Multi-target wind change preference decision-making method for cable crane operation path

Abstract

The invention discloses a cable crane operation path multi-target wind change preference decision-making method, which comprises the following steps of S1, constructing an actual wind speed probability density function based on a predicted value based on wind speed prediction error statistical data, and calculating wind power according to the actual wind speed obtained by the probability density function; s2, traditional cable crane operation path optimization targets are established, wherein the targets comprise a cable crane working efficiency target and a dam pouring space exposure risk target; s3, establishing a wind change non-inferior front edge and a wind change target preference weight; S4, establishing a cable crane operation multi-target optimization model considering wind change preference; and S5, adopting a KnEA algorithm to solve a non-dominated solution set of the cable crane operation path multi-objective optimization model considering the wind change preference decision. The method has the characteristics of effectively coping with uncertain wind power and enabling a path optimization result to be more rigorous and accurate.

Description

Technical Field

[0001] The invention relates to a cable crane operation path, in particular to a multi-objective wind change preference decision method for a cable crane operation path. Background Art

[0002] In the process of dam construction at home and abroad, various large-scale construction machinery is widely used. Among them, cable cranes play an important role in the entire dam construction process because of their advantages such as being suitable for construction sites with complex terrain and difficult to pass, convenient use, large hoisting range, and little impact from floods. At present, China's dam construction center is gradually shifting to high-altitude, remote and windy areas in the west, which has brought great challenges to the operation and management of cable cranes and also put forward higher requirements. The traditional multi-objective offline pre-control optimization method of cable crane operation path is too conservative and requires huge costs to deal with the worst case of uncertain wind force.

[0003] Chinese Patent Application No.: 202110106611.4 "A Multi-objective Optimization Method for Cableway Tank Transportation Path" discloses a multi-objective optimization method for cableway tank transportation path, establishes a multi-objective optimization model for cableway tank transportation path, and considers the static collision risk and dynamic collision risk in cableway operation, making the transportation path optimization more accurate and reasonable. However, the patent does not consider the impact of wind on cableway operation, resulting in a lack of robustness in the cableway operation path.

[0004] Chinese Patent Application No.: 202210011249.7 "A Cableway Route Planning Method for Suppressing Wind Random Disturbance" discloses a cableway route planning method for suppressing wind random disturbance, including a multi-objective robust optimization method for the cableway tank transportation path in response to the multi-objective requirements and wind uncertainty problems in the operation of the cableway tank. However, the patent focuses on analyzing the competitiveness and coordination between objectives, and does not consider the changes in decision makers' preferences for objectives as the uncertain factor wind changes.

[0005] In view of this, conducting research on online control methods for cable crane operation paths and suppressing the disturbance of uncertain wind forces are of great significance for achieving project goals and ensuring construction safety. Summary of the invention

[0006] The purpose of the present invention is to provide a multi-objective wind change preference decision method for a cable car operation path. The present invention has the characteristics of effectively coping with uncertain wind forces and making the path optimization result more rigorous and accurate.

[0007] The technical solution of the present invention is a multi-objective wind change preference decision method for a cableway operation path, comprising the following steps:

[0008] S1. During the dam pouring process, for the cable crane operation, based on the statistical data of wind speed prediction error, a normal or skew function fitting is used to construct the actual wind speed probability density function based on the predicted value, and the actual wind speed v obtained according to the probability density function is w Calculate wind force F w ;

[0009] S2. Establish optimization targets for the traditional cable crane operation path, including the cable crane work efficiency target f1 and the dam pouring space exposure risk target f2;

[0010] S3. Establish wind-variable non-inferior frontier and wind-variable target preference weights: solve the multi-objective optimization model of the traditional cable crane operation path for different wind force levels, obtain the non-inferior frontier under different wind conditions, and then form a wind-variable Pareto front cluster to obtain the time-varying preference weights of the cable crane work efficiency target and the spatial exposure risk target;

[0011] S4. Establish a multi-objective optimization model for cable crane operation considering wind change preference. The objective functions are work efficiency function and spatial exposure risk function. The constraints are time constraint, static collision constraint and dynamic collision constraint.

[0012] S5. The KnEA algorithm is used to solve the non-dominated solution set of the multi-objective optimization model of the cable crane operation path considering wind change preference decision.

[0013] In the aforementioned multi-objective wind variation preference decision method for a cable crane operation path, in step S1, the actual wind speed probability density function is set as: In the formula, v w is the actual wind speed, To predict wind speed, Δv w is the prediction error; p′ is the probability density function, is a normal distribution, σ w is the standard deviation of the wind speed prediction error.

[0014] In the aforementioned multi-objective wind change preference decision method for a cableway operation path, in step S1, the wind force F w =ρrhv w 2 ; In the formula, ρ is the air density ρ, r is the radius of the hanging tank, and h is the height of the hanging tank.

[0015] In the aforementioned multi-objective wind change preference decision method for a cable car operation path, in step S2, the calculation method of the work efficiency target f1 includes the following steps:

[0016] S201, calculate the round trip time of the cable car from the loading point to the unloading point In the formula, t i The operating time of each operation stage of the cable car;

[0017] S202. Calculate the efficiency target of the cable crane Where, T plan Plan your round trip time for the cable car.

[0018] In the aforementioned cable crane operation path multi-objective wind change preference decision method, in step S2, the calculation method of the dam pouring space exposure risk target f2 includes the following steps:

[0019] S203. Calculate the probability of the hanging tank appearing in the spatial exposure area: Where, t a T is the time when the hanging tank appears in the space exposure area; a It is the round trip time of the cable crane from the loading point to the unloading point;

[0020] S204. Calculate the probability of vibrating machinery appearing in the spatial exposure area: Where, t z is the time when the vibrating machine appears in the spatial exposure area, T z is the total construction time of the pouring layer;

[0021] S205. Calculate the exposure risk target of the dam pouring space: In the formula, v lb To robustly control wind speed, V f is the wind speed at each level near the dam, P1 is the actual wind speed v at each level near the dam site obtained based on the actual wind speed probability density function and the actual wind speed w probability.

[0022] In the aforementioned cable crane operation path multi-objective wind change preference decision method, in step S3, the method for calculating the wind change non-inferior front and the wind change target preference weight includes the following steps:

[0023] S301, define the average value of the slope of the line connecting any point and two adjacent points in the Pareto front cluster as the average variability of the point, calculate the average variability of the construction period and spatial exposure risk targets for each point of the non-inferior front one by one; let f1 and f2 be the work efficiency target and the spatial exposure risk target respectively; are the average variation rates of f1 relative to f2 and f2 relative to f1 at point m, i.e., the average variation rates of work efficiency target and spatial exposure risk target; M is the total number of non-inferior solutions; m is the number of non-inferior solutions sorted in ascending order according to the objective function value;

[0024] S302, define the ratio of the two average variation rates of the non-inferior solution m to the corresponding objective function values ​​as the sensitivity ratio, They are work efficiency sensitivity ratio and spatial exposure risk sensitivity ratio respectively;

[0025] S303, perform dimension normalization on the sensitivity ratio to obtain the dimensionally normalized work efficiency sensitivity ratio and spatial exposure risk sensitivity ratio

[0026] S304, compare the dominance relationship of the dimensionally normalized sensitivity ratio sets {ε1} and {ε2}, obtain a non-inferior solution subset through secondary screening, and include the most sensitive solution individual into the decision support set X * :

[0027] S305. Calculate decision support set X * The relative weight of the sensitivity ratio ε1 and ε2 of each individual in is defined as the bias of the individual relative to f1 and f2: Where: are the biases towards work efficiency and spatial exposure risk targets respectively; N is X * The total number of non-inferior solutions in ; n is the number of elements in the non-inferior solution subset from small to large;

[0028] S306, using the quantified bias degree sets {ω1}, {ω2} as decision support, comprehensively evaluate the decision maker's target preferences at different times j, select the corresponding non-inferior solution individual n, and convert the corresponding bias degree As the time-varying preference weights of work efficiency target and spatial exposure risk target, β eff-j , β exp-j .

[0029] In the aforementioned cable crane operation path multi-objective wind change preference decision method, in step S4, the method for establishing a cable crane operation multi-objective optimization model considering wind change preference includes the following steps:

[0030] S401. According to the preference of the construction period target under the j-th wind force condition, the work efficiency function is obtained:

[0031]

[0032] Where, T plan Plan the round trip time for the cable car, T a is the round trip time of the cable car from the loading point to the unloading point, β eff-j is the time-varying preference weight for work efficiency goals;

[0033] S402. According to the preference of the spatial exposure risk target under the j-th wind force condition, the spatial exposure risk function is obtained:

[0034]

[0035] In the formula, β exp-j is the time-varying preference weight for the spatial exposure risk target;

[0036] S403, establish time constraints:

[0037] T amin ≤T a ≤T amax ; T zmin ≤T z ≤T zmax ;

[0038] Where, T amin and T amax are the shortest and longest time for a round trip of the cable car; T zmin T is the shortest time for vertical transportation of a certain layer of dam blocks by cable crane; zmax The longest total construction time of the cable crane for the concrete construction of a certain layer of dam blocks;

[0039] S404. Establish static collision constraint conditions: d h <d static ; In the formula, d h is the cable car buffer distance, d static The minimum distance between the hanging tank and the set obstacle point;

[0040] S405. According to the distance w between adjacent hanging tanks, the swing radii of adjacent hanging tanks are denoted as r1 and r2 respectively, and a dynamic collision constraint condition is established as: r1+r2≤w.

[0041] In the aforementioned cable crane operation path multi-objective wind change preference decision method, in step S5, the solution process of the KnEA algorithm includes the following steps:

[0042] S501, input the information required by the cable crane and set the driving parameters of the dam surface compactor;

[0043] S502, inputting initial variables, and using the initial variables to generate an initial path population;

[0044] S503, for the path population, calculate the objective function of the cable crane operation multi-objective optimization model; re-sort by using the fast non-dominated sorting method, then perform the selection operation by using the binary tournament mechanism, and perform the mutation crossover operation to generate the first generation path population R;

[0045] S504, constructing a mating pool B for individuals in the path population R;

[0046] S505, performing selection, mutation and crossover operations on the mating pool B to generate a subpopulation C, taking the union of the path population R and the subpopulation C to obtain a new path population U;

[0047] S506, performing non-dominated sorting on the path population U, and calculating the solution of the objective function, to obtain the current Pareto frontier set W;

[0048] S507, performing an adaptive KneePoint identification strategy on the individuals in the set W to obtain a set N;

[0049] S508, perform environment selection operation on set W and set N to obtain a new population V, and increase the number of iterations by 1 to determine whether the number of iterations is equal to the maximum number of iterations; if so, execute the next step; otherwise, return to S504 to loop;

[0050] S509, searching for non-dominated individuals in the Pareto optimal front after iteration in the population V, selecting the individuals that simultaneously minimize the objective function value as the optimal solution set in the non-dominated solution set formed by the individuals, obtaining the optimal cable crane operation path after multi-objective optimization, and then terminating the entire path optimization.

[0051] In the aforementioned multi-objective wind-variability preference decision method for a cable crane operation path, in step S501, the information required by the cable crane includes the size of the hoisting tank, the acceleration of the hoisting tank operation, and the uniform speed under various working conditions.

[0052] In the aforementioned multi-objective wind-variable preference decision method for a cable crane operation path, in step S502, the initial variables include network parameters, constraints, maximum number of iterations, population size, congestion distance and coordinates of termination conditions, wherein the network parameters include relevant information of the cable crane tank transportation route.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention studies the online control method of the cable crane operation path, suppresses the disturbance of uncertain wind force, and can characterize the multi-objective feedback relationship and dynamic changes of demand of the cable crane operation path under different wind levels, so as to effectively guarantee the specific goals in the critical period and ensure the construction safety. It solves the problem that the existing cable crane operation line planning method is difficult to characterize the changes in target emphasis and competition relationship, and also solves the impact of uncertain factors on the operation of the cable crane.

[0055] Therefore, the present invention has the characteristics of effectively coping with the influence of uncertain wind force on the operation of the cable crane and making the path optimization result more rigorous and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is the wind speed distribution diagram of the cable crane operation of the present invention.

[0057] Figure 2 It is a diagram of the solution steps of the KnEA algorithm of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be further described below in conjunction with the embodiments, but they are not intended to limit the present invention.

[0059] Example:

[0060] A multi-objective wind change preference decision method for a cableway operation path comprises the following steps:

[0061] S1. During the dam pouring process, for the cable crane operation, based on the statistical data of wind speed prediction error, a normal or skew function fitting is used to construct the actual wind speed probability density function based on the predicted value, and the actual wind speed v obtained according to the probability density function is w Calculate wind force F w ;

[0062] Wind force F w The calculation method is:

[0063] S101. Construct the probability density function of actual wind speed: Due to the random fluctuation of wind speed during cableway operation, it cannot be accurately predicted. The actual wind speed v w There is a certain prediction error Δv w , and different prediction methods will show different error distributions;

[0064] like Figure 1 As shown, Figure 1 In is the lower limit of wind speed, is the upper limit of wind speed. Without loss of generality, it is assumed that the wind speed prediction error of the vertical operation of the cable car follows a normal distribution after fitting. The actual wind speed probability density function when the cable car is running vertically is expressed as:

[0065]

[0066] Where p′ is the probability density function, σ w is the standard deviation of the wind speed prediction error.

[0067] S102. Calculate wind force F w :According to the Bernoulli equation wind pressure formula, the actual wind speed v is calculated according to the air density ρ, the radius r of the hanging tank and the height h of the hanging tank. w Switch to wind force F w .

[0068] F w =ρrhv w 2

[0069] S2. Establish optimization targets for the traditional cable crane operation path, for example: cable crane work efficiency target f1, dam pouring space exposure risk target f2 under wind action.

[0070] The calculation method of the traditional cable crane operation path optimization target is as follows:

[0071] The continuous operation of the cableway mainly includes: loading, safety inspection, lifting of the hanging tank, heavy-load operation, lowering of the hanging tank, alignment, unloading, lifting of the hanging tank, empty return, lowering of the hanging tank to the loading platform and other n cycles;

[0072] Since the processes of concrete loading and empty return have the same operating rules as the heavy-load operation process, the operating time t of each operation stage of the cable crane is obtained. i .

[0073] S201, calculate the round trip time T of the cable car from the loading point to the unloading point a :

[0074]

[0075] S202, according to the cable car plan round trip time T plan , calculate the cable crane working efficiency target f1:

[0076]

[0077] S203. Calculate the probability P2 of the hanging tank appearing in the space exposure area: Under certain wind speed conditions, the probability P2 of the hanging tank appearing in the space exposure area is equal to the time t when the hanging tank appears in the space exposure area. a The round trip time T of the cable car from the loading point to the unloading point a Ratio:

[0078]

[0079] S204. Calculate the probability P3 of the vibrating machine appearing in the space exposure area: Similarly, under certain wind speed conditions, the probability P3 of the vibrating machine appearing in the space exposure area is the time t z The total construction time of the pouring layer is T z Ratio:

[0080]

[0081] S205. Calculate the exposure risk target f2 of the dam pouring space: According to the cable crane operation wind force prediction result of the actual wind speed probability density function and the actual wind speed near the dam for one year, the actual wind speed v at each level near the dam site is obtained. w The probability P1, if the robust control wind speed is v lb , and the joint probability is used to obtain the exposure risk target f2 of the dam pouring space under wind action:

[0082] Where V f are the wind speeds at various levels near the dam.

[0083] S3. Establish wind-variable non-inferior frontier and wind-variable target preference weights: Solve the multi-objective optimization model of the traditional cable crane operation path for different wind force levels, obtain the non-inferior frontier under different wind conditions, and then form a wind-variable Pareto frontier cluster, and obtain the time-varying preference weights of the cable crane work efficiency target and the spatial exposure risk target as β eff-j , β exp-j (β eff-j and β eff-j between 0-1).

[0084] The calculation method of the wind change non-inferior front and wind change target preference weights is as follows:

[0085] S301. Define the average slope of the line connecting any point in the Pareto front cluster with two adjacent points as the average rate of change of the point. Calculate the average rate of change of the construction period and spatial exposure risk targets for each point on the non-inferior front, that is, the absolute change of the spatial exposure risk target when the construction period target value changes by one unit. Assume that f1 and f2 are the work efficiency target and the spatial exposure risk target respectively; are the average variability of f1 relative to f2 and f2 relative to f1 at point m, that is, the average variability of work efficiency target and spatial exposure risk target; M is the total number of non-inferior solutions; m is the number of non-inferior solutions sorted from small to large according to the objective function value.

[0086] S302, define the ratio of the two average variation rates of the non-inferior solution m to the corresponding objective function values ​​as the sensitivity ratio, They are work efficiency sensitivity ratio and spatial exposure risk sensitivity ratio respectively.

[0087] S303. Considering the different units and magnitudes of work efficiency and spatial exposure risk, the sensitivity ratio is dimensionally normalized to obtain the dimensionally normalized work efficiency sensitivity ratio. and spatial exposure risk sensitivity ratio

[0088] S304. Since there are many individuals in the Pareto solution set, in order to improve the simplicity of trade-offs and optimize the decision-making process, the concept of non-dominated sorting is used to simplify the selection range, and the dominance relationship of the dimensionally normalized sensitivity ratio sets {ε1} and {ε2} is compared. The non-inferior solution subset is obtained by secondary screening, and the most sensitive solution individual is included in the decision support set X. * :

[0089] S305. Calculate decision support set X * The relative weight of the sensitivity ratio ε1 and ε2 of each individual in is defined as the bias of the individual relative to f1 and f2:

[0090]

[0091]

[0092] Where: are the biases towards work efficiency and spatial exposure risk targets respectively; N is X * The total number of non-inferior solutions in the set; n is the number of elements in the non-inferior solution subset from small to large.

[0093] S306, using the quantified bias degree sets {ω1}, {ω2} as decision support, comprehensively evaluate the decision maker's target preferences at different times j, select the corresponding non-inferior solution individual n, and convert the corresponding bias degree As the time-varying preference weights of work efficiency target and spatial exposure risk target, β eff-j , β exp-j .

[0094] S4. A multi-objective optimization model for cable crane operation considering wind change preference is established. The objective functions are work efficiency function and spatial exposure risk function, and the constraints are time constraint, static collision constraint and dynamic collision constraint.

[0095] The method for establishing the multi-objective optimization model of cable crane operation considering wind variation preference is as follows:

[0096] S401. According to the preference of the construction period target under the j-th wind force condition, the work efficiency function is obtained:

[0097]

[0098] Where, T plan Plan the round trip time for the cable car, T a is the round trip time of the cable car from the loading point to the unloading point, β eff-j is the time-varying preference weight for work efficiency goals.

[0099] S402. According to the preference of the spatial exposure risk target under the j-th wind force condition, the spatial exposure risk function is obtained:

[0100]

[0101] In the formula, β exp-j is the time-varying preference weight for the spatial exposure risk target.

[0102] S403, establish time constraints:

[0103] The construction period is an important control factor in engineering construction. Without affecting the concrete construction of a certain layer of dam block, the maximum total construction time of the cable crane is T zmax Under these conditions, the time constraint is expressed as:

[0104] T amin ≤T a ≤T amax ;

[0105] T zmin ≤T z ≤T zmax ;

[0106] Where, T amin and T amax are the shortest and longest time for a round trip of the cable car; T zmin The shortest time for vertical transportation of cable crane for construction of a certain layer of dam blocks.

[0107] S404, establish static collision constraint conditions:

[0108] d h <d static ;

[0109] Where, d h is the cable car buffer distance, d static It is the minimum distance between the hanging tank and the set obstacle point.

[0110] S405, according to the distance w between adjacent hanging tanks, the swing radius of adjacent hanging tanks is recorded as r1 and r2 respectively, and the dynamic collision constraint condition is established as follows:

[0111] r1+r2≤w.

[0112] S5. The KnEA algorithm is used to solve the non-dominated solution set of the multi-objective optimization model of the cable crane operation path considering wind change preference decision.

[0113] The solution steps are as follows:

[0114] S501, input the information required for the cable crane, including the size of the hanging tank, the acceleration of the hanging tank operation, and the uniform speed under various working conditions; set the driving parameters of the dam surface compactor;

[0115] S502: Input initial variables, and use the initial variables to generate an initial path population.

[0116] The initial variables include:

[0117] a. Network parameters: relevant information on the cableway tank transportation route;

[0118] b. The constraints in step S4;

[0119] c. Coordinates of the maximum number of iterations, population size, crowding distance, and termination criteria.

[0120] S503, for the path population, calculate the solution of the objective function of the cable car operation multi-objective optimization model in step S4; re-sort by using a fast non-dominated sorting method, then perform a selection operation by using a binary tournament mechanism, and perform a mutation crossover operation to generate a first generation path population R;

[0121] S504, constructing a mating pool B using a mating pool selection strategy for individuals in the path population R;

[0122] S505, using a binary tournament mechanism to perform selection, mutation and crossover operations on the mating pool B to generate a subpopulation C, taking the union of the first-generation path population R and the subpopulation C to obtain a new path population U;

[0123] S506, performing non-dominated sorting on the path population U, and calculating the solution of the objective function of the cable crane operation multi-objective optimization model in step S4, to obtain the current Pareto frontier set W;

[0124] S507, performing an adaptive KneePoint identification strategy on the individuals in the set W to obtain a set N;

[0125] S508, perform environment selection operation on set W and set N to obtain a new population V, and increase the number of iterations by 1 to determine whether the number of iterations is equal to the maximum number of iterations; if so, execute the next step; otherwise, return to S504 to loop;

[0126] S509, searching for non-dominated individuals in the Pareto optimal front after iteration in the population V, selecting the individuals that simultaneously minimize the objective function value as the optimal solution set in the non-dominated solution set formed by the individuals, obtaining the optimal cable crane operation path after multi-objective optimization, and then terminating the entire path optimization.

[0127] It should be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. For those skilled in the art, the technical solutions described in the above embodiments can be modified, or some of the technical features therein can be replaced by equivalents; and all these modifications and replacements should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A multi-objective wind change preference decision method for a cableway operation path, characterized by: The steps include: S1. During the dam pouring process, for the cable crane operation, based on the statistical data of wind speed prediction error, a normal or skew function fitting is used to construct the actual wind speed probability density function based on the predicted value, and the actual wind speed v obtained according to the probability density function is w Calculate wind force F w ; S2. Establish optimization targets for the traditional cable crane operation path, including the cable crane work efficiency target f1 and the dam pouring space exposure risk target f2; S3. Establish wind-variable non-inferior frontier and wind-variable target preference weights: solve the multi-objective optimization model of the traditional cable crane operation path for different wind force levels, obtain the non-inferior frontier under different wind conditions, and then form a wind-variable Pareto front cluster to obtain the time-varying preference weights of the cable crane work efficiency target and the spatial exposure risk target; S4. Establish a multi-objective optimization model for cable crane operation considering wind change preference. The objective functions are work efficiency function and spatial exposure risk function. The constraints are time constraint, static collision constraint and dynamic collision constraint. S5. The KnEA algorithm is used to solve the non-dominated solution set of the multi-objective optimization model of the cable crane operation path considering wind change preference decision.

2. A cableway operation path multi-objective wind change preference decision method according to claim 1, characterized in that: In step S1, the actual wind speed probability density function is set as: In the formula, v w is the actual wind speed, To predict wind speed, Δv w is the prediction error; p′ is the probability density function, is a normal distribution, σ w is the standard deviation of the wind speed prediction error.

3. A multi-objective wind change preference decision method for a cableway operation path according to claim 1, characterized in that: In step S1, the wind force F w =ρrhv w 2 ; In the formula, ρ is the air density ρ, r is the radius of the hanging tank, and h is the height of the hanging tank.

4. A multi-objective wind change preference decision method for a cableway operation path according to claim 1, characterized in that: In step S2, the method for calculating the work efficiency target f1 includes the following steps: S201, calculate the round trip time of the cable car from the loading point to the unloading point Where, t i The operating time of each operation stage of the cable car; S202. Calculate the efficiency target of the cable crane Where, T plan Plan your round trip time for the cable car.

5. The cableway operation path multi-objective wind change preference decision method according to claim 1 is characterized by: In step S2, the calculation method of the dam pouring space exposure risk target f2 includes the following steps: S203. Calculate the probability that the hanging tank appears in the spatial exposure area: Where, t a T is the time when the hanging tank appears in the space exposure area; a It is the round trip time of the cable crane from the loading point to the unloading point; S204. Calculate the probability of vibrating machinery appearing in the spatial exposure area: Where, t z is the time when the vibrating machine appears in the spatial exposure area, T z is the total construction time of the pouring layer; S205. Calculate the exposure risk target of the dam pouring space: In the formula, v lb For robust control of wind speed, V f is the wind speed at each level near the dam, P1 is the actual wind speed v at each level near the dam site obtained based on the actual wind speed probability density function and the actual wind speed w probability.

6. A cableway operation path multi-objective wind change preference decision method according to claim 1, characterized in that: In step S3, the method for calculating the wind-variable non-inferior front and the wind-variable target preference weight includes the following steps: S301, define the average value of the slope of the line connecting any point and two adjacent points in the Pareto front cluster as the average variability of the point, calculate the average variability of the construction period and spatial exposure risk targets for each point of the non-inferior front one by one; let f1 and f2 be the work efficiency target and the spatial exposure risk target respectively; are the average variation rates of f1 relative to f2 and f2 relative to f1 at point m, i.e., the average variation rates of work efficiency target and spatial exposure risk target; M is the total number of non-inferior solutions; m is the number of non-inferior solutions sorted in ascending order according to the objective function value; S302, define the ratio of the two average variation rates of the non-inferior solution m to the corresponding objective function values ​​as the sensitivity ratio, They are work efficiency sensitivity ratio and spatial exposure risk sensitivity ratio respectively; S303, perform dimension normalization on the sensitivity ratio to obtain the dimensionally normalized work efficiency sensitivity ratio and spatial exposure risk sensitivity ratio S304, compare the dominance relationship of the dimensionally normalized sensitivity ratio sets {ε1} and {ε2}, obtain a non-inferior solution subset through secondary screening, and include the most sensitive solution individual into the decision support set X * : S305. Calculate decision support set X * The relative weight of the sensitivity ratio ε1 and ε2 of each individual in is defined as the bias of the individual relative to f1 and f2: Where: are the biases towards work efficiency and spatial exposure risk targets respectively; N is X * The total number of non-inferior solutions in ; n is the number of elements in the non-inferior solution subset from small to large; S306, using the quantified bias degree sets {ω1}, {ω2} as decision support, comprehensively evaluate the decision maker's target preferences at different times j, select the corresponding non-inferior solution individual n, and convert the corresponding bias degree As the time-varying preference weights of work efficiency target and spatial exposure risk target, β eff-j , β exp-j .

7. A multi-objective wind change preference decision method for a cableway operation path according to claim 5, characterized in that: In step S4, the method for establishing a multi-objective optimization model for cable crane operation considering wind variation preference includes the following steps: S401. According to the preference of the construction period target under the j-th wind force condition, the work efficiency function is obtained: Where, T plan Plan the round trip time for the cable car, T a is the round trip time of the cable car from the loading point to the unloading point, β eff-j is the time-varying preference weight for work efficiency goals; S402. According to the preference of the spatial exposure risk target under the j-th wind force condition, the spatial exposure risk function is obtained: In the formula, β exp-j is the time-varying preference weight for the spatial exposure risk target; S403, establish time constraints: T amin ≤T a ≤T amax ;T zmin ≤T z ≤T zmax ; Where, T amin and T amax are the shortest and longest time for a round trip of the cable car; T zmin T is the shortest time for vertical transportation of a certain layer of dam blocks by cable crane; zmax The longest total construction time of the cable crane for the concrete construction of a certain layer of dam blocks; S404. Establish static collision constraint conditions: d h <d static ; In the formula, d h is the cable car buffer distance, d static The minimum distance between the hanging tank and the set obstacle point; S405. According to the distance w between adjacent hanging tanks, the swing radii of adjacent hanging tanks are denoted as r1 and r2 respectively, and a dynamic collision constraint condition is established as: r1+r2≤w.

8. The cableway operation path multi-objective wind change preference decision method according to claim 1 is characterized by: In step S5, the solution process of the KnEA algorithm includes the following steps: S501, input the information required by the cable crane and set the driving parameters of the dam surface compactor; S502, inputting initial variables, and using the initial variables to generate an initial path population; S503, for the path population, calculate the objective function of the cable crane operation multi-objective optimization model; re-sort by using the fast non-dominated sorting method, then perform the selection operation by using the binary tournament mechanism, and perform the mutation crossover operation to generate the first generation path population R; S504, constructing a mating pool B for individuals in the path population R; S505, performing selection, mutation and crossover operations on the mating pool B to generate a subpopulation C, taking the union of the path population R and the subpopulation C to obtain a new path population U; S506, performing non-dominated sorting on the path population U, and calculating the solution of the objective function, to obtain the current Pareto frontier set W; S507, performing an adaptive KneePoint identification strategy on the individuals in the set W to obtain a set N; S508, perform environment selection operation on set W and set N to obtain a new population V, and increase the number of iterations by 1 to determine whether the number of iterations is equal to the maximum number of iterations; if so, execute the next step; otherwise, return to S504 to loop; S509, searching for non-dominated individuals in the Pareto optimal front after iteration in the population V, selecting the individuals that simultaneously minimize the objective function value as the optimal solution set from the non-dominated solution set formed by the individuals, obtaining the optimal cable crane operation path after multi-objective optimization, and then terminating the entire path optimization.

9. A cableway operation path multi-objective wind change preference decision method according to claim 8, characterized in that: In step S501, the information required by the cable crane includes the size of the hanging tank, the acceleration of the hanging tank operation, and the uniform speed under various working conditions.

10. A cableway operation path multi-objective wind change preference decision method according to claim 8, characterized in that: In step S502, the initial variables include network parameters, constraints, maximum number of iterations, population size, crowding distance and coordinates of termination conditions, wherein the network parameters include relevant information of the cable crane tank transportation route.

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