Inspection robot dog scheduling method considering charging planning in electric energy meter detection scene

Abstract

The invention discloses an inspection robot dog scheduling method considering charging planning in an electric energy meter detection scene, and belongs to the technical field of inspection scheduling. The technical scheme of the invention comprises the following steps: deeply fusing inspection task allocation and charging planning decision, accurately calculating the shortest feasible path of a robot dog between equipment points and between the equipment points and charging piles, and calculating the optimal path of the robot dog; by means of the technical scheme, the inspection task execution efficiency of the inspection robot dog is improved, the practicability and reliability of the inspection robot dog scheduling scheme are improved, the algorithm response speed is high, dynamic events such as newly-added inspection tasks and robot dog state changes can be flexibly processed, and the service life of the inspection robot dog is prolonged. Rapid replanning of the inspection task sequence is realized, and efficient and stable execution of the inspection task is ensured.

Description

Technical Field

[0001] This invention belongs to the field of inspection and scheduling technology, and in particular relates to a scheduling method for inspection robot dogs that takes charging planning into account in the scenario of electricity meter detection. Background Technology

[0002] As a crucial component of the power grid system, accurate measurement of electricity consumption by electricity meters is fundamental to ensuring the efficient and stable operation of the grid. Both newly manufactured and recycled electricity meters undergo comprehensive functional verification on a meter calibration line. This ensures the accuracy of newly manufactured meters and assesses the condition of recycled meters to determine their reusability, thus improving their utilization rate. Currently, the meter calibration line requires regular staff inspections to check the equipment's operation and handle unexpected anomalies (such as retrieving faulty meters or fixing line blockages). The inspection robot, with its flexible mobility, good environmental adaptability, and ability to carry various intelligent devices, has become an ideal tool for inspecting machine status and handling anomalies on the meter calibration line. However, efficiently scheduling multiple inspection robot dogs to collaboratively complete inspection and emergency handling tasks, while effectively managing their limited battery power, especially timely charging to avoid task interruption, has become a key challenge in improving the overall inspection efficiency of the electricity meter calibration production line and ensuring its continuous, efficient, and stable operation. Currently, research on robot dog collaborative scheduling technology that integrates task allocation and charging planning for such specific scenarios remains insufficient.

[0003] In the relevant technologies of production line system scheduling and planning, existing methods can be mainly summarized into three categories: scheduling methods based on static rules rely on preset priorities or simple rules (such as shortest distance priority) to allocate tasks, and have the characteristics of high computational efficiency; methods based on global optimization models (such as mixed integer programming models) aim to seek theoretically optimal solutions; and methods based on intelligent algorithms (such as genetic algorithms and ant colony algorithms) focus on dealing with problems with complex constraints.

[0004] In the field of robot dog inspection path planning, existing methods rarely consider how the robot dog's movement path between different equipment points adapts to the actual production line layout (including equipment location, size, passage restrictions, obstacle distribution, etc.) in the context of multiple inspection task scheduling. This leads to inaccurate estimation of the robot dog's power consumption during inspections. Furthermore, there is a lack of research specifically addressing the integrated planning of robot dog charging paths and task paths. Existing technologies often treat charging as an independent event or simply pre-set charging points, failing to embed it into the inspection task scheduling sequence for collaborative optimization. This results in delayed charging decisions, impacting the overall efficiency of inspection task completion.

[0005] In summary, current technologies have significant limitations when applied to scheduling robot dog inspection tasks in electricity meter testing production lines: First, there is a lack of an integrated scheduling scheme specifically designed for robot dogs in complex production line environments, simultaneously optimizing inspection task allocation and charging planning, leading to interruptions in inspection tasks or excessively long charging wait times; second, existing path planning methods often ignore the fine-grained layout of the workshop (such as equipment size and narrow passages) or fail to efficiently and accurately integrate layout information into the shortest path calculation, affecting the practicality of the generated paths and the accuracy of power consumption prediction; third, when faced with dynamic situations such as new inspection tasks or changes in robot dog status, existing scheduling algorithms are slow to replan and lack flexibility, making it difficult to guarantee the continuous and efficient operation of the testing production line. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method that introduces a robot dog to perform inspection tasks, deeply integrates inspection task allocation and charging planning decisions, improves the efficiency of the robot dog's inspection task execution, accurately calculates the shortest feasible path between equipment points and between equipment points and charging piles, and accurately estimates the power consumption and charging timing of task execution, improves the practicality and reliability of the robot dog scheduling scheme, can flexibly handle dynamic events such as new inspection tasks and changes in robot dog status, realizes rapid replanning of inspection task sequences, and ensures efficient and stable execution of inspection tasks.

[0007] like Figure 1 As shown, to solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A scheduling method for inspection robot dogs considering charging planning in the scenario of electricity meter detection includes the following steps: S1: Obtain static scene information of the electricity meter detection pipeline and construct a static shortest path library through an algorithm; S2: Obtain relevant information about inspection tasks and construct a set of inspection tasks; S3: Select the earliest available inspection robot dog and calculate the shortest path time between its available location and the locations of each inspection task robot. S4: Calculate the sum of the shortest path time and the inspection task time, and select the inspection task with the smallest sum of time as the next inspection task for the selected robot dog. S5: Determine whether the remaining battery power of the selected robot dog is sufficient to complete the inspection task and return to the nearest charging station when it is first available; if yes, proceed to step S7; otherwise, proceed to step S6. S6: Select a charging station for the selected robot dog according to the preset rules, insert the charging task into the end of the current task sequence of the selected robot dog, and update the earliest available time and the node where the available time is located. S7: Insert the selected inspection task at the end of the current task sequence of the selected robot dog; S8: Update the earliest available time of the selected robot dog; S9: Determine whether all inspection tasks have been planned; if yes, proceed to step S10; otherwise, return to step S3. S10: Dispatch all robot dogs back to the charging station closest to the node where their last inspection task was located, and charge them.

[0008] Preferably, in step S1: S11: Obtain the internal layout of the testing workshop, including the location and size of equipment, passage restrictions, obstacle dimensions, and charging pile locations, and draw a layout plan; S12: Model the interior of the workshop using a grid map method, and plan the static shortest path between each piece of equipment and charging pile node using the A* algorithm.

[0009] Preferably, in step S2: S21: Obtain the equipment nodes corresponding to each inspection task, the power required for the inspection task, and the time consumed by the inspection task, and construct a set of inspection tasks.

[0010] Preferably, in step S3: S31: Select the earliest available inspection robot dog, read the node where it was first available as the current node, and calculate the shortest path time between the current node and the device nodes corresponding to each inspection task based on the static shortest path library.

[0011] Preferably, in step S5: S51: Obtain the remaining battery power of the selected robot dog when it is first available. Calculate the sum of the power consumption of the robot dog on the path from the current node to the device node corresponding to the inspection task, the power consumption required for the inspection task, and the power consumption of the shortest path from the inspection machine to the nearest charging pile. Determine whether the remaining battery power of the robot dog when it is first available is greater than the sum of the above power consumption. If it is greater, proceed to step S7; otherwise, proceed to step S6.

[0012] Preferably, in step S6: S61: Obtain the shortest distance between each charging station and the earliest available node of the selected robot dog, calculate the arrival time of the robot dog to each charging station and the remaining battery power upon arrival. S62: Calculate the time it takes for the robot dog to fully charge at each charging station based on the remaining battery power when it arrives at each charging station. S63: Plan the charging time window for the robot dog at each charging station: The start time of this time window must not be earlier than the arrival time of the robot dog; the length of the time window is the time required for the robot dog to be fully charged at the charging station; this time window must not overlap with the charging time windows of other robot dogs already planned at the charging station. S64: Select the charging station with the earliest charging completion time, insert a charging task to that charging station at the end of the current task sequence of the selected robot dog, update the earliest available time and the node where the available time is located of the selected robot dog, and update the occupancy time window set of the selected charging station.

[0013] Preferably, in step S8: S81: Update the available time of the selected robot dog to the completion time of the current inspection task.

[0014] Through the above design scheme, the present invention can bring the following beneficial effects: (1) For the electricity meter calibration production line scenario, the inspection robot dog is introduced to perform inspection tasks, deeply integrating the inspection task allocation and charging planning decision, filling the relevant technical gaps, and improving the inspection task execution efficiency of the inspection robot dog; (2) By obtaining the static scene information of the electricity meter testing production line, the refined layout model of the production line workshop is carried out. On this basis, the algorithm is used to accurately calculate the shortest feasible path of the robot dog between equipment points and between equipment points and charging piles, and accurately estimate the power consumption of task execution and charging timing, thereby improving the practicality and reliability of the inspection robot dog scheduling scheme; (3) The algorithm has a fast response speed and can flexibly handle dynamic events such as new inspection tasks and robot dog status changes, realize the rapid replanning of the inspection task sequence, and ensure the efficient and stable execution of the inspection task. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: Figure 1 This is a flowchart of an embodiment of the inspection robot dog scheduling method considering charging planning in the scenario of electricity meter detection according to the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the following detailed description, in conjunction with the accompanying drawings, provides a more comprehensive overview of the inspection robot dog scheduling method considering charging planning in the context of electricity meter detection. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0017] Example

[0018] A scheduling method for inspection robot dogs considering charging planning in the scenario of electricity meter detection includes the following steps: S1: Obtain static scene information of the electricity meter testing pipeline and build a static shortest path library; The static scene information of the testing workshop includes the following steps: S11: Obtain information on the internal layout of the calibration workshop, including the location and size of equipment, passage restrictions, obstacle dimensions, and charging pile locations, and draw a layout plan. The steps involved in constructing the static shortest path library are as follows: S12: Two-dimensional planar modeling of the workshop interior using the grid mapping method: discretizing the workshop interior into... grid nodes Construct a square grid map with the following map matrix: , Among them, when node When a node is an obstacle, its label variable at its corresponding position in the map matrix. Similarly, when node When it is a channel node, When node When it is a device node, When node When it is a charging pile node, .

[0019] S13: Select obstacle nodes, passage nodes, device nodes, and charging pile nodes from all nodes in the grid map to construct an obstacle node set. B Channel node set S Device node set M Charging pile node set P As shown in formulas (2)-(5): , pass The algorithm calculates the values ​​of each device and charging pile node (i.e., any two nodes). The static shortest path between ( ).

[0020] The general steps of the algorithm are as follows: (1) Initialization: Define open set (Initialized to empty), used to store information about the raster nodes to be inspected. Define a closed set. (Initialized to empty), used to store information about raster nodes that do not require inspection. Collection and The elements inside are node information vectors: , in, Starting node Through nodes arrive The known shortest distance, for The parent node. The starting node information. Add to open collection (default starting node) The parent node is itself), and As the current node .

[0021] Node to be inspected expansion: Identify the current node n The set of neighboring nodes For all adjacent nodes ,like and Then, first calculate the node according to formula (7). Evaluation function value: , in, To ignore obstacle nodes, from To the target node The estimated shortest distance. Secondly, if Not If a record is made, the open set is updated according to formula (8). : , like Already Record, and the recorded information is Then, the open set is updated according to formula (9). : , Finally, update and As shown in formula (10): , (3) Current node update: As shown in formula (11), select the node with the minimum evaluation function value from the open set. As the new current node n and will The relevant information is from Remove from the middle and put in at the same time middle: , (4) Target verification and iterative execution: S1: If the current node n For the target node Then the iterative search ends, starting from the target node. Backtracking the parent node yields the shortest path; otherwise, repeat steps (2) to (3) until the target node is found. or open set It is empty (i.e., there is no feasible path).

[0022] S2: Obtain the inspection task set KFor each inspection task Obtain the corresponding device node Electricity required for inspection tasks Inspection task time Construct the inspection task feature matrix: , S3: Inspection robot dog in the system Define its task sequence sequence The elements inside are vectors containing information about the tasks performed by the robot dog: , in, The first execution performed by the robot dog i The device nodes corresponding to each task; To execute the first... i Remaining battery power after completing one task; To execute the first... i Time window for each task. Sequence Initially, it contains only unique vectors. ,in The charging station node where the robot dog started; This refers to the robot dog's battery capacity (the robot dog is initially fully charged by default). It also retrieves the earliest available time for each robot dog. Choose the earliest available robot dog: , Read the node where the robot dog is available. ,calculate With each inspection task equipment node Shortest path time between: , in, This represents the average moving speed of the robot dog. For reading from the static shortest path library and The shortest path length between; S4: Calculate the sum of path completion time and inspection task time: , choose The minimum inspection task is selected for the robot dog. The next inspection task is as shown in formula (17): , S5: Read the remaining battery power of the selected robot dog when it is available. Computer dog From the node where it is available Head to the node corresponding to the selected inspection task Power consumption during walking: , in, For robot dogs The amount of electricity required to travel a unit distance. Similarly, the power consumption of the robot dog can be calculated from the selected inspection task's corresponding node. Head to The nearest charging station node Power consumption during walking The safe battery level for the robot dog is: , like Then the robot dog Sufficient power to perform the selected task If not, proceed to step S7; otherwise, proceed to step S6.

[0023] S6: The selected robot dog is configured according to preset rules. Select a charging station and insert the charging task into the end of the current task sequence of the selected robot dog. Update the earliest available time and the node where the available time is located. The steps are as follows: S61: Obtain information from each charging station based on a static shortest path library. The node where the selected robot dog is available. Shortest distance, computer dog Depend on Arrival time to each charging station and remaining battery power upon arrival: , , S62: Based on the robot dog's arrival at each charging station Remaining battery power at that time Calculate the time required for the battery to fully charge at each charging station: , in, For the robot dog at the charging station The charging rate at the location; S63: Attempt to select the robot dog Plan its installation at each charging station Charging time window on the charging pile This time window needs to satisfy: ① The start time must not be earlier than the time the robot dog arrives at the charging station: , ② The time window length is the time it takes for the robot dog to fully charge at that charging station: , ③ The time window must not overlap with the time window already scheduled for charging of other robot dogs on the same charging station. Existing time window set : , S64: Select the charging station with the earliest charging completion time: , As in formula (27), a destination The charging task is inserted at the end of the current task sequence of the selected robot dog, and the planned charging time window is added to the selected charging station. Within the set of occupied time windows, update the node where the selected robot dog is available, the remaining battery power during the available time, and the earliest available time: , S7: Select the inspection task Insert the selected robot dog End of current task sequence: , S8: Update the earliest available time of the selected robot dog to the completion time of the current inspection task: , S9: Determine whether all inspection tasks have been planned. If yes, proceed to step S10; otherwise, return to step S3. S10: Schedule all robot dogs Return to the charging station closest to the node where its last inspection task was located, and start charging.

[0024] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0025] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A scheduling method for inspection robot dogs considering charging planning in the scenario of electricity meter detection, characterized by: The steps are as follows: S1: Obtain static scene information of the electricity meter detection pipeline and construct a static shortest path library through an algorithm; S2: Obtain relevant information about inspection tasks and construct a set of inspection tasks; S3: Select the earliest available inspection robot dog and calculate the shortest path time between its available location and the locations of each inspection task robot. S4: Calculate the sum of the shortest path time and the inspection task time, and select the inspection task with the smallest sum of time as the next inspection task for the selected robot dog. S5: Determine whether the remaining battery power of the selected robot dog is sufficient to complete the inspection task and return to the nearest charging station when it is first available; if yes, proceed to step S7; otherwise, proceed to step S6. S6: Select a charging station for the selected robot dog according to the preset rules, insert the charging task into the end of the current task sequence of the selected robot dog, and update the earliest available time and the node where the available time is located. S7: Insert the selected inspection task at the end of the current task sequence of the selected robot dog; S8: Update the earliest available time of the selected robot dog; S9: Determine whether all inspection tasks have been planned; if yes, proceed to step S10; otherwise, return to step S3. S10: Dispatch all robot dogs back to the charging station closest to the node where their last inspection task was located, and charge them.

2. The inspection robot dog scheduling method considering charging planning in the electricity meter detection scenario according to claim 1, characterized in that: In step S1: S11: Obtain the internal layout of the testing workshop, including the location and size of equipment, passage restrictions, obstacle dimensions, and charging pile locations, and draw a layout plan; S12: Model the interior of the workshop using a grid map method, and plan the static shortest path between each piece of equipment and charging pile node using the A* algorithm.

3. The inspection robot dog scheduling method considering charging planning in the energy meter detection scenario according to claim 1, characterized in that: In step S2: S21: Obtain the equipment nodes corresponding to each inspection task, the power required for the inspection task, and the time consumed by the inspection task, and construct a set of inspection tasks.

4. The inspection robot dog scheduling method considering charging planning in the energy meter detection scenario according to claim 1, characterized in that: In step S3: S31: Select the earliest available inspection robot dog, read the node where it was first available as the current node, and calculate the shortest path time between the current node and the device nodes corresponding to each inspection task based on the static shortest path library.

5. The inspection robot dog scheduling method considering charging planning in the electricity meter detection scenario according to claim 1, characterized in that: In step S5: S51: Obtain the remaining battery power of the selected robot dog when it is first available. Calculate the sum of the power consumption of the robot dog on the path from the current node to the device node corresponding to the inspection task, the power consumption required for the inspection task, and the power consumption of the shortest path from the inspection machine to the nearest charging pile. Determine whether the remaining battery power of the robot dog when it is first available is greater than the sum of the above power consumption. If it is greater, proceed to step S7; otherwise, proceed to step S6.

6. The inspection robot dog scheduling method considering charging planning in the electricity meter detection scenario according to claim 1, characterized in that: In step S6: S61: Obtain the shortest distance between each charging station and the earliest available node of the selected robot dog, calculate the arrival time of the robot dog to each charging station and the remaining battery power upon arrival. S62: Calculate the time it takes for the robot dog to fully charge at each charging station based on the remaining battery power when it arrives at each charging station. S63: Plan the charging time window for the robot dog at each charging station: The start time of this time window must not be earlier than the arrival time of the robot dog; the length of the time window is the time required for the robot dog to be fully charged at the charging station; this time window must not overlap with the charging time windows of other robot dogs already planned at the charging station. S64: Select the charging station with the earliest charging completion time, insert a charging task to that charging station at the end of the current task sequence of the selected robot dog, update the earliest available time and the node where the available time is located of the selected robot dog, and update the occupancy time window set of the selected charging station.

7. The inspection robot dog scheduling method considering charging planning in the scenario of electricity meter detection according to claim 1, characterized in that: In step S8: S81: Update the available time of the selected robot dog to the completion time of the current inspection task.

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