Tower crane cluster task scheduling method based on multi-objective genetic algorithm and storage medium

Abstract

The application discloses a tower crane cluster task scheduling method based on a multi-objective genetic algorithm and a storage medium, and comprises the following steps: acquiring a task list of a tower crane cluster; determining a first constraint condition and a multi-objective optimization model; wherein the multi-objective optimization model comprises a first target optimization function with the optimization target of minimizing the total distance of the tower crane cluster in an idle state and a second target optimization function with the optimization target of minimizing the total number of operation conflicts of the tower crane cluster; based on the task list and the first constraint condition, N chromosomes are coded as an initial population; wherein each chromosome comprises the task identification of all to-be-executed planned tasks, and the to-be-executed planned task is a task with the task state of a to-be-executed state and the task type of a non-temporary type; based on the first constraint condition and the multi-objective optimization model, a preset genetic algorithm is used to iteratively optimize the initial population to obtain a Pareto optimal solution set; and based on the Pareto optimal solution set, a scheduling strategy of the tower crane cluster is determined.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and storage medium for scheduling tower crane cluster tasks based on a multi-objective genetic algorithm. Background Technology

[0002] Current mainstream solutions employ single-objective optimization models (such as minimizing total completion time or total hoisting distance), simplifying key factors like safety conflicts, energy consumption, and multi-tower coordination into rigid constraints. However, in actual construction, managers must simultaneously weigh multiple conflicting objectives, including efficiency, safety risks, energy costs, and fairness in construction team collaboration. Single-objective optimization often comes at the expense of other dimensions, failing to provide a Pareto-optimal scheduling solution that considers the needs of all parties, thus preventing the overall system efficiency from being maximized.

[0003] There is currently no effective solution to the technical problem that traditional methods in existing technologies cannot simultaneously achieve both efficiency and safety. Summary of the Invention

[0004] The purpose of this invention is to provide a tower crane cluster task scheduling method and storage medium based on a multi-objective genetic algorithm, which realizes the collaborative optimization of idle distance and number of conflicts, and solves the technical problem that traditional methods cannot simultaneously take into account efficiency and safety.

[0005] According to one aspect of the present invention, a method for scheduling tower crane cluster tasks based on a multi-objective genetic algorithm is provided, comprising:

[0006] Obtain the task list of the tower crane cluster; wherein, the task list includes the task identifier, task status and task type of each task; The first constraint condition and the multi-objective optimization model are determined; wherein, the multi-objective optimization model includes a first objective optimization function with the objective of minimizing the total unloaded distance of the tower crane cluster, and a second objective optimization function with the objective of minimizing the total number of operational conflicts of the tower crane cluster; Based on the task list and the first constraint, N chromosomes are encoded as the initial population; wherein, each chromosome includes the task identifiers of all planned tasks to be executed, and the planned tasks to be executed are tasks whose task status is pending execution and whose task type is non-temporary. Based on the first constraint and the multi-objective optimization model, a preset genetic algorithm is used to iteratively optimize the initial population to obtain the Pareto optimal solution set; Based on the Pareto optimal solution set, the scheduling strategy for the tower crane cluster is determined.

[0007] Optionally, the step of iteratively optimizing the initial population using a preset genetic algorithm based on the first constraint and the multi-objective optimization model to obtain the Pareto optimal solution set includes: Based on the first constraint, each chromosome in the initial population is decoded to obtain the task execution sequence associated with each chromosome; wherein, each task execution sequence includes all task identifiers belonging to the same tower crane in the task list, and the order of the task identifiers is used to characterize the execution order of the corresponding tasks; Based on the task execution sequence associated with each chromosome, the value of the first objective optimization function is calculated to obtain the first optimization objective value corresponding to each chromosome, and the value of the second objective optimization function is calculated to obtain the second optimization objective value corresponding to each chromosome. A new generation population is generated based on the first and second optimization target values ​​for each chromosome. The genetic algorithm is then used to iteratively optimize the new generation of population to obtain the Pareto optimal solution set.

[0008] Optionally, task identifiers belonging to the same tower crane are arranged consecutively within each chromosome, and task identifiers belonging to different tower cranes are separated by a preset separator; based on the first constraint, each chromosome in the initial population is decoded to obtain the task execution sequence associated with each chromosome, including: The chromosome is divided into multiple task segments based on the preset separator; each task segment includes task identifiers for all planned tasks to be executed belonging to the same tower crane; Determine from the task list whether there are any tower cranes associated with the task segment whose task status is "in execution" and / or whose task type is "temporary" for the task segment; If a task in the execution state exists and no temporary task exists, then the task identifier of the task in the execution state is inserted at the beginning of the task segment, and the inserted task segment is used as the task execution sequence. If there is no task in the execution state but there is a task of the temporary type, then the task identifier of the temporary type task is inserted at the beginning of the task segment, and the inserted task segment is used as the task execution sequence. If both the task in the execution state and the temporary task exist simultaneously, the task identifier of the task in the execution state is inserted at the beginning of the task segment, and the task identifier of the temporary task is inserted in the task segment immediately after the task in the execution state. The inserted task segment is then used as the task execution sequence. If there are no tasks in the execution state or tasks of the temporary type, then the task segment is directly used as the task execution sequence.

[0009] Optionally, based on the task execution sequence associated with each chromosome, the value of the first objective optimization function is calculated respectively to obtain the first optimization objective value corresponding to each chromosome, including: Determine the initial position of the tower crane corresponding to each task execution sequence associated with the chromosome; wherein, when there is a task identifier of a task in the execution state in the task execution sequence, the initial position is the end position of the task in the execution state; otherwise, the initial position is the current hook position of the tower crane corresponding to the task execution sequence. Calculate the first idle distance from each initial position to the starting position of the first planned task to be executed in the corresponding task execution sequence; The task identifiers of the planned tasks to be executed in each task execution sequence associated with the chromosome are traversed, and the second idle distance from the end position of the task corresponding to the current traversed task identifier to the start position of the task corresponding to the next task identifier is calculated. The first empty distance and the second empty distance of all task execution sequences associated with the chromosome are summed to obtain the first optimization target value corresponding to the chromosome.

[0010] Optionally, based on the task execution sequence associated with each chromosome, the value of the second objective function is calculated respectively to obtain the second optimization objective value corresponding to each chromosome, including: Obtain a pre-constructed conflict mapping relationship; wherein the conflict mapping relationship is used to characterize the potential conflict relationship between two planned tasks to be executed in any task pair due to overlapping operation paths, and the two planned tasks to be executed in the task pair belong to different tower cranes; Determine the job time period for each planned task to be executed in each task execution sequence associated with the chromosome; wherein the job time period includes the job start time and the job end time; The number of task pairs with potential conflict relationships and overlapping time periods is determined and used as the second optimization target value for the chromosome.

[0011] Optionally, the conflict mapping relationship includes: a first mapping relationship between the slewing intersection area and the task pairs whose operation paths pass through the slewing intersection area and have potential conflict relationships, and a second mapping relationship between each planned task to be executed in the task pairs with potential conflict relationships and the slewing intersection area passed through by the operation path of the planned task to be executed; wherein, the slewing intersection area is the overlapping area of ​​the slewing coverage of any two tower cranes on the horizontal plane.

[0012] Optionally, determining the job time period for each planned task to be executed in each task execution sequence associated with the chromosome includes: Read the pre-calculated movement time information; wherein, the movement time information includes: the first movement time of each tower crane from the hook position at the sampling point to the starting position of each planned task to be executed for the corresponding tower crane, and the second movement time of each tower crane from the end position of each planned task to be executed for the corresponding tower crane to the starting position of each other planned task to be executed; The preset operation path planning module is invoked to calculate the third movement time for each tower crane to move from the starting position of each planned task to the ending position of each planned task in the corresponding task execution sequence; Read the end time of each task in execution status and the end time of each temporary task from the task list; Based on the first movement duration, the second movement duration, the third movement duration, the end time of the task in the execution state, and the end time of the temporary type task, the job time period of each planned task to be executed in each task execution sequence associated with the chromosome is determined.

[0013] Optionally, based on the Pareto optimal solution set, the scheduling strategy for the tower crane cluster is determined, including: Target solutions that meet business requirements are selected from the Pareto optimal solution set; wherein, the target solution includes: a target chromosome, and a first optimization target value and a second optimization target value corresponding to the target chromosome; Based on the first constraint, the target chromosome is decoded to obtain all target task execution sequences associated with the target chromosome; Determine the time interval for each task in the execution sequence of each target task; The job path planning module is invoked to plan the job path for each planned task to be executed in all target task execution sequences, and the reachability results of each job path are verified. Calculate the first optimization effect of the first optimization target value corresponding to the target chromosome compared with the first optimization target value corresponding to the task list; wherein, the task list also includes the initial execution order of each task in the tower crane set; Calculate the second optimization effect of the second optimization target value corresponding to the target chromosome compared to the second optimization target value corresponding to the task list; Based on the first and second optimized target values ​​corresponding to the target chromosome, the execution sequence of all target tasks, the operation time period of each task, the operation path of each planned task to be executed, and the reachability results, a scheduling scheme for the multiple tower cranes is generated.

[0014] Optionally, the first constraint and the multi-objective optimization model are determined, including: Determine whether there are any tower crane pairs in the tower crane cluster whose slewing coverage areas overlap on the horizontal plane; When there are one or more tower crane pairs, determine the first constraint and the multi-objective optimization model; When there are no tower crane pairs, the second constraint condition and the single-objective optimization model are determined; wherein the single-objective optimization model is either the first objective optimization function or the second objective optimization function.

[0015] To achieve the above objectives, the present invention further provides a tower crane cluster task scheduling device based on a multi-objective genetic algorithm, comprising: The acquisition module is used to acquire the task list of the tower crane cluster; wherein, the task list includes the task identifier, task status and task type of each task; The first determining module is used to determine the first constraint conditions and the multi-objective optimization model; wherein, the multi-objective optimization model includes a first objective optimization function with the objective of minimizing the total unloaded distance of the tower crane cluster and a second objective optimization function with the objective of minimizing the total number of operational conflicts of the tower crane cluster; The encoding module is used to encode N chromosomes based on the task list and the first constraint condition, as an initial population; wherein each chromosome includes the task identifiers of all planned tasks to be executed, and the planned tasks to be executed are tasks whose task status is pending execution and whose task type is non-temporary. An optimization module is used to iteratively optimize the initial population using a preset genetic algorithm based on the first constraint and the multi-objective optimization model to obtain the Pareto optimal solution set. The second determining module is used to determine the scheduling strategy of the tower crane cluster based on the Pareto optimal solution set.

[0016] To achieve the above objectives, the present invention also provides a computer device, the computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the tower crane cluster task scheduling method based on a multi-objective genetic algorithm described above.

[0017] To achieve the above objectives, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is used to implement the steps of the tower crane cluster task scheduling method based on a multi-objective genetic algorithm described above.

[0018] This invention provides a tower crane cluster task scheduling method and storage medium based on a multi-objective genetic algorithm. By constructing a multi-objective optimization model with the dual objectives of minimizing the total unloaded distance of the tower crane cluster and the total number of operational conflicts, and combining this with iterative optimization using a genetic algorithm, it achieves coordinated optimization of efficiency and safety in multi-tower crane collaborative operations. This scheme not only significantly reduces the unloaded travel distance of tower cranes and improves operational efficiency while ensuring operational safety, but also provides decision-makers with scheduling schemes of different trade-offs by generating Pareto optimal solution sets, enhancing the flexibility and adaptability of scheduling decisions. Simultaneously, by classifying and constraining tasks based on task status and task type, it ensures that the scheduling process conforms to the actual operational rules of the construction site, improving the feasibility and practicality of the scheduling scheme. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 The flowchart of the tower crane cluster task scheduling method based on multi-objective genetic algorithm provided in Example 1; Figure 2 This is a schematic diagram of the tower crane cluster task scheduling process based on a multi-objective genetic algorithm provided in Example 1; Figure 3 A schematic diagram of the optimization iteration process provided in Example 1; Figure 4 This is a block diagram of the tower crane cluster task scheduling device based on a multi-objective genetic algorithm provided in Embodiment 2; Figure 5 This is a block diagram of a computer device suitable for implementing a tower crane cluster task scheduling method based on a multi-objective genetic algorithm, as provided in Embodiment 3. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0021] Example 1 Embodiment 1 of the present invention provides a tower crane cluster task scheduling method based on a multi-objective genetic algorithm, such as... Figure 1 As shown, the method includes steps S1 to S5, wherein: Step S1: Obtain the task list of the tower crane cluster; wherein the task list includes the task identifier, task status and task type of each task.

[0022] Task status includes in-process and pending status; task types include temporary and non-temporary types, with non-temporary types also known as planned types. Tasks in the in-process status are currently being executed but have not yet been completed; tasks in the pending status are tasks waiting to be executed; temporary tasks are urgent tasks that are suddenly inserted and need to be executed first, i.e., unplanned hoisting tasks; non-temporary tasks are planned hoisting tasks.

[0023] The task list includes task information for all tower cranes in the tower crane cluster. The task list may also include: the tower crane identifier of each task, the start and end points of each task, and the size and type of the load being lifted for each task.

[0024] It should be noted that, to ensure the accuracy of subsequent calculations, the coordinates of all location points used in this application need to be standardized. For example, the pixel coordinates need to be converted to the Cartesian coordinate system of the map, and then the Cartesian coordinate system of the map needs to be converted to the polar coordinate system.

[0025] Step S2: Determine the first constraint and the multi-objective optimization model; wherein the multi-objective optimization model includes a first objective optimization function with the objective of minimizing the total unloaded distance of the tower crane cluster and a second objective optimization function with the objective of minimizing the total number of operational conflicts of the tower crane cluster.

[0026] The first constraints include: tasks in the execution state and temporary tasks do not participate in chromosome encoding and optimization of the genetic algorithm; only tasks awaiting execution participate. In the task execution sequence obtained after chromosome decoding, tasks in the execution state are arranged first, temporary tasks are arranged immediately after tasks in the execution state, and tasks awaiting execution are arranged last. The end position and end time of tasks in the execution state cannot be adjusted. The job time period of temporary tasks serves as a fixed constraint for the time extrapolation of subsequent tasks awaiting execution.

[0027] The first objective optimization function aims to minimize the total unloaded distance of the tower crane cluster, and is used to measure the operational efficiency of the scheduling scheme. The total unloaded distance refers to the sum of the unloaded distances traveled by all tower cranes during task execution, from the hook position at the sampling point to the first task start point, and from one task end point to the next task start point.

[0028] The second objective optimization function aims to minimize the total number of operational conflicts in the tower crane cluster, and is used to measure the operational safety of the scheduling scheme. The total number of operational conflicts refers to the sum of the number of conflicts arising from overlapping work periods of different tower cranes within all slewing intersection areas. The slewing intersection area is the horizontal overlap of the slewing coverage of any two tower cranes.

[0029] It should be noted that in the genetic algorithm optimization process, the first constraint, as a rigid rule, determines the basic framework of chromosome encoding, decoding, and hoisting time extrapolation. For example, by constraining the time and location information of tasks in execution to be unadjustable, seamless integration between the scheduling scheme and the actual on-site conditions is ensured; temporary tasks do not participate in the genetic algorithm optimization but are directly inserted at the front of the scheduling queue, demonstrating the system's rapid response capability to dynamic changes at the construction site; and the planned tasks to be executed serve as the core object of optimization, with the optimal execution order searched through the genetic algorithm. This processing mechanism solves the technical problems of existing technologies that cannot effectively distinguish between tasks of different priorities and have weak dynamic response capabilities.

[0030] A multi-objective optimization model serves as the optimization guide, directing the algorithm to search for Pareto optimal scheduling schemes that balance efficiency and safety while satisfying constraints. For example, traditional methods treat conflicts as insurmountable hard constraints, directly excluding solutions with conflict risks. This leads to a significant compression of the solution space in task-intensive scenarios, easily resulting in a no-solution dilemma. This embodiment transforms the number of conflicts from a hard constraint into a quantifiable optimization objective, treating conflicts as an optimizable cost indicator, enabling the algorithm to explore high-quality solutions in a larger solution space.

[0031] Step S3: Based on the task list and the first constraint, N chromosomes are encoded as the initial population; wherein, each chromosome includes the task identifiers of all planned tasks to be executed, and the planned tasks to be executed are tasks whose task status is pending execution and whose task type is non-temporary, and N is an integer greater than 1.

[0032] Chromosome encoding follows a first constraint: only tasks awaiting execution are encoded; tasks in execution or temporary tasks are excluded. Each chromosome includes task identifiers for all tasks awaiting execution in the task list. Task identifiers belonging to the same task on each chromosome are arranged consecutively, while task identifiers belonging to different task on different task on different task on different task on different task on the same task on the same task on different ..., while providing clear boundary constraints for subsequent genetic operations and avoiding illegal solutions.

[0033] The task identifiers of each tower crane are arranged in different orders across different chromosomes, thus forming a diverse initial population and providing a foundation for the subsequent global search of the genetic algorithm. N chromosomes are generated by randomly arranging the task identifiers within each tower crane, with each chromosome representing a different execution order scheme for the planned tasks.

[0034] For example, a tower crane cluster has three tower cranes: A, B, and C. The task list includes task identifiers A1, A2, and A3 for tower crane A, B1 and B2 for tower crane B, and C1, C2, and C3 for tower crane C. A1 represents the task identifier for a task in execution, A2 represents a temporary task, and the rest are task identifiers for planned tasks awaiting execution. According to the first constraint, only planned tasks awaiting execution (A3, B1, B2, C1, C2, C3) participate in the encoding. Assuming the default separator is a semicolon, one chromosome can be: {A3; B1, B2; C1, C2, C3}, and another chromosome can be: {A3; B2, B1; C2, C1, C3}. During decoding, the chromosome can be divided into three task segments based on the semicolon, corresponding to the order of the planned tasks awaiting execution for each of the three tower cranes.

[0035] The coding method in this step reflects the core requirement of the first constraint: only planned tasks awaiting execution are coded; tasks in progress and temporary tasks are not. Task identifiers belonging to the same crane within each chromosome are arranged consecutively, and task identifiers from different cranes are separated by a preset separator.

[0036] Step S4: Based on the first constraint and the multi-objective optimization model, the initial population is iteratively optimized using a preset genetic algorithm to obtain the Pareto optimal solution set.

[0037] In this step, a multi-objective genetic algorithm is used to iteratively optimize the initial population. The genetic algorithm can be from the NSGA (Non-dominated Sorting Genetic Algorithm) series, such as NSGA-II and NSGA-III. In each iteration, each chromosome in the current population is first decoded to obtain the task execution sequence corresponding to each tower crane. The task execution sequence includes the task identifiers of all tasks for each tower crane. Then, based on the decoding results, the total idle distance value for each chromosome is calculated according to the first objective optimization function, and the total number of job conflicts for each chromosome is calculated according to the second objective optimization function. Each chromosome corresponds to a set of objective values: total idle distance and total number of job conflicts.

[0038] Based on the two target values ​​for each chromosome, the chromosomes in the current population are divided into multiple Pareto levels using a non-dominated sorting algorithm. The first level consists of non-dominated solutions that are not dominated by any other chromosome. Simultaneously, the crowding density of each chromosome within the same level is calculated to measure the distribution density of solutions in the target space. Based on the non-dominated levels and crowding density, superior chromosomes are selected as parents using methods such as tournament selection. Offspring chromosomes are generated through crossover and mutation operations, forming a new generation of the population.

[0039] The above iterative process is repeated until a preset termination condition is met. The termination condition can be reaching the maximum number of iterations or reaching the maximum computation time. For multi-tower collaborative scenarios, setting an upper limit on computation time can ensure that the algorithm outputs a high-quality solution set within a finite time, meeting the real-time requirements of the construction site.

[0040] After iteration, all non-dominated solutions are extracted from the last generation of the population, forming a Pareto optimal solution set. The Pareto optimal solution set includes multiple Pareto optimal solutions, each consisting of: a chromosome, the value of the first objective function corresponding to that chromosome, and the value of the second objective function. These solutions are non-dominated in both the total unloaded distance and the total number of job conflicts, collectively constituting a scheme that balances efficiency and safety integrity, providing decision-makers with a flexible choice space.

[0041] Optionally, based on the first constraint and the multi-objective optimization model, a preset genetic algorithm is used to iteratively optimize the initial population to obtain the Pareto optimal solution set, including: Based on the first constraint, each chromosome in the initial population is decoded to obtain the task execution sequence associated with each chromosome; wherein, each task execution sequence includes all task identifiers belonging to the same tower crane in the task list, and the order of the task identifiers is used to characterize the execution order of the corresponding tasks; Based on the task execution sequence associated with each chromosome, the value of the first objective optimization function is calculated to obtain the first optimization objective value corresponding to each chromosome, and the value of the second objective optimization function is calculated to obtain the second optimization objective value corresponding to each chromosome. A new generation population is generated based on the first and second optimization target values ​​for each chromosome. The genetic algorithm is then used to iteratively optimize the new generation of population to obtain the Pareto optimal solution set.

[0042] Specifically, the decoding process transforms the order of planned tasks represented by the chromosomes into a complete sequence of tasks that each tower crane needs to execute during actual construction. The decoding process strictly adheres to the first constraint: tasks in the execution state are listed first, temporary tasks are placed immediately after tasks in the execution state, and planned tasks are listed last. The number of task execution sequences associated with each chromosome after decoding is the same as the number of tower cranes in the tower crane cluster, with each task execution sequence corresponding to one tower crane. This design ensures that regardless of chromosome changes, the final task execution sequence always satisfies the on-site operation rules, avoiding the problem of algorithm optimization being disconnected from on-site execution.

[0043] For example, combining the above examples, for chromosome {A3; B2, B1; C2, C1, C3}, after decoding, we get: the task execution sequence of tower crane A is (A1, A2, A3), the task execution sequence of tower crane B is (B2, B1), and the task execution sequence of tower crane B is (C2, C1, C3).

[0044] The value of the first objective optimization function is denoted as the first optimization objective value; the value of the second objective optimization function is denoted as the second optimization objective value. Based on the task execution sequence associated with each chromosome, the value of the first objective optimization function is calculated to obtain the first optimization objective value for each chromosome, and the value of the second objective optimization function is calculated to obtain the second optimization objective value for each chromosome. Specifically, based on the task execution sequence associated with each chromosome, the values ​​of the first objective optimization function and the second objective optimization function are calculated to obtain the first and second optimization objective values ​​for each chromosome. The first optimization objective value is calculated by accumulating the unloaded travel distance of all tower cranes during task execution, and the second optimization objective value is calculated by counting the number of conflicts caused by overlapping operation time periods between all tower cranes.

[0045] Optionally, task identifiers belonging to the same tower crane are arranged consecutively within each chromosome, and task identifiers belonging to different tower cranes are separated by a preset separator; based on the first constraint, each chromosome in the initial population is decoded to obtain the task execution sequence associated with each chromosome, including: The chromosome is divided into multiple task segments based on the preset separator; each task segment includes task identifiers for all planned tasks to be executed belonging to the same tower crane; Determine from the task list whether there are any tower cranes associated with the task segment whose task status is "in execution" and / or whose task type is "temporary" for the task segment; If a task in the execution state exists and no temporary task exists, then the task identifier of the task in the execution state is inserted at the beginning of the task segment, and the inserted task segment is used as the task execution sequence. If there is no task in the execution state but there is a task of the temporary type, then the task identifier of the temporary type task is inserted at the beginning of the task segment, and the inserted task segment is used as the task execution sequence. If both the task in the execution state and the temporary task exist simultaneously, the task identifier of the task in the execution state is inserted at the beginning of the task segment, and the task identifier of the temporary task is inserted in the task segment immediately after the task in the execution state. The inserted task segment is then used as the task execution sequence. If there are no tasks in the execution state or tasks of the temporary type, then the task segment is directly used as the task execution sequence.

[0046] Specifically, taking the decoding of a certain chromosome as an example, the chromosome decoding process will be explained in detail. The decoding process of other chromosomes is the same as or similar to that in this embodiment.

[0047] Since task identifiers belonging to the same tower crane are arranged consecutively within each chromosome, and task identifiers from different tower cranes are separated by a preset delimiter, each task segment obtained after partitioning corresponds to one tower crane. Each task segment contains the task identifiers of all planned tasks to be executed for that tower crane, and their arrangement order determines the execution order of those planned tasks. For each task segment, the task list is queried to determine if any tower crane associated with that segment has a task status of "in execution" or a task type of "temporary." According to the first constraint, although tasks in execution and temporary tasks do not participate in chromosome encoding, they must be inserted into the corresponding tower crane's task sequence according to fixed rules when generating the final task execution sequence. Through these decoding rules, the chromosome is converted into a set of task execution sequences equal to the number of tower cranes in the tower crane cluster. Each task execution sequence includes all task identifiers of the corresponding tower crane, and the arrangement order of the task identifiers precisely represents the order in which the tower crane executes the tasks according to the scheduling scheme.

[0048] Optionally, based on the task execution sequence associated with each chromosome, the value of the first objective optimization function is calculated respectively to obtain the first optimization objective value corresponding to each chromosome, including: Determine the initial position of the tower crane corresponding to each task execution sequence associated with the chromosome; wherein, when there is a task identifier of a task in the execution state in the task execution sequence, the initial position is the end position of the task in the execution state; otherwise, the initial position is the current hook position of the tower crane corresponding to the task execution sequence. Calculate the first idle distance from each initial position to the starting position of the first planned task to be executed in the corresponding task execution sequence; The task identifiers of the planned tasks to be executed in each task execution sequence associated with the chromosome are traversed, and the second idle distance from the end position of the task corresponding to the current traversed task identifier to the start position of the task corresponding to the next task identifier is calculated. The first empty distance and the second empty distance of all task execution sequences associated with the chromosome are summed to obtain the first optimization target value corresponding to the chromosome.

[0049] Specifically, if a task is in progress, it indicates that the tower crane is currently performing a task, and its hook position will move as the task progresses. Therefore, the endpoint of the task in progress is used as the initial position. If no task is in progress, it indicates that the tower crane is idle, and its hook is at the endpoint of the previous task or the default standby point. In this case, the current hook position of the tower crane is used as the initial position. If a task execution sequence contains only one planned task to be executed, there is no second idle distance. If it contains multiple planned tasks to be executed, the second idle distance must be calculated between every two adjacent tasks. The first idle distance reflects the distance the tower crane needs to travel without load from its initial position to the starting point of the first task. The second idle distance reflects the distance the tower crane needs to travel without load to the starting point of the next task after completing one task. The total idle distance is used to measure the operational efficiency of the scheduling scheme; the smaller this value, the higher the efficiency of the scheduling scheme.

[0050] Optionally, based on the task execution sequence associated with each chromosome, the value of the second objective function is calculated respectively to obtain the second optimization objective value corresponding to each chromosome, including: Obtain a pre-constructed conflict mapping relationship; wherein the conflict mapping relationship is used to characterize the potential conflict relationship between two planned tasks to be executed in any task pair due to overlapping operation paths, and the two planned tasks to be executed in the task pair belong to different tower cranes; Determine the job time period for each planned task to be executed in each task execution sequence associated with the chromosome; wherein the job time period includes the job start time and the job end time; The number of task pairs with potential conflict relationships and overlapping time periods is determined and used as the second optimization target value for the chromosome.

[0051] Specifically, the conflict mapping relationship is a pre-calculated and stored data structure based on the working radius and spatial location of each tower crane, as well as the start and end points of each planned task to be executed. It is used to characterize the potential conflict between any two planned tasks belonging to different tower cranes due to the spatial overlap of their respective work paths. The construction of this mapping relationship is independent of specific chromosomes and can be completed all at once before the optimization process begins, improving the efficiency of subsequent iterative calculations. The conflict mapping relationship may include: a first mapping relationship between the slewing intersection area and the task pairs whose work paths pass through the slewing intersection area and have a potential conflict relationship; and a second mapping relationship between each planned task in the task pair with a potential conflict relationship and the slewing intersection area traversed by the work path of that planned task. The slewing intersection area is the overlapping area of ​​the slewing coverage of any two tower cranes on the horizontal plane. Optionally, the conflict mapping relationship may also include: a third mapping relationship between the task pairs with potential conflicts and the conflict type and severity. Conflict types include: full conflict and partial conflict. Full conflict indicates that the operation path of the task pair completely passes through the corresponding turn-over area, while partial conflict indicates that the operation path of the task pair partially passes through the corresponding turn-over area.

[0052] Determining the work period is crucial for judging whether two tasks will actually conflict. For each task execution sequence, the time taken from the end of the previous task to the end of the next task is calculated. Using the end time of the task in the corresponding tower crane's execution state or the end time of a temporary task as the start time, the durations of the corresponding tasks are accumulated sequentially to obtain the start and end times of each planned task to be executed. Existing algorithms can be used to calculate the time taken for each task.

[0053] Based on the conflict mapping relationship, potentially conflicting task pairs are identified within the chromosome. Then, for each potentially conflicting task pair, the execution time slots of the two tasks are obtained, and it is determined whether these two time slots overlap. If they overlap, it means that the two tasks will simultaneously occupy the same spatial region during actual execution, thus constituting a valid conflict, and the conflict count is incremented. After traversing all potentially conflicting task pairs, the number of task pairs with overlapping execution time slots is accumulated to obtain the second optimization objective value corresponding to the chromosome. This value is used to measure the job safety of the scheduling scheme; the smaller the value, the safer the scheduling scheme.

[0054] It should be noted that although the conflict mapping relationship may contain a third mapping relationship, the number of conflicts can be directly used as the basic metric when calculating the second optimization objective value. If it is necessary to differentiate the conflict severity, the number of conflicts can also be weighted based on the third mapping relationship to obtain the weighted total number of conflicts as the second optimization objective value.

[0055] Optionally, determining the job time period for each planned task to be executed in each task execution sequence associated with the chromosome includes: Read the pre-calculated movement time information; wherein, the movement time information includes: the first movement time of each tower crane from the hook position at the sampling point to the starting position of each planned task to be executed for the corresponding tower crane, and the second movement time of each tower crane from the end position of each planned task to be executed for the corresponding tower crane to the starting position of each other planned task to be executed; The preset operation path planning module is invoked to calculate the third movement time for each tower crane to move from the starting position of each planned task to the ending position of each planned task in the corresponding task execution sequence; Read the end time of each task in execution status and the end time of each temporary task from the task list; Based on the first movement duration, the second movement duration, the third movement duration, the end time of the task in the execution state, and the end time of the temporary type task, the job time period of each planned task to be executed in each task execution sequence associated with the chromosome is determined.

[0056] Specifically, the first and second travel durations are calculated based on a preset time precision model. This time precision model includes gear shifting time calculation, multi-dimensional time accumulation, and a dynamic adjustment mechanism.

[0057] Gear switching time calculation: Tower cranes typically have multiple gears for luffing, slewing, and hoisting movements. The time accuracy model automatically selects the optimal gear combination based on the travel distance, decomposing each movement into four stages: acceleration, constant speed, deceleration, and braking, and calculating the time for each stage separately. For the first gear, the total time is the sum of the times for acceleration, constant speed, deceleration, and braking. For intermediate gears, the total time is the current gear distance minus the travel time corresponding to the previous gear distance, plus the current gear deceleration distance minus the deceleration time corresponding to the previous gear deceleration distance. For the highest gear, the remaining distance is traveled at the highest gear speed at a constant speed, and the acceleration, deceleration, and braking times are included.

[0058] Multi-dimensional time accumulation: Calculate the luffing time, slewing time, and hoisting time required from the starting point to the ending point. Since luffing and slewing can be performed simultaneously, the larger of the two values ​​is taken as the combined luffing and slewing motion time. Hoisting usually occurs after the luffing and slewing are in place, therefore the total motion time = max(luffing time, slewing time) + hoisting time.

[0059] Dynamic adjustment mechanism: Real-time monitoring of tower crane position and task status changes. When the tower crane position changes due to task execution or a new temporary task is inserted, the relevant time data is automatically recalculated and the cached results in the inter-task movement time matrix are updated to ensure that the time data always reflects the latest status.

[0060] The task path planning module is used to plan the task's task path based on the task's start and end points, load size, and load type. Then, it calculates the third movement time for each task based on the task path and a precise time model.

[0061] For each task execution sequence, the start time of the corresponding tower crane is first determined. If the tower crane has a task in execution, the end time of that task is used as the start time. If there is no task in execution but there is a temporary task, the end time of that temporary task is used as the start time. If neither exists, the sampling point time is used as the start time. Then, for the first planned task to be executed in the task execution sequence, the start time is the start time. The overall operation duration is the sum of the idle time and the operation duration, i.e., the overall operation duration is the sum of the first movement duration, the second movement duration, and the third movement duration. The operation end time is equal to the sum of the operation start time and the overall operation duration.

[0062] Traditional methods require recalculation from scratch for each scheduling task, which can take minutes or even hours in complex scenarios, making them unsuitable for real-time scheduling demands in dynamic construction. This invention addresses this by constructing a multi-level caching system, including multi-dimensional time caching, path planning caching, and conflict detection caching, through result reuse and intelligent caching mechanisms. It intelligently identifies task and location changes, directly reusing previous results even with minor changes, resulting in a response speed improvement of hundreds of times and achieving a transformation from offline computation to real-time response.

[0063] Step S5: Based on the Pareto optimal solution set, determine the scheduling strategy for the tower crane cluster.

[0064] The Pareto optimal solution set is a set of non-dominated solutions obtained through iterative optimization using a multi-objective genetic algorithm. These solutions are not dominant to each other in terms of the two optimization objectives: total idle distance and total number of job conflicts. That is, it is impossible to further reduce the total idle distance without increasing the number of conflicts, nor is it possible to further reduce the number of conflicts without increasing the total idle distance.

[0065] Based on the actual needs of the construction site, a target solution is selected from the Pareto optimal solution set. The target solution includes: a target chromosome, and the corresponding first and second optimization target values. Based on the selected target solution, a scheduling strategy for the tower crane cluster is generated. The scheduling strategy includes: the first and second optimization target values ​​corresponding to the target chromosome, the execution sequence of all target tasks obtained after decoding the target chromosome, the operation time period of each task, the operation path of each planned task to be executed, and the reachability result of the operation path. If the second optimization target value is not 0, the scheduling strategy also includes: the task identifier of each planned task to be executed in task pairs with potential conflicts and overlapping operation time periods.

[0066] Optionally, based on the Pareto optimal solution set, the scheduling strategy for the tower crane cluster is determined, including: Target solutions that meet business requirements are selected from the Pareto optimal solution set; wherein, the target solution includes: a target chromosome, and a first optimization target value and a second optimization target value corresponding to the target chromosome; Based on the first constraint, the target chromosome is decoded to obtain all target task execution sequences associated with the target chromosome; Determine the time interval for each task in the execution sequence of each target task; The job path planning module is invoked to plan the job path for each planned task to be executed in all target task execution sequences, and the reachability results of each job path are verified. Calculate the first optimization effect of the first optimization target value corresponding to the target chromosome compared with the first optimization target value corresponding to the task list; wherein, the task list also includes the initial execution order of each task in the tower crane set; Calculate the second optimization effect of the second optimization target value corresponding to the target chromosome compared to the second optimization target value corresponding to the task list; Based on the first and second optimized target values ​​corresponding to the target chromosome, the execution sequence of all target tasks, the operation time period of each task, the operation path of each planned task to be executed, and the reachability results, a scheduling scheme for the multiple tower cranes is generated.

[0067] Specifically, referring to the above embodiments for calculating the first and second optimization target values ​​of the chromosome, the first and second optimization target values ​​corresponding to the task list can be calculated based on the initial execution order. Calculating the first optimization effect can be done by calculating the difference between the first optimization target value corresponding to the task list and the first optimization target value corresponding to the target chromosome, and then calculating the percentage of the ratio of this difference to the first optimization target value corresponding to the task list. Calculating the second optimization effect can be done by calculating the difference between the second optimization target value corresponding to the task list and the second optimization target value corresponding to the target chromosome, and then calculating the percentage of the ratio of this difference to the second optimization target value corresponding to the task list.

[0068] Traditional methods cannot fully quantify the optimization effect of scheduling schemes, and decisions often rely on experience-based judgment. This embodiment provides comprehensive output to ensure the high feasibility of the scheduling scheme in actual construction, providing a reliable guarantee for the efficient collaborative operation of tower crane clusters.

[0069] Optionally, the first constraint and the multi-objective optimization model are determined, including: Determine whether there are any tower crane pairs in the tower crane cluster whose slewing coverage areas overlap on the horizontal plane; When there are one or more tower crane pairs, determine the first constraint and the multi-objective optimization model; When there are no tower crane pairs, the second constraint condition and the single-objective optimization model are determined; wherein the single-objective optimization model is either the first objective optimization function or the second objective optimization function.

[0070] Specifically, the second constraint is essentially the same as the first constraint, also used to standardize task processing rules, including: tasks in the execution state and temporary tasks do not participate in genetic algorithm encoding, only tasks awaiting execution participate in optimization; the time information of tasks in the execution state is used as a fixed constraint; temporary tasks have a fixed insertion position after decoding; in the task execution sequence obtained after chromosome decoding, tasks in the execution state come first, temporary tasks second, and tasks awaiting execution last. The difference from the first constraint is that, since there is no overlapping region, conflict detection is not required during optimization, thus simplifying some conflict-related constraints, such as eliminating the need to construct conflict mapping relationships, but the task priority and time extrapolation rules remain consistent.

[0071] The adaptive selection mechanism in this embodiment can dynamically adjust the optimization strategy according to the actual spatial layout of the tower crane cluster. This not only improves the adaptability of the algorithm in different scenarios, but also optimizes the use of computing resources, ensuring that a high-quality scheduling scheme can still be generated within a limited time in complex multi-tower scenarios.

[0072] The following is combined with Figure 2 and Figure 3Explain the specific implementation process.

[0073] First, obtain the task list of the tower crane cluster, then perform coordinate transformation to unify the coordinates used subsequently. Second, construct the conflict mapping relationship, calculate and cache the first and second movement times. Third, check whether there is a turning intersection area in the tower crane set. If not, execute the single-objective optimization algorithm; if so, execute the multi-objective optimization algorithm. When executing the multi-objective optimization algorithm: determine the first constraint and the multi-objective optimization model; generate N chromosomes based on all planned tasks to be executed, as the initial population; calculate the two objective values ​​for each chromosome in the initial population; perform non-dominated sorting on these chromosomes based on the two objective values ​​for each chromosome; for chromosomes at the same level, maintain the diversity of the population through the reference point selection mechanism (NSGA-III) or crowding distance calculation (NSGA-II); based on the non-dominated level and reference point association (or crowding distance), select excellent individuals from the current population as parents to generate the next generation population using methods such as tournament selection; perform crossover operation on the selected parent chromosomes to generate offspring chromosomes; perform mutation operation on the offspring chromosomes; merge the parent population and the offspring population, perform non-dominated sorting and reference point selection (or crowding calculation) on the merged population, and select N excellent individuals as the new generation population to enter the next iteration; repeat the above process until the termination condition is met, and output the Pareto optimal solution set. Furthermore, target solutions that meet business requirements are selected from the Pareto optimal solution set, and the final tower crane cluster scheduling scheme is generated based on the target solutions.

[0074] Example 2 This invention provides a tower crane cluster task scheduling device based on a multi-objective genetic algorithm, such as... Figure 4 As shown, the tower crane cluster task scheduling device 40 based on a multi-objective genetic algorithm specifically includes the following components: The acquisition module 401 is used to acquire the task list of the tower crane cluster; wherein, the task list includes the task identifier, task status and task type of each task; The first determining module 402 is used to determine the first constraint conditions and the multi-objective optimization model; wherein, the multi-objective optimization model includes a first objective optimization function with the optimization objective of minimizing the total unloaded distance of the tower crane cluster and a second objective optimization function with the optimization objective of minimizing the total number of operation conflicts of the tower crane cluster; The encoding module 403 is used to encode N chromosomes based on the task list and the first constraint condition, as an initial population; wherein each chromosome includes the task identifiers of all planned tasks to be executed, and the planned tasks to be executed are tasks whose task status is pending execution and whose task type is non-temporary. The optimization module 404 is used to iteratively optimize the initial population using a preset genetic algorithm based on the first constraint and the multi-objective optimization model to obtain the Pareto optimal solution set. The second determining module 405 is used to determine the scheduling strategy of the tower crane cluster based on the Pareto optimal solution set.

[0075] Optionally, the optimization module is specifically used for: Based on the first constraint, each chromosome in the initial population is decoded to obtain the task execution sequence associated with each chromosome; wherein, each task execution sequence includes all task identifiers belonging to the same tower crane in the task list, and the order of the task identifiers is used to characterize the execution order of the corresponding tasks; Based on the task execution sequence associated with each chromosome, the value of the first objective optimization function is calculated to obtain the first optimization objective value corresponding to each chromosome, and the value of the second objective optimization function is calculated to obtain the second optimization objective value corresponding to each chromosome. A new generation population is generated based on the first and second optimization target values ​​for each chromosome. The genetic algorithm is then used to iteratively optimize the new generation of population to obtain the Pareto optimal solution set.

[0076] Optionally, task identifiers belonging to the same tower crane are arranged consecutively within each chromosome, and task identifiers belonging to different tower cranes are separated by a preset separator; when the optimization module decodes each chromosome in the initial population based on the first constraint to obtain the task execution sequence associated with each chromosome, it is specifically used for: The chromosome is divided into multiple task segments based on the preset separator; each task segment includes task identifiers for all planned tasks to be executed belonging to the same tower crane; Determine from the task list whether there are any tower cranes associated with the task segment whose task status is "in execution" and / or whose task type is "temporary" for the task segment; If a task in the execution state exists and no temporary task exists, then the task identifier of the task in the execution state is inserted at the beginning of the task segment, and the inserted task segment is used as the task execution sequence. If there is no task in the execution state but there is a task of the temporary type, then the task identifier of the temporary type task is inserted at the beginning of the task segment, and the inserted task segment is used as the task execution sequence. If both the task in the execution state and the temporary task exist simultaneously, the task identifier of the task in the execution state is inserted at the beginning of the task segment, and the task identifier of the temporary task is inserted in the task segment immediately after the task in the execution state. The inserted task segment is then used as the task execution sequence. If there are no tasks in the execution state or tasks of the temporary type, then the task segment is directly used as the task execution sequence.

[0077] Optionally, when the optimization module executes the task execution sequence based on each chromosome association, calculates the value of the first objective optimization function respectively, and obtains the first optimization objective value corresponding to each chromosome, it is specifically used for: Determine the initial position of the tower crane corresponding to each task execution sequence associated with the chromosome; wherein, when there is a task identifier of a task in the execution state in the task execution sequence, the initial position is the end position of the task in the execution state; otherwise, the initial position is the current hook position of the tower crane corresponding to the task execution sequence. Calculate the first idle distance from each initial position to the starting position of the first planned task to be executed in the corresponding task execution sequence; The task identifiers of the planned tasks to be executed in each task execution sequence associated with the chromosome are traversed, and the second idle distance from the end position of the task corresponding to the current traversed task identifier to the start position of the task corresponding to the next task identifier is calculated. The first empty distance and the second empty distance of all task execution sequences associated with the chromosome are summed to obtain the first optimization target value corresponding to the chromosome.

[0078] Optionally, when the optimization module executes the task execution sequence based on each chromosome association, calculates the value of the second objective optimization function respectively, and obtains the second optimization objective value corresponding to each chromosome, it is specifically used for: Obtain a pre-constructed conflict mapping relationship; wherein the conflict mapping relationship is used to characterize the potential conflict relationship between two planned tasks to be executed in any task pair due to overlapping operation paths, and the two planned tasks to be executed in the task pair belong to different tower cranes; Determine the job time period for each planned task to be executed in each task execution sequence associated with the chromosome; wherein the job time period includes the job start time and the job end time; The number of task pairs with potential conflict relationships and overlapping time periods is determined and used as the second optimization target value for the chromosome.

[0079] Optionally, the conflict mapping relationship includes: a first mapping relationship between the slewing intersection area and the task pairs whose operation paths pass through the slewing intersection area and have potential conflict relationships, and a second mapping relationship between each planned task to be executed in the task pairs with potential conflict relationships and the slewing intersection area passed through by the operation path of the planned task to be executed; wherein, the slewing intersection area is the overlapping area of ​​the slewing coverage of any two tower cranes on the horizontal plane.

[0080] Optionally, when the optimization module executes the job time period for each planned task to be executed in each task execution sequence that determines the chromosome association, it is specifically used for: Read the pre-calculated movement time information; wherein, the movement time information includes: the first movement time of each tower crane from the hook position at the sampling point to the starting position of each planned task to be executed for the corresponding tower crane, and the second movement time of each tower crane from the end position of each planned task to be executed for the corresponding tower crane to the starting position of each other planned task to be executed; The preset operation path planning module is invoked to calculate the third movement time for each tower crane to move from the starting position of each planned task to the ending position of each planned task in the corresponding task execution sequence; Read the end time of each task in execution status and the end time of each temporary task from the task list; Based on the first movement duration, the second movement duration, the third movement duration, the end time of the task in the execution state, and the end time of the temporary type task, the job time period of each planned task to be executed in each task execution sequence associated with the chromosome is determined.

[0081] Optionally, the second determining module is specifically used for: Target solutions that meet business requirements are selected from the Pareto optimal solution set; wherein, the target solution includes: a target chromosome, and a first optimization target value and a second optimization target value corresponding to the target chromosome; Based on the first constraint, the target chromosome is decoded to obtain all target task execution sequences associated with the target chromosome; Determine the time interval for each task in the execution sequence of each target task; The job path planning module is invoked to plan the job path for each planned task to be executed in all target task execution sequences, and the reachability results of each job path are verified. Calculate the first optimization effect of the first optimization target value corresponding to the target chromosome compared with the first optimization target value corresponding to the task list; wherein, the task list also includes the initial execution order of each task in the tower crane set; Calculate the second optimization effect of the second optimization target value corresponding to the target chromosome compared to the second optimization target value corresponding to the task list; Based on the first and second optimized target values ​​corresponding to the target chromosome, the execution sequence of all target tasks, the operation time period of each task, the operation path of each planned task to be executed, and the reachability results, a scheduling scheme for the multiple tower cranes is generated.

[0082] Optionally, the first determining module is specifically used for: Determine whether there are any tower crane pairs in the tower crane cluster whose slewing coverage areas overlap on the horizontal plane; When there are one or more tower crane pairs, determine the first constraint and the multi-objective optimization model; When there are no tower crane pairs, the second constraint condition and the single-objective optimization model are determined; wherein the single-objective optimization model is either the first objective optimization function or the second objective optimization function.

[0083] Example 3 This embodiment also provides a computer device, such as a smartphone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server, or cabinet server (including a standalone server or a server cluster composed of multiple servers), etc., capable of executing programs. Figure 5 As shown, the computer device 50 in this embodiment includes, but is not limited to, a memory 501 and a processor 502 that are communicatively connected to each other via a system bus. It should be noted that... Figure 5 Only a computer device 50 with components 501-502 is shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0084] In this embodiment, the memory 501 (i.e., the readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 501 may be an internal storage unit of the computer device 50, such as the hard disk or memory of the computer device 50. In other embodiments, the memory 501 may also be an external storage device of the computer device 50, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 50. Of course, the memory 501 may include both the internal storage unit and the external storage device of the computer device 50. In this embodiment, the memory 501 is typically used to store the operating system and various application software installed on the computer device 50. In addition, the memory 501 may also be used to temporarily store various types of data that have been output or will be output.

[0085] In some embodiments, processor 502 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. This processor 502 is typically used to control the overall operation of computer device 50.

[0086] Specifically, in this embodiment, the processor 502 is used to execute the program of the tower crane cluster task scheduling method based on multi-objective genetic algorithm stored in the memory 501.

[0087] For a detailed description of the above method steps, please refer to Example 1. This example will not be repeated here.

[0088] Example 4 This embodiment also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc., which stores a computer program. When the computer program is executed by a processor, it is used to implement the steps of the tower crane cluster task scheduling method based on a multi-objective genetic algorithm.

[0089] For a detailed description of the above method steps, please refer to Example 1. This example will not be repeated here.

[0090] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0091] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0093] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A tower crane cluster task scheduling method based on a multi-objective genetic algorithm, characterized in that, include: Obtain the task list of the tower crane cluster; wherein, the task list includes the task identifier, task status and task type of each task; The first constraint condition and the multi-objective optimization model are determined; wherein, the multi-objective optimization model includes a first objective optimization function with the objective of minimizing the total unloaded distance of the tower crane cluster, and a second objective optimization function with the objective of minimizing the total number of operational conflicts of the tower crane cluster; Based on the task list and the first constraint, N chromosomes are encoded as the initial population; wherein, each chromosome includes the task identifiers of all planned tasks to be executed, and the planned tasks to be executed are tasks whose task status is pending execution and whose task type is non-temporary. Based on the first constraint and the multi-objective optimization model, a preset genetic algorithm is used to iteratively optimize the initial population to obtain the Pareto optimal solution set; Based on the Pareto optimal solution set, the scheduling strategy for the tower crane cluster is determined.

2. The tower crane cluster task scheduling method based on multi-objective genetic algorithm according to claim 1, characterized in that, Based on the first constraint and the multi-objective optimization model, a preset genetic algorithm is used to iteratively optimize the initial population to obtain the Pareto optimal solution set, including: Based on the first constraint, each chromosome in the initial population is decoded to obtain the task execution sequence associated with each chromosome; wherein, each task execution sequence includes all task identifiers belonging to the same tower crane in the task list, and the order of the task identifiers is used to characterize the execution order of the corresponding tasks; Based on the task execution sequence associated with each chromosome, the value of the first objective optimization function is calculated to obtain the first optimization objective value corresponding to each chromosome, and the value of the second objective optimization function is calculated to obtain the second optimization objective value corresponding to each chromosome. A new generation population is generated based on the first and second optimization target values ​​for each chromosome. The genetic algorithm is then used to iteratively optimize the new generation of population to obtain the Pareto optimal solution set.

3. The tower crane cluster task scheduling method based on multi-objective genetic algorithm according to claim 2, characterized in that, Within each chromosome, task identifiers belonging to the same tower crane are arranged consecutively, and task identifiers belonging to different tower cranes are separated by a preset separator; based on the first constraint, each chromosome in the initial population is decoded to obtain the task execution sequence associated with each chromosome, including: The chromosome is divided into multiple task segments based on the preset separator; each task segment includes task identifiers for all planned tasks to be executed belonging to the same tower crane; Determine from the task list whether there are any tower cranes associated with the task segment whose task status is "in execution" and / or whose task type is "temporary" for the task segment; If a task in the execution state exists and no temporary task exists, then the task identifier of the task in the execution state is inserted at the beginning of the task segment, and the inserted task segment is used as the task execution sequence. If there is no task in the execution state but there is a task of the temporary type, then the task identifier of the temporary type task is inserted at the beginning of the task segment, and the inserted task segment is used as the task execution sequence. If both the task in the execution state and the temporary task exist simultaneously, the task identifier of the task in the execution state is inserted at the beginning of the task segment, and the task identifier of the temporary task is inserted in the task segment immediately after the task in the execution state. The inserted task segment is then used as the task execution sequence. If there are no tasks in the execution state or tasks of the temporary type, then the task segment is directly used as the task execution sequence.

4. The tower crane cluster task scheduling method based on multi-objective genetic algorithm according to claim 2, characterized in that, Based on the task execution sequence associated with each chromosome, the value of the first objective optimization function is calculated respectively to obtain the first optimization objective value corresponding to each chromosome, including: Determine the initial position of the tower crane corresponding to each task execution sequence associated with the chromosome; wherein, when there is a task identifier of a task in the execution state in the task execution sequence, the initial position is the end position of the task in the execution state; otherwise, the initial position is the current hook position of the tower crane corresponding to the task execution sequence. Calculate the first idle distance from each initial position to the starting position of the first planned task to be executed in the corresponding task execution sequence; The task identifiers of the planned tasks to be executed in each task execution sequence associated with the chromosome are traversed, and the second idle distance from the end position of the task corresponding to the current traversed task identifier to the start position of the task corresponding to the next task identifier is calculated. The first empty distance and the second empty distance of all task execution sequences associated with the chromosome are summed to obtain the first optimization target value corresponding to the chromosome.

5. The tower crane cluster task scheduling method based on multi-objective genetic algorithm according to claim 2, characterized in that, Based on the task execution sequence associated with each chromosome, the value of the second objective function is calculated respectively to obtain the second optimization objective value corresponding to each chromosome, including: Obtain a pre-constructed conflict mapping relationship; wherein the conflict mapping relationship is used to characterize the potential conflict relationship between two planned tasks to be executed in any task pair due to overlapping operation paths, and the two planned tasks to be executed in the task pair belong to different tower cranes; Determine the job time period for each planned task to be executed in each task execution sequence associated with the chromosome; wherein the job time period includes the job start time and the job end time; The number of task pairs with potential conflict relationships and overlapping time periods is determined and used as the second optimization target value for the chromosome.

6. The tower crane cluster task scheduling method based on multi-objective genetic algorithm according to claim 5, characterized in that, The conflict mapping relationship includes: a first mapping relationship between the slewing intersection area and the task pairs whose operation paths pass through the slewing intersection area and have potential conflict relationships; and a second mapping relationship between each planned task to be executed in the task pairs with potential conflict relationships and the slewing intersection area passed through by the operation path of the planned task to be executed; wherein, the slewing intersection area is the overlapping area of ​​the slewing coverage of any two tower cranes on the horizontal plane.

7. The tower crane cluster task scheduling method based on multi-objective genetic algorithm according to claim 5, characterized in that, Determining the job time period for each planned task to be executed in each task execution sequence associated with the chromosome includes: Read the pre-calculated movement time information; wherein, the movement time information includes: the first movement time of each tower crane from the hook position at the sampling point to the starting position of each planned task to be executed for the corresponding tower crane, and the second movement time of each tower crane from the end position of each planned task to be executed for the corresponding tower crane to the starting position of each other planned task to be executed; The preset operation path planning module is invoked to calculate the third movement time for each tower crane to move from the starting position of each planned task to the ending position of each planned task in the corresponding task execution sequence; Read the end time of each task in execution status and the end time of each temporary task from the task list; Based on the first movement duration, the second movement duration, the third movement duration, the end time of the task in the execution state, and the end time of the temporary type task, the job time period of each planned task to be executed in each task execution sequence associated with the chromosome is determined.

8. The tower crane cluster task scheduling method based on multi-objective genetic algorithm according to claim 7, characterized in that, Based on the Pareto optimal solution set, the scheduling strategy for the tower crane cluster is determined, including: Target solutions that meet business requirements are selected from the Pareto optimal solution set; wherein, the target solution includes: a target chromosome, and a first optimization target value and a second optimization target value corresponding to the target chromosome; Based on the first constraint, the target chromosome is decoded to obtain all target task execution sequences associated with the target chromosome; Determine the time interval for each task in the execution sequence of each target task; The job path planning module is invoked to plan the job path for each planned task to be executed in all target task execution sequences, and the reachability results of each job path are verified. Calculate the first optimization effect of the first optimization target value corresponding to the target chromosome compared with the first optimization target value corresponding to the task list; wherein, the task list also includes the initial execution order of each task in the tower crane set; Calculate the second optimization effect of the second optimization target value corresponding to the target chromosome compared to the second optimization target value corresponding to the task list; Based on the first and second optimized target values ​​corresponding to the target chromosome, the execution sequence of all target tasks, the operation time period of each task, the operation path of each planned task to be executed, and the reachability results, a scheduling scheme for the multiple tower cranes is generated.

9. The tower crane cluster task scheduling method based on multi-objective genetic algorithm according to claim 1, characterized in that, Determine the first constraint and the multi-objective optimization model, including: Determine whether there are any tower crane pairs in the tower crane cluster whose slewing coverage areas overlap on the horizontal plane; When there are one or more tower crane pairs, determine the first constraint and the multi-objective optimization model; When there are no tower crane pairs, the second constraint condition and the single-objective optimization model are determined; wherein the single-objective optimization model is either the first objective optimization function or the second objective optimization function.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it is used to implement the steps of the method according to any one of claims 1 to 9.

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