denotes the total number of slices; denotes the slice the projection coordinates on the plane .

3. The cable machine group intelligent scheduling method considering both quality and safety double pre-control standards according to claim 1, characterized in that, In step S3, the expression of the multi-objective optimization function is: ， ， In the formula, T represents the total pouring time of the cable crane in the optimization target, represents the cable crane total pouring time; N represents the total number of cable cranes; represents the longest pouring time in all cable cranes; represents the average value of the pouring time of each cable crane; represents the mean square deviation of the pouring time of the cable crane in the optimization target; ， wherein denotes the cable crane casting strip total duration used; denotes the cable crane travel speed; denotes the strip assigned to the cable crane decision variable; W denotes the total number of strips; denotes the strip centroid ordinate of the strip denotes the strip centroid ordinate of the strip 4. The cable machine group intelligent scheduling method considering both quality and safety double pre-control standards of claim 1, wherein, The initial working boundary of the crane satisfies the following conditions: ， ， wherein represents the lower boundary of the cable machine ; represents the upper boundary of the cable machine ; represents the safety distance between two adjacent cable machines.

5. The cable machine group intelligent scheduling method considering both quality and safety double pre-control standards according to claim 1, characterized in that, Step S5 specifically includes: S51, a greedy initial allocation strategy is used for the strip sequence to be allocated to obtain an initial allocation result; S52、in the case where the greedy initial allocation result is obtained, re-computing the global total pouring duration based on the multi-objective optimization model and the global mean square deviation and selecting two adjacent strips for local search optimization, the two adjacent strips being two strips of the adjacent cable crane pouring boundaries after the greedy initial allocation; S53、selecting an upward or downward optimization direction to perform the first optimization, and recalculating the cable crane pouring duration after the first optimization based on the multi-objective optimization model and the cable crane pouring duration mean square error and comparing with the greedy initial allocation result; S54, compare the cable machine pouring time after the first optimization and the cable machine pouring time variance whether less than the greedy initial allocation result: if not, select the heterogeneous optimization direction to re-optimize the first round; if yes, select the same optimization direction to optimize the second round; S55, enter the loop optimization process, and compare the optimization result of each round with the optimization result of the previous round until the convergence condition is met; S56, output the best matching scheme as the final strip allocation result.

6. The cable machine group intelligent scheduling method considering both quality and safety double pre-control standards according to claim 5, characterized in that, The expression of the greedy initial allocation result is: For each strip of the sequence of strips to be allocated satisfying the relationship The optimal cable machine selection expression for each strip of the sequence of strips to be allocated is: ， wherein represents a strip of optimal cable machines, represents a strip of centroid coordinates longitudinal coordinate values, represents a lower boundary of a cable machine , represents an upper boundary of a cable machine , represents a strip of paving duration mean square errors assigned to cable machines , represents a strip of duration mean square errors assigned to cable machines , represents a base paving duration mean square error.

7. The cable machine group intelligent scheduling method considering both quality and safety double pre-control standards according to claim 5, characterized in that, In step S55, the convergence condition includes: the total pouring time decreases by no more than 0.5h; the mean square deviation decreases by less than 1%; When continuous multiple rounds of optimization cannot further improve the system performance index, or both upward optimization and downward optimization cannot be performed, the loop optimization process is terminated.

8. The cable machine group intelligent scheduling method considering both quality and safety double pre-control standards according to claim 1, wherein, The construction quality inspection in step S6 specifically includes: the final cable machine and strip matching scheme must satisfy that the total construction time of any cable machine under any warehouse surface is less than or equal to the initial setting time of the material to be poured; if a satisfactory solution that satisfies all constraints cannot be found in the optimization process of step S5, it is prompted to adjust the initial parameters or constraint conditions and then re-execute the optimization process.

Images