Control method, apparatus, device, and storage medium for intelligent mobility assistance systems

Abstract

The present invention provides a control method, apparatus, device, and storage medium for a smart mobility assistance system, wherein the method comprises: a step of classifying a mobility robot into a first robot and a second robot according to the available power value of the mobility robot; a step of generating a task distribution density map including a plurality of task density areas based on an issued task and a preset navigation map; a step of determining available charging stations based on the task density areas and the preset navigation map and obtaining charging location information of the available charging stations; a step of determining a first work area and a second work area according to the task distribution density map and charging location information when the number of task density areas is greater than the number of the first robot, wherein the number of the first work area is equal to the number of the first robot and the second work area includes at least one available charging station; and a step of controlling the first robot to perform a task included in one first work area and a plurality of second robots to alternately perform a task included in one second work area.

Description

Technology Field

[0001] The present invention relates to the field of automation control technology, and in particular to a control method, apparatus, device, and storage medium for a smart mobility assistance system. Background Technology

[0002] Currently, the application of robots in modern industrial automation and the service sector is becoming increasingly widespread, and robots are playing a more significant role, ranging from manufacturing production lines to customer hospitality. With the advancement of robot technology, collaborative work among multiple robots has established itself as a crucial method for improving work efficiency and handling complex tasks. However, in actual implementation, many robot systems face a series of challenges, one of the most prominent being the conflict between task bursts and robot power management. When multiple tasks appear simultaneously or surge in large quantities over a short period, it is necessary to respond by rapidly deploying available robot resources. In this case, efficiently allocating tasks to each robot and ensuring that all robots complete their tasks while maintaining sufficient power is an urgent task that needs to be addressed. Since each robot has different work intensity and duration during operation, there are significant differences in remaining power consumption, which further increases the difficulty of integrated scheduling. The problem to be solved

[0003] The present invention provides a control method, apparatus, device, and storage medium for a smart mobility assistance system to improve the scheduling efficiency of a mobility robot when task density occurs. means of solving the problem

[0004] In a first aspect, an embodiment of the present invention provides a method for controlling a smart mobility assistance system, said method,

[0005] When entering a task increase mode, a step of obtaining the available power value of the mobility robot and classifying the mobility robot into a first robot and a second robot according to the available power value;

[0006] A step of generating a task distribution density map including multiple task density areas based on published tasks and a preset navigation map;

[0007] A step of determining available charging stations based on the above task density area and the above preset navigation map, and obtaining charging location information of the available charging stations;

[0008] When the number of task density areas is greater than the number of the first robot, a step of determining a first work area and a second work area according to the task distribution density map and the charging location information - the number of the first work area is equal to the number of the first robot, and the second work area includes at least one available charging station - ; and

[0009] The method includes a step of controlling the first robots to each perform the one task included in the first work area, and the plurality of second robots to alternately perform the one task included in the second work area.

[0010] In a second aspect, an embodiment of the present invention provides a control device for a smart mobility assistance system, said device,

[0011] A power measurement calculation module used to obtain the available power value of a mobility robot when entering a task increase mode, and to classify the mobility robot into a first robot and a second robot according to the available power value;

[0012] A task management module used to generate a task distribution density map containing multiple task density areas based on published tasks and a preset navigation map;

[0013] A charging management control module used to determine available charging stations based on the above task density area and the above preset navigation map, and to acquire charging location information of the available charging stations;

[0014] When the number of task-dense areas is greater than the number of the first robot, an area division module used to determine a first work area and a second work area according to the task distribution density map and the charging location information - the number of the first work area is equal to the number of the first robot, and the second work area includes at least one available charging station -; and

[0015] It includes a motion control module used to control the first robots to each perform a task included in one of the first work areas and the plurality of second robots to alternately perform a task included in one of the second work areas.

[0016] In a third aspect, an embodiment of the present invention provides an electronic device, said electronic device comprising a memory and a processor;

[0017] The above memory is for storing computer programs, and

[0018] The above processor is used to execute the computer program and to implement a control method for a smart mobility assistance system according to any one of the embodiments of the present invention when executing the computer program.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, said computer-readable storage medium has a computer program stored therein, and when said computer program is executed by a processor, said processor implements a control method for a smart mobility assistance system according to any one of the embodiments of the present invention. Effects of the invention

[0020] An embodiment of the present invention provides a control method for a smart mobility assistance system, the method comprising: a step of obtaining an available power value of a mobility robot when entering a task increase mode and classifying the mobility robot into a first robot and a second robot according to the available power value; a step of generating a task distribution density map including a plurality of task dense areas based on an issued task and a preset navigation map; a step of determining available charging stations based on the task dense areas and the preset navigation map and obtaining charging location information of the available charging stations; a step of determining a first work area and a second work area according to the task distribution density map and charging location information when the number of task dense areas is greater than the number of the first robot, wherein the number of the first work area is equal to the number of the first robot and the second work area includes at least one available charging station; and a step of controlling the first robot to perform a task included in one first work area and a plurality of second robots to alternately perform a task included in one second work area. Through the above method, robots are classified into a first robot (high power) and a second robot (low power) based on power consumption values; the work area is rationally divided by combining a task distribution density map and charging station location information; after identifying multiple task-dense areas, task jurisdiction areas are allocated according to the number of task-dense areas and the number of first robots; task-dense areas adjacent to charging stations are preferentially set as the second work area to ensure that the robots can easily charge while performing tasks, and task-dense areas located far from charging stations are classified as the first work area. This not only optimizes resource allocation but also reduces unnecessary movement, prevents service interruptions due to power shortages, and guarantees efficient and stable task completion even when a large number of simultaneous task requests occur, thereby significantly improving overall service quality and user experience. Brief explanation of the drawing

[0021] To more clearly explain the technical method of the embodiments of the present invention, the accompanying drawings necessary for describing the embodiments are briefly introduced below. The drawings described below are some embodiments of the present invention, and it is obvious to those skilled in the art that other accompanying drawings can be obtained based on these accompanying drawings without creative labor. FIG. 1 is a schematic flowchart of a control method for a smart mobility assistance system provided in an embodiment of the present invention. FIG. 2 is a schematic block diagram of a control device for a smart mobility assistance system provided in an embodiment of the present invention. Specific details for implementing the invention

[0022] The technical method of the embodiments of the present invention is clearly and completely explained by combining the drawings attached to the embodiments of the present invention. It is obvious that the described embodiments are only some embodiments of the present invention and not the entirety of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person skilled in the art without creative labor fall within the scope of protection of the present invention.

[0023] The flowcharts depicted in the attached drawings are merely illustrative and do not necessarily include all contents, tasks, or steps, nor must they be performed in the order described. For example, some tasks or steps may be further divided, combined, or partially merged, so the actual order of execution may vary depending on the actual situation.

[0024] It should also be understood that the terms used in this specification are intended solely for the purpose of describing specific embodiments and are not intended to limit the invention. As used in this specification and the appended claims, unless the context clearly indicates otherwise, the singular forms of “one,” “one,” and “corresponding” are used with the intent to include the plural forms as well.

[0025] It is further to be understood that the term "and / or" as used in the specification and appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes all such combinations.

[0026] Referring to FIG. 1, FIG. 1 is a schematic flowchart of a control method for a smart mobility assistance system provided in an embodiment of the present invention. As illustrated in FIG. 1, the specific steps of the control method for the smart mobility assistance system include steps S101 to S105.

[0027] In step S101, when entering the task increase mode, the available power value of the mobility robot is obtained, and the mobility robot is classified into a first robot and a second robot according to the available power value.

[0028] For example, if the number of tasks received by the control system of a mobility robot exceeds a preset increment threshold, it enters task increment mode. The preset increment threshold may be determined based on the number of mobility robots or the task execution status. Scenarios for task increment mode include factories, restaurants, and airports. Even during normal operations, unpredictable factors (such as weather changes or holiday effects) may cause more task requests than usual during specific times. Adopting task increment mode allows the mobility assistance system to respond more flexibly to these fluctuations, ensuring that the service is always efficient and reliable. Before performing task reallocation, the available power value of the mobility robot must be obtained. The available power value is the difference between the mobility robot's current power value and the return power value, and the return power value is the power value required to traverse the longest path between the task point and the charging station.

[0029] When distinguishing robot types, a single preset power threshold must be used as the distinction criterion, and said preset power threshold may be artificially set or determined based on historical data; for example, in task escalation mode, the initial power values ​​of mobility robots requiring charging may be statistically analyzed, and the maximum value among the initial power values ​​may be set as the preset power threshold.

[0030] First robot (high power): The first robot is equipped with sufficient battery capacity for long-term operation and can handle food delivery tasks that involve moving between floors or crossing zones. For example, during lunch peak hours, the robot can handle food delivery from a restaurant on the first floor to a guest room on an upper floor.

[0031] Second Robot (Low Power): The second robot is better suited for performing tasks on the same floor or near available charging stations. For example, these robots can handle room service near elevator entrances, ensuring continuous service capabilities by allowing them to quickly return to available charging stations to replenish energy even when power levels are low.

[0032] A mobility robot classification strategy based on power consumption values ​​is a key element in improving scheduling efficiency in task scaling mode, ensuring the maintenance of efficient and stable service levels even in the face of routine tasks or sudden high demand situations.

[0033] In step S102, a task distribution density map including multiple task density areas is generated based on the published tasks and the preset navigation map.

[0034] For example, a task distribution density map is generated based on deployed tasks and a pre-configured navigation map. The task distribution density map indicates the degree of task density in each area, that is, which areas have high task demand. Generally, this includes a comprehensive analysis of factors such as task locations, time requirements, and task types.

[0035] It needs to be explained that the task distribution density map can be in a 2D or 3D form; for example, a 2D task distribution density map is constructed for a single-story factory, and a 3D task distribution density map is constructed for a multi-story hotel. By combining this with the current visual neural network and processing according to the task distribution density map, task area classification can be performed efficiently.

[0036] Additionally, it must be explained that the task-dense area must satisfy the following conditions: 1) The area of ​​the task-dense area is larger than the preset area. 2) The task path density within the task-dense area is greater than the preset density threshold. Accordingly, after the buffered mobility robot completes the task in the task-dense area, the remaining power value becomes 15%-25%.

[0037] Visual representation facilitates decision-makers' intuitive identification of task-concentrated hotspots and provides a basis for assigning tasks in the next stage.

[0038] In step S103, available charging stations are determined based on the task density area and the preset navigation map, and charging location information of the available charging stations is obtained.

[0039] For example, after determining the task density area, available charging stations within and around that area must be identified, and specific location information of the available charging stations must be collected. This is crucial for establishing a charging plan, and is even more important for the second robot, which has a low power consumption.

[0040] It needs to be clarified that an available charging station refers to a charging station in an idle state. Since multiple charging stations may exist within a scenario and other mobility robots may be charging, a charging station in an idle state (a state where no mobility robot is currently charging) is considered an available charging station.

[0041] By adding specific location information for available charging stations, work areas can be planned more rationally, ensuring service continuity by allowing mobility robots to replenish energy in a timely manner while completing tasks. Furthermore, this provides an important reference standard for subsequent work area partitioning.

[0042] In step S104, if the number of task-dense areas is greater than the number of first robots, a first work area and a second work area are determined according to the task distribution density map and charging location information, wherein the number of first work areas is equal to the number of first robots, and the second work area includes at least one available charging station.

[0043] For example, if it is confirmed that the number of task-dense areas exceeds the number of first robots (high-power robots), second robots must cooperate to complete the corresponding tasks. Two types of work areas, a first work area and a second work area, are defined based on the task distribution density map and the location information of available charging stations. The number of first work areas exactly matches the number of first robots, meaning that each area of ​​the first work area is exclusively handled by a single robot with sufficient power. On the other hand, the second work area is designed to include at least one available charging station, allowing multiple second robots with lower power to perform tasks alternately and charge in a timely manner.

[0044] It needs to be explained that when the number of task-dense areas is detected to exceed the number of first robots (high-power robots), a second work area is planned first to ensure that the second work area is the area closest to the available charging station among the task-dense areas, and then the first work area is planned.

[0045] After confirming the existence of a task-dense area exceeding the number of first robots, a second work area is distinguished based on whether it is adjacent to an available charging station, and a key feature of the second work area is that it includes at least one available charging station. This is intended to solve the power replenishment problem of the second robots. By placing these second robots in a location close to the charging station, it is facilitated to charge them alternately by utilizing the task execution interval, thereby extending the work cycle. The available charging station in the second work area allows only the robots corresponding to the second work area to use it in the task escalation mode.

[0046] In some embodiments, if the availability of charging stations is guaranteed to be sufficient, a first work area may be determined preferentially. This first work area is generally assigned to a robot with sufficient power to perform the task. The selection criteria for the first work area may include multiple factors, such as the urgency of the task, the estimated completion time, and the complexity of the task within the area. The goal is to ensure that each first work area can be efficiently completed by a single first robot.

[0047] Through the above method, the smart mobility assistance system enables the efficient allocation of resources and ensures that it maintains good operational performance even when faced with complex and diverse work environments. It improves the utilization rate of individual mobility robots while simultaneously strengthening the overall system's ability to respond to unexpected situations.

[0048] In step S105, the first robot is controlled to perform a task included in one first work area, and a plurality of second robots are controlled to alternately perform a task included in one second work area.

[0049] For example, commands are sent to each first robot so that each first robot is responsible for all tasks within a single first work area. For the second work area, a rotation system is adopted, and multiple second robots perform the tasks in that area sequentially or according to demand.

[0050] This approach not only ensures the efficient utilization of limited resources even during peak times but also considers the sustainability of mobility robots, preventing a decline in overall efficiency caused by the overuse of individual robots. Through this precise management, the entire smart mobility assistance system can maintain an efficient and stable operational state even when faced with a large volume of concurrent requests.

[0051] To explain the technical solution of the present invention more clearly, the technical solution of the present invention is further explained through specific embodiments below. It should be noted that such specific embodiments are intended only to extend and explain the technical solution of the present invention and are not intended to limit the present invention.

[0052] In some embodiments, the step of determining available charging piles based on a task-dense area and a preset navigation map includes the step of determining work charging stations and non-work charging stations based on the preset navigation map. An expansion area is determined according to the available power value of the second robot, and the expansion area is determined according to the expansion area and the task-dense area. If the number of work charging stations within the expansion area is greater than a preset difference value, a non-work charging station corresponding to the preset difference value is set as an available charging station.

[0053] For example, based on a pre-configured navigation map, all known charging station locations are first identified. Furthermore, these charging stations are classified into two states: "working" and "non-working." "Working charging stations" are charging stations currently in use, while "non-working charging stations" are charging stations in an idle state. Based on the available power value of the second robot, a reasonable "expansion area" is calculated. This area is estimated based on the maximum travel range achievable with the robot's remaining power. The specific numerical value of the expansion area can be calculated based on empirical data from actual application or a pre-configured formula. For instance, the average travel distance at different power levels can be obtained through historical data analysis, and the corresponding area can be estimated. By combining information on task-dense areas, a single "expansion area" centered on the task-dense area is determined based on the expansion area obtained by the above calculation. This area is intended to include all potential task points that may require separate charging station support. The boundary of the expansion area must consider the maximum possible distance a robot can travel from the task point to the nearest charging station, ensuring that the robot can return for charging even in the worst-case scenario. Non-working charging stations are selected based on a preset difference value and set as available charging stations; this preset difference value is the difference between the task-dense area and the first robot. Selection criteria may include factors such as the charging station's proximity to the task-dense area and ease of access.

[0054] By dynamically allocating work areas, all available resources can be utilized more efficiently during task peak times, service interruptions caused by a shortage of charging stations can be prevented, and low-power robots can be provided with more charging opportunities to maintain sufficient power while performing tasks, thereby extending the continuous working time of the entire team.

[0055] In some embodiments, the step of generating a task distribution density map including a plurality of task density areas based on a published task and a preset navigation map includes the step of determining task start location information according to the published task. A task path planning map is generated based on a preset path planning algorithm, task start location information, and a preset navigation map, and the task path planning map includes a plurality of task paths. The task path planning map is divided into a plurality of first grid cells, and a task path density within the first grid cells is determined based on the plurality of task paths and the first grid cells. If the task path density is greater than a preset first density threshold, the first grid cell is set as a second grid cell. A task density area is determined based on a preset density clustering algorithm, a preset distance weighting parameter, and the second grid cells.

[0056] For example, the starting location of each published task is obtained according to the published task. This location information is based on user demand or pre-configured task points. One or multiple possible paths are generated for each task by combining the task starting location and a pre-configured navigation map using a pre-configured path planning algorithm. These paths are optimized to reduce time and energy consumption by considering factors such as obstacles and terrain. The generated task path planning map contains multiple task paths, each representing a route traversable from the starting point to the end point of one or more tasks. The entire navigation map is divided into a series of first grid cells of equal size. For each first grid cell, task path density is calculated based on the number of task paths passing through that unit. Task path density reflects the concentration of tasks within a specific area. By setting a first density threshold as a reference, first grid cells with a task path density greater than that threshold are marked as second grid cells. Second grid cells represent areas with high task density. All second grid cells are processed using a preset density clustering algorithm (e.g., DBSCAN). Algorithm parameters include an adjacent radius and a minimum number of adjacent area points, and are used to identify high-density areas that are closely connected to each other. As a result, some initial task areas and third grid cells not included therein are primarily defined. For the second grid cells within each initial task area, a first weighted distance value between them is calculated using preset distance weight parameters. Based on the first weighted distance value, the initial task area is further subdivided into oversized and undersized areas. For subsequent adjustment, second grid cells located at the edges of the oversized area are reclassified as third grid cells. A second weighted distance value is calculated between the third grid cells and the undersized area.Based on the second weighted distance value, the initial task area is updated into the first update area and the second update area by assigning the third grid cell to the nearest undersized area. Finally, the boundaries of each area are constructed using Voronoi diagram techniques, and smoothing processing is performed to obtain the final task dense area distribution.

[0057] By efficiently planning task paths, unnecessary travel distances are reduced, thereby lowering the robot's energy consumption and extending the operating time after a single charge. By scientifically analyzing the task distribution, areas where tasks are highly concentrated can be identified more quickly and accurately, which helps in establishing a more rational robot scheduling strategy.

[0058] In some embodiments, the step of determining a task density area based on a preset clustering algorithm, a preset distance weight parameter, and a second grid cell involves performing density clustering processing on the second grid cell according to a preset adjacent radius and a preset minimum number of adjacent area points to obtain an initial task area and a third grid cell outside the initial task area. A first weighted distance value for each second grid cell within the initial task area is determined according to the preset distance weight parameter. Based on the first weighted distance value, the initial task area is divided into an oversized area and an undersized area. A second grid cell located at the edge of the oversized area is set as a third grid cell. A second weighted distance value between the third grid cell and the undersized area is calculated. Based on the second weighted distance value, the third grid cell is assigned to the corresponding undersized area, the oversized area is updated as a first update area, and the undersized area is updated as a second update area. Based on the first update region and the second update region, a Voronoi diagram is constructed and boundary smoothing is performed to generate task density regions.

[0059] For example, density clustering processing is performed on a second grid cell using a preset adjacency radius and a minimum number of adjacency points. The adjacency radius defines the range considered around each unit, and the minimum number of adjacency points specifies the minimum number of units required to form a cluster. Through a density clustering algorithm (e.g., DBSCAN), a high-density area is identified as an initial task area, while a third grid cell not included within that area is determined. For a second grid cell within each initial task area, a first weighted distance value with other units within that area is calculated using a preset distance weighting parameter. The distance weighting parameter can be calculated based on Euclidean distance, Manhattan distance, or other suitable distance measurement methods, and combines weighting factors to reflect the relative importance or degree of closeness between units. Based on the first weighted distance value, the initial task area is divided into an oversized area and an undersized area. An oversized area refers to an area where the distance between internal units is far and the distribution is sparse, while an undersized area refers to an area where the distance between internal units is close and the distribution is dense. Specific division criteria can be set according to actual demand; for example, an oversized area and an undersized area can be distinguished by setting a single threshold. A second grid cell located at the edge of the oversized area is subdivided into a third grid cell to reduce the area of ​​the oversized region and make it denser. A second weighted distance value is calculated between the third grid cell and the undersized area, and the boundary between the oversized and undersized areas is updated by assigning the third grid cell to the nearest undersized area based on that distance value. Based on the first updated area and the second updated area, a Voronoi diagram is constructed. A Voronoi diagram is a spatial partitioning method that divides a plane into multiple areas, where each area includes all locations where the distance to a specific point is closer than the distance to any other arbitrary point.Boundary smoothing is performed on the generated Voronoi diagram to remove unnecessary sharp edges and irregular shapes, making the boundaries of task-dense regions more natural and smooth. Through this step, an optimized distribution of task-dense regions is obtained. These task-dense regions not only reflect the actual distribution of tasks but also take into account path planning and robot scheduling efficiency.

[0060] In some embodiments, when the number of task-dense areas is less than the number of first robots, the method further includes the step of detecting the task path density of the task-dense areas. A task-dense area where the task path density is greater than a preset second density threshold is set as a first work area, and a task-dense area where the task path density is less than or equal to the preset second density threshold is set as a second work area. The first robot is set as a third robot and a fourth robot, respectively, wherein the number of third robots is equal to the number of second work areas. Each third robot is controlled to perform a task included in one second work area. A plurality of fourth robots are controlled to jointly perform a task included in one first work area, or a plurality of second robots are controlled to jointly perform a task included in one first work area alternately with the third robot.

[0061] For example, if the number of task-dense areas is less than the number of first robots, an evaluation of each task-dense area must be performed first. The actual workload of the task-dense area is determined by calculating the task path density of each grid cell within the task path planning map. Task-dense areas where the corresponding task path density is higher than a preset second density threshold are defined as the first work area. These areas must be processed preferentially due to the high volume of tasks. Conversely, task-dense areas where the task path density is lower than or equal to the second density threshold are defined as the second work area. These areas may contain a small volume of tasks or have a sparse distribution. A sufficient number of robots (equal to the number of first work areas) are selected from the existing first robots and designated as the third robots. The third robots are dedicated to performing tasks within the second work area. The remaining first robots are designated as the fourth robots. The fourth robots can jointly perform tasks within a single first work area or complete tasks in cooperation with the second robots. The third robots move independently to their respective second work areas and perform all tasks assigned to them within those areas. For the first work area, to increase efficiency, multiple fourth robots may be selected to work together, or the fourth and second robots may perform tasks alternately; this ensures task completion while also allowing for consideration of robot charging requirements. During the actual operation, the placement of the third and fourth robots can be adjusted according to real-time conditions. For example, if a task in a specific second work area is completed in advance, the third robot responsible for that area may be reassigned to another task-dense area or assigned to support task processing in the first work area. Similarly, for the second work area, the number of participating robots and their combination methods are dynamically adjusted based on changes in tasks and robot status to ensure the maximization of resource utilization.

[0062] By dividing the task-dense area into a first work area and a second work area based on task path density, and correspondingly reassigning the roles of the robots to the third and fourth robots, this strategy optimizes robot task allocation. This not only ensures that high-density task areas are processed preferentially but also enables efficient coverage of low-density task areas. Consequently, overall work efficiency is improved, while the power consumption of each robot is efficiently utilized through rational scheduling, and the flexibility and adaptability of the system are enhanced.

[0063] In some embodiments, the step of controlling a plurality of second robots to alternately perform a task included in a second work area includes setting one second robot with a larger available power value as a work robot and setting another second robot as a reserve robot. The work robot is controlled to perform a task included in the second work area, and the reserve robot is controlled to move to an available charging station corresponding to the second work area to perform charging. If the available power value of the work robot is lower than a preset first power threshold, or if the available power value of the reserve robot is greater than a preset second power threshold, the reserve robot is controlled to perform a task included in the second work area, and the work robot is controlled to move to an available charging station corresponding to the second work area to perform charging.

[0064] For example, in the second work area, multiple second robots are classified into working robots and backup robots based on their available power values. Specifically, a robot with a higher power value is selected as the working robot to handle task execution, while another robot with a lower power value is selected as the backup robot to prepare for charging or await commands. The working robot begins performing tasks within the second work area. During execution, the robot's available power value is continuously monitored to ensure the smooth completion of the assigned task. Simultaneously, the backup robot is controlled to move to an available charging station within or near the second work area to perform charging. This ensures that the backup robot can immediately replace the working robot when necessary. It monitors whether the working robot's available power value is lower than a preset first power threshold, or whether the backup robot's available power value is higher than a preset second power threshold. If the working robot's power is lower than the first power threshold, it indicates that it may be unable to continue performing the remaining task, at which point replacement is required. If the power level of the backup robot exceeds the second power threshold, it indicates that it is sufficiently charged to take over the work of the operating robot. If any of the above conditions are met, a role switch is performed immediately. That is, the backup robot switches to the new operating robot and begins performing the unfinished task. The existing operating robot switches to the new backup robot, moves to the charging station to recharge, and prepares for the next task. By continuously monitoring the status of both robots and continuously adjusting their roles according to actual conditions, the continuity and efficiency of the task are ensured.

[0065] In some embodiments, the step of classifying a mobility robot into a first robot and a second robot according to an available power value includes: a step of setting a mobility robot whose available power value belongs to a preset first power value range as the first robot; and a step of setting a mobility robot whose available power value belongs to a preset second power value range as the second robot.

[0066] For example, when entering task escalation mode, the current available power data of all mobility robots awaiting task assignment is first collected. This step serves as the basis for classifying the robot types. Two pre-configured power value ranges are defined as the first power value range and the second power value range. These two ranges are set based on actual demand and the robot's operational performance. The first power value range generally refers to a high power level and is suitable for performing long-duration or long-distance tasks. The second power value range refers to a low but still sufficient power level and is suitable for performing short-duration or short-distance tasks and rapidly performing charging changes.

[0067] Depending on current task demands and the specific conditions of each work area, the ratio of the first and second robots is flexibly adjusted to maximize overall work efficiency while maintaining excellent energy management.

[0068] Referring to FIG. 2, FIG. 2 is a schematic block diagram of a control device of a smart mobility assistance system provided in an embodiment of the present invention, and the control device (200) of the smart mobility assistance system is for performing the control method of the smart mobility assistance system described above. Here, the control device (200) of the smart mobility assistance system may be placed on a server.

[0069] Here, the server may be a standalone server or a server cluster, and may be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain services, security services, Content Delivery Networks (CDNs), and big data and artificial intelligence platforms.

[0070] For example, as shown in FIG. 2, the control unit (200) of the smart mobility assistance system includes a power amount measurement calculation module (201), a task management module (202), a charge management control module (203), an area division module (204), and a motion control module (205).

[0071] The power amount measurement calculation module (201) is used to obtain the available power amount value of the mobility robot when entering the task increase mode, and to classify the mobility robot into a first robot and a second robot according to the available power amount value.

[0072] The task management module (202) is used to generate a task distribution density map containing multiple task density areas based on published tasks and a preset navigation map.

[0073] The charging management control module (203) is used to determine available charging stations based on a task-dense area and a preset navigation map, and to obtain charging location information of the available charging stations.

[0074] The area division module (204) is used to determine a first work area and a second work area according to a task distribution density map and charging location information when the number of task-dense areas is greater than the number of first robots, wherein the number of first work areas is equal to the number of first robots and the second work area includes at least one available charging station.

[0075] The motion control module (205) is used to control the first robot to perform a task included in each of the first work areas, and to control the plurality of second robots to perform a task included in each of the second work areas alternately.

[0076] An embodiment of the present invention provides an electronic device, the electronic device comprising a memory and a processor; the memory is used to store a computer program; the processor executes the computer program and is used to implement a control method for a smart mobility assistance system according to any one of the embodiments of the present invention when the computer program is executed.

[0077] An embodiment of the present invention provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the processor implements a control method for a smart mobility assistance system according to any one of the embodiments of the present invention.

[0078] The foregoing description is merely a specific embodiment of the present invention and is not limited thereto. A person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and all such modifications or substitutions should be included within the scope of protection of the present invention. Accordingly, the scope of protection of the present invention follows the scope of protection of the claims.

Claims

Claim 1 A control method for a smart mobility assistance system characterized by comprising: a step of obtaining an available power value of a mobility robot when entering a task increase mode and classifying the mobility robot into a first robot and a second robot according to the available power value; a step of generating a task distribution density map including a plurality of task density areas based on an issued task and a preset navigation map; a step of determining available charging stations based on the task density areas and the preset navigation map and obtaining charging location information of the available charging stations; a step of determining a first work area and a second work area according to the task distribution density map and the charging location information when the number of task density areas is greater than the number of the first robot - wherein the number of the first work area is equal to the number of the first robot, and the second work area includes at least one available charging station -; and a step of controlling the first robot to perform a task included in one of the first work areas and the plurality of the second robots to alternately perform a task included in one of the second work areas. Claim 2 A control method for a smart mobility assistance system according to claim 1, wherein the step of determining available charging stations based on the task-dense area and the preset navigation map comprises: determining work charging stations and non-work charging stations based on the preset navigation map; determining an expansion area according to the available power value of the second robot and determining an expansion area according to the expansion area and the task-dense area; and, if the number of work charging stations within the expansion area is greater than a preset difference value, setting a non-work charging station corresponding to the preset difference value as the available charging station. Claim 3 A method for controlling a smart mobility assistance system according to claim 1, wherein the step of generating a task distribution density map including a plurality of task density areas based on the issued task and a preset navigation map comprises: a step of determining task start location information according to the issued task; a step of generating a task path planning map including a plurality of task paths based on a preset path planning algorithm, the task start location information, and the preset navigation map; a step of dividing the task path planning map into a plurality of first grid cells and determining the task path density within the first grid cells based on the plurality of task paths and the first grid cells; a step of setting a first grid cell in which the task path density is greater than a preset first density threshold as a second grid cell; and a step of determining the task density area based on a preset density clustering algorithm, a preset distance weighting parameter, and the second grid cells. Claim 4 In claim 3, the step of determining the task density area based on the preset density clustering algorithm, the preset distance weight parameter, and the second grid cell comprises: a step of performing density clustering processing on the second grid cell according to a preset adjacent radius and a preset minimum number of adjacent area points to obtain an initial task area and a third grid cell outside the initial task area; a step of determining a first weighted distance value for each of the second grid cells within the initial task area according to the preset distance weight parameter; a step of dividing the initial task area into an oversized area and an undersized area according to the first weighted distance value; a step of setting the second grid cell at the edge of the oversized area as the third grid cell; a step of calculating a second weighted distance value between the third grid cell and the undersized area; and a step of assigning the third grid cell to the corresponding undersized area according to the second weighted distance value, updating the oversized area to a first update area, and updating the undersized area to a second update area. A control method for a smart mobility assistance system characterized by including the step of constructing a Voronoi diagram according to the first update area and the second update area, and performing boundary smoothing processing to generate the task density area. Claim 5 A method for controlling a smart mobility assistance system according to claim 1, wherein, when the number of task-dense areas is less than the number of first robots, the method further comprises: a step of detecting the task path density of the task-dense areas; a step of setting a task-dense area where the task path density is greater than a preset second density threshold as a first work area, and a step of setting a task-dense area where the task path density is less than or equal to the preset second density threshold as a second work area; a step of setting the first robots as a third robot and a fourth robot, respectively, wherein the number of the third robots is equal to the number of the second work areas; a step of controlling the third robots to each perform a task included in one of the second work areas; and a step of controlling a plurality of the fourth robots to jointly perform a task included in one of the first work areas, or controlling a plurality of the second robots to alternately jointly perform a task included in one of the first work areas with the third robots. Claim 6 A method for controlling a smart mobility assistance system according to claim 1, wherein the step of controlling a plurality of the second robots to alternately perform a task included in one of the second work areas comprises: setting one of the second robots with a larger available power value as a work robot and setting another of the second robots as a reserve robot; controlling the work robot to perform a task included in the second work area and controlling the reserve robot to move to the available charging station corresponding to the second work area to perform charging; and, when the available power value of the work robot is lower than a preset first power threshold or the available power value of the reserve robot is greater than a preset second power threshold, controlling the reserve robot to perform a task included in the second work area and controlling the work robot to move to the available charging station corresponding to the second work area to perform charging. Claim 7 A control method for a smart mobility assistance system according to claim 1, wherein the step of classifying the mobility robot into a first robot and a second robot according to the available power value comprises: a step of setting the mobility robot that belongs to a preset first power value range as the first robot; and a step of setting the mobility robot that belongs to a preset second power value range as the second robot. Claim 8 A power measurement calculation module used to acquire the available power value of a mobility robot when entering a task increase mode and to classify the mobility robot into a first robot and a second robot according to the available power value; a task management module used to generate a task distribution density map including a plurality of task dense areas based on an issued task and a preset navigation map; a charging management control module used to determine available charging stations based on the task dense areas and the preset navigation map and to acquire charging location information of the available charging stations; and a region division module used to determine a first work area and a second work area according to the task distribution density map and the charging location information when the number of task dense areas is greater than the number of the first robot - wherein the number of the first work area is equal to the number of the first robot, and the second work area includes at least one available charging station -; A control device for a smart mobility assistance system characterized by including a motion control module used to control each of the first robots to perform a task included in one of the first work areas and a plurality of the second robots to alternately perform a task included in one of the second work areas. Claim 9 An electronic device comprising a memory and a processor; wherein the memory is for storing a computer program, the processor is used to execute the computer program, and when executing the computer program, the electronic device is characterized by implementing a control method for a smart mobility assistance system according to any one of claims 1 to 7. Claim 10 A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the processor enables the implementation of a control method for a smart mobility assistance system according to any one of claims 1 to 7.

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