Workshop equipment intelligent scheduling method, equipment and medium

Abstract

The invention relates to the field of intelligent equipment scheduling, in particular to a workshop equipment intelligent scheduling method, equipment and a medium. Comprising the following steps that in the process that an intelligent forklift to be dispatched operates according to an initial dispatching path, local path correction operation is executed according to a preset interval so as to correct part of paths in the initial dispatching path. According to the method, the local path correction operation can be used for correcting part of paths in the initial scheduling paths of the intelligent forklifts with lower priorities under the condition that the two intelligent forklifts possibly collide, so that the bypassing paths of the forklifts with the lower priorities are re-planned in a small-range area; therefore, efficient operation of the intelligent forklift with the higher priority can be guaranteed, and it can be guaranteed that the intelligent forklift with the lower priority cannot be started and stopped repeatedly in the operation process, and a large amount of energy consumption is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent device scheduling, in particular to a workshop device intelligent scheduling method, device and medium. BACKGROUND

[0002] Cargo handling is an indispensable part in many industries such as logistics, manufacturing workshop, retail, etc., which involves the process of moving goods from one location to another. With the development of technology, the way of cargo handling has gradually developed from traditional manual handling to automation and intelligence. Intelligent forklift combines advanced technologies such as automation technology, robotics, sensor technology and artificial intelligence to realize the task of automated material handling. In large warehouses or between production lines, there will be a transfer task of goods.

[0003] Usually, multiple intelligent forklifts will be configured in the same manufacturing workshop to jointly complete the cargo transfer task. Therefore, in the same workshop map, multiple intelligent forklifts may be running at the same time. As a result, collisions may occur due to the intersection of different intelligent forklift paths. In the prior art, in order to prevent the intelligent forklift from colliding, sensors such as integrated laser radar, ultrasonic sensor and camera are installed on the intelligent forklift to monitor the situation on the running path in real time, and cooperate with the related obstacle avoidance algorithm to control one of the intelligent forklifts to stop running before the collision occurs, so as to give way to the other intelligent forklift, and then start running again after the other intelligent forklift passes. However, since the weight of the material transported by the intelligent forklift in the manufacturing workshop is large, the energy consumption of the intelligent forklift is large and the time consumed is long during the process of stopping and restarting, thereby affecting the logistics transfer efficiency. SUMMARY

[0004] In view of one of the above technical problems, the technical solution adopted by the present application is:

[0005] According to one aspect of the present application, a workshop device intelligent scheduling method is provided, which comprises the following steps:

[0006] In the scheduling area corresponding to the grid navigation map, the initial scheduling path of the to-be-scheduled intelligent forklift from the starting point to the target point is obtained. Each node constituting the initial scheduling path is the center point of the obstacle-free grid in the grid navigation map.

[0007] During the operation of the to-be-scheduled intelligent forklift according to the initial scheduling path, the local path correction operation is performed at a predetermined interval to correct part of the initial scheduling path, generate the target scheduling path of the to-be-scheduled intelligent forklift, and control the to-be-scheduled intelligent forklift.

[0008] The local path correction operation comprises:

[0009] If the current time T now In the corresponding future period [T now +△T, T now +2△T], if the segment scheduling path corresponding to the to-be-scheduled intelligent forklift truck intersects with the segment scheduling path corresponding to other intelligent forklift trucks, a first bounding box of the segment scheduling path corresponding to the to-be-scheduled intelligent forklift truck in the grid navigation map is obtained.

[0010] The length and width of the first bounding box are both increased by two grid lengths to expand to the four directions, forming a second bounding box.

[0011] In the local grid navigation map framed by the second bounding box, a local scheduling path between the segment start point and the segment end point corresponding to the segment scheduling path is reacquired. The local grid navigation map includes the obstacle grid corresponding to the inherent obstacle in the local grid navigation map, and the running path of other intelligent forklift trucks in the local grid navigation map corresponds to the obstacle grid in [T now +△T, T now +2△T].

[0012] The local scheduling path is used to replace the segment scheduling path.

[0013] According to a second aspect of the present application, a non-transitory computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned intelligent scheduling method for workshop equipment.

[0014] According to a third aspect of the present application, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned intelligent scheduling method for workshop equipment when executing the computer program.

[0015] The present application has at least one of the following beneficial effects:

[0016] In the present application, during the operation of the intelligent forklift truck according to the initially planned initial scheduling path, a local path correction operation is also performed at a preset interval. In the case that the two intelligent forklift trucks may collide, the local path correction operation can correct part of the initial scheduling path of the intelligent forklift truck with a lower priority in the local grid navigation map framed by the second bounding box, so as to re-plan the detour route of the low-priority forklift truck in a small range of area. This not only can control the mutual avoidance detour of the vehicles and reduce the energy consumption, but also can avoid the collision.

[0017] At the same time, since the local path correction operation is performed each time based on the current time T now corresponding to the future period [Tnow +△T, T now +2△T] is analyzed, and it can be judged in advance whether a vehicle collision will occur, and the avoidance planning of the local detour route is performed in advance, so that the avoidance detour path planned in advance can be automatically executed when the vehicle runs to the corresponding position, thereby ensuring the efficient operation of the intelligent forklift with high priority, and ensuring that the intelligent forklift with low priority will not be repeatedly started and stopped during operation, causing a large amount of energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 A flow chart of a workshop equipment intelligent scheduling method provided by the embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] As a possible embodiment of the present application, as shown in Figure 1 A workshop equipment intelligent scheduling method is provided, and the method comprises the following steps:

[0022] S100: In the scheduling area corresponding to the grid navigation map, the initial scheduling path of the to-be-scheduled intelligent forklift from the starting point to the target point is obtained. Each node in the initial scheduling path is the center point of the obstacle-free grid in the grid navigation map.

[0023] Before S100, a corresponding grid navigation map of the operable area of the smart forklift in the workshop needs to be made. Since the spatial distribution of the factory workshop is usually rectangular, the corresponding running ground of the smart forklift is also rectangular. Therefore, when constructing the grid navigation map, the continuous rectangular space can be directly divided into discrete small areas (i.e. grids), each of which can contain information about the area (such as whether it is passable, terrain type, etc.). The completed grid navigation map can be stored in the form of a two-dimensional array. Specifically, each element in the array corresponds to the state of a grid. Since the state of the ground in the workshop is basically consistent, the influence on the passage of the smart forklift is basically consistent, so when constructing the grid navigation map, it is not necessary to distinguish the ground state, and only whether the corresponding grid area has an obstacle needs to be considered. For example, the corresponding numbers in the two-dimensional array can be used to represent whether the area has an obstacle, such as 0 for a passable area and 1 for an obstacle.

[0024] In addition, when constructing the grid navigation map, when determining whether there is an obstacle in each grid, the projection of the obstacle on the ground that may affect the passage of the smart forklift may intersect with the grid in the grid navigation map to determine whether the grid is passable. In this use scenario, there are usually some immovable inherent obstacles in the grid navigation map, such as shelves, production lines, and machining tools, and there are also some movable dynamic obstacles, such as smart forklifts.

[0025] When the running ground of the workshop is not a square structure, the filling method can be used to surround the entire map with a larger rectangular area, and the non-map part is filled as an unreachable obstacle area, so as to facilitate the representation and storage of the grid map using a two-dimensional array.

[0026] After the corresponding grid navigation map is constructed, the dispatching path from the starting point to the target point can be planned according to the transfer task currently received by each smart forklift. Specifically, in this embodiment, the A* algorithm is used to plan the dispatching path in the corresponding grid navigation map.

[0027] The A* path search algorithm (also known as the A* algorithm) is an algorithm for finding the shortest path between two points on a graph plane with a starting point and an ending point.

[0028] Specifically, in this embodiment, the A* path search algorithm is used to plan the path on the grid navigation map, and each node in the path is the center point of the grid.

[0029] During the execution of the A* path search algorithm, the core of the search control is the setting of the cost function (g(n)) and the heuristic function (h(n)).

[0030] g(n) represents the actual cost from the starting point to the current node n. This value can be adjusted according to the actual application scenario. In this embodiment, since the state of the entire passable ground is basically uniform, all passable grids have the same passable cost, and the cost of each step can be simply set to 1, that is, g(n) can be calculated based on the distance of movement.

[0031] h(n) is an estimated value representing the estimated cost from the current node n to the target node. In this embodiment, Manhattan distance or Euclidean distance can be selected to calculate the estimated cost according to the limitation of the moving direction of the intelligent forklift during operation. Manhattan distance is suitable for the case where movement is only allowed in four basic directions (up, down, left, and right). Euclidean distance is suitable for the case where movement is allowed in diagonal directions.

[0032] Specifically, before S100, the initial scheduling path is obtained according to the following steps:

[0033] S110: In the first grid navigation map, the path length L1 between the starting point and the target point of the to-be-scheduled intelligent forklift is obtained. The first grid navigation map includes obstacle grids corresponding to inherent obstacles in the scheduling area corresponding grid navigation map, and obstacle grids corresponding to the running paths of other intelligent forklifts in the scheduling area corresponding grid navigation map during the to-be-scheduled intelligent forklift corresponding operation period.

[0034] Specifically, the operation period of the path corresponding to L1 can be obtained according to the following steps:

[0035] S111: The average running time length of a plurality of historical paths corresponding to the running time length is obtained.

[0036] S112: The operation period of the path corresponding to L1 is generated according to the preset starting time of the to-be-scheduled intelligent forklift corresponding scheduling task and the average running time length.

[0037] Since, in the production operation process of the workshop, goods are usually produced in large quantities and for a long time, the carrying starting point and target point of the same goods are basically consistent, that is, the carrying route of the same goods has a consistent time consumption, so the average running time length of a plurality of historical paths corresponding to the running time length can be used to determine the running time length of the path corresponding to L1, and then the starting time of the task is combined to obtain the operation period of the path corresponding to L1. According to the type of the path between each two adjacent nodes in the entire path, such as the starting and accelerating phase, the uniform speed driving phase, the deceleration and stopping phase, or the turning phase, the acceleration, speed, and path length corresponding to each section of the path can be determined, and the running time consumption of the path between two adjacent nodes can be estimated to configure the corresponding arrival time for each node in the entire operation path.

[0038] S120: In the second grid navigation map, the path length L2 between the starting point and the target point of the to-be-scheduled intelligent forklift is obtained. The second grid navigation map only includes the obstacle grids corresponding to the inherent obstacles in the scheduling area corresponding grid navigation map.

[0039] S130: If then in the third grid navigation map, the initial scheduling path between the starting point and the target point of the to-be-scheduled intelligent forklift is obtained. The third grid navigation map includes the obstacle grids corresponding to the inherent obstacles in the scheduling area corresponding grid navigation map, and the obstacle grids corresponding to the running paths of the other intelligent forklifts with scheduling priorities greater than the scheduling priority of the to-be-scheduled intelligent forklift in the to-be-scheduled intelligent forklift corresponding running period in the scheduling area corresponding grid navigation map. Y1 is an energy consumption threshold. Y1 can be 0.4.

[0040] S140: If then the path obtained in the first grid navigation map between the starting point and the target point of the to-be-scheduled intelligent forklift is taken as the target scheduling path.

[0041] In this embodiment, the path corresponding to L1 is a most safe scheduling path formed by considering the inherent obstacles and all other intelligent forklifts that may become obstacles in the running process. However, the scheduling path may have a large increase in length and thus increase the energy consumption in the scheduling process due to too many obstacles to avoid. The path corresponding to L2 is a scheduling route formed by considering only the inherent obstacles. The scheduling route has a shortest length of the scheduling path due to fewer obstacles to avoid, and thus is the most energy-saving route in the scheduling process. Therefore, from it can be concluded that how much energy is newly consumed in the path corresponding to L1 than in the path corresponding to L2. In this embodiment, a threshold comparison scheme is used to determine whether L1 can be directly used as the final target scheduling path.

[0042] Specifically, if it indicates that the path corresponding to L1 has an increase in energy consumption, but the increase is within an acceptable range, so that a certain energy consumption can be sacrificed to ensure absolute safety.

[0043] If it indicates that the increase in energy consumption of the path corresponding to L1 exceeds the acceptable range, that is, the path corresponding to L1 is no longer suitable for use, and the obstacles to be considered need to be reduced to form a scheduling path with less energy consumption.

[0044] In the embodiment, the specific reduced obstacles are other intelligent forklifts whose scheduling priorities are less than or equal to the scheduling priority of the to-be-scheduled intelligent forklift. Due to the difference in production efficiency of different parts in the same workshop, the corresponding transfer speed also needs to be different to avoid stacking. Therefore, different scheduling priorities can be configured for intelligent forklifts transferring each kind of material to ensure the right of way.

[0045] Therefore, in the embodiment, the other intelligent forklifts whose scheduling priorities are relatively lower than the scheduling priority of the to-be-scheduled intelligent forklift are not considered in the initial scheduling path planning. Thus, the dynamic obstacles formed by the intelligent forklifts are reduced in the third grid navigation map, and the path length formed by the planning is also reduced to reduce the energy consumption during operation, while the avoidance function of the other intelligent forklifts with higher scheduling priorities is still retained.

[0046] S200: During the operation of the to-be-scheduled intelligent forklift according to the initial scheduling path, a local path correction operation is performed at a preset interval to correct part of the initial scheduling path, generate a target scheduling path of the to-be-scheduled intelligent forklift, and perform scheduling control on the to-be-scheduled intelligent forklift.

[0047] S200 can also be replaced by:

[0048] S210: During the operation of the to-be-scheduled intelligent forklift according to the initial scheduling path, real-time position information of other intelligent forklifts whose scheduling priorities are less than or equal to the scheduling priority of the to-be-scheduled intelligent forklift is obtained.

[0049] S220: If the distance between the other intelligent forklift and the to-be-scheduled intelligent forklift is less than a preset distance threshold, a local path correction operation is performed.

[0050] Since the obtained initial scheduling path does not consider avoiding other intelligent forklifts with lower scheduling priorities during the planning process, the to-be-scheduled intelligent forklift may collide with other intelligent forklifts with lower scheduling priorities during subsequent operation. Based on this, the local path correction operation is provided in the application to correct part of the initial scheduling path to avoid the possible collision.

[0051] Corresponding to the judgment of the starting time of the local path correction operation, two methods are provided in the embodiment. One is that the local path correction operation is performed once at a preset interval (such as 5 seconds) in S200. Or, the execution of the local path correction operation is started by judging the distance between the to-be-scheduled intelligent forklift and other intelligent forklifts (especially other intelligent forklifts whose scheduling priorities are less than or equal to the scheduling priority of the to-be-scheduled intelligent forklift) in S210 to S220.

[0052] The local path correction operation includes:

[0053] S201: If the current time T now In the corresponding future period [T now +△T, T now +2△T], the segment scheduling path corresponding to the to-be-scheduled intelligent forklift and the segment scheduling path corresponding to other intelligent forklifts have intersection points, then a first bounding box of the segment scheduling path corresponding to the to-be-scheduled intelligent forklift in the grid navigation map is obtained.

[0054] The first bounding box in the embodiment can be determined according to the left lower corner point coordinate and the right upper corner point coordinate in the segment scheduling path, and a corresponding coordinate system can be established based on the grid navigation map, wherein each grid corresponds to an independent coordinate point, and the coordinate point represents the position of the grid in the entire grid navigation map. Correspondingly, the left lower corner point coordinate and the right upper corner point coordinate are the coordinates of the grid where they are located, so that the bounding box can take the grid as the boundary in the embodiment. For example, the left lower corner point coordinate (2, 3) and the right upper corner point coordinate (5, 6), the boundary of the first bounding box is the rectangle bounding box formed by the two corner points, that is, the left lower corner and the right upper corner.

[0055] Whether the segment scheduling path corresponding to the to-be-scheduled intelligent forklift and the segment scheduling path corresponding to other intelligent forklifts have intersection points is determined according to the following steps:

[0056] S211: The position of each grid passed by the segment scheduling path corresponding to the to-be-scheduled intelligent forklift in the grid navigation map is taken as a tuple to generate a path set A corresponding to the to-be-scheduled intelligent forklift.

[0057] S221: The position of each grid passed by the segment scheduling path corresponding to all other intelligent forklifts in the grid navigation map is taken as a tuple to generate a path set B corresponding to other intelligent forklifts.

[0058] S231: If The segment scheduling path corresponding to the to-be-scheduled intelligent forklift and the segment scheduling path corresponding to other intelligent forklifts have intersection points.

[0059] In this step, whether the two paths have intersection points is determined by set intersection, and the position of the grid can be determined according to the coordinate system established when the bounding box is determined.

[0060] Since each time the local path correction operation is performed, it is based on the current time T now The corresponding future period [T now +△T, T nowThe operation in the range of [T - 2△T, T + 2△T] is analyzed and judged, and whether the vehicle collision will occur is judged in advance, and the local bypass route is planned in advance to avoid, and the bypass route planned in advance is automatically executed when the vehicle runs to the corresponding position, so that the efficient operation of the intelligent forklift with high priority can be ensured, and the intelligent forklift with low priority can not be repeatedly started and stopped in the running process, thereby causing a large amount of energy consumption.

[0061] S202: The length and width of the first bounding box are increased by two grid lengths to expand to the four directions to form a second bounding box.

[0062] S203: In the local grid navigation map framed by the second bounding box, the local scheduling path between the section start point and the section end point corresponding to the section scheduling path is reacquired. The local grid navigation map includes the obstacle grid corresponding to the inherent obstacle in the local grid navigation map, and the obstacle grid corresponding to the running path of the other intelligent forklift in the range of [T - 2△T, T + 2△T] in the local grid navigation map. now +△T, T now +2△T] in the local grid navigation map.

[0063] S204: The local scheduling path is used to replace the section scheduling path.

[0064] In the embodiment, the intelligent forklift runs according to the initial scheduling path planned in advance, and also performs a local path correction operation at a preset interval. The operation can enlarge the first bounding box region in the corresponding path section to form a second bounding box in the case that the two intelligent forklifts may collide, and correct part of the initial scheduling path of the intelligent forklift with lower priority in the local grid navigation map framed by the second bounding box, to re-plan the bypass route of the intelligent forklift with lower priority in a small range of area. The energy consumption of mutual bypass of the vehicles can be controlled, and the energy consumption caused by repeated start and stop of the vehicles can be avoided. At the same time, the bypass mode still follows the principle of lower priority giving way to higher priority, to ensure the efficient operation of the intelligent forklift with high priority.

[0065] In addition, although the steps of the method in the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. In addition or alternatively, some steps can be omitted, a plurality of steps can be combined into one step, and / or one step can be divided into a plurality of steps, etc.

[0066] Those skilled in the art can clearly understand, through the description of the above embodiments, that the example embodiments described herein can be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a plurality of instructions to make a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) execute the method according to the embodiments of the present disclosure.

[0067] In the example embodiments of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0068] Those skilled in the art can understand that each aspect of the present disclosure can be implemented as a system, a method or a program product. Therefore, each aspect of the present disclosure can be embodied in the form of a complete hardware, a complete software (including firmware, microcode, etc.), or a combination of hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system".

[0069] The electronic device according to this embodiment of the present disclosure. The electronic device is only an example, and should not bring any limitation to the function and use range of the embodiments of the present disclosure.

[0070] The electronic device is in the form of a general computing device. The components of the electronic device can include but are not limited to the above-mentioned at least one processor, the above-mentioned at least one storage, a bus connecting different system components (including storage and processor).

[0071] The storage stores program codes which can be executed by the processor, so that the processor executes the steps according to various example embodiments of the present disclosure described in the above "example method" section of the present specification.

[0072] The storage can include a readable medium in the form of a volatile storage, such as a random access memory (RAM) and / or a cache memory, and can further include a read-only memory (ROM).

[0073] The storage can also include programs / utilities with a set of (at least one) program modules, such as operating systems, one or more application programs, other program modules, and program data, each of which or some combination of which can include the implementation of a network environment.

[0074] The bus can be one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, a graphics bus, a processor or local bus using any of a variety of bus architectures.

[0075] The electronic device can also communicate with one or more external devices such as a keyboard or a pointing device, through an I / O interface. The I / O interface can also include devices such as a Bluetooth device, a universal serial bus (USB) device, a serial device, a parallel device, or a game port. The electronic device can communicate with one or more devices that enable a user to interact with the electronic device through the I / O interface. The electronic device can also include a communication interface that can enable the electronic device to communicate with one or more other electronic devices. The communication interface can include a modem, a network interface card, a communication port, or a wireless communication device, among other possibilities. The electronic device can communicate with one or more networks, such as a local area network (LAN), a general area network (GAN), a wide area network (WAN), or the Internet, among other possibilities, through the communication interface. The communication interface can include logic encoded in software and / or hardware in a dedicated processing device for enabling communications between the electronic device and one or more networks. In some embodiments, the communication interface can include software drivers

[0076] Those skilled in the art will readily understand that the example embodiments described herein can be implemented by software and / or by software in combination with the necessary hardware. Thus, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB, a mobile hard disk, or the like) or a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to perform the methods according to the embodiments of the present disclosure.

[0077] In the example embodiments of the present disclosure, a computer readable storage medium is also provided, which stores a program product capable of implementing the above-mentioned method of the present disclosure. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program codes for causing a terminal device to perform the steps according to various example embodiments of the present disclosure described in the above-mentioned “example method” section of the present specification when the program product is run on the terminal device.

[0078] The program product can employ any combination of one or more computer-readable media. The computer-readable media can be a computer-readable storage medium or a computer-readable signal medium. The computer-readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0079] The computer-readable signal medium can include a computer-readable storage medium that is communicated, propagated, or transported, for example, over a communication link, a wireless link, or a hard-wired link. The computer-readable signal medium can also be a computer-readable storage medium that is embodied into a computer-readable storage medium or used to manufacture a computer-readable storage medium. The computer-readable storage medium can be any appropriate medium (including the one or more computer-readable media described above) that participates in providing instructions to an instruction execution system, apparatus, or device such that the instructions, which properly render the instruction-execution system, apparatus, or device into a

[0080] The program code embodied on the computer-readable media can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0081] The program code can be executed by one or more programmable processors, which can be individual or grouped processors, to perform the methods disclosed herein. The program code can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0082] In addition, the above-described flowcharts are merely illustrative of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to limit the purpose. It is easily understood that the processes shown in the above-described flowcharts do not indicate or limit the time sequence of the processes. In addition, it is easily understood that the processes can be executed synchronously or asynchronously, for example, in a plurality of modules.

[0083] It should be noted that, although several modules or units of the devices for action execution are mentioned in the above detailed description, the division into such modules or units is not mandatory. Indeed, according to an embodiment of the present disclosure, the features and functionalities of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functionalities of one of the above-described modules or units can be further divided into several modules or units.

[0084] The above merely shows the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any changes or replacements within the technical scope disclosed by the present application can be easily conceived by those skilled in the art, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for intelligent scheduling of workshop equipment, characterized in that, The method includes the following steps: In the grid navigation map corresponding to the scheduling area, obtain the initial scheduling path of the intelligent forklift to be scheduled from the starting point to the target point; Each node in the initial scheduling path is the center point of an obstacle-free grid in the grid navigation map; During the process of the intelligent forklift to be scheduled running according to the initial scheduling path, a local path correction operation is performed at a preset interval to correct part of the path in the initial scheduling path, generate the target scheduling path of the intelligent forklift to be scheduled, and perform scheduling control on the intelligent forklift to be scheduled. The local path correction operation includes: If the current time T now The corresponding future time period [T] now +△T,T now If the section scheduling path of the intelligent forklift to be scheduled intersects with the section scheduling path of other intelligent forklifts, then the first bounding box of the section scheduling path of the intelligent forklift to be scheduled in the grid navigation map is obtained. The first enclosing box is expanded outwards by increasing its length and width by two grids to form a second enclosing box; Within the local grid navigation map selected by the second bounding box, the local scheduling path between the segment start point and the segment end point corresponding to the segment scheduling path is re-acquired; the local grid navigation map includes obstacle grids corresponding to inherent obstacles in the local grid navigation map, and in [T now +△T,T now In +2△T], the obstacle grid corresponding to the running path of other intelligent forklifts in the local grid navigation map; Replace the segment scheduling path with a local scheduling path.

2. The method according to claim 1, characterized in that, The initial scheduling path is obtained according to the following steps: In the first grid navigation map, the path length L1 between the starting point and the target point of the intelligent forklift to be scheduled is obtained; the first grid navigation map includes the obstacle grid corresponding to the inherent obstacles in the grid navigation map corresponding to the scheduling area, and the obstacle grid corresponding to the running path of other intelligent forklifts in the grid navigation map corresponding to the scheduling area during the corresponding running period of the intelligent forklift to be scheduled. In the second grid navigation map, obtain the path length L2 of the smart forklift to be scheduled from the starting point to the target point; the second grid navigation map only includes the obstacle grid corresponding to the inherent obstacles in the grid navigation map of the scheduling area. like Then, in the third grid navigation map, obtain the initial scheduling path of the intelligent forklift to be scheduled from the starting point to the target point; The third grid navigation map includes obstacle grids corresponding to inherent obstacles in the grid navigation map of the scheduling area, and obstacle grids corresponding to the running paths of other intelligent forklifts with a scheduling priority higher than that of the intelligent forklift to be scheduled during the corresponding running period of the intelligent forklift to be scheduled; Y1 is the energy consumption threshold.

3. The method according to claim 2, characterized in that, After obtaining L2, the method further includes: like The path from the starting point to the target point of the intelligent forklift to be scheduled, obtained from the first grid navigation map, will then be used as the target scheduling path.

4. The method according to claim 1, characterized in that, The A* algorithm is used to obtain the scheduling path from the corresponding grid navigation map.

5. The method according to claim 2, characterized in that, The runtime segment of the path corresponding to L1 is determined based on the runtime of the historical path between the starting point and the target point.

6. The method according to claim 5, characterized in that, Based on the runtime of the historical path from the starting point to the target point, determine the runtime segment of the path corresponding to L1, including: Get the average runtime of multiple historical paths between the starting point and the target point; Based on the preset start time of the scheduling task corresponding to the intelligent forklift to be scheduled and the average running time, the running segment of the path corresponding to L1 is generated.

7. The method according to claim 1, characterized in that, During the process of the intelligent forklift to be scheduled running according to the initial scheduling path, a local path correction operation is performed at preset intervals, replacing: During the process of the intelligent forklift to be scheduled running according to the initial scheduling path, the location information of other intelligent forklifts with scheduling priorities less than or equal to the scheduling priority of the intelligent forklift to be scheduled is obtained in real time. If the distance between other smart forklifts and the smart forklift to be scheduled is less than a preset distance threshold, a local path correction operation is performed.

8. The method according to claim 1, characterized in that, Whether the segment dispatching path corresponding to the intelligent forklift to be dispatched intersects with the segment dispatching paths corresponding to other intelligent forklifts is determined according to the following steps: The location of each grid cell that the intelligent forklift to be dispatched passes through in the grid navigation map is taken as a tuple to generate the path set A corresponding to the intelligent forklift to be dispatched. Generate a path set B corresponding to the other intelligent forklifts by taking the position of each grid cell passed through in the grid navigation map as a tuple for the segment scheduling paths corresponding to all other intelligent forklifts. like The section scheduling path corresponding to the intelligent forklift to be scheduled intersects with the section scheduling paths corresponding to the other intelligent forklifts.

9. A non-transitory computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a workshop equipment intelligent scheduling method as described in any one of claims 1 to 8.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a workshop equipment intelligent scheduling method as described in any one of claims 1 to 8.

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