5_AOI指令学习心得

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RSLogix5000 AOI 用户自定义指令学习心得

Created by Jackwell Cheung, Copyright of Jackwell Automation

www.plcab.cn

jackwellcheung@gmail.com

1、定义 AOI 中的输入参数 Input Parameter

Input Paramter 用法：

For an Add-On Instruction, an Input Parameter defines data that is passed

into the instruction. Input Parameters can accept a literal value or a tag as an

argument. The value is passed to the instruction before execution. The data

type of an Input Parameter can be any valid atomic data type. The parameter's

value is also accessible through a member of a tag, which is of the Add-On

Instruction's data type.

Input Parameter - passed by value into the Add-On Instruction. Must be a

SINT, INT, DINT, REAL, or BOOL data type. In our example, Input_a1 is

defined to be an Input Parameter. Input_a1 can be accessed from outside of

the AOI.

Input Parameter 用来传递用户自定义功能块中的输入变量。传递的给功能块

的变量类型要与定义功能块时的对应变量类型相匹配。

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AOI操作说明书

AOI设备的简介，测试原理，设备安装及保养，设备的操作，测试程序制作，系统参数设置，常见问题及解决方法。

omron AOI中级培训教材

网络信息安全学习心得

网络信息安全学习心得文档、拿走，不谢。

s3://spacenet-dataset/spacenet/SN5_roads/tarballs/SN5_roads_test_public_AOI_9_San_Juan.tar.gz如何使用

s3://spacenet-dataset/spacenet/SN5_roads/tarballs/SN5_roads_test_public_AOI_9_San_Juan.tar.gz 是一个 Amazon S3 存储桶中的文件路径，指向一个名为 "SN5_roads_test_public_AOI_9_San_Juan.tar.gz" 的压缩文件...

ra_t_comm_aoi_v0.1.zip下载

ra_t_comm_aoi_v0.1.zip是一个压缩文件，用于下载该文件，首先需要找到一个可靠的来源。可能的来源包括官方网站、开发者的网站或其他可信赖的技术论坛。一旦找到了可信赖的来源，在下载之前需要确保你的设备有足够的...

Temp = cv.imread(r"D:\PCQ_KB_AOI_x64_20191023\luosi\temp\E1504.jpg", 0)%Model怎样把机种改为变量

你可以通过在程序中定义变量来实现将机种作为变量。...Temp = cv.imread(r"D:\PCQ_KB_AOI_x64_20191023\luosi\temp\" + model_name + ".jpg", 0) ``` 这样，当你需要更改机种时，只需要更改model_name变量的值即可。

Temp = cv.imread(r"D:\PCQ_KB_AOI_x64_20191023\luosi\temp\%+Model+.jpg", 0)这是个变量格式吗

Temp = cv.imread(r"D:\PCQ_KB_AOI_x64_20191023\luosi\temp\%s.jpg" % Model, 0) ``` 在这个字符串中，`%s`是一个占位符，表示要插入一个字符串。字符串后面的`%`是格式化字符串的标志，它告诉Python解释器，要将...

input_tours_for_drones = 20 len_input_tours_for_drones = 7 aoi = utility.build_random_aoi(width_area, height_area, n_target, n_depots, hovering_time=5, seed=seed) depots = aoi.depots depot_first_drone = depots[0] tours_first_drone=[build_random_tour(aoi,depot_first_drone,np.random.randint(len_input_tours_for_drones - 5,len_input_tours_for_drones + 5)) for i in range(input_tours_for_drones)] depot_second_drone = depots[1] tours_second_drone=[build_random_tour(aoi,depot_second_drone,np.random.randint(len_input_tours_for_drones-5, len_input_tours_for_drones + 5)) for i in range(input_tours_for_drones)] uavs_to_tours = {drones[0]: tours_first_drone, drones[1]: tours_second_drone model = TotalCoverageModel(aoi, uavs_to_tours, max_rounds, debug=False) model.build() model.optimize() mrs = model.solution assert mrs is not None, "optimal solution not found" print("TC-OPT covers", mrs.coverage_score(), "targets using", mrs.max_rounds, "rounds") mrs.plot("TC-OPT") # for big istances (over 200/300 points) remove this plot mrs.plot_cumulative_coverage_for_round("TC-OPT") 以上为用Gurobi求解最优解问题，请解释以上程序： depot_first_drone = depots[0] tours_first_drone=[build_random_tour(aoi,depot_first_drone,np.random.randint(len_input_tours_for_drones - 5,len_input_tours_for_drones + 5)) for i in range(input_tours_for_drones)] depot_second_drone = depots[1] tours_second_drone=[build_random_tour(aoi,depot_second_drone,np.random.randint(len_input_tours_for_drones-5, len_input_tours_for_drones + 5)) for i in range(input_tours_for_drones)] uavs_to_tours = {drones[0]: tours_first_drone, drones[1]: tours_second_drone是什么意思

2. 通过调用 build_random_aoi 函数生成一个随机的区域，包括了 n_target 个目标点和 n_depots 个起飞点，并指定了每个起飞点的停留时间为 5 秒。 3. 从生成的起飞点中选择了第一个起飞点为无人机的起飞点，并通过...

seed = 50 n_target = 50 n_depots = 2 width_area = 2000 height_area = 2000 max_rounds = 5 input_tours_for_drones = 20 len_input_tours_for_drones = 7 np.random.seed(seed) drones = test3(plot=False) drones = drones[:2] aoi = utility.build_random_aoi(width_area, height_area, n_target, n_depots, hovering_time=5, seed=seed) depots = aoi.depots depot_first_drone = depots[0] tours_first_drone = [build_random_tour(aoi, depot_first_drone, np.random.randint(len_input_tours_for_drones - 5,len_input_tours_for_drones + 5)) for i in range(input_tours_for_drones)] depot_second_drone = depots[1] tours_second_drone = [build_random_tour(aoi, depot_second_drone, np.random.randint(len_input_tours_for_drones - 5,len_input_tours_for_drones + 5)) for i in range(input_tours_for_drones)] uavs_to_tours = {drones[0]: tours_first_drone, drones[1]: tours_second_drone model = TotalCoverageModel(aoi, uavs_to_tours, max_rounds, debug=False) model.build() model.optimize() mrs = model.solution assert mrs is not None, "optimal solution not found" print("TC-OPT covers", mrs.coverage_score(), "targets using", mrs.max_rounds, "rounds") mrs.plot("TC-OPT") # for big istances (over 200/300 points) remove this plot mrs.plot_cumulative_coverage_for_round("TC-OPT") 以上为用Gurobi求解最优解问题，请解释以上程序

这段程序主要是使用Gurobi求解一个最优化模型，该模型是一个TotalCoverageModel，其中包括了一个区域AOI，一些无人机UAVs和它们的巡航路径tours，以及一些限制条件，如最大巡航轮次max_rounds等。程序中，首先生成一...

input_tours_for_drones = 20 len_input_tours_for_drones = 7 np.random.seed(seed) drones = test3(plot=False) drones = drones[:2] aoi = utility.build_random_aoi(width_area, height_area, n_target, n_depots, hovering_time=5, seed=seed) depots = aoi.depots depot_first_drone = depots[0] tours_first_drone = [build_random_tour(aoi, depot_first_drone, np.random.randint(len_input_tours_for_drones - 5,len_input_tours_for_drones + 5)) for i in range(input_tours_for_drones)] uavs_to_tours = {drones[0]: tours_first_drone, drones[1]: tours_second_drone model = TotalCoverageModel(aoi, uavs_to_tours, max_rounds, debug=False) model.build() model.optimize() mrs = model.solution assert mrs is not None, "optimal solution not found" print("TC-OPT covers", mrs.coverage_score(), "targets using", mrs.max_rounds, "rounds") mrs.plot("TC-OPT") # for big istances (over 200/300 points) remove this plot mrs.plot_cumulative_coverage_for_round("TC-OPT") 以上为用Gurobi求解最优解问题，请解释以上程序，input_tours_for_drones和len_input_tours_for_drones分别是什么意思

在程序中，生成巡航路径时，使用了np.random.randint(len_input_tours_for_drones - 5, len_input_tours_for_drones + 5)来控制每个巡航路径的长度（目标点数量）在一个范围内波动，范围是以len_input_tours_for_...

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class AbstractGreedyAndPrune(): def init(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> MultiRoundSolution: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] visited_points |= set(opt_tour.targets_indexes) # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()) class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序，使其能返回所有已经探索过的目标点visited_points的数量，请用代码表示

def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi....

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