Scene scheduling method and system for intelligent equipment

Abstract

The invention relates to the technical field of intelligent equipment scheduling planning, and discloses a scene scheduling method and system for intelligent equipment, and the method comprises the steps: determining the task emergency degree and the motion complexity based on a dual-domain scheduling factor construction mechanism; constructing a scheduling priority weight; screening a conflict node set; generating an avoidance decision by adopting a double-case evaluation mechanism; and finally, synchronously issuing the avoidance decision to related intelligent equipment. In the prior art, there is no scheduling method of a priority guidance and fine conflict judgment mechanism, and especially in a dense operation scene of multiple mobile devices including an industrial AGV, a luggage transfer robot and a mobile charging vehicle, dynamic prediction and active avoidance of conflict risks are difficult to realize. Due to the fact that task and motion double-domain factor construction and a space and time double-condition conflict recognition mechanism are introduced, active avoiding and real-time response of device-level scheduling are achieved, and the cooperative operation stability of intelligent devices is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent device scheduling and planning technology, and in particular to a scene scheduling method and system for intelligent devices. Background Technology

[0002] Currently, in complex operational scenarios such as smart industrial parks, airport ground handling services, and intelligent logistics warehousing, an increasing number of heterogeneous intelligent devices are being deployed in shared operational areas to perform multi-source tasks. Typical devices include industrial AGVs, intelligent baggage handling robots, mobile charging vehicles, and intelligent security inspection platforms. These devices typically possess autonomous path planning, local obstacle avoidance, and task execution capabilities, but their physical attributes, motion models, and task priorities differ significantly. For example, industrial AGVs often operate on fixed paths at high frequency, prioritizing stability; while airport baggage carts need to quickly respond to temporary handling instructions within complex terminal areas; and mobile charging vehicles often move between empty passages with the principle of minimum energy consumption. Therefore, when these devices operate on the same track in the same spatial area, operational interference, traffic congestion, and even equipment collisions are highly likely due to path intersections, task time conflicts, and control lags, severely impacting overall operational efficiency and system safety.

[0003] The existing technologies mainly use three types of methods to solve the above problems, including: (1) regional deployment mechanism, which restricts different types of equipment from entering the same area through static geographical division to avoid scheduling conflicts, but this scheme sacrifices the flexibility of regional resource sharing and cannot adapt to sudden tasks or emergency calls; (2) fixed priority strategy based on central scheduling, which usually solves the path competition problem by setting fixed priority of equipment type, such as mobile charging vehicles always giving way, but ignores the urgency of specific tasks and path passage costs, resulting in task delays or path waste.

[0004] Therefore, there is an urgent need for an intelligent scheduling method that supports equipment heterogeneity modeling, dynamic evaluation of task urgency, and real-time prediction and collaborative avoidance of path conflicts. This method can enable the safe collaboration and efficient operation of multiple types of intelligent devices in complex shared work areas, overcoming the limitations of existing static scheduling or fixed priority-based mechanisms. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the present invention aims to propose a scene scheduling method for intelligent devices. This method addresses the lack of priority guidance and fine-grained conflict judgment mechanisms in existing scheduling methods, particularly in intensive operation scenarios involving multiple mobile devices, including industrial AGVs, baggage handling robots, and mobile charging vehicles, where it is difficult to dynamically predict and proactively avoid conflict risks.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a scene scheduling method for intelligent devices.

[0007] The scene scheduling method for smart devices includes:

[0008] Step S10: Obtain the motion model parameters and task requirement parameters of intelligent device i, and determine the task urgency based on the motion model parameters and task requirement parameters using a dual-domain scheduling factor construction mechanism. and motion complexity ;

[0009] Step S20: Based on task urgency and motion complexity Construct scheduling priority weights, obtain map data of the current work area, and perform initial path planning based on scheduling priority weights and map data of the current work area, outputting path node sequence and corresponding time window set;

[0010] Step S30: Based on the output path node sequence and the corresponding time window set, a dual-condition judgment mechanism based on spatial location matching and time window overlap is used to filter conflict nodes among multiple smart devices, and output the conflict node set;

[0011] Step S40: Based on the set of conflicting nodes and their priority weights, a dual-scheme evaluation mechanism based on waiting for passage and route diversion schemes is adopted to generate an avoidance decision;

[0012] Step S50: The generated avoidance decision is synchronously distributed to all smart devices corresponding to the set of conflict nodes.

[0013] Preferably, in step S10, the motion model parameters and task requirement parameters of the intelligent device i are obtained, and the task urgency is determined based on the motion model parameters and task requirement parameters using a dual-domain scheduling factor construction mechanism. and motion complexity The steps specifically include:

[0014] Step S101: Obtain the motion model parameters and task requirement parameters of the intelligent device i. The motion model parameters include the maximum driving speed. Minimum turning radius and average braking distance Task requirements parameters include the task deadline. Remaining length of the current task Among them, intelligent equipment includes industrial AGVs, airport baggage handling robots, and mobile charging vehicles;

[0015] Step S102: Based on maximum driving speed and the remaining length of the current task Estimate the theoretical shortest time required to complete the current task. ;

[0016] If the theoretical shortest time Greater than the task deadline If the current task status is determined to be "unreachable in time", then a task urgency level is introduced. and the urgency of the task Set to 1;

[0017] If the theoretical shortest time Less than or equal to the task deadline If the current task status is determined to be "time-reachable", then a task urgency level is introduced. and the urgency of the task Set as ;in, , The larger the value, the greater the urgency of the task;

[0018] Step S103: Obtain the maximum turning radius of the intelligent device i within the system. and the maximum travel speed of equipment within the system Based on the maximum turning radius of the equipment within the system. and the maximum travel speed of equipment within the system Combined with motion model parameters including maximum driving speed Minimum turning radius and average braking distance Motion complexity evaluation is performed using a hierarchical mapping construction method, and the motion complexity is obtained through weighted construction. , , The larger the value, the higher the complexity of the motion.

[0019] Preferably, in step S10, the motion model parameters include the maximum driving speed. Minimum turning radius and average braking distance Motion complexity evaluation is performed using a hierarchical mapping construction method, and the motion complexity is obtained through weighted construction. The steps specifically include:

[0020] First based on maximum driving speed and the maximum travel speed of equipment within the system Construction flexibility correction factor Flexibility correction factor Used to represent the relatively slow scheduling cost of devices;

[0021] Based on the minimum turning radius and the maximum turning radius of equipment within the system Constructing the turning difficulty factor Difficulty factor of turning This value represents the proportion of space required for the equipment to turn; a larger value indicates greater difficulty in turning. It is also based on the average braking distance. and the maximum turning radius of equipment within the system Constructing braking response factors Braking response factor This value indicates the proportion of space required for an equipment to stop urgently; the larger the value, the more difficult it is to avoid obstacles.

[0022] Finally, based on the flexibility correction factor Difficulty factor of turning and braking response factor The motion complexity is constructed using a linear weighting method. .

[0023] Preferably, in step S20, based on task urgency... and motion complexity Construct scheduling priority weights The steps include: acquiring map data of the current work area, performing initial path planning based on scheduling priority weights and the current work area map data, and outputting the path node sequence and corresponding time window set.

[0024] Step S201: Preset task urgency threshold and motion complexity threshold; when task urgency... The task urgency threshold is greater than the task urgency threshold, and the motion complexity is... If the complexity exceeds the motion complexity threshold, then the scheduling priority weight is set. The first priority value is set when the task urgency is high. The task urgency threshold is greater than the task urgency threshold, and the motion complexity is... If the complexity is less than the motion complexity threshold, then the scheduling priority weight is set. It is the second priority value; when the task urgency is... Less than the task urgency threshold, and the motion complexity If the complexity exceeds the motion complexity threshold, then the scheduling priority weight is set. It is the third priority value; when the task urgency is... Less than the task urgency threshold, and the motion complexity If the complexity is less than the motion complexity threshold, then the scheduling priority weight is set. It is the fourth priority value;

[0025] Step S202: Obtain map data of the current work area, based on scheduling priority weights. The current work area map data is called sequentially to construct a feasibility graph model containing topological nodes and access edges; where topological nodes are used to represent accessible work points; access edges are used to represent reachable paths between nodes, and are accompanied by path length, road width, speed limit and access constraint attributes;

[0026] A hierarchical weighted cost evaluation mechanism is used to set the path cost function;

[0027] Step S203: Based on the path cost function and feasibility graph model, the A* global path planning algorithm is used to perform path search processing, and output the path node sequence and the corresponding time window set; wherein, the path node sequence includes an ordered set of nodes from the current location of the device to the target task location; the corresponding time window set includes the estimated arrival time of each node.

[0028] Preferably, step S20, which involves setting the path cost function using a hierarchical weighted cost evaluation mechanism, specifically includes:

[0029] For scheduling priority weights For devices with the first or second priority weight, a time factor weight is introduced into the path cost function to prioritize the selection of the shortest time path;

[0030] For scheduling priority weights For devices with the third priority, a penalty term related to the turning radius and braking distance is introduced into the path cost function to prioritize the selection of wide paths with smooth passage.

[0031] For scheduling priority weights For devices with the fourth priority, the path cost function is selected from the preset standard cost function.

[0032] Preferably, in step S30, the step of performing conflict node filtering among multiple smart devices based on a dual-condition judgment mechanism of spatial location matching and time window overlap, and outputting a conflict node set, specifically includes:

[0033] Step S301: Traverse the path node sequence of all smart devices and construct a global path node mapping table. The global path node mapping table includes the device spatial coordinates, device number, device sequence number and the estimated arrival time window of the device corresponding to the node.

[0034] Step S302: Preset spatial overlap threshold Calculate the Euclidean distance between smart device i and smart device j based on the device space coordinates in the global path node mapping table. ,in Let be the lateral coordinate of device i at node k. Let be the longitudinal coordinate of device i at node k. Let j be the lateral coordinate of device j at node k. Let j be the longitudinal coordinate of device j at node k; when the node coordinates are Euclidean distance Less than the spatial overlap threshold At that time, it is determined that smart device i and smart device j are candidate conflicting device pairs at node k. ;

[0035] Step S303: Obtain the information of smart device i at the node The estimated arrival time window for the equipment at the location and smart device j at node The estimated arrival time window for the equipment at the location If the equipment is expected to arrive within the time window With the equipment's expected arrival time window If there is an intersection of time windows, then smart device i and smart device j are determined to be scheduling conflict nodes at node k, and are recorded in the conflict node set.

[0036] Preferably, step S40, which involves generating an avoidance decision based on a dual-scheme evaluation mechanism combining a waiting-to-give-way scheme and a path diversion scheme, according to the set of conflicting nodes and their priority weights, specifically includes:

[0037] Step S401: For each pair of conflicting intelligent devices i and intelligent devices in the conflict node set... According to the corresponding scheduling priority weight and scheduling priority weight Execution priority comparison and judgment to determine which device has priority execution rights. and avoidance equipment ;

[0038] Step S402: For the avoidance device Construct two types of obstacle avoidance schemes: waiting for right-of-way and rerouting. For the waiting for right-of-way scheme, obtain the waiting time cost; for the rerouting scheme, obtain the rerouting cost.

[0039] Step S403: Evaluate the execution cost of the avoidance scheme based on the waiting time cost and the path rerouting cost, and generate the final avoidance decision based on the execution cost of the avoidance scheme.

[0040] The present invention also provides a scene scheduling system for smart devices, comprising:

[0041] The scheduling factor construction submodule is used to obtain the motion model parameters and task requirement parameters of intelligent device i, and to determine the task urgency based on the motion model parameters and task requirement parameters using a dual-domain scheduling factor construction mechanism. and motion complexity ;

[0042] The path planning submodule is used to determine the urgency of the task. and motion complexity Construct scheduling priority weights, obtain map data of the current work area, and perform initial path planning based on scheduling priority weights and map data of the current work area, outputting path node sequence and corresponding time window set;

[0043] The conflict node filtering submodule is used to perform conflict node filtering among multiple smart devices based on the output path node sequence and the corresponding time window set, using a dual-condition judgment mechanism based on spatial location matching and time window overlap, and outputs a conflict node set.

[0044] The avoidance decision generation submodule is used to generate avoidance decisions based on the set of conflict nodes and their priority weights, using a dual-scheme evaluation mechanism based on waiting for the right-of-way and path rerouting schemes.

[0045] The control command distribution submodule is used to synchronously distribute the generated avoidance decision to all smart devices corresponding to the set of conflict nodes.

[0046] The present invention also provides a scene scheduling device for smart devices, comprising: a memory, a processor, and a scene scheduling program for smart devices stored in the memory and executable on the processor. When the scene scheduling program for smart devices is executed by the processor, a scene scheduling method for smart devices is implemented.

[0047] The present invention also provides a computer program product, including a scene scheduling program for smart devices, wherein the scene scheduling program for smart devices implements the scene scheduling method for smart devices when executed by a processor.

[0048] The beneficial effects of this invention are as follows: By constructing a dual-domain scheduling factor that combines task urgency and motion complexity, and identifying conflict nodes based on the spatial location and time window overlap of path nodes, this invention effectively solves the problem of frequent path conflicts caused by differences in equipment maneuverability and inconsistent task rhythms in traditional scheduling methods. It realizes a differentiated scheduling strategy under task guidance and improves the coordination and safety of multi-device co-track operation.

[0049] This invention introduces an evaluation mechanism for two dynamic strategies—waiting for right-of-way and route rerouting—in the decision-making stage of conflict avoidance. By combining the evaluation of the two schemes with the weighted execution of equipment priority, it achieves rapid response and local scheduling optimization for conflicts in different scenarios, significantly reduces the overhead of global route replanning, and improves the ability to quickly adapt to sudden congestion and scheduling bottlenecks. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating the first embodiment of a scene scheduling method for smart devices according to the present invention.

[0052] Figure 2 This is a schematic diagram of device distribution under a dual-domain scheduling factor, representing a first embodiment of a scene scheduling method for smart devices according to the present invention.

[0053] Figure 3 This is a schematic diagram of multi-device scheduling priority path planning, which is a first embodiment of the scene scheduling method for smart devices according to the present invention.

[0054] Figure 4 This is a schematic diagram of a scene scheduling method for smart devices according to the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the scene scheduling method for smart devices according to the present invention, which presents the first embodiment of the scene scheduling method for smart devices according to the present invention.

[0057] In the first embodiment, the scene scheduling method for smart devices includes:

[0058] Step S10: Obtain the motion model parameters and task requirement parameters of intelligent device i, and determine the task urgency based on the motion model parameters and task requirement parameters using a dual-domain scheduling factor construction mechanism. and motion complexity ;

[0059] It should be noted that the dual-domain scheduling factor construction mechanism refers to extracting key scheduling reference indicators from the task execution requirement domain and the equipment's own motion performance domain, respectively. The former constructs the "task urgency," and the latter constructs the "motion complexity." The task urgency is based on the remaining length of the current task and the task deadline, reflecting the urgency of the task. The motion complexity is based on parameters such as maximum speed, minimum turning radius, and average braking distance, combined with the average motion capabilities of other equipment, and normalized to reflect the equipment's adaptability to the traffic environment and path planning complexity.

[0060] Understandably, this step expands the scheduling constraints from the traditional single "task priority" to a two-factor evaluation of "task urgency and motion complexity". This not only allows for advance path arrangement of high-priority tasks, but also gives priority to equipment with poor motion performance in complex areas, thereby improving the global stability of the scheduling strategy and the ability to avoid path conflicts.

[0061] It should be understood that, compared with the "static task priority" or "fixed device priority" strategies commonly used in the prior art, the dual-domain scheduling factor construction mechanism adopted in this invention can dynamically adapt to the device operating status and task timeliness requirements, and has stronger scheduling flexibility and scalability. It is especially suitable for practical application scenarios with a large number of heterogeneous devices, complex topology of the work area, and frequent task generation.

[0062] For example, such as Figure 2 As shown, the scheduling characteristics of 10 smart devices are illustrated: the upper right area represents devices with urgent tasks and weak mobility, which should be given priority to avoid to prevent path conflicts; the upper left area represents devices with non-urgent tasks but high mobility, suitable for reserving safe passage space; the lower right area represents devices with good mobility but urgent tasks, which can be given priority to execute tasks; and the lower left area represents devices with non-urgent tasks and strong mobility, with the greatest scheduling flexibility. Through a dual-domain factor model, the timeliness of tasks and the mobility of devices can be comprehensively evaluated, and the avoidance priority can be dynamically adjusted, thereby significantly improving the safety and global controllability of path scheduling in high-density task scenarios.

[0063] Step S20: Based on task urgency and motion complexity Construct scheduling priority weights, obtain map data of the current work area, and perform initial path planning based on scheduling priority weights and map data of the current work area, outputting path node sequence and corresponding time window set;

[0064] It should be noted that scheduling priority weight refers to a comprehensive scheduling factor calculated in scheduling decisions based on task urgency (such as time pressure to complete the task) and motion complexity (such as the ease with which the equipment can traverse obstacle areas). This factor is used to determine the preference weight for different travel paths during path planning. Map data includes, but is not limited to: a gridded topology map of the work area, edge weights between nodes (length, passage width, speed limits, etc.), and obstacle constraint information. Path planning processing employs a weight-driven cost function optimization algorithm. The output path node sequence represents the set of spatial coordinates that the intelligent device must sequentially traverse from its current location to the target location. The corresponding time window set indicates the estimated time range for the device to travel at each node, used for subsequent conflict avoidance judgments.

[0065] Understandably, this step, by coupling the path cost function with scheduling priority weights, can dynamically adjust the path selection strategy based on the urgency of the current task and the differences in mobility of different devices. For example, urgent tasks tend to choose the shortest path, while highly complex devices tend to choose routes with ample space and gentler turns. This improves the personalized adaptability of the overall path planning while avoiding local path congestion and scheduling deadlock caused by a one-size-fits-all path strategy.

[0066] It should be understood that, compared to the single-objective strategy of traditional path planning methods that only considers the shortest path or the minimum cost, this invention constructs a multi-objective path planning mechanism driven by scheduling priority weights, so that path planning not only serves the efficiency of individual task completion, but also takes into account operational coordination and equipment movement safety, making it particularly suitable for complex operating environments where multiple types of intelligent devices operate in parallel.

[0067] For example, such as Figure 3 As shown, the path planning results of multiple devices under different scheduling priorities are illustrated. Each curve represents the optimal path planned by a device from the starting point (marked by S) to the ending point (marked by E). The path is influenced by both the task urgency and motion complexity of the devices. Devices with higher scheduling priorities (such as those with tight remaining task time or poorer motion capabilities) tend to obtain shorter paths with fewer turns and smoother node passage; while devices with lower priorities choose suboptimal paths that avoid the main path to reduce the overall system conflict probability. The distribution of paths in the figure clearly reflects the impact of scheduling priority on path selection strategies. Some paths branch off early, bypassing the main path to avoid key nodes occupied by other high-priority devices, thus effectively mitigating scheduling conflicts caused by overlapping paths of multiple devices. In addition, the direction of travel and transition angle of each path near key nodes reflect the adaptability to motion complexity (such as the minimum turning radius and braking distance of the device), ensuring the executability of each device under physical constraints.

[0068] Step S30: Based on the output path node sequence and the corresponding time window set, a dual-condition judgment mechanism based on spatial location matching and time window overlap is used to filter conflict nodes among multiple smart devices, and output the conflict node set;

[0069] It should be noted that the "conflict node screening" mentioned in this step refers to identifying and marking pairs of nodes that have spatial and temporal overlap in all scheduling paths to construct a candidate set of potential conflict points. Specifically, two criteria are used: "spatial location matching" and "temporal window overlap." The former determines whether nodes in two or more device paths are at the same physical location (or are close neighbors within a preset distance threshold), and the latter determines whether multiple devices have overlapping time windows at that node. Only nodes that simultaneously satisfy both spatial overlap and temporal overlap are identified as "conflict nodes" and added to the conflict node set.

[0070] Understandably, using a dual-condition decision-making mechanism to filter path nodes for conflict resolution can significantly reduce the false positive rate (for example, relying solely on spatial location matching may lead to situations where static overlap but dynamic misalignment is mistakenly identified as a conflict), improving the accuracy of conflict detection and scheduling fault tolerance. Furthermore, this mechanism supports a unified conflict judgment standard in scenarios with varying path complexities across different devices, serving as a prerequisite for subsequent multi-device dynamic avoidance and scheduling coordination.

[0071] It should be understood that, compared to most existing methods that judge path conflicts solely based on space occupancy, this invention effectively avoids the problems of "false conflicts" and "delayed judgment" by introducing a time window overlap analysis mechanism. For example, in AGV path planning, although two devices pass through the same road segment, the time window interval is sufficient, and there is no need to take an avoidance strategy; traditional methods will misjudge them as conflicts and cause unnecessary path changes or stagnation, affecting overall efficiency.

[0072] For example, suppose the path node sequence for device A is {N1, N2, N3, N4}, with corresponding time windows of {[2s, 4s], [5s, 6s], [7s, 8s], [9s, 10s]}; and the path node sequence for device B is {N0, N2, N5}, with corresponding time windows of {[3s, 4s], [5.5s, 7s], [8s, 9s]}. At node N2, it is found that the spatial location is the same, and the time windows overlap within the interval [5.5s, 6s], which is a true conflict node. This node is added to the conflict node set and used as input for subsequent steps to determine avoidance priority and adjust the path. Actual deployment results show that in a scenario with 100 devices and 2000 path nodes, the number of effective conflict nodes identified using this invention's mechanism is reduced by 31% compared to traditional algorithms, and the scheduling response time is shortened by approximately 18%.

[0073] Step S40: Based on the set of conflicting nodes and their priority weights, a dual-scheme evaluation mechanism based on waiting for passage and route diversion schemes is adopted to generate an avoidance decision;

[0074] It should be noted that the "avoidance decision" mentioned in this step refers to the evaluation of two avoidance schemes, namely "waiting for passage" and "path rerouting," when multiple intelligent devices have spatial and temporal conflicts at the same node. The "waiting for passage" scheme refers to setting a waiting time before the conflict node for devices with lower priority. The "path rerouting" scheme refers to avoiding the conflict node by reconstructing a local path to ensure the timeliness of the overall task.

[0075] Understandably, by introducing a "dual-scheme evaluation mechanism," the scheduling order of high-priority devices can be preserved while low-priority devices can be flexibly adjusted, thus avoiding the high computational complexity and execution cost brought about by "global path replanning."

[0076] It should be understood that traditional scheduling systems often employ fixed yield rules or global obstacle avoidance strategies when handling path conflicts, making it difficult to balance task urgency with energy efficiency. This invention introduces task priority weights as decision factors, making the yielding strategy more differentiated and adaptable. For example, when a high-priority device's task is within a critical time window at a conflict node, it tends to instruct a low-priority device to yield briefly; conversely, when a low-priority device's path rerouting only causes a slight delay and avoids multiple conflicts, path reconstruction is automatically prioritized to ensure overall efficiency.

[0077] For example, device A and device B conflict at node N4. Device A's task deadline is 12:00, the remaining path distance is shorter, and its priority is higher. Device B's task deadline is 12:08, the current path is longer, device mobility is low (high movement complexity), and its priority is lower. First, the waiting-and-give-way option is evaluated: device B waiting 4 seconds before N4 ensures device A's priority passage without causing a task timeout, which is deemed feasible. Simultaneously, the path rerouting option is evaluated: device B's path rerouting requires detouring through 3 nodes, delaying by approximately 40 seconds, causing its task to fail to complete on time. Ultimately, the waiting-and-give-way option is chosen as the avoidance decision, and the instruction "wait 4 seconds before node N3" is synchronously sent to device B.

[0078] Step S50: The generated avoidance decision is synchronously distributed to all smart devices corresponding to the set of conflict nodes.

[0079] It should be noted that the "synchronous issuance of avoidance decisions" in this step refers to the process of synchronously issuing the corresponding control commands in a unified format to all intelligent devices related to the conflicting node after generating the avoidance decision (including waiting for passage or path rerouting) based on the mapping relationship between the task device ID and the path node. This process must ensure consistency in the timing accuracy, communication link stability, and device execution response of the issued commands to guarantee the real-time execution of the avoidance scheme and the effective implementation of the scheduling strategy.

[0080] Understandably, by binding avoidance decisions to the set of conflict nodes, it is possible to accurately identify all devices affected by the conflict and push targeted scheduling instructions, avoiding redundant broadcasts or mis-issuance, and improving scheduling efficiency and instruction execution accuracy.

[0081] For example, in a smart park delivery scenario, AGV-A and robot B experience a path conflict at node N7. After generating an avoidance decision in step S40 of "AGV-A has priority to pass, robot B's path is diverted to N7," control commands are immediately issued based on the device binding relationship at the conflict node N7: "AGV-A maintains its current path and continues execution," and "Robot B executes the path diversion plan." This synchronous issuance process is achieved through the MQTT low-latency channel, completing command distribution within 10ms, with a device execution synchronization rate of 98.7%. Compared to the traditional CAN bus broadcast method, command response latency is reduced by approximately 64%, and conflict recovery time is shortened by 31%.

[0082] Example 2: Furthermore, the present invention provides a scene scheduling system for smart devices, employing a scene scheduling method for smart devices as described in the above embodiments, which can solve a technical problem related to scene scheduling for smart devices. Compared with the prior art, the beneficial effects of the scene scheduling system for smart devices provided by the present invention are the same as those of the scene scheduling method for smart devices provided in the above embodiments, and other technical features of the scene scheduling system for smart devices are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0083] Example 3: This invention provides a scene scheduling device for intelligent devices. Please refer to... Figure 4A scene scheduling device for smart devices includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to execute the scene scheduling method for smart devices described in Embodiment 1 above. The scene scheduling device for smart devices in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This scene scheduling device for smart devices is merely an example and should not limit the functionality or scope of use of the embodiments of this invention. A scene scheduling device for smart devices may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of a scene scheduling device for a smart device. The processing unit 1001, read-only memory 1002, and random access memory 1004 are interconnected via a bus 1005. The I / O interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows a scene scheduling device for a smart device to communicate wirelessly or wiredly with other devices to exchange data. Although a scene scheduling device for a smart device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0084] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the scene scheduling method for smart devices as described above. The computer program product provided by this invention can solve a technical problem related to scene scheduling for smart devices. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the scene scheduling method for smart devices provided in the above embodiments, and will not be repeated here.

[0085] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0086] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0087] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for scene scheduling for an intelligent device, the method comprising: receiving a scene scheduling request from a user; determining a scene to be scheduled based on the scene scheduling request; and scheduling the scene to be scheduled. The method comprises: Step S10: Obtain the motion model parameters and task demand parameters of the intelligent device i, and determine the task urgency based on the motion model parameters and task demand parameters using a dual-domain scheduling factor construction mechanism and motion complexity ; Step S20: based on task urgency and motion complexity construct a scheduling priority weight, obtain current job area map data, and perform initial path planning processing according to the scheduling priority weight and the current job area map data, output a path node sequence and a corresponding time window set; Step S30: performing conflict node screening among the plurality of intelligent devices based on the output path node sequence and the corresponding time window set using a double-condition judgment mechanism based on spatial position matching and time window overlap, and outputting a conflict node set; Step S40: generating an avoidance decision according to the conflict node set in combination with a priority weight using a double-scheme evaluation mechanism based on a waiting yielding scheme and a path rerouting scheme; Step S50: synchronously issuing the generated avoidance decision to all intelligent devices corresponding to the conflict node set. 2.The method of claim 1, wherein, In step S10, the motion model parameters and the task demand parameters of the intelligent device i are acquired, and the task urgency is determined based on the motion model parameters and the task demand parameters by using a dual-domain scheduling factor construction mechanism and the steps of motion complexity , specifically including: Step S101: Obtain the motion model parameters and task demand parameters of the intelligent device i, the motion model parameters including the maximum driving speed , the minimum turning radius , and the average braking distance ; the task demand parameters including the task deadline and the current task remaining length ; wherein the intelligent device includes industrial AGV, airport baggage handling robot, and mobile charging vehicle; Step S102: estimating the theoretical shortest time required for completing the current task based on the maximum driving speed and the remaining length of the current task estimating the theoretical shortest time required for completing the current task ; If the theoretical shortest time is greater than the task deadline , then the state of the current task is determined to be "time unreachable", the task urgency is introduced, and the task urgency is set to 1. If the shortest time is less than or equal to the task deadline , the state of the current task is determined as "time reachable", the task urgency is introduced, and the task urgency is set as ; wherein, , the greater the value of the task urgency , the stronger the task urgency. Step S103: Obtain the maximum turning radius of the intelligent device i in the system and the maximum driving speed of the intelligent device in the system ; based on the maximum turning radius of the intelligent device in the system and the maximum driving speed of the intelligent device in the system , combined with the motion model parameters including the maximum driving speed , the minimum turning radius and the average braking distance Perform motion complexity evaluation processing using hierarchical mapping construction method, and obtain motion complexity , , The larger the value represents the higher the motion complexity. 3.The method of claim 2, wherein, In step S10, the motion model parameters include maximum driving speed , minimum turning radius , and average braking distance The motion complexity evaluation process is performed using a hierarchical mapping construction method, and the motion complexity is obtained by weighting construction. First, based on the maximum travel speed and the maximum travel speed of the device within the system construction flexibility correction factor , flexibility correction factor for indicating the scheduling cost of the device relative slow speed; Based on minimum turning radius and maximum turning radius of the device within the system Construct a turning difficulty factor , turning difficulty factor Used to represent the proportion of space required for the device to turn, the larger the value, the more difficult it is to turn; at the same time, based on the average braking distance and maximum turning radius of the device within the system Construct a brake response factor , brake response factor Used to represent the proportion of space required for the device to stop, the larger the value, the more difficult it is to avoid obstacles; Finally based on flexibility correction factor , turning difficulty factor and brake response factor Adopt linear weighting way to construct motion complexity . 4.The method of claim 1, wherein, In step S20, based on the task urgency and the motion complexity constructing the scheduling priority weight , obtaining the current job area map data, and performing an initial path planning process according to the scheduling priority weight and the current job area map data, outputting a path node sequence and a corresponding time window set, specifically including: Step S201: presetting a task urgency threshold and a motion complexity threshold; when the task urgency is greater than the task urgency threshold, and the motion complexity is greater than the motion complexity threshold, setting a scheduling priority weight as a first priority value; when the task urgency is greater than the task urgency threshold, and the motion complexity is less than the motion complexity threshold, setting the scheduling priority weight as a second priority value; when the task urgency is less than the task urgency threshold, and the motion complexity is greater than the motion complexity threshold, setting the scheduling priority weight as a third priority value; when the task urgency is less than the task urgency threshold, and the motion complexity is less than the motion complexity threshold, setting the scheduling priority weight as a fourth priority value. ​​​​​​​​​​​​ Step S202: Obtain the current work area map data, and determine the scheduling priority weight based on the current work area map data The current work area map data is sequentially called to construct a feasibility graph model containing topology nodes and passable edges. The topology nodes are used to represent passable work points. The passable edges are used to represent reachable paths between the nodes, and are attached with path length, road width, speed limit and passable constraint attributes. The path cost function is set using a hierarchical weighted cost evaluation mechanism; Step S203: performing path search processing based on the path cost function and the feasibility graph model using an A* global path planning algorithm, and outputting a path node sequence and a corresponding time window set; wherein the path node sequence comprises an ordered node set from the current position of the device to the target task position; and the corresponding time window set comprises the predicted arrival time of each node. 5.The method of claim 4, wherein, In step S20, the step of setting the path cost function using the hierarchical weighted cost evaluation mechanism specifically comprises: For scheduling priority weight For the device of the first priority value or the second priority value, a time factor weight is introduced in the path cost function, which is used for preferentially selecting the shortest time path. For scheduling priority weight For the third priority value device, the path cost function introduces a penalty term related to the turning radius and braking distance, which is used to preferentially select wide and smooth paths; For scheduling priority weight For the fourth priority value, the path cost function selects a pre-defined standard cost function. 6.The method of claim 1, wherein, In step S30, the step of performing conflict node screening among the plurality of intelligent devices based on the output path node sequence and the corresponding time window set using the double-condition judgment mechanism based on spatial position matching and time window overlap, and outputting a conflict node set specifically comprises: Step S301: traversing the path node sequences of all intelligent devices, and constructing a global path node mapping table, the global path node mapping table comprising the device spatial coordinates, device number, device serial number, and device predicted arrival time window corresponding to the node; Step S302: presetting a spatial coincidence threshold , calculating a node coordinate Euclidean distance between the intelligent device i and the intelligent device j based on the device spatial coordinates in the global path node mapping table , wherein is a transverse coordinate of the device i at the node k, is a longitudinal coordinate of the device i at the node k, is a transverse coordinate of the device j at the node k, is a longitudinal coordinate of the device j at the node k; when the node coordinate Euclidean distance is less than the spatial coincidence threshold , determining that the intelligent device i and the intelligent device j are a candidate conflict device pair at the node k ; Step S303: Obtain the information of smart device i at the node The estimated arrival time window for the equipment at the location and smart device j at node The estimated arrival time window for the equipment at the location If the equipment is expected to arrive within the time window With the equipment's expected arrival time window If there is an intersection of time windows, then smart device i and smart device j are determined to be scheduling conflict nodes at node k, and are recorded in the conflict node set. 7.The method of claim 1, wherein, In step S40, the step of generating an avoidance decision according to the conflict node set in combination with a priority weight using the double-scheme evaluation mechanism based on the waiting yielding scheme and the path rerouting scheme specifically comprises: Step S401: For each set of conflicting smart devices i and smart devices j in the conflict node set, performing a priority comparison judgment according to the corresponding scheduling priority weight , and the scheduling priority weight , and determining the priority execution right belongs to the device or the device avoiding the device ; Step S402: obtaining avoidance device Two types of avoidance schemes are constructed, including a waiting-to-yield scheme and a path-replanning scheme; for the waiting-to-yield scheme, a waiting time cost is obtained; for the path-replanning scheme, a path-replanning cost is obtained; Step S403: evaluating the avoidance scheme execution cost based on the waiting time cost and the path rerouting cost, and generating a final avoidance decision based on the avoidance scheme execution cost. 8.A scene scheduling system for smart devices, applied to the scene scheduling method for smart devices in any one of claims 1 to 7, characterized in that, The scene scheduling system for intelligent devices comprises: The scheduling factor construction submodule is configured to acquire the motion model parameters and the task demand parameters of the intelligent device i, and determine the task urgency based on the motion model parameters and the task demand parameters by using a dual-domain scheduling factor construction mechanism and motion complexity ; The path planning sub-module is configured to perform initial path planning based on the task urgency and the motion complexity and the motion complexity construct a scheduling priority weight, acquire current job area map data, and perform initial path planning processing according to the scheduling priority weight and the current job area map data, and output a path node sequence and a corresponding time window set; A conflict node screening submodule, configured to perform conflict node screening among the plurality of intelligent devices based on the output path node sequence and the corresponding time window set using a double-condition judgment mechanism based on spatial position matching and time window overlap, and output a conflict node set; An avoidance decision generation submodule, configured to generate an avoidance decision according to the conflict node set in combination with a priority weight using a double-scheme evaluation mechanism based on a waiting yielding scheme and a path rerouting scheme; A control instruction issuing submodule, configured to synchronously issue the generated avoidance decision to all intelligent devices corresponding to the conflict node set. 9.A scene scheduling device for a smart device, the device comprising: The scene scheduling device for intelligent devices comprises a memory, a processor, and a scene scheduling program for intelligent devices stored on the memory and executable on the processor, and the scene scheduling program for intelligent devices, when executed by the processor, implements the scene scheduling method for intelligent devices in any one of claims 1 to 7.

10. A computer program product, characterised in that, The computer program product comprises a scene scheduler for smart devices, which, when executed by a processor, implements a scene scheduling method for smart devices as claimed in any one of claims 1 to 7.

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