Data processing and task scheduling method and system for mobile charging robot

Abstract

The invention discloses a data processing and task scheduling method and system for a mobile charging robot, and belongs to the technical field of robot control, and the method comprises the steps: periodically receiving tasks of a cloud task center according to a robot task quantity capacity value, collecting the coordinate information and state information of each task, and determining the emergency degree of each task; when the electric quantity of the robot is sufficient, the task priority is dynamically planned according to the coordinate information and the emergency degree; when the electric quantity of the robot is insufficient, an execution path between the tasks is constructed according to the coordinate information of the tasks; and determining the priority of the task according to the position information of the charging station and the position relationship of the execution path. The task priority is dynamically planned according to the electric quantity of the robot and the task emergency degree, and the task priority is determined according to the position relation between the execution path and the charging station when the electric quantity is insufficient, so that efficient execution of the task and reasonable charging of the robot can be realized, the task execution efficiency is improved, and the service life of the robot is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a data processing and task scheduling method and system for a mobile charging robot. Background Art

[0002] In the current mobile robot application scenarios, cloud-based task scheduling systems have been widely used, which can effectively improve the efficiency and accuracy of task execution. Among them, mobile charging robots, as an important type of intelligent device, play an increasingly important role in daily life and industrial scenarios. However, in actual application scenarios, mobile charging robots often need to cope with the dual challenges of power and task requirements, that is, they need to reasonably plan the robot's driving path while meeting the task allocation to ensure that the robot can effectively complete the pre-assigned tasks and recharge in time.

[0003] Current mobile robot task scheduling systems mostly rely on traditional priority planning methods, such as allocating tasks in chronological order or by the principle of shortest distance. This is more applicable to robots with sufficient power, but for robots with limited power, simply following the above rules often cannot effectively balance the relationship between tasks and power, causing the robot to frequently be in a low-power state, affecting task execution efficiency and machine service life. In addition, static path planning directly based on task distance and power is also difficult to respond quickly to sudden changes, and cannot achieve optimal path planning in a dynamic environment. Summary of the invention

[0004] To solve the above problems, the present invention provides a data processing and task scheduling method and system for a mobile charging robot, which dynamically plans the task priority according to the robot's power level and the task urgency, and determines the task priority according to the execution path and the location relationship of the charging station when the power is insufficient. This can achieve efficient execution of tasks and reasonable charging of the robot, thereby improving the task execution efficiency and the service life of the robot.

[0005] The above objectives can be achieved through the following solutions: A data processing and task scheduling method for a mobile charging robot comprises: periodically accepting tasks from a cloud task center according to the robot task quantity capacity value, and collecting coordinate information and status information of each task; determining the urgency of the task according to the status information; when the robot has sufficient power, dynamically planning the task priority according to the coordinate information and the urgency; when the robot has insufficient power, constructing an execution path between each task according to the coordinate information of each task; and determining the priority of the task according to the position relationship between the location information of the charging station and the execution path.

[0006] Optionally, determining the urgency of the task according to the status information includes: establishing fuzzy sets of time limit, impact range and user priority respectively according to the status information; establishing membership functions of the time limit, impact range and user priority respectively; establishing a membership function of urgency according to the membership functions of the time limit, impact range and user priority, calculating the urgency membership value of the task, and The urgency membership value ,have , In the formula, is the weight of the time limit, is the weight of the impact range, is the weight of user priority, For the The membership function of the time limit of each task is For the The membership function of the influence range of each task, For the The urgency degree membership value is normalized to obtain an urgency coefficient value.

[0007] Optionally, when the robot has sufficient power, dynamically planning the task priority according to the coordinate information and the urgency includes: when the robot power is greater than or equal to a first threshold, setting a first weight of the coordinate information and a second weight of the urgency coefficient value, wherein for the first weight With the second weight relationship, ; Using the first weight and the second weight, a priority calculation function is established to calculate the priority result value. The priority result value of ,have , In the formula, Indicates that according to The distance measurement value between the coordinate information of each task and the robot position coordinates, For the The urgency coefficient value of each task is obtained; the priority result value is normalized to obtain the priority coefficient value; and the tasks are sorted according to the priority coefficient value.

[0008] Optionally, when the robot is low on power, constructing execution paths between tasks according to the coordinate information of each task includes: when the robot power is greater than or equal to a second threshold and less than the first threshold, sending tasks in the task list whose priority coefficient values ​​are greater than or equal to a third threshold to a cloud task center; constructing the shortest paths between tasks whose priority coefficient values ​​are less than the third threshold, obtaining multiple first predicted paths, and With the task The first predicted path ,have , In the formula, Indicates the task With the task The first predicted path The coordinates of the point on .

[0009] Optionally, when the robot is low on power, constructing execution paths between tasks according to the coordinate information of each task includes: when the robot power is less than the second threshold, sending tasks whose priority coefficient values ​​in the task list are less than the third threshold and greater than or equal to the fourth threshold to the cloud task center; constructing the shortest paths between tasks whose priority coefficient values ​​are less than the fourth threshold, obtaining multiple second predicted paths, and With the task The second predicted path ,have , In the formula, Indicates the task With the task The first predicted path The coordinates of the point on .

[0010] Optionally, determining the priority of the task based on the positional relationship between the location information of the charging station and the execution path includes: acquiring the location information of the charging station to obtain the coordinates of the charging station; obtaining the charging path based on the distance between the charging station coordinates and the coordinates of each point on the execution path; setting the priorities of the two tasks corresponding to the charging path to the first priority and the second priority according to the size of the priority coefficient value; calculating the priority result values ​​of the remaining tasks according to the priority calculation function to obtain the priority coefficient values ​​of the remaining tasks; sorting the remaining tasks starting from the third priority according to the size of the priority coefficient values; wherein the execution path includes the first predicted path and the second predicted path, and the robot will enter the charging station to charge on the way to the second priority task after completing the first priority task.

[0011] Optionally, obtaining the charging path according to the distance between the charging station coordinates and the point coordinates of each execution path includes: calculating the distance between the charging station coordinates and the point coordinates of each execution path to obtain multiple docking distance values; judging whether there is a docking distance value less than a fifth threshold; if there is a docking distance value less than the fifth threshold, selecting the execution path corresponding to the docking distance value as the charging path.

[0012] Optionally, the determination of whether there is a docking distance value less than the fifth threshold value also includes: if there is no docking distance value less than the fifth threshold value, obtaining the coordinate information and status information of the task in the cloud task center; calculating multiple backup paths based on the coordinate information of each task in the cloud task center and the coordinate information of the next task to be executed; calculating the distance between the charging station coordinates and the point coordinates of each of the backup paths to obtain multiple backup distance values; sorting the multiple backup distance values ​​from small to large, and selecting the backup path corresponding to the first-ranked backup distance value as the charging emergency path; receiving the task in the cloud task center corresponding to the charging emergency path, and inserting the priority of the task in the cloud task center corresponding to the charging emergency path after the next task to be executed.

[0013] Optionally, if there is a docking distance value less than the fifth threshold, selecting the execution path corresponding to the docking distance value as the charging path includes: if there is a docking distance value less than the fifth threshold, selecting the execution path corresponding to the docking distance value as the charging path; if there are multiple docking distance values ​​less than the fifth threshold, selecting the execution paths corresponding to multiple docking distance values ​​less than the fifth threshold to construct a path set; sorting the path set from small to large according to the distance value between the coordinate information of each task in the path set and the position coordinates of the robot; selecting the execution path corresponding to the first distance value as the charging path.

[0014] Based on the same inventive concept, the present invention also provides a data processing and task scheduling system for a mobile charging robot, the system comprising: a task receiving module, used to periodically receive tasks from a cloud task center according to the robot task quantity capacity value; the task receiving module is also used to send tasks whose task list priority coefficient value is greater than or equal to a third threshold to the cloud task center when the robot power is greater than or equal to a second threshold and less than a first threshold; the task receiving module is also used to send tasks whose task list priority coefficient value is less than the third threshold and greater than or equal to a fourth threshold to the cloud task center when the robot power is less than the second threshold; the task receiving module is also used to receive the tasks in the cloud task center corresponding to the charging emergency path if there is no docking distance value less than the fifth threshold. Task; a task information collection module for collecting coordinate information and status information of each task of the robot; the task information collection module is also used to collect coordinate information and status information of tasks in the cloud task center; a task urgency evaluation module is used to determine the urgency of the task according to the status information; a task priority planning module is used to dynamically plan the task priority by calculating the task priority coefficient according to the coordinate information and the urgency when the robot has sufficient power; the task priority planning module is also used to construct an execution path between tasks according to the coordinate information of each task when the robot is low on power, and determine the priority of the task according to the positional relationship between the location information of the charging station and the execution path; a task execution module is used to guide the mobile charging robot to perform tasks according to the task priority and the execution path.

[0015] Compared with the prior art, the present invention has the following advantages: 1. Dynamically plan the priority of tasks based on the robot's power status and the urgency of the task. When the power is sufficient, the task coordinate information and urgency are comprehensively considered to determine the execution order of the tasks; when the power is insufficient, the execution path is constructed based on the task coordinate information, and the priority of the task is determined in combination with the location information of the charging station; this dynamic planning method can more effectively balance the relationship between task execution and power consumption; 2. By establishing fuzzy sets and membership functions of time limits, impact ranges, and user priorities, the urgency of tasks can be evaluated more accurately. This evaluation system can comprehensively consider multiple factors, making the determination of task priorities more reasonable and scientific. 3. Adopt different task scheduling strategies according to the power level of the robot. When the power is high, it can accept and execute more tasks; when the power is low, it will prioritize tasks with higher urgency or shorter distances, and send some tasks back to the cloud task center to reduce power consumption; at the same time, the planning of charging paths is also considered to ensure that the robot can be charged in time while performing tasks; 4. The method and system of the present invention can adapt to different application scenarios and task requirements; whether the power is sufficient or insufficient, task scheduling and path planning can be performed according to the specific situation to ensure efficient execution of tasks and continuous operation of the robot.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a data processing and task scheduling method for a mobile charging robot according to an embodiment of the present invention.

[0019] Figure 2 It is a flow chart of calculating the urgency coefficient value according to an embodiment of the present invention.

[0020] Figure 3 It is an execution flow chart of a data processing and task scheduling method for a mobile charging robot according to an embodiment of the present invention.

[0021] Figure 4 It is an execution flow chart of task priority determination according to an embodiment of the present invention.

[0022] Figure 5 It is a structural schematic diagram of a data processing and task scheduling system for a mobile charging robot according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Reference Figure 1An embodiment of the present invention proposes a data processing and task scheduling method for a mobile charging robot, which adopts a method of dynamically planning task priorities according to the robot's power level and the task urgency, and determining the task priority according to the execution path and the charging station position relationship when the power is insufficient. It can achieve efficient task execution and reasonable charging of the robot, thereby improving the task execution efficiency and the service life of the robot.

[0025] The method of this embodiment specifically includes: According to the robot's task quantity and capacity, it periodically accepts tasks from the cloud task center and collects the coordinate information and status information of each task; Specifically, a mobile charging robot with task execution capability has a task capacity value of 10, which means that it can accept and process up to 10 tasks at a time; the cloud task center is a server that centrally manages and assigns tasks. It maintains a task list, each task contains coordinate information and status information. The status information includes time limit, scope of influence and user priority; the robot will connect to the cloud task center at regular intervals (for example, every hour) to check whether there are new tasks to accept.

[0026] determining the urgency of the task according to the status information; Specifically, the urgency of the task will be calculated using a preset algorithm based on the task's time limit, scope of impact, and user priority.

[0027] When the robot has sufficient power, dynamically plan the task priority according to the coordinate information and the urgency; Specifically, when the robot has sufficient power, it will dynamically plan the priority of the tasks based on the coordinate information and urgency of the tasks. This means that the robot will comprehensively consider the location of each task and its urgency, and thus more intelligently decide which task to perform first.

[0028] When the robot is low on power, an execution path between tasks is constructed based on the coordinate information of each task; The priority of the task is determined according to the positional relationship between the location information of the charging station and the execution path.

[0029] Specifically, when the robot is low on power, it will build an execution path between tasks based on the coordinate information of each task, and determine the priority of the tasks at both ends of the path based on whether there is a charging station nearby. This allows the robot to charge along the way when it needs to, use task scheduling to reasonably plan the charging time, and complete the task efficiently.

[0030] Alternatively, if Figure 2 As shown, determining the urgency of the task according to the status information includes: According to the state information, fuzzy sets of time limit, impact range and user priority are respectively established; Specifically, the time limit fuzzy set represents the time urgency of task completion, the impact range fuzzy set represents the impact of the task on the system or user after completion, and the user priority fuzzy set represents the importance the user places on task completion.

[0031] Exemplarily, the time limit fuzzy set can be "very urgent", "urgent", "general", "loose", etc., the impact range fuzzy set can be "wide impact", "medium impact", "limited impact", "no impact", etc., and the user priority fuzzy set can be "high priority", "medium priority", "low priority", "no priority", etc.

[0032] Establishing membership functions of the time limit, the impact range and the user priority respectively; For example, for the task , according to the time limit of the task (such as the deadline), determine the degree to which it belongs to the fuzzy set of "very urgent", "urgent", "normal", "loose", etc. For example, the membership function of the time limit can be defined using common membership function shapes such as S-shaped function or trapezoidal function ; For tasks , according to the degree of impact on the system or user after the task is completed, determine the degree to which it belongs to the fuzzy set such as "wide impact", "medium impact", "limited impact", "no impact", etc. Similarly, the membership function of the impact range can be defined using the S-shaped function, trapezoidal function or other suitable membership function shapes ; On the task , according to the importance the user places on task completion, the degree to which it belongs to the fuzzy set of "high priority", "medium priority", "low priority", "no priority", etc. A similar membership function shape can be used to define the membership function of user priority .

[0033] The membership function of the urgency is established according to the membership function of the time limit, the scope of influence and the user priority, and the urgency membership value of the task is calculated. The urgency membership value ,have , In the formula, is the weight of the time limit, is the weight of the impact range, is the weight of user priority, For the The membership function of the time limit of each task is For the The membership function of the influence range of each task, For the The membership function of the user priority of each task; For example, the weights of time limit, impact range and user priority are determined according to the actual situation. , , , these weights can be determined by expert scoring, data analysis or other methods; assuming , , , and for a task , =0.8 (indicates that the task time is very tight), =0.6 (indicating that the task has a wide impact), = 0.9 (indicating that the user has a high priority for this task), then =0.5x0.8+0.3x0.6+0.2x0.9=0.76.

[0034] The urgency degree membership value is normalized to obtain an urgency degree coefficient value.

[0035] Exemplarily, the urgency membership values ​​are normalized to a certain range (such as [0,1]) for subsequent comparison and analysis, and maximum and minimum normalization, standard normalization or other suitable normalization methods may be used; for example, using the maximum and minimum normalization method, The values ​​are mapped to the range [0,1].

[0036] For example, suppose there are three tasks , and , their time limits, impact scope, and user priorities are as follows: Task :Time is tight ( =0.9), with wide impact ( =0.8), user high priority ( =0.7); task : Time is generally =0.5), medium impact ( =0.5), user priority ( =0.6); task : Time is relaxed ( =0.2), the impact is limited ( =0.3), user low priority ( =0.4); Assumption weight , , ,but =0.79, =0.53, =0.27, normalized to [0,1], we get the task The urgency coefficient is 0.79. The urgency coefficient is 0.53. The urgency coefficient is 0.27.

[0037] Alternatively, if Figure 3 As shown, when the robot has sufficient power, the dynamic planning task priority includes: When the robot power is greater than or equal to the first threshold, the first weight of the coordinate information and the second weight of the urgency coefficient value are set, wherein the first weight With the second weight relationship, ; Using the first weight and the second weight, a priority calculation function is established to calculate the priority result value. The priority result value of ,have , In the formula, Indicates that according to The distance measurement value between the coordinate information of each task and the robot position coordinates, For the The urgency coefficient value of each task; Normalizing the priority result value to obtain a priority coefficient value; The tasks are sorted according to the priority coefficient values.

[0038] For example, assume that the robot's current position coordinates are (0,0) and there are three tasks: Task The coordinate information is (2,3), and the urgency coefficient value is =0.8; Task The coordinate information is (1,1), and the urgency coefficient value is =0.6; Task The coordinate information is (4,4), and the urgency coefficient value is =0.9; Assume that the first threshold is a certain percentage of the robot's battery, such as 50%; In this example, assume that the robot's battery is greater than or equal to 50%, and assume that the decision =0.6 (i.e. the distance metric accounts for a larger proportion), then =1-0.6=0.4 (the urgency coefficient value accounts for a smaller proportion); use the Euclidean distance formula to calculate the distance from each task to the robot's current position: =3.61, =1.41, =5.66; Calculate the priority value of each task and get =0.6x3.61+0.4x0.8=2.486, =0.6x1.41+0.4x0.6=1.086, =0.6x5.66+0.4x0.9=3.756; In order to convert the priority result values ​​into priority coefficient values ​​between 0 and 1, they can be divided by the maximum value, which is 3.756, then the task The priority coefficient of the task is 0.662. The priority coefficient is 0.289, and the task The priority coefficient is 1. The tasks are sorted according to the priority coefficient value, and the task priority order is , , ; Therefore, according to this example, the robot will first perform the task , followed by tasks , and finally the task ; This ranking is based on the distance from the task to the robot and the urgency coefficient of the task, calculated using the set weights.

[0039] Alternatively, if Figure 3 As shown, when the robot is low on power, constructing execution paths between tasks according to the coordinate information of each task includes: When the robot power is greater than or equal to the second threshold and less than the first threshold, the tasks in the task list whose priority coefficient value is greater than or equal to the third threshold are sent to the cloud task center; Construct the shortest path between the tasks whose priority coefficient values ​​are less than the third threshold value, and obtain multiple first predicted paths. With the task The first predicted path ,have , In the formula, Indicates the task With the task The first predicted path On Point coordinates.

[0040] Exemplarily, the first threshold is 70% of the robot's battery power, the second threshold is 30% of the robot's battery power, and the third threshold is 0.7 of the priority coefficient value; consider the case where the robot's battery power is between 30% and 70%, and there is a set of tasks whose priority coefficient values ​​have been calculated according to the previous steps; assume that the task list and their priority coefficient values ​​are as follows: Task The priority coefficient value is 0.662 (less than 0.7), and the task The priority coefficient value is 0.289 (less than 0.7), and the task The priority coefficient value is 1 (greater than 0.7). Since the robot's battery power is between 30% and 70%, the priority coefficient value of each task will be checked. If it is greater than or equal to 0.7, the task will be sent to the cloud task center. In this example, the task The priority coefficient value is greater than 0.7, so it will be sent to the cloud task center; for tasks with a priority coefficient value less than 0.7 and tasks , we need to construct the shortest path between them and get the task and tasks The shortest path between ; In order to construct the shortest paths between these tasks, graph theory algorithms can be used. What is more important is to understand that paths need to be constructed for low-priority tasks and that these paths are calculated based on their relative positions and possible obstacles (which are not considered in this example); Ultimately, these paths can be used for subsequent decision making, such as deciding which task should be executed first to minimize the robot's movement distance and time.

[0041] Optionally, when the robot is low on power, constructing execution paths between tasks according to the coordinate information of each task includes: When the battery level of the robot is less than the second threshold, the tasks in the task list whose priority coefficient value is greater than or equal to the fourth threshold are sent to the cloud task center; Construct the shortest path between the tasks whose priority coefficient values ​​are less than the fourth threshold value, and obtain multiple second predicted paths. With the task The second predicted path ,have , In the formula, Indicates the task With the task The first predicted path On Point coordinates.

[0042] Exemplarily, the first threshold is 70% of the robot's battery power, the second threshold is 30% of the robot's battery power, and the fourth threshold is 0.3 of the priority coefficient value; consider the case where the robot's battery power is below 30%, and there is a set of tasks whose priority coefficient values ​​have been calculated according to the previous steps; task The priority coefficient value is 0.662 (greater than 0.3), and the task The priority coefficient value is 0.289 (less than 0.3), and the task The priority coefficient value is 1 (greater than 0.3); the task Tasks with a priority coefficient value of 0.45 (greater than 0.3) The priority coefficient value is 0.25 (less than 0.3), then check the priority coefficient value of each task, if it is greater than or equal to 0.3, then send the task to the cloud task center; in this example, the task ,Task ,Task The priority coefficient value of meets the conditions, so it will be sent to the cloud task center; for tasks with a priority coefficient value less than 0.3, in this example, it is task and tasks , we need to construct the shortest path between them and get the task and tasks The shortest path between ; Use graph theory algorithms (such as Dijkstra, A*, Bellman-Ford, etc.) or heuristic methods (such as greedy algorithms) to find the shortest path between these tasks; It is important to understand that this path construction process is calculated based on the relative positions between tasks and possible environmental obstacles; In practical applications, factors such as the robot's kinematic constraints, battery life, and environmental uncertainties also need to be considered; In addition, because this task allocation and path planning problem is a complex optimization problem, it is usually necessary to use more advanced algorithms and techniques to find the optimal solution or approximate optimal solution.

[0043] Alternatively, if Figure 4 As shown, the determining the priority of the task according to the position relationship between the location information of the charging station and the execution path includes: Get the location information of the charging station and obtain the coordinates of the charging station; Obtaining a charging path according to the distance between the charging station coordinates and the coordinates of each point of the execution path; According to the value of the priority coefficient, the priorities of the two tasks corresponding to the charging path are set to the first priority and the second priority; Calculate the priority result value of each remaining task according to the priority calculation function to obtain the priority coefficient value of each remaining task; According to the priority coefficient values ​​of the remaining tasks, the tasks are sorted starting from the third priority level; The execution path includes the first predicted path and the second predicted path. After completing the task of the first priority, the robot will enter the charging station to charge on the way to the task of the second priority.

[0044] Exemplarily, the coordinates of the charging station are determined as ( , ), for example, (5,5), the corresponding execution path is calculated according to the robot power. For example, if the robot power is less than 30% of the second threshold, the second predicted path is calculated according to the condition. ,These paths consist of a series of point coordinates representing the paths that the robot may take, e.g. , indicating the task To the task A possible path; for each point on the execution path, calculate its path to the charging station ( , ), there is a point whose distance is less than the fifth threshold , then the path For the charging path; and tasks The priority coefficient values ​​of the task are compared. The priority coefficient is greater than the task The priority coefficient of the task The priority of the task will become the first priority. The priority of the task will be set to the second priority, and the other tasks in the task list will be sorted according to the priority coefficient, starting from the third priority. The robot will perform the task next. ;Robots perform tasks To the task When you pass by , when the robot reaches point , then enter the charging station to charge, and after charging is completed, you will go to the mission Perform tasks at the location , and then continue to execute tasks according to the task priority of the task list; please note that this example is simplified for the purpose of illustrating the process, and more factors need to be considered in actual applications, such as the current position of the robot, power consumption rate, time constraints between tasks, etc.

[0045] Optionally, obtaining the charging path according to the distance between the charging station coordinates and the point coordinates of each execution path includes: Calculating the distance between the coordinates of the charging station and the coordinates of each point of the execution path to obtain a plurality of parking distance values; Determine whether the stopping distance value is less than a fifth threshold; If there is a parking distance value that is smaller than the fifth threshold, the execution path corresponding to the parking distance value is selected as the charging path.

[0046] Exemplarily, the coordinates of the charging station are determined as ( , ), for example (10,10); when the robot's battery level is between the first threshold of 70% and the second threshold of 30%, the calculation is ={(7,7),(8,8),(9,9)}, indicating that from the task To the task possible paths; ={(1,1),(2,2),(3,4),(5,6)}, indicating that from the task To the task possible paths; setting the fifth threshold D = 2, means that if the distance from a point on a path to the charging station is less than or equal to 2, this point is considered to be a suitable stop point; through calculation, we know The distance from all points on the charging station is less than 2, so we can choose as a charging path.

[0047] Optionally, the determining whether the stopping distance value is less than a fifth threshold value further includes: If there is no docking distance value less than the fifth threshold, obtaining the coordinate information and status information of the task in the cloud task center; Based on the coordinate information of each task in the cloud task center and the coordinate information of the next task to be executed, multiple backup paths are calculated; Calculating the distance between the coordinates of the charging station and the coordinates of each point on the backup path to obtain a plurality of backup distance values; sorting the plurality of standby distance values ​​from small to large, and selecting the standby path corresponding to the standby distance value that is ranked first as the charging emergency path; Receive the task in the cloud task center corresponding to the charging emergency path, and insert the priority of the task in the cloud task center corresponding to the charging emergency path after the next task to be executed.

[0048] Exemplarily, the coordinates of the charging station are determined as ( , ), for example (6,7); when the robot's battery power is between the first threshold of 70% and the second threshold of 30%, the robot is currently executing a task path from task point A (1,1) to task point B (4,4); assuming that the distance from the point on the current task path to the charging station is greater than the fifth threshold D = 2, it is necessary to obtain new task information from the cloud task center to plan the charging emergency path; the cloud task center stores the coordinate information and status information of multiple tasks, such as task C (4,8), task D (12,4), and these tasks are currently Executable state; calculate the distance from all points on the current task path A to B to the charging station, and find that they are all greater than the fifth threshold. Since there is no suitable stop on the current task path, the robot obtains the coordinate information and status information of tasks C and D from the cloud task center; according to the coordinate information of the cloud task and the coordinate information of the next task B to be executed, calculate multiple backup paths; for example, backup path 1: from B (4,4) to C (4,8), backup path 2: from B (4,4) to D (12,4); calculate the charging station coordinates (6,7) and each The distances of the coordinates of the points on the backup paths are calculated to obtain multiple backup distance values; for example, on backup path 1, there is a point (4,7) with the smallest distance to the charging station, and the distance from point (4,7) to the charging station is 2; on backup path 2, there is a point (6,4) with the smallest distance to the charging station, and the distance from point (6,4) to the charging station is 3; since multiple backup distance values ​​are sorted from small to large and the backup path with the smallest backup distance value is selected, the minimum value between the minimum distance from backup path 1 to the charging station and the minimum distance from backup path 2 to the charging station meets the requirements; since the minimum distance from backup path 1 to the charging station is 2, and the minimum distance from backup path 2 to the charging station is 3 and greater than 2, backup path 1 is the charging emergency path; the robot receives task C in the cloud task center corresponding to the charging emergency path, and prioritizes it after the next task B to be executed; this means that after completing task B, the robot will enter the charging station to charge on the way to the location of task C, and then continue to execute task C and subsequent tasks, and the subsequent tasks will sort the tasks according to the priority coefficient value and execute them in sequence.

[0049] Optionally, if there is a parking distance value that is less than the fifth threshold, selecting the execution path corresponding to the parking distance value as the charging path includes: If there is a parking distance value that is less than the fifth threshold, selecting the execution path corresponding to the parking distance value as the charging path; Specifically, if there is only one path that meets the requirement that the stopping distance value is less than the fifth threshold, then this path can be considered as a charging path.

[0050] If there are multiple stopping distance values ​​smaller than the fifth threshold, selecting the execution paths corresponding to the multiple stopping distance values ​​smaller than the fifth threshold to construct a path set; According to the distance value between the coordinate information of each task in the path set and the position coordinate of the robot, the paths are sorted from small to large; The execution path corresponding to the first distance value is selected as the charging path.

[0051] Exemplarily, the fifth threshold is set to 4, the minimum distance from the point on execution path 1 to the charging station is 1, the minimum distance from the point on execution path 2 to the charging station is 3, the minimum distance from the point on execution path 3 to the charging station is 6, and the minimum distance from the point on execution path 4 to the charging station is 2, and a path set is constructed, including execution path 1, execution path 2, execution path 3 and execution path 4; the minimum distances from the charging station to the execution path 1, execution path 2, execution path 3 and execution path 4 are sorted from small to large, the first one is execution path 1, the second one is execution path 4, the third one is execution path 2, and the fourth one is execution path 3, so execution path 1 is the charging path.

[0052] Based on the same inventive concept, Figure 5 As shown, the present invention also provides a data processing and task scheduling system for a mobile charging robot, the system comprising: A task receiving module is used to periodically receive tasks from a cloud task center according to the robot task quantity capacity value; the task receiving module is also used to send tasks whose task list priority coefficient values ​​are greater than or equal to a third threshold to the cloud task center when the robot power is greater than or equal to a second threshold and less than a first threshold; the task receiving module is also used to send tasks whose task list priority coefficient values ​​are less than the third threshold and greater than or equal to a fourth threshold to the cloud task center when the robot power is less than the second threshold; the task receiving module is also used to receive tasks in the cloud task center corresponding to the charging emergency path if there is no docking distance value less than the fifth threshold; A task information collection module is used to collect coordinate information and status information of each task of the robot; the task information collection module is also used to collect coordinate information and status information of tasks in the cloud task center; A task urgency evaluation module, used to determine the urgency of the task according to the status information; The task priority planning module is used to dynamically plan the task priority by calculating the task priority coefficient according to the coordinate information and the urgency when the robot has sufficient power; the task priority planning module is also used to construct the execution path between the tasks according to the coordinate information of each task when the robot has insufficient power, and determine the priority of the task according to the position relationship between the location information of the charging station and the execution path; The task execution module is used to guide the mobile charging robot to perform tasks according to task priorities and execution paths.

[0053] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between the lines. The indirect connection method can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention.

[0054] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptive change of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not described in the present invention.

Claims

1. A data processing and task scheduling method for a mobile charging robot, It is characterized in that The method comprises: According to the robot's task quantity and capacity, it periodically accepts tasks from the cloud task center and collects the coordinate information and status information of each task; determining the urgency of the task according to the status information; When the robot has sufficient power, dynamically plan the task priority according to the coordinate information and the urgency; When the robot is low on power, an execution path between tasks is constructed based on the coordinate information of each task; The priority of the task is determined according to the positional relationship between the location information of the charging station and the execution path.

2. A data processing and task scheduling method for a mobile charging robot according to claim 1, It is characterized in that Determining the urgency of the task according to the status information includes: According to the state information, fuzzy sets of time limit, impact range and user priority are respectively established; Establishing membership functions of the time limit, the impact range and the user priority respectively; The membership function of the urgency is established according to the membership function of the time limit, the scope of influence and the user priority, and the urgency membership value of the task is calculated. The urgency membership value ,have ， In the formula, is the weight of the time limit, is the weight of the impact range, is the weight of user priority, For the The membership function of the time limit of each task is For the The membership function of the influence range of each task, For the The membership function of the user priority of each task; The urgency degree membership value is normalized to obtain an urgency degree coefficient value.

3. A data processing and task scheduling method for a mobile charging robot according to claim 2, It is characterized in that The method further comprises: When the robot power is greater than or equal to the first threshold, the first weight of the coordinate information and the second weight of the urgency coefficient value are set, wherein the first weight With the second weight relationship, ； Using the first weight and the second weight, a priority calculation function is established to calculate the priority result value. The priority result value of ,have ， In the formula, Indicates that according to The distance measurement value between the coordinate information of each task and the robot position coordinates, For the The urgency coefficient value of each task; Normalizing the priority result value to obtain a priority coefficient value; The tasks are sorted according to the priority coefficient values.

4. A data processing and task scheduling method for a mobile charging robot according to claim 3, It is characterized in that The method further comprises: When the robot power is greater than or equal to the second threshold and less than the first threshold, the tasks in the task list whose priority coefficient value is greater than or equal to the third threshold are sent to the cloud task center; Construct the shortest path between the tasks whose priority coefficient values ​​are less than the third threshold value, and obtain multiple first predicted paths. With the task The first predicted path ,have ， In the formula, Indicates the task With the task The first predicted path The coordinates of the point on .

5. A data processing and task scheduling method for a mobile charging robot according to claim 4, It is characterized in that The method further comprises: When the battery level of the robot is less than the second threshold, the tasks in the task list whose priority coefficient value is less than the third threshold and greater than or equal to the fourth threshold are sent to the cloud task center; Construct the shortest path between the tasks whose priority coefficient values ​​are less than the fourth threshold value, and obtain multiple second predicted paths. With the task The second predicted path ,have ， In the formula, Representation Task With the task The first predicted path The coordinates of the point on .

6. A data processing and task scheduling method for a mobile charging robot according to claim 5, It is characterized in that The method further comprises: Get the location information of the charging station and obtain the coordinates of the charging station; Obtaining a charging path according to the distance between the charging station coordinates and the coordinates of each point of the execution path; According to the value of the priority coefficient, the priorities of the two tasks corresponding to the charging path are set to the first priority and the second priority; Calculate the priority result value of each remaining task according to the priority calculation function to obtain the priority coefficient value of each remaining task; According to the priority coefficient values ​​of the remaining tasks, the tasks are sorted starting from the third priority level; The execution path includes the first predicted path and the second predicted path. After completing the first priority task, the robot will enter the charging station to charge on the way to the second priority task.

7. A data processing and task scheduling method for a mobile charging robot according to claim 6, It is characterized in that The step of obtaining the charging path according to the distance between the charging station coordinates and the coordinates of each point of the execution path comprises: Calculating the distance between the coordinates of the charging station and the coordinates of each point of the execution path to obtain a plurality of parking distance values; Determine whether the stopping distance value is less than a fifth threshold; If there is a parking distance value that is smaller than the fifth threshold, the execution path corresponding to the parking distance value is selected as the charging path.

8. A data processing and task scheduling method for a mobile charging robot according to claim 7, It is characterized in that The determining whether the stopping distance value is less than the fifth threshold value further includes: If there is no docking distance value less than the fifth threshold, obtaining the coordinate information and status information of the task in the cloud task center; Based on the coordinate information of each task in the cloud task center and the coordinate information of the next task to be executed, multiple backup paths are calculated; Calculating the distance between the coordinates of the charging station and the coordinates of each point on the backup path to obtain a plurality of backup distance values; sorting the plurality of standby distance values ​​from small to large, and selecting the standby path corresponding to the standby distance value that is ranked first as the charging emergency path; Receive the task in the cloud task center corresponding to the charging emergency path, and insert the priority of the task in the cloud task center corresponding to the charging emergency path after the next task to be executed.

9. A data processing and task scheduling method for a mobile charging robot according to claim 7, It is characterized in that If the parking distance value is less than the fifth threshold, selecting the execution path corresponding to the parking distance value as the charging path includes: If there is a parking distance value that is less than the fifth threshold, selecting the execution path corresponding to the parking distance value as the charging path; If there are multiple stopping distance values ​​smaller than the fifth threshold, selecting the execution paths corresponding to the multiple stopping distance values ​​smaller than the fifth threshold to construct a path set; According to the distance value between the coordinate information of each task in the path set and the position coordinate of the robot, the paths are sorted from small to large; The execution path corresponding to the first distance value is selected as the charging path.

10. A data processing and task scheduling system for a mobile charging robot, It is characterized in that The system comprises: A task receiving module is used to periodically receive tasks from a cloud task center according to the robot task quantity capacity value; the task receiving module is also used to send tasks whose task list priority coefficient values ​​are greater than or equal to a third threshold to the cloud task center when the robot power is greater than or equal to a second threshold and less than a first threshold; the task receiving module is also used to send tasks whose task list priority coefficient values ​​are less than the third threshold and greater than or equal to a fourth threshold to the cloud task center when the robot power is less than the second threshold; the task receiving module is also used to receive tasks in the cloud task center corresponding to the charging emergency path if there is no docking distance value less than the fifth threshold; A task information collection module is used to collect coordinate information and status information of each task of the robot; the task information collection module is also used to collect coordinate information and status information of tasks in the cloud task center; A task urgency evaluation module, used to determine the urgency of the task according to the status information; The task priority planning module is used to dynamically plan the task priority by calculating the task priority coefficient according to the coordinate information and the urgency when the robot has sufficient power; the task priority planning module is also used to construct the execution path between the tasks according to the coordinate information of each task when the robot has insufficient power, and determine the priority of the task according to the position relationship between the location information of the charging station and the execution path; The task execution module is used to guide the mobile charging robot to perform tasks according to task priorities and execution paths.

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