Automatic mobile robot work scheduling method, system, equipment and medium

Abstract

The invention provides an automatic mobile robot work scheduling method, system and device and a medium. The number of automatic mobile robots completing a current task is determined by combining the actual utilization rate of the automatic mobile robots, the starting number of the devices is dynamically adjusted, unnecessary device operation time is shortened, energy waste is reduced, and reasonable utilization of device resources is ensured. In addition, the automatic mobile robot is monitored in real time, whether the scheduling task is added or not is determined according to the state of the automatic mobile robot, and the situation that the automatic mobile robot completing the task is added into the scheduling task and task completion is delayed is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field related to intelligent warehousing, and in particular relates to a method, system, equipment and medium for scheduling work of an automatic mobile robot. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In modern warehouses and logistics centers, autonomous mobile robots (AMRs) are widely used for handling and transporting goods.

[0004] Currently, when completing related tasks, automatic mobile robots mostly make corresponding scheduling plans based on the difficulty of the task, the task route, and whether the automatic mobile robots currently have task conflicts. However, the status of the automatic mobile robots themselves is ignored, and there is a lack of real-time monitoring and accurate analysis of the status of the automatic mobile robots. As a result, the automatic mobile robots cannot complete the corresponding tasks well due to their own reasons. In addition, there is a lack of quantitative basis for the scheduling of automatic mobile robots, resulting in too many or too few automatic mobile robots being turned on, causing low efficiency, energy waste, and increased risk of equipment failure. Summary of the Invention

[0005] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method, system, equipment and medium for scheduling the work of an automatic mobile robot. By real-time monitoring of the automatic mobile robot, it is determined whether to join the scheduling task based on the state of the automatic mobile robot, thereby avoiding the automatic mobile robot that has completed the task from joining the scheduling task and delaying the completion of the task.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for scheduling work of an automatic mobile robot, comprising: Establishing a device working model for each autonomous mobile robot based on its charging status and working status; Determine the actual utilization rate of the automatic mobile robot according to the duration of different working states of the automatic mobile robot; Determining a first number of automatic mobile robots required to complete the current task based on the current task situation and the actual utilization rate of the automatic mobile robots; According to the current real-time operation data of each automatic mobile robot and based on the corresponding equipment working model, the automatic mobile robots belonging to the first number of automatic mobile robots are determined, and task scheduling is performed on the automatic mobile robots belonging to the first number of automatic mobile robots.

[0007] As an implementation method, the actual utilization rate of the automatic mobile robot is determined based on the duration of different working states of the automatic mobile robot, specifically: The actual utilization rate of the autonomous mobile robot is determined according to the ratio of the sum of the time the autonomous mobile robot spends performing tasks and the charging time in the first time interval to the total time of the first time interval.

[0008] As an implementation method, the first number of autonomous mobile robots required to complete the current task is determined based on the current task situation and the actual utilization rate of the autonomous mobile robots, specifically: Determine the number of tasks of a single autonomous mobile robot based on its single cargo capacity and production rhythm; Determine the current task requirement time based on the number of tasks of a single automatic mobile robot and the time it takes for a single task of a single automatic mobile robot; A first number of automatic mobile robots required to complete the current task is determined based on the determined current task requirement time, available time corresponding to the production cycle, and an efficiency loss coefficient.

[0009] As an implementation method, it further includes: Determining a second number of autonomous mobile robots required to complete the current task based on the current task situation and the charging requirements of the autonomous mobile robots; The final number of autonomous mobile robots required to complete the current task is determined based on the determined first number of autonomous mobile robots and the determined second number of autonomous mobile robots.

[0010] As an implementation method, the second number of autonomous mobile robots required to complete the current task is determined based on the current task situation and the charging requirements of the autonomous mobile robots. Specifically, According to the endurance and charging time of the automatic mobile robot, determine the total charging time of the automatic mobile robot within the time corresponding to the production cycle; The second number of automatic mobile robots required to complete the current task is determined based on the current task requirement time and the total charging time of the automatic mobile robots within the time corresponding to the production tact.

[0011] As an implementation method, the automatic mobile robot to be used for task scheduling is determined based on the current real-time operating data of each automatic mobile robot and the corresponding device working model, specifically: According to the current real-time operation data of each automatic mobile robot and the corresponding equipment working model, the automatic mobile robot that is in an idle state within the corresponding time of the current task is determined; If the number of automatic mobile robots that are idle during the time corresponding to the current task is greater than the number of automatic mobile robots determined for task scheduling, the fault condition of the corresponding automatic mobile robot is determined based on the historical data of the automatic mobile robots that are idle during the time corresponding to the current task, and the automatic mobile robot used for task scheduling is determined in combination with the relative relationship between the current position of the automatic mobile robot and the starting position of the task, as well as the relative relationship between the starting position of the task and the position where the task is completed once and the charging position of the automatic mobile robot.

[0012] As an implementation manner, the efficiency loss coefficient is determined as follows: Determine the path conflict loss based on the time delay caused by the conflict scenarios when the autonomous mobile robot completes the task; Determine the charging loss based on the time delay when the autonomous mobile robot is charging in addition to the normal charging time; Determine random losses based on historical failures of autonomous mobile robots; The efficiency loss coefficient is determined according to the path conflict loss, the charging loss, and the random loss.

[0013] In a second aspect, the present invention provides an automatic mobile robot work scheduling system, comprising: A model building module is configured to: build a device working model of each autonomous mobile robot according to the charging state and working state of each autonomous mobile robot; a calculation module configured to: determine an actual utilization rate of the automatic mobile robot according to durations of different working states of the automatic mobile robot; a quantity determination module configured to: determine a first quantity of autonomous mobile robots required to complete the current task based on the current task situation and in combination with actual utilization rates of the autonomous mobile robots; The scheduling module is configured to: determine the automatic mobile robots belonging to the first number of automatic mobile robots based on the current real-time operation data of each automatic mobile robot and the corresponding equipment working model, and perform task scheduling on the automatic mobile robots belonging to the first number of automatic mobile robots.

[0014] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0016] One or more of the above technical solutions have the following beneficial effects: In the present invention, based on the charging status and working status of each automatic mobile robot, an equipment working model of each automatic mobile robot is established; based on the duration of different working states of the automatic mobile robot, the actual utilization rate of the automatic mobile robot is determined; based on the current task situation, the first number of automatic mobile robots that complete the current task is determined in combination with the actual utilization rate of the automatic mobile robot; based on the current real-time operation data of each automatic mobile robot and the corresponding equipment working model, the automatic mobile robots belonging to the first number of automatic mobile robots are determined, and task scheduling is performed on the automatic mobile robots belonging to the first number of automatic mobile robots. The present invention determines the number of automatic mobile robots that complete the current task in combination with the actual utilization rate of the automatic mobile robots, dynamically adjusts the number of equipment startups, reduces unnecessary equipment operating time, reduces energy waste, and ensures the rational use of equipment resources; in addition, by real-time monitoring of the automatic mobile robots, it is determined whether to join the scheduling task according to the state of the automatic mobile robots, so as to avoid the automatic mobile robots that have completed the task from joining the scheduling task and delaying the completion of the task.

[0017] In the present invention, the actual utilization rate of the automatic mobile robots is taken into consideration when determining the first number of automatic mobile robots. The impact of the charging time of the automatic mobile robots on the actual available time is taken into consideration when determining the second number of automatic mobile robots, to ensure that there are still enough AMRs to perform tasks after deducting the charging time. The first number of automatic mobile robots and the second number of automatic mobile robots are combined to obtain the final number. The final number of automatic mobile robots is quantitatively analyzed using a two-dimensional calculation method, which not only ensures production continuity but also saves resource costs.

[0018] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0020] Figure 1 This is an overall flow chart of the automatic mobile robot work scheduling method in an embodiment of the present invention; Figure 2 This is a flow chart for determining the final number of automatic mobile robots in an embodiment of the present invention; Figure 3 This is a flow chart for determining the second number of automatic mobile robots in an embodiment of the present invention; Figure 4This is a flow chart for determining the efficiency loss coefficient in an embodiment of the present invention; Figure 5 This is a flow chart of the automatic mobile robot work scheduling system in an embodiment of the present invention; Figure 6 is a schematic structural diagram of an electronic device in an embodiment of the present invention; Figure 7 FIG. 2 is a schematic structural diagram of another electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0022] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0023] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0024] like Figure 1 As shown, this embodiment discloses a method for scheduling work of an automatic mobile robot, including: S101: Establishing a device working model of each autonomous mobile robot according to the charging status and working status of each autonomous mobile robot.

[0025] In this embodiment, key indicator data under different working states are obtained according to the working state of the automatic mobile robot. The working state is divided into idle state, busy state, charging state, system fault state, and hardware fault state; the busy state is further divided into six states: docking, jacking, moving, traffic control, descending, and disengagement. Each state is divided into four stages, namely start, in progress, completion, and abnormality.

[0026] Among them, the key indicator data corresponding to each state include the working current of the drive system motor, battery voltage, battery temperature, remaining power, gyroscope, encoder, operating speed, mileage, load weight, etc.; as well as navigation radar, safety touch edge, emergency stop button data, real-time working current data of the drive motor, lifting motor, and pallet rotation motor; and establish a floating range for each indicator, construct each key indicator and indicator floating range, and the mapping relationship between the corresponding working states to obtain the equipment working model.

[0027] S102: Determine the actual utilization rate of the automatic mobile robot according to the duration of different working states of the automatic mobile robot.

[0028] In this embodiment, the actual utilization rate of the autonomous mobile robot is determined based on the ratio of the sum of the time the autonomous mobile robot spends performing tasks and charging in the first time interval to the total time of the first time interval.

[0029] Specifically,

[0030] Among them, totalTime represents the total time of the first time interval, unit: minute; taskTime represents the sum of the time the AMR performs tasks in the first time interval, unit: minute; chargeTime represents the sum of the AMR charging time in the first time interval, unit: minute.

[0031] Optionally, the state duration of different AMRs in the same task, such as moving time and charging time, may vary due to factors such as hardware performance and path planning. The actual utilization rate of each automatic mobile robot is calculated, and then the actual utilization rate of each automatic mobile robot is averaged to determine the actual utilization rate of the automatic mobile robot. This can avoid the problem of inaccurate subsequent plans due to individual differences of automatic mobile robots, which can help improve equipment utilization efficiency and reduce energy consumption.

[0032] S103: Determine a first number of autonomous mobile robots required to complete the current task based on the current task situation and the actual utilization rate of the autonomous mobile robots.

[0033] In this embodiment, the first number of autonomous mobile robots required to complete the current task is determined based on the current task situation and the actual utilization rate of the autonomous mobile robots. Specifically, the first number is: Determine the number of tasks of a single autonomous mobile robot based on its single cargo capacity and production rhythm; Determine the current task requirement time based on the number of tasks of a single automatic mobile robot and the time it takes for a single task of a single automatic mobile robot; A first number of automatic mobile robots required to complete the current task is determined based on the determined current task requirement time, available time corresponding to the production cycle, and an efficiency loss coefficient.

[0034] Specifically, the calculation formula for the first number of automatic mobile robots that complete the current task is:

[0035] in, Q represents Single load capacity refers to the number of pieces of material transported by the AMR each time, unit: piece / time; taskCount The total number of production tasks is the total number of materials that need to be delivered, unit: taskMinute Single-task operation time (including loading, unloading, and waiting), unit: minutes / task; taskMilesSingle mission mileage refers to the round-trip path length of the AMR, unit: km / mission; speed indicates AMR driving speed, to be converted into kilometers per minute: , unit: meter / second; T 可用 express Available time window, unit: minute; U 目标 express Target utilization rate, in this embodiment, is 75%-85%, unit: percentage; η table Show Efficiency loss coefficient.

[0036] In this embodiment, if Figure 4 As shown in Figure 2, the efficiency loss coefficient is determined as follows: S401: Determine a path conflict loss based on a time delay caused by a conflict scenario when the autonomous mobile robot completes a task; S402: Determine charging loss based on the time delay when the autonomous mobile robot is charging other than the normal charging time; S403: Determine random loss based on historical failure conditions of the automatic mobile robot; S404: Determine the efficiency loss coefficient according to the path conflict loss, the charging loss, and the random loss.

[0037] Specifically, path conflict scenarios are classified and losses are quantified. Conflict scenarios include waiting in congestion on main roads, avoiding intersections, speed reduction in mixed traffic areas, and multi-AMR path interference. For waiting in congestion on main roads, the scenario loss factor is calculated as: congestion duration = (actual travel time - theoretical travel time) / theoretical travel time × 100%; for avoiding intersections, the scenario loss factor is calculated as: waiting time = game theory decision time (≤5 seconds) + speed reduction time; for speed reduction in mixed traffic areas, the scenario loss factor is calculated as: speed loss rate = (baseline speed - actual speed) / baseline speed × 100%; for multi-AMR path interference, the scenario loss factor is calculated as: number of interferences * single avoidance time / total mission time.

[0038]

[0039] Charging loss scenarios are classified and losses are quantified. Charging loss scenarios include the proportion of charging time, charging waiting time, and restart calibration time after charging.

[0040]

[0041] The random loss is determined based on the ratio of the number of historical failures and the number of normal operations of the autonomous mobile robot over a period of time.

[0042] The efficiency loss coefficient is determined by adding the path conflict loss, charging loss, and random loss.

[0043] For example, given the following conditions: Cycle time: 60 JPH (60 vehicles per hour, two shifts of 16 hours) → Total task count = 60 × 16 = 960 items; Single cargo capacity: Q=20 pieces / time; Single task operation time: taskMinute=5 minutes / task; Single task mileage: taskMiles=1.2 km / task; AMR speed: speed = 1.2 m / s; Available time: Tavailable = 16 × 60 = 960 minutes; Target utilization: Utarget = 80%; Efficiency loss coefficient: η=15%.

[0044] The specific calculation is: 1. Actual number of AMR tasks: Number of tasks = =48 times; 2. Single-task driving time (km → m, speed m / s → m / min): Driving time = = ≈16.67 minutes; 3. Total time consumed for a single task: T single task = 5 + 16.67 = 21.67 minutes; 4. Total task time requirement: T 总任务 =48×21.67≈1,040 minutes; 5. Theoretical AMR quantity: N 理论 = ≈1.08 units; 6. Utilization correction: N 实际 = = 1.35 = 2 units; 7. Efficiency loss correction: N 修正 = 2×1.15 = 2.3 → 3 units.

[0045] S104: Determine the first number of automatic mobile robots according to the current real-time operation data of each automatic mobile robot and based on the corresponding equipment working model, and perform task scheduling on the first number of automatic mobile robots.

[0046] In this embodiment, the automatic mobile robot to be used for task scheduling is determined based on the current real-time operating data of each automatic mobile robot and the corresponding device working model, specifically: According to the current real-time operation data of each automatic mobile robot and the corresponding equipment working model, the automatic mobile robot that is in an idle state within the corresponding time of the current task is determined; If the number of automatic mobile robots that are idle during the time corresponding to the current task is greater than the number of automatic mobile robots determined for task scheduling, the fault condition of the corresponding automatic mobile robot is determined based on the historical data of the automatic mobile robots that are idle during the time corresponding to the current task, and the automatic mobile robot used for task scheduling is determined in combination with the relative relationship between the current position of the automatic mobile robot and the starting position of the task, as well as the relative relationship between the starting position of the task and the position where the task is completed once and the charging position of the automatic mobile robot.

[0047] Specifically, the path distance and estimated travel time between the current position of each idle robot and the task start position are calculated, as well as the path distance and corresponding estimated travel time between the task start position and the task single completion position and the charging position of each idle robot are calculated; a comprehensive evaluation model of idle automatic mobile robots is established based on the calculated indicators, a corresponding weight is assigned to each evaluation indicator, a comprehensive score is given to each automatic mobile robot, the automatic mobile robots are sorted according to the comprehensive score, and the automatic mobile robots for scheduling tasks are selected according to the sorting results.

[0048] In this embodiment, the automatic mobile robot may need to be charged during the task completion process, but there are different models of automatic mobile robots in the warehouse, and the charging areas of different models of automatic mobile robots are different. The distances between the task start position and the task single completion position and the charging position of each idle robot are taken into consideration, so as to shorten the driving time as much as possible, reduce energy consumption, and improve the overall resource utilization efficiency; and the distance between the current position of the automatic mobile robot and the start of the task is taken into consideration, so that the automatic mobile robot that can quickly reach the task start position can be selected, and the power loss of the automatic mobile robot in the process of reaching the task start position can also be reduced.

[0049] In this embodiment, if Figure 2 As shown, it also includes: S201: Determine a second number of autonomous mobile robots required to complete the current task based on the current task status and the charging requirements of the autonomous mobile robots.

[0050] In this embodiment, if Figure 3 As shown, based on the current task situation and the charging requirements of the automatic mobile robots, the second number of automatic mobile robots that complete the current task is determined, specifically: S301: Determine the total charging time of the automatic mobile robot within the production cycle based on the battery life and charging time of the automatic mobile robot; S302: Determine a second number of automatic mobile robots required to complete the current task based on the current task requirement time and the total charging time of the automatic mobile robots within the time corresponding to the production cycle.

[0051] S202: Determine the final number of autonomous mobile robots required to complete the current task based on the determined first number of autonomous mobile robots and the determined second number of autonomous mobile robots.

[0052] For example, assuming an AMR has a 4-hour battery life and requires 30 minutes to charge, the number of times each AMR is charged per day is: = 4 times → Total charging time 4×30=1204×30=120 minutes; Corrected usable time: 960−120=840 minutes; Corrected charging time: N Charge = = 1.55 = 2 units.

[0053] Take the maximum value max(3,2) = 3 units, add 1 spare unit → deploy 4 units.

[0054] In this embodiment, after determining the number of autonomous mobile robots used to complete the current task, an energy consumption-efficiency balance model is established to optimize the first number of autonomous mobile robots: min(α*N +β*total driving distance +γ*number of charging times) Among them, the weight coefficients α, β, and γ are set according to the actual cost; the total driving distance is affected by the loading strategy, that is, the single cargo capacity and path planning; the number of charging times is positively correlated with the energy efficiency of the AMR.

[0055] Maximize the single cargo volume through the backpack strategy and reduce the number of tasks:

[0056] in, Wi It is the material volume / weight constraint.

[0057] In this embodiment, the task processing volume and energy consumption per unit time corresponding to different speed gears are statistically analyzed to construct a speed-flux-energy consumption matrix. A load-mileage matrix is ​​constructed based on the relationship between single cargo load and the number of missions. A range-charging matrix is ​​constructed based on the correlation between energy consumption and charging frequency. The speed-flux-energy consumption matrix influences the number of devices and energy costs, indirectly relating to total driving distance. The load-mileage matrix's core function is to optimize total driving distance by reducing the number of trips by adjusting cargo load. The range-charging matrix corresponds to the number of charges and quantifies charging frequency and cost using energy consumption data. The speed-flux-energy consumption matrix satisfies the constraint that task throughput < processing volume corresponding to the speed gear * N; the load-mileage matrix satisfies the constraint that the single load must not exceed the device's maximum load capacity; and the range-charging matrix satisfies the constraint that the charging time must be less than the task time window.

[0058] The weight coefficient determines the priority of the matrix data. For example, if the energy consumption cost accounts for a high proportion, the low-speed gear with low energy consumption in the matrix is ​​preferred; if production efficiency is prioritized, the high-speed gear with high flux is selected.

[0059] Specifically, based on the task processing volume and energy consumption per unit time corresponding to different speed gears, the number of trips and total driving distance corresponding to a single loading and unloading amount, and the number of charging times corresponding to different gears, the number of devices under different gears and their corresponding total driving distance and charging times are determined, and the corresponding objective function values ​​are calculated; the optimal solution is determined based on the calculated objective function values.

[0060] The total driving distance in the energy consumption-efficiency balance model is optimized by adjusting the speed of the AGV dynamically according to the area type, real-time traffic density and task priority.

[0061] For example,

[0062] Let's take a specific usage scenario as an example: a welding section needs to deliver 60 sets of parts per hour (60 JPH). The path includes: an 8-meter straight main road (AGV priority); a 3-meter-wide intersection (mixed pedestrian and vehicle traffic); and a 2-meter narrow passage (frequently crossed by workers).

[0063] Parameter settings: Base speed: 1.2 m / s; AGV load: 200 kg; Response time requirement: <2 seconds.

[0064] Speed ​​adjustment scheme: 1. Main road section (8 meters): Increase speed to 1.32 m / s (+10%); use Kalman filtering to predict road conditions 50 meters ahead and maintain a constant speed; energy consumption calculation: ; 2. Intersection (3 meters): Reduce speed to 0.84 m / s (-30%); A game theory model is used to determine the passage order, with a maximum waiting time of ≤5 seconds; Energy consumption calculation: (including brake friction energy consumption); 3. Human-machine mixed traffic area (2 meters): Reduce speed to 0.6 m / s (-50%); Enable safety force control mode (maximum thrust <150N, machine stops upon collision); Energy consumption calculation: ; Total energy consumption during the whole process: ; Comparison with the fixed speed solution (1.2 m / s throughout): ; Energy saving effect: ; Energy consumption-efficiency balance model: Profit = min(α*N +β*total driving distance +γ*number of charging times).

[0065] Parameter example: α =5 yuan / piece (production efficiency weight); β=0.02 yuan / kilojoule (energy cost weight); γ=50 yuan / time (safety risk weight).

[0066] Optimization process: Establish a speed-energy consumption-risk relationship matrix:

[0067] Calculate the benefits of each gear: high speed: 5×68−0.02×2,500−50×0.05=340−50−2.5=287.5 yuan / hour; Medium speed: 5×60−0.02×1,800−50×0.02=300−36−1=263 yuan / hour Low speed: 5×45−0.02×1,200−50×0.005=225−24−0.25=200.75 yuan / hour; The key parameters of ARM are optimized based on dynamic loading optimization, mixed traffic management and energy consumption-efficiency balance model. The number of ARM startups is corrected based on the optimized key parameters to determine the final number of ARM startups.

[0068] like Figure 5 As shown, this embodiment also provides an automatic mobile robot work scheduling system, including: A model building module is configured to: build a device working model of each autonomous mobile robot according to the charging state and working state of each autonomous mobile robot; a calculation module configured to: determine an actual utilization rate of the automatic mobile robot according to durations of different working states of the automatic mobile robot; a quantity determination module configured to: determine a first quantity of autonomous mobile robots required to complete the current task based on the current task situation and in combination with actual utilization rates of the autonomous mobile robots; The scheduling module is configured to: determine the automatic mobile robots belonging to the first number of automatic mobile robots based on the current real-time operation data of each automatic mobile robot and the corresponding equipment working model, and perform task scheduling on the automatic mobile robots belonging to the first number of automatic mobile robots.

[0069] Figure 6 This is a block diagram of an electronic device 600 provided in an embodiment of the present application. The electronic device 600 may be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The electronic device 600 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other similar terminology.

[0070] Typically, the electronic device 600 includes a processor 601 and a memory 602 .

[0071] The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 601 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 601 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 601 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 601 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0072] The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 602 is used to store at least one instruction, which is executed by the processor 601 to implement the automatic mobile robot work scheduling method provided in the method embodiment of the present application.

[0073] In some embodiments, electronic device 600 may optionally include a peripheral device interface 603 and at least one peripheral device. Processor 601, memory 602, and peripheral device interface 603 may be connected via a bus or signal lines. Each peripheral device may be connected to peripheral device interface 603 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 604, a touchscreen display 605, a camera 606, an audio circuit 607, a positioning component 608, and a power supply 609.

[0074] The peripheral device interface 603 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 601 and the memory 602. In some embodiments, the processor 601, the memory 602, and the peripheral device interface 603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 601, the memory 602, and the peripheral device interface 603 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0075] The radio frequency circuit 604 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 604 communicates with communication networks and other communication devices via electromagnetic signals. The radio frequency circuit 604 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 604 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The radio frequency circuit 604 can communicate with other terminal devices via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G, and 5G), a wireless local area network, and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 604 may also include circuits related to NFC (Near Field Communication), which is not limited in the present embodiment.

[0076] Display screen 605 is used to display a user interface (UI). This UI can include graphics, text, icons, videos, or any combination thereof. When display screen 605 is a touchscreen display, it can also capture touch signals on or above the surface of display screen 605. These touch signals can be input as control signals to processor 601 for processing. Display screen 605 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be a single display screen 605, located on the front panel of electronic device 600. In other embodiments, there can be at least two display screens 605, located on different surfaces of electronic device 600 or in a foldable design. In still other embodiments, display screen 605 can be a flexible display, located on a curved or foldable surface of electronic device 600. Display screen 605 can also be configured as a non-rectangular, irregular shape, also known as a special-shaped screen. Display screen 605 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0077] The camera assembly 606 is used to capture images or videos. Optionally, the camera assembly 606 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal device, and the rear camera is arranged on the back of the terminal device. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 606 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0078] The audio circuit 607 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals to be input into the processor 601 for processing, or input into the radio frequency circuit 604 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, there can be multiple microphones, which are respectively arranged in different parts of the electronic device 600. The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signals from the processor 601 or the radio frequency circuit 604 into sound waves. The speaker can be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signals into sound waves audible to humans, but also convert the electrical signals into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 607 may also include a headphone jack.

[0079] Positioning component 608 is used to locate the current geographic location of electronic device 600 to implement navigation or LBS (Location Based Service). Positioning component 608 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system or Russia's Galileo system.

[0080] Power supply 609 is used to power the various components in electronic device 600. Power supply 609 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 609 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0081] In some embodiments, the electronic device 600 further includes one or more sensors 610 , including but not limited to: an acceleration sensor 611 , a gyroscope sensor 612 , a pressure sensor 613 , a fingerprint sensor 614 , an optical sensor 615 , and a proximity sensor 616 .

[0082] The accelerometer 611 can detect the magnitude of acceleration along the three coordinate axes of the coordinate system established by the electronic device 600. For example, the accelerometer 611 can be used to detect the components of gravity acceleration along the three coordinate axes. The processor 601 can control the touch screen display 605 to display the user interface in a landscape or portrait view based on the gravity acceleration signal collected by the accelerometer 611. The accelerometer 611 can also be used to collect game or user motion data.

[0083] The gyroscope sensor 612 can detect the orientation and rotation angle of the electronic device 600. It can work in conjunction with the accelerometer 611 to collect the user's 3D movements of the electronic device 600. Based on the data collected by the gyroscope sensor 612, the processor 601 can implement the following functions: motion sensing (for example, changing the UI based on the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0084] The pressure sensor 613 can be set on the side frame of the electronic device 600 and / or the lower layer of the touch screen display 605. When the pressure sensor 613 is set on the side frame of the electronic device 600, it can detect the user's grip signal of the electronic device 600, and the processor 601 performs left and right hand recognition or shortcut operations based on the grip signal collected by the pressure sensor 613. When the pressure sensor 613 is set on the lower layer of the touch screen display 605, the processor 601 controls the operable controls on the UI interface based on the user's pressure operation on the touch screen display 605. The operable controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0085] The fingerprint sensor 614 is used to collect the user's fingerprint. The processor 601 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 614, or the fingerprint sensor 614 identifies the user's identity based on the collected fingerprint. When the user's identity is recognized as a trusted identity, the processor 601 authorizes the user to perform relevant sensitive operations, such as unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 614 can be set on the front, back, or side of the electronic device 600. When a physical button or manufacturer logo is set on the electronic device 600, the fingerprint sensor 614 can be integrated with the physical button or manufacturer logo.

[0086] The optical sensor 615 is used to detect ambient light intensity. In one embodiment, the processor 601 can control the display brightness of the touchscreen display 605 based on the ambient light intensity detected by the optical sensor 615. Specifically, when the ambient light intensity is high, the display brightness of the touchscreen display 605 is increased; when the ambient light intensity is low, the display brightness of the touchscreen display 605 is decreased. In another embodiment, the processor 601 can also dynamically adjust the shooting parameters of the camera assembly 606 based on the ambient light intensity detected by the optical sensor 615.

[0087] Proximity sensor 616, also known as a distance sensor, is typically located on the front panel of electronic device 600. Proximity sensor 616 is used to detect the distance between the user and the front of electronic device 600. In one embodiment, when proximity sensor 616 detects that the distance between the user and the front of electronic device 600 is gradually decreasing, processor 601 controls touchscreen display 605 to switch from the screen-on state to the screen-off state. When proximity sensor 616 detects that the distance between the user and the front of electronic device 600 is gradually increasing, processor 601 controls touchscreen display 605 to switch from the screen-off state to the screen-on state.

[0088] Those skilled in the art will understand that Figure 6 The structure shown in the figure does not constitute a limitation on the electronic device 600, and the electronic device 600 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0089] Figure 7 7 is a schematic diagram of the structure of a server provided in an embodiment of the present application. The server may be the scheduling server described above. The server 700 includes a central processing unit (CPU) 701, a system memory 704 including a random access memory (RAM) 702 and a read-only memory (ROM) 703, and a system bus 705 connecting the system memory 704 and the central processing unit 701. The server 700 also includes a basic input / output system (I / O system) 706 that helps transmit information between various components within the computer, and a mass storage device 707 for storing an operating system 713, application programs 714, and other program modules 715.

[0090] The basic input / output system 706 includes a display 708 for displaying information and an input device 709, such as a mouse and keyboard, for user input. Both the display 708 and the input device 709 are connected to the central processing unit 701 via an input / output controller 710 connected to the system bus 705. The basic input / output system 706 may also include an input / output controller 710 for receiving and processing input from a variety of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 710 also provides output to a display screen, printer, or other types of output devices.

[0091] The mass storage device 707 is connected to the central processing unit 701 through a mass storage controller (not shown) connected to the system bus 705. The mass storage device 707 and its associated computer-readable media provide non-volatile storage for the server 700. That is, the mass storage device 707 may include computer-readable media (not shown) such as a hard disk or CD-ROM drive.

[0092] Without loss of generality, computer-readable media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technologies, CD-ROM, DVD or other optical storage, magnetic cassettes, magnetic tape, disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media is not limited to the aforementioned types. The above-mentioned system memory 704 and mass storage device 707 may be collectively referred to as memory.

[0093] According to various embodiments of the present application, the server 700 may also be connected to a remote computer on a network such as the Internet for operation. That is, the server 700 may be connected to the network 712 via the network interface unit 711 connected to the system bus 705, or the network interface unit 711 may be used to connect to other types of networks or remote computer systems (not shown).

[0094] The memory also includes one or more programs, which are stored in the memory and configured to be executed by the CPU.

[0095] In some embodiments, a computer-readable storage medium is also provided, which stores a computer program. When executed by a processor, the computer program implements the steps of the method for automatically scheduling work for a mobile robot in the above-described embodiments. For example, the computer-readable storage medium may be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0096] It is worth noting that the computer-readable storage medium mentioned in the embodiments of the present application may be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0097] It should be understood that all or part of the steps for implementing the above embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the steps may be implemented in the form of a computer program product. The computer program product may include one or more computer instructions. The computer instructions may be stored in the computer-readable storage medium.

[0098] That is, in some embodiments, a computer program product containing instructions is also provided, which, when executed on a computer, enables the computer to execute the steps of the above-mentioned automatic mobile robot work scheduling method.

[0099] It should be understood that the "at least one" mentioned herein refers to one or more, and "a plurality of" refers to two or more. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0100] The above description is an embodiment provided for this application and is not intended to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. A method for automatically scheduling a mobile robot's work, characterized in that: include: Establishing a device working model for each autonomous mobile robot based on its charging status and working status; Determine the actual utilization rate of the automatic mobile robot according to the duration of different working states of the automatic mobile robot; Determining a first number of automatic mobile robots required to complete the current task based on the current task situation and the actual utilization rate of the automatic mobile robots; According to the current real-time operation data of each automatic mobile robot and based on the corresponding equipment working model, the automatic mobile robots belonging to the first number of automatic mobile robots are determined, and task scheduling is performed on the automatic mobile robots belonging to the first number of automatic mobile robots.

2. The automatic mobile robot work scheduling method according to claim 1, characterized in that: According to the duration of different working states of the automatic mobile robot, the actual utilization rate of the automatic mobile robot is determined, specifically: The actual utilization rate of the autonomous mobile robot is determined according to the ratio of the sum of the time the autonomous mobile robot spends performing tasks and the charging time in the first time interval to the total time of the first time interval.

3. The automatic mobile robot work scheduling method according to claim 1, characterized in that: According to the current task situation and the actual utilization rate of the automatic mobile robots, the first number of automatic mobile robots required to complete the current task is determined, specifically: Determine the number of tasks of a single autonomous mobile robot based on its single cargo capacity and production rhythm; Determine the current task requirement time based on the number of tasks of a single automatic mobile robot and the time it takes for a single task of a single automatic mobile robot; A first number of automatic mobile robots required to complete the current task is determined based on the determined current task requirement time, available time corresponding to the production cycle, and an efficiency loss coefficient.

4. The automatic mobile robot work scheduling method according to any one of claims 1 to 3, characterized in that: Depending on the current task, it also includes: Determining a second number of autonomous mobile robots required to complete the current task based on the current task situation and the charging requirements of the autonomous mobile robots; The final number of autonomous mobile robots required to complete the current task is determined based on the determined first number of autonomous mobile robots and the determined second number of autonomous mobile robots.

5. The automatic mobile robot work scheduling method according to claim 4, characterized in that: According to the current task situation and the charging requirements of the autonomous mobile robots, the second number of autonomous mobile robots required to complete the current task is determined as follows: According to the endurance and charging time of the automatic mobile robot, determine the total charging time of the automatic mobile robot within the time corresponding to the production cycle; The second number of automatic mobile robots required to complete the current task is determined based on the current task requirement time and the total charging time of the automatic mobile robots within the time corresponding to the production tact.

6. The automatic mobile robot work scheduling method according to claim 1, characterized in that: According to the current real-time operating data of each automatic mobile robot and the corresponding equipment working model, the automatic mobile robot used for task scheduling is determined as follows: According to the current real-time operation data of each automatic mobile robot and the corresponding equipment working model, the automatic mobile robot that is in an idle state within the corresponding time of the current task is determined; If the number of automatic mobile robots that are idle during the time corresponding to the current task is greater than the number of automatic mobile robots determined for task scheduling, the fault condition of the corresponding automatic mobile robot is determined based on the historical data of the automatic mobile robots that are idle during the time corresponding to the current task, and the automatic mobile robot used for task scheduling is determined in combination with the relative relationship between the current position of the automatic mobile robot and the starting position of the task, as well as the relative relationship between the starting position of the task and the position where the task is completed once and the charging position of the automatic mobile robot.

7. The automatic mobile robot work scheduling method according to claim 1, characterized in that: The efficiency loss coefficient is determined as follows: Determine the path conflict loss based on the time delay caused by the conflict scenarios when the autonomous mobile robot completes the task; Determine the charging loss based on the time delay when the autonomous mobile robot is charging in addition to the normal charging time; Determine random losses based on historical failures of autonomous mobile robots; The efficiency loss coefficient is determined according to the path conflict loss, the charging loss, and the random loss.

8. Automatic mobile robot work scheduling system, characterized in that: include: A model building module is configured to: build a device working model of each autonomous mobile robot according to the charging state and working state of each autonomous mobile robot; a calculation module configured to: determine an actual utilization rate of the automatic mobile robot according to durations of different working states of the automatic mobile robot; a quantity determination module configured to: determine a first quantity of autonomous mobile robots required to complete the current task based on the current task situation and in combination with actual utilization rates of the autonomous mobile robots; The scheduling module is configured to: determine the automatic mobile robots belonging to the first number of automatic mobile robots based on the current real-time operation data of each automatic mobile robot and the corresponding equipment working model, and perform task scheduling on the automatic mobile robots belonging to the first number of automatic mobile robots.

9. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 7 is completed.

10. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, complete the method according to any one of claims 1 to 7.

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