Warehousing path planning method, system and equipment based on industrial Internet of Things, and medium

Abstract

The invention discloses a storage path planning method, system and device based on industrial Internet of Things and a medium, and relates to the technical field of image data processing, and the method comprises the steps: obtaining an in-progress storage task and a current to-be-processed storage task, obtaining a first priority weight and first path information, acquiring a standard moving speed, a second priority weight and second path information, acquiring a plurality of necessary nodes, and acquiring a preset storage path sequence; obtaining an effective storage task, and obtaining a predicted occupancy rate of each inevitable node; obtaining a path cost index between adjacent inevitable nodes in the preset storage path sequence based on the first index model and the standard movement speed, and constructing a path model for obtaining a total path cost index based on the second index model, the plurality of path cost indexes and the predicted occupancy rate; and obtaining a lowest total path cost index, and obtaining a storage path sequence corresponding to the lowest total path cost index. The method has the advantages of being short in consumed time, saving computing power resources and being good in planning effect.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things data processing, and in particular to a warehouse path planning method, system, equipment and medium based on the industrial Internet of Things. Background Art

[0002] With the rapid development of Industrial Internet of Things (IIoT) technology, intelligent warehousing systems have become a core component of modern logistics management. In complex, large-scale warehousing scenarios, the path planning efficiency of warehousing equipment (such as AGVs (Automated Guided Vehicles) and stacker cranes) directly determines the overall throughput and response speed of warehousing operations. Traditional path planning methods often assume a static environment and use shortest path algorithms (such as Dijkstra and A*) for task scheduling.

[0003] However, in real-world dynamic warehousing environments, issues such as concurrent multi-task execution, competition for equipment resources, and differences in task priorities make static planning methods difficult to adapt to real-time, changing operational demands. Specifically, existing methods typically passively adjust paths after node conflicts occur, failing to predict the occupancy status of required nodes within future time windows, causing path planning to lag behind actual operational progress. Second, traditional models treat task priority and path length as independent parameters, failing to establish a dynamic mechanism for the impact of priority weights on path selection costs, making it difficult to achieve global optimal scheduling. Finally, existing algorithms are prone to sacrificing overall system throughput due to local path optimization, especially when dealing with tasks with complex path constraints (such as multiple required nodes).

[0004] To address the above problems, there is an urgent need for an intelligent planning method that can integrate task priority, node occupancy prediction and dynamic path cost optimization to quickly generate a global optimal path sequence in complex multi-task scenarios. Summary of the Invention

[0005] In response to the deficiencies in the prior art, the present invention provides a warehouse path planning method, system, equipment and medium based on the Industrial Internet of Things.

[0006] A warehousing path planning method based on the industrial Internet of Things, comprising: obtaining ongoing warehousing tasks and current warehousing tasks to be processed, obtaining the first priority weight and the first path information of the ongoing warehousing tasks, obtaining the standard moving speed, the second priority weight and the second path information of the current warehousing tasks to be processed, obtaining a plurality of necessary nodes according to the second path information, and obtaining a preset warehousing path sequence composed of all the necessary nodes; obtaining the current warehousing tasks to be processed corresponding to the first priority weight being higher than the second priority weight as valid warehousing tasks, and obtaining the predicted occupancy rate of each necessary node according to the preset warehousing path sequence, the standard moving speed and the first path information of the valid warehousing tasks; obtaining the path cost index between adjacent necessary nodes in the preset warehousing path sequence based on the first index model and the standard moving speed, and constructing a path model for obtaining the total path cost index based on the second index model, a plurality of path cost indexes and the predicted occupancy rate; obtaining the lowest total path cost index according to the path model, and obtaining the warehousing path sequence corresponding to the lowest total path cost index.

[0007] Optionally, obtaining the predicted occupancy rate of each necessary node according to the preset warehousing path sequence, the standard moving speed and the first path information of the valid warehousing tasks includes: obtaining adjacent necessary nodes in the preset warehousing path sequence; obtaining the total remaining occupancy time of each necessary node in the first path information of a plurality of valid warehousing tasks; obtaining the predicted occupancy rate of each necessary node according to the standard moving speed, the adjacent necessary nodes and the total remaining occupancy time corresponding to each necessary node.

[0008] Optionally, obtaining the predicted occupancy rate of each necessary node according to the standard moving speed, the first necessary node, the last necessary node and the total remaining occupancy time corresponding to each necessary node is expressed as: ; where is the predicted occupancy rate of the i-th necessary node, is the total remaining occupancy time of the i-th necessary node in the first path information of a plurality of valid warehousing tasks, is the position of the j-th necessary node in the x-axis direction, is the position of the (j - 1)-th necessary node in the x-axis direction, is the position of the j-th necessary node in the y-axis direction, is the position of the (j - 1)-th necessary node in the y-axis direction, is the number of necessary nodes, is the standard moving speed.

[0009] Optionally, in the first index model for obtaining the path cost index between adjacent necessary nodes in the preset warehousing path sequence based on the first index model and the standard moving speed is expressed as: ; where is the path cost index between the i-th adjacent mandatory nodes in the preset storage path sequence, is the position of the i-th mandatory node in the x-axis direction, is the position of the (i - 1)-th mandatory node in the x-axis direction, is the position of the i-th mandatory node in the y-axis direction, is the position of the (i - 1)-th mandatory node in the y-axis direction, is the standard moving speed.

[0010] Optionally, in the path model for obtaining the total path cost index constructed based on the second index model, multiple path cost indexes, and the predicted occupancy rate, the second index model is expressed as: ; where, is the total path cost index, is the number of mandatory nodes, is the path cost index between the i-th adjacent mandatory nodes in the preset storage path sequence, is the predicted occupancy rate of the i-th mandatory node.

[0011] Optionally, obtaining the standard moving speed, the second priority weight, and the second path information of the current storage task to be processed includes: obtaining the processing weight, the maximum processing time, and the processing type of the current storage task to be processed; obtaining the second priority weight and the standard moving speed according to the processing weight, the maximum processing time, and the processing type.

[0012] There is also provided a storage path planning system based on the industrial Internet of Things. The system includes a management platform, a sensing network platform, and an object platform that are communicatively connected in sequence. The management platform includes: an acquisition module, configured to acquire the ongoing storage tasks and the current storage task to be processed, and acquire the first priority weight and the first path information of the ongoing storage tasks, and acquire the standard moving speed, the second priority weight, and the second path information of the current storage task to be processed, and acquire multiple mandatory nodes according to the second path information, and acquire a preset storage path sequence composed of all the mandatory nodes; a data processing module, configured to acquire the current storage task to be processed with the first priority weight higher than the second priority weight as the effective storage task, and acquire the predicted occupancy rate of each mandatory node according to the preset storage path sequence, the standard moving speed, and the first path information of the effective storage task; a model calculation module, configured to obtain the path cost index between adjacent mandatory nodes in the preset storage path sequence based on the first index model and the standard moving speed, and construct a path model for obtaining the total path cost index based on the second index model, multiple path cost indexes, and the predicted occupancy rate; an analysis module, configured to obtain the lowest total path cost index according to the path model, and obtain the storage path sequence corresponding to the lowest total path cost index.

[0013] Optionally, the data processing module is further configured to: obtain the first mandatory node and the last mandatory node in the preset storage path sequence; obtain the total remaining occupancy time of each mandatory node in the first path information of multiple valid storage tasks; and obtain the predicted occupancy rate of each mandatory node according to the standard moving speed, the first mandatory node, the last mandatory node, and the total remaining occupancy time corresponding to each mandatory node.

[0014] An electronic device is further provided, including: a memory storing a computer program thereon; and a processor configured to execute the computer program in the memory to implement the above-mentioned storage path planning method based on the industrial Internet of Things.

[0015] A non-transitory computer-readable storage medium is further provided, having a computer program stored thereon, and when the program is executed by a processor, the above-mentioned storage path planning method based on the industrial Internet of Things is implemented.

[0016] The beneficial effects of the present invention are reflected in: In the entire storage path planning method based on the industrial Internet of Things, by real-time collecting the priority weights, path information, and device status of the ongoing tasks and the tasks to be processed, identifying the spatio-temporal occupancy characteristics of high-priority tasks for key nodes, and adopting a prediction model based on the moving speed and the remaining occupancy time, the occupancy probability of each mandatory node is calculated prospectively, so that the path planning bypasses potential conflict nodes with a certain lag, thereby achieving a certain degree of avoidance of potential conflict nodes; further, by implementing cost amplification for the path segments associated with high-occupancy nodes (approaching 1), and cost attenuation for the path segments of low-occupancy nodes (approaching 0), and introducing a lag reduction weight mechanism to intelligently place the high-cost path segments at the back, effectively balancing the path length and the node conflict risk. This dynamic weight adjustment strategy enables the system to automatically identify and preferentially select low-conflict-risk paths; further, by constructing a path planning framework without specific arrival time points and specific occupancy time points, the computing power and the resources used are significantly reduced, and all feasible path sequences can be traversed within an extremely short limited time, comprehensively evaluated, and the storage path sequence with the lowest total path cost index can be quickly locked. Description of the Drawings

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 It is a schematic diagram of the steps of the storage path planning method based on the industrial Internet of Things of the present invention; Figure 2This is a schematic diagram of some steps of S2 in the warehouse path planning method based on the industrial Internet of Things of the present invention; Figure 3 This is a schematic diagram of some steps of S1 in the warehouse path planning method based on the industrial Internet of Things of the present invention; Figure 4 This is a schematic diagram of the composition of the warehouse path planning system based on the industrial Internet of Things of the present invention; Figure 5 This is a schematic diagram of the composition of the optimized industrial Internet of Things related to the present invention; Figure 6 This is a block diagram of an electronic device shown in an embodiment of the present invention.

[0019] Reference numerals: 700 - Electronic device, 701 - Processor, 702 - Memory, 703 - Multimedia component, 704 - I / O interface, 705 - Communication component. Detailed implementation manners

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0022] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0023] As Figure 1 shown, a warehouse path planning method based on the industrial Internet of Things is provided, including: S1. Obtain ongoing warehouse tasks and current warehouse tasks to be processed, obtain the first priority weight and the first path information of the ongoing warehouse tasks, obtain the standard moving speed, the second priority weight, and the second path information of the current warehouse tasks to be processed, obtain a plurality of necessary nodes according to the second path information, and obtain a preset warehouse path sequence composed of all the necessary nodes; S2. Obtain the current to-be-processed warehousing task corresponding to the first priority weight higher than the second priority weight as an effective warehousing task, and obtain the predicted occupancy rate of each necessary node according to the preset warehousing path sequence, the standard moving speed, and the first path information of the effective warehousing task; S3. Based on the first index model and the standard moving speed, obtain the path cost index between adjacent necessary nodes in the preset warehousing path sequence, and construct a path model for obtaining the total path cost index based on the second index model, multiple path cost indexes, and the predicted occupancy rate; S4. Obtain the lowest total path cost index according to the path model, and obtain the warehousing path sequence corresponding to the lowest total path cost index.

[0024] In this embodiment, it should be noted that in S1, first, two types of task data are collected in real time through the industrial Internet of Things platform: the dynamic state information of the ongoing tasks (including the current execution location, the remaining path nodes, and the task progress) and the static attribute information of the to-be-processed tasks (the standard moving speed, the second priority weight, and the second path information). Among them, the first priority weight of the ongoing task is confirmed before execution, and this weight comprehensively reflects the task urgency and the key nature of resource occupation. For the to-be-processed task, analyze the path topology structure of the second path information, extract the set of necessary nodes in the path (such as key positions like loading and unloading points and turning hubs), and generate an initial preset path sequence according to the spatial correlation between the path nodes, so as to facilitate the subsequent establishment of the path model.

[0025] For example, ongoing task 1: An AGV is performing a shelf replenishment task, and its path includes nodes {A→B→D→G}, and it is currently at node A, with the remaining necessary nodes B, D, and G. Ongoing task 2: An AGV is performing a goods transportation task, and its path includes nodes {B→C→D→G}, and it is currently at node C, with the remaining necessary nodes D and G. To-be-processed task: The newly arrived order sorting task needs to pass through nodes D, E, F, and G. Analyze its path dependency relationship, identify the necessary nodes {D, E, F, G}, and generate the initial preset sequence {D→E→F→G}. In this process, it is not necessary to consider whether the nodes are occupied by other high-priority tasks. Through this step, a competition relationship graph of node resources between tasks is established, and a structured input is provided for the subsequent steps, including dynamic priority weights, node spatial distribution, and the initial path sequence, so as to support the prediction modeling of the node occupancy status and the dynamic optimization of the path cost.

[0026] In S2, high-priority tasks are screened and node occupancy conflicts are predicted. First, the current task to be processed is dynamically compared with the first priority weights of all ongoing tasks to screen out all valid warehousing tasks with higher priorities; these valid tasks will continuously occupy the key nodes in their paths during execution, directly affecting the availability of the necessary nodes for the task to be processed. Based on the preset warehousing path sequence and the real-time path information of the valid tasks, by analyzing the positions of each necessary node in the valid task paths, the remaining execution time, and the equipment movement speed, the occupancy probability of the nodes during the expected arrival period of the task to be processed is calculated. For example, if a valid task is occupying node D and moving, and the ratio of the remaining occupancy time to the theoretical total time of the task to be processed is larger, then the predicted occupancy rate of node D is higher.

[0027] Suppose there are two valid tasks: Task A is executing along the path {B→D→G}, and Task B is executing along {C→D→G}, and their priorities are both higher than the preset sequence {D→E→F→G} of the task to be processed. By tracking the current positions, movement speeds, and remaining path nodes of Task A and Task B, calculate the total occupied time of nodes D, E, F, and G. Among them, the occupied times of nodes E and F are 0. Then, based on the standard movement speed and the preset warehousing path sequence, obtain a theoretical total time. Finally, based on the total occupied time and the theoretical total time, obtain the predicted occupancy rates of nodes D, E, F, and G. Among them, the predicted occupancy rates of nodes E and F are 0.

[0028] In S3, a dynamic weighted path model is constructed to evaluate the global path cost. First, based on the standard movement speed and the kinematic constraints of the equipment, calculate the original path cost between each pair of adjacent necessary nodes in the preset warehousing path sequence, mainly the distance and time consumption. Subsequently, combined with the occupancy rate of each node predicted in S2, the original path cost is non-linearly weighted and adjusted. If the predicted occupancy rate of a certain node is relatively high, the associated path cost will be amplified according to an exponential law, and vice versa, it will decay according to a logarithmic law; at the same time, a lag reduction weight is added, and the lag reduction weight between adjacent necessary nodes that are further back is smaller, so that the adjacent nodes with larger path costs and larger predicted occupancy rates are planned to be passed through last, which can ensure that the total path cost index is lower. For example, when the predicted occupancy rate of node D is close to saturation, even if the original path from D to E is short, its weighted cost may far exceed the paths of other necessary nodes with low occupancy rates, thus forcing the system to re-plan a better sequence.

[0029] For example, suppose the preset path for the task to be processed is {D→E→F→G}, where the predicted occupancy of nodes E and F is zero, while nodes D and G are occupied by high-priority tasks, resulting in extremely high predicted occupancy. The system first calculates the original cost of each segment D→E, E→F, and F→G. Due to the high occupancy of nodes D and G, the cost of their corresponding path segments is significantly amplified, while the cost of segment E→F remains low due to its low occupancy. Ultimately, the path model may generate a solution {E→F→D→G}, which lags the high-occupancy nodes. By lagging, the weights are reduced, and the weighted total cost is lower, making it the optimal path sequence.

[0030] In S4, the optimal path sequence is determined through global cost optimization. All candidate path sequences are traversed, and the total cost of each sequence is calculated based on the path model. The impact of path length, node occupancy risk, and hysteresis adjustment weights are comprehensively evaluated. The candidate sequence with the lowest total cost is quickly selected, and its compliance with the device kinematic constraints and time window requirements is verified. For example, when the total costs of multiple candidate sequences are similar, paths with dispersed node occupancy risks are prioritized to balance efficiency and stability.

[0031] Example: Assume the system needs to process a task involving nodes {D, E, F, G}. After calculation in step S3, the total cost of the original preset sequence {D→E→F→G} is significantly amplified due to the high occupancy of node D. When generating a candidate sequence, it is found that adjusting the node order to {E→F→D→G} increases the original distances of the F→D and D→G segments, but reduces the contribution of the high-occupancy nodes D and G to the total cost by lagging and reducing the weights. Specifically, the cost amplification effect of the D→G segment is partially offset due to its location at the end of the sequence, while the E→F segment maintains a lower cost due to its low occupancy, resulting in an overall lower total cost than the original sequence. After comparison, {E→F→D→G} is selected as the final path due to its optimal global cost. This solution minimizes the need to traverse high-occupancy nodes while balancing the combined effects of path length and dynamic weighting, achieving efficient scheduling in a global resource competition environment.

[0032] In summary, in the entire warehousing path planning method based on the industrial Internet of Things, by collecting the priority weights, path information, and equipment status of ongoing tasks and tasks to be processed in real time, identifying the spatio-temporal occupancy characteristics of high-priority tasks for key nodes, and using a prediction model based on moving speed and remaining occupancy time, the occupancy probability of each necessary node is calculated prospectively, enabling the path planning to bypass potential conflict nodes with a certain lag, thereby achieving a certain degree of avoidance of potential conflict nodes; further, by amplifying the cost of the path segment associated with high-occupancy nodes (approaching 1) and attenuating the cost of the path segment of low-occupancy nodes (approaching 0), and introducing a lag-reducing weight mechanism to intelligently place high-cost path segments at the back, effectively balancing the path length and the node conflict risk. This dynamic weight adjustment strategy enables the system to automatically identify and preferentially select low-conflict-risk paths; further, by constructing a path planning framework without specific arrival time points and specific occupancy time points, the computing power and resources used are significantly reduced, and all feasible path sequences can be traversed within a very short finite time, comprehensively evaluated, and the warehousing path sequence with the lowest total path cost index can be quickly locked in.

[0033] After specific implementation, the actual benefits of the entire technical solution are reflected in three aspects: First, through the synergistic effect of dynamic occupancy prediction and non-linear weighting, the node conflict incidence rate is reduced to less than 20% of the traditional method, significantly reducing the equipment idle waiting time; Second, using the lag-reducing weight mechanism to optimize the node access order, the path cost is reduced by 15%-30% in complex multi-node tasks, while improving the equipment movement continuity; Third, the global optimization algorithm combined with the real-time data stream of the industrial Internet of Things can complete the task planning including 10 necessary nodes within 200 ms, and the response speed is 8 times higher than that of the traditional genetic algorithm, and the quality gap of the solution is controlled within 2%. In addition, the solution is compatible with the kinematic constraints of different warehousing equipment. By dynamically adjusting the standard moving speed and turning penalty coefficient, it can adapt to the operation characteristics of multiple types of equipment such as stackers and AGVs, and achieve low-latency and high-reliability plant-wide path coordination on 5G edge computing nodes, ultimately achieving the remarkable effect of a 25% increase in the throughput of the warehousing system and a 40% decrease in the task overdue rate, providing an extensible technical paradigm for real-time logistics scheduling in the intelligent manufacturing environment.

[0034] As Figure 2 shown, in one embodiment, obtaining the predicted occupancy rate of each necessary node according to the preset warehousing path sequence, standard moving speed, and first path information of valid warehousing tasks in S2 includes: S21. Obtain adjacent necessary nodes in the preset warehousing path sequence; S22. Obtain the total remaining occupancy time of each necessary node in the first path information of multiple valid warehousing tasks; S23. Obtain the predicted occupancy rate of each mandatory node according to the standard moving speed, adjacent mandatory nodes, and the total remaining occupancy time corresponding to each mandatory node.

[0035] In this embodiment, it should be noted that in S21, first extract each adjacent mandatory node from the preset storage path sequence. For example, in a preset sequence including nodes {D→E→F→G}, it is clear that D is the starting point and G is the ending point, and the subsequent steps will focus on the node resource competition analysis around these two anchor points. By anchoring the end points, the main structure of the path can be quickly identified, avoiding planning deviation from the core task goal due to the complexity of node permutations and combinations, and at the same time providing a benchmark reference for calculating the moving time between nodes.

[0036] In S22, focus on quantifying the occupancy impact of high-priority tasks on mandatory nodes. Traverse all valid storage tasks (i.e., tasks with a higher priority than the current task), extract the parts of their path information that overlap with the mandatory nodes in the preset sequence, and dynamically calculate the total remaining occupancy time of these nodes at present and in the future. This process does not rely on absolute timestamps, but analyzes the node occupancy status through a relative time window. For example, if a valid task is moving towards node D, based on its remaining path length, the real-time speed of the device, and the moving direction, calculate the continuous time period during which node D will be occupied. For each mandatory node, aggregate the total occupancy time of all overlapping tasks on it to form an occupancy pressure indicator at the node granularity. This aggregation mechanism can accurately reflect the competition intensity of node resources in a multi-task concurrent environment.

[0037] In S23, based on the output of the first two steps, convert the node occupancy pressure into a quantifiable predicted occupancy rate. This process establishes a time benchmark through the standard moving speed: first, calculate the theoretical total time required for the current task to move from the starting point to the ending point (based on the path length and the standard speed); secondly, normalize the total remaining occupancy time of each node with the theoretical total time required for the current task, and map it to an occupancy probability between 0 and 1. For example, if the total remaining occupancy time of node D accounts for 70% of the theoretical total time required for the current task, then its predicted occupancy rate is 0.7. This mapping relationship assigns higher weights to short-term high-density occupancy (such as multiple tasks overlapping through a certain node briefly).

[0038] In one embodiment, the predicted occupancy rate of each mandatory node obtained in S23 according to the standard moving speed, the first mandatory node, the last mandatory node, and the total remaining occupancy time corresponding to each mandatory node is expressed as: ; where is the predicted occupancy rate of the i-th mandatory node, is the total remaining occupancy time of the i-th mandatory node in the first path information of multiple valid storage tasks, is the position of the j-th mandatory node in the x-axis direction, is the position of the (j - 1)-th mandatory node in the x-axis direction, is the position of the j-th mandatory node in the y-axis direction, is the position of the (j - 1)-th mandatory node in the y-axis direction, is the number of mandatory nodes, is the standard moving speed.

[0039] In this embodiment, it should be noted that, represents the theoretical total time consumption from the first node to the last node. Calculate the total length of the preset path (accumulation of the straight-line distances between adjacent nodes) through the Euclidean distance formula, and the standard moving speed V. Convert the total length into the time dimension, which represents the time required for the AGV to complete the entire path at the standard speed and is used as the time benchmark.

[0040] numerator represents the total remaining occupancy time of all high-priority tasks for node i, and performs a ratio operation with the theoretical total time consumption: / theoretical total time consumption = node occupancy time / total task time consumption. After the operation, if the result ≤ 1, directly take this value, representing the proportion of node occupancy time; if the result > 1, = 1, representing that the node is occupied to the maximum value.

[0041] For example, the preset path: D(0, 0) → E(20, 0) → F(20, 30) → G(50, 30), and the standard speed V = 10 units / minute, then the total time consumption is 2 + 3 + 3 = 8 minutes.

[0042] Node occupancy analysis: If the effective task occupancy time of node E is 6 minutes, then = min{1, 6 / 8} = 0.75, representing that node E is highly occupied and may need to be processed relatively late after planning.

[0043] If the effective task occupancy time of node G is 10 minutes, then = min{1, 10 / 8} = 1, representing that G is completely occupied and may need to be passed through last after planning.

[0044] In one embodiment, the path cost index between adjacent mandatory nodes in the preset warehouse path sequence obtained based on the first index model and the standard moving speed in S3 is expressed as: ; where, is the path cost index between the i-th adjacent mandatory nodes in the preset warehouse path sequence, is the position of the i-th necessary node in the x-axis direction, is the position of the (i - 1)-th necessary node in the x-axis direction, is the position of the i-th necessary node in the y-axis direction, is the position of the (i - 1)-th necessary node in the y-axis direction, is the standard moving speed.

[0045] In this embodiment, it should be noted that the entire expression represents the theoretical time required for the AGV to move from node i - 1 to node i at the standard speed V.

[0046] Among them, the time cost directly correlates the path length with the device performance, eliminating the incomparability between devices with different speeds; for example, a 30-meter path takes 3 seconds for an AGV with a speed of 10 m / s, while it takes 6 seconds for an AGV with a speed of 5 m / s; after unifying the time dimension, the path costs of different devices can be directly compared and optimized; traditional static planning only optimizes the path length and does not consider the device performance differences, resulting in the disconnection between the scheduling scheme and the actual execution efficiency.

[0047] Furthermore, in the warehousing environment, the AGV runs along a straight line or a preset track, and the Euclidean distance can accurately represent the actual moving path length, avoiding the direction constraint error of the grid algorithm (such as A*). At the same time, based on the nominal speed V of the device, the path length is converted into the time cost, providing a standardized input for subsequent dynamic adjustment (such as node occupancy rate weighting).

[0048] For example, the preset path: D(0, 0) → E(20, 0) → F(20, 30) → G(50, 30), and the standard speed V = 10 units / minute. In D(0, 0) → E(20, 0), = 2; in E(20, 0) → F(20, 30), = 3; in F(20, 30) → G(50, 30), = 3.

[0049] In one embodiment, in the path model for obtaining the total path cost index constructed based on the second index model, multiple path cost indicators, and the predicted occupancy rate in S3, the second index model is expressed as: ; where, is the total path cost index, is the number of necessary nodes, is the path cost index between the i-th adjacent necessary nodes in the preset warehousing path sequence, is the predicted occupancy rate of the i-th necessary node.

[0050] In this embodiment, it should be noted that the non-linear weighting can achieve dynamic risk quantification. The node occupancy rate ∈[0, 1] is mapped to a non-linear growth interval. For example: When = 0, = 0, the path cost is not amplified and is uniformly 0; when = 0.5, e^{0.5}-1 ≈ 0.65, and the cost is amplified by 65%; when = 1, e^{1}-1 ≈ 1.718, and the cost is amplified by 171.8%. This design significantly increases the path segment cost of high-occupancy nodes, forcing the system to avoid them preferentially. In this way, the exponential weighting dynamically couples the node occupancy risk to the path cost, achieving global optimization and avoiding the method of independently processing priorities and path lengths in traditional methods.

[0051] Furthermore, is the lag reduction weight in the foregoing embodiment. As the node position i in the path sequence increases (i.e., the more backward), the weight gradually decreases. For example: when i = 2 (the second node), ≈ 0.91; when i = 4 (the fourth node), ≈ 0.62. This means that the cost amplification effect of the backward nodes will be weakened, encouraging the placement of high-conflict nodes at the back. It avoids excessive detours of the path due to avoiding the front-end nodes, balances the path length and conflict risk, and improves the overall throughput.

[0052] Furthermore, in the dynamic adjustment of the total cost, the path cost is affected by both the occupancy amplification and the position attenuation; for example, the total cost contribution of a high-occupancy but backward node ( = 0.8, i = 4) is 0.76ci; compared with the similar cost (1.11ci) of the front-end node (i = 2), the influence of the back-end node is significantly lower. In complex multi-node tasks, traditional algorithms sacrifice global efficiency for local optimization, while this model achieves spatial dispersion of conflict risks through position weight allocation.

[0053] In actual use, for a task with 10 nodes, the traditional genetic algorithm takes 650 ms to find a path with an 8% cost gap, while this method can find an optimized path with a gap ≤ 2% within 85 ms.

[0054] As Figure 3 shown, in one embodiment, obtaining the standard moving speed, the second priority weight, and the second path information of the current warehousing task to be processed in S1 includes: S11. Obtain the processing weight, the maximum processing time, and the processing type of the current warehousing task to be processed; S12. Obtain the second priority weight and the standard moving speed according to the processing weight, the maximum processing time, and the processing type.

[0055] In this embodiment, it should be noted that in S11, the physical attributes and time constraints of the task to be processed are analyzed to provide basic data input for subsequent priority determination and equipment parameter setting. Three key parameters are obtained through the industrial Internet of Things interface: the processing weight, which reflects the physical load of goods transportation and directly affects the dynamic performance (such as acceleration, braking distance) and energy consumption level of the AGV. The greater the weight, the more power the equipment needs to allocate to maintain operation, and the standard speed may need to be reduced to ensure safety; the maximum processing time, which defines the deadline window for task completion and characterizes the urgency of the task. The shorter the required time, the higher the urgency of task scheduling, and resources need to be allocated preferentially; the processing type, which identifies the task type (such as normal temperature transportation, cold chain distribution, hazardous material handling). Different types correspond to different equipment configuration requirements (such as refrigeration activation, explosion-proof mode switching). The type parameter determines whether the equipment can execute the task and the applicable safety regulations.

[0056] In S12, based on the input of S11, the second priority weight and the standard moving speed are dynamically generated through a predefined rule library and a weight mapping table: the second priority weight, which comprehensively considers the processing weight, the maximum processing time, and the processing type, quantifies the global importance of the task through a multi-objective decision-making model. For example, a task with a high weight and a short processing time may be assigned a higher weight because it occupies a large amount of equipment resources and is time-sensitive; while a task with a low weight and a long processing time has a lower weight; the standard moving speed, which determines the upper limit of the safe operating speed of the equipment according to the processing weight and type. For example, for a hazardous material transportation task, even if the weight is light, a lower standard speed needs to be set to meet the explosion-proof requirements, while ordinary goods can match the peak speed of the equipment within the allowable weight range to improve efficiency.

[0057] For example, assume that a warehouse receives two tasks to be processed: Task A: It is necessary to transport heavy machinery parts (with a large processing weight), and it is required to be completed within 2 hours (with a short maximum processing time), and the type is ordinary goods (no special requirements); it is determined that its second priority weight is relatively high (such as 10), but the standard speed needs to be reduced to a medium level to ensure stability under heavy loads (such as 5). Task B: It is necessary to distribute medical reagents (with a small processing weight), and 6 hours are allowed for completion (with a loose processing time), but it belongs to the cold chain type (special processing type); although the time requirement is low, due to limited cold chain equipment resources and the time sensitivity of reagents, it is still given a higher priority (such as 15), and the standard speed is set to medium to maintain the power consumption balance of the incubator (such as 10).

[0058] Such as Figure 4As shown in the figure, a warehousing path planning system based on the industrial Internet of Things is also provided. The system includes a management platform, a sensing network platform, and an object platform that are communicatively connected in sequence. The management platform includes: An acquisition module, configured to acquire ongoing warehousing tasks and current warehousing tasks to be processed, acquire the first priority weight and the first path information of the ongoing warehousing tasks, acquire the standard moving speed, the second priority weight, and the second path information of the current warehousing tasks to be processed, acquire a plurality of necessary nodes according to the second path information, and acquire a preset warehousing path sequence composed of all the necessary nodes; A data processing module, configured to acquire the current warehousing tasks to be processed for which the first priority weight is higher than the second priority weight as effective warehousing tasks, and acquire the predicted occupancy rate of each necessary node according to the preset warehousing path sequence, the standard moving speed, and the first path information of the effective warehousing tasks; A model calculation module, configured to obtain the path cost index between adjacent necessary nodes in the preset warehousing path sequence based on the first index model and the standard moving speed, and construct a path model for obtaining the total path cost index based on the second index model, a plurality of path cost indexes, and the predicted occupancy rate; An analysis module, configured to obtain the lowest total path cost index according to the path model, and obtain the warehousing path sequence corresponding to the lowest total path cost index.

[0059] In one embodiment, the data processing module is further configured to: acquire the first necessary node and the last necessary node in the preset warehousing path sequence; acquire the total remaining occupancy time of each necessary node in the first path information of a plurality of effective warehousing tasks; and acquire the predicted occupancy rate of each necessary node according to the standard moving speed, the first necessary node, the last necessary node, and the total remaining occupancy time corresponding to each necessary node.

[0060] In this embodiment, it should be noted that regarding the above-mentioned warehousing path planning system based on the industrial Internet of Things, the specific manner of performing operations has been described in detail in the embodiments of the warehousing path planning method based on the industrial Internet of Things, and will not be elaborated here.

[0061] It should also be noted that the entire warehousing path planning system based on the industrial Internet of Things can be applied to the optimized industrial Internet of Things, such as Figure 5 As shown in the figure, the optimized industrial Internet of Things includes a user platform, a service platform, a management platform, a sensing network platform, and an object platform that are communicatively connected in sequence; The user platform is configured to provide the function of front-end services to users; users obtain the required perception service information through the user platform, process the perception service information, and convert it into user perception information; users analyze the user perception information and make corresponding decisions in combination with their own wishes, and convert the user perception information into user control information through the corresponding information system and send it to the service platform, thereby showing the corresponding service demand wishes of users.

[0062] The physical entities of the user platform include various user terminals, such as mobile phones, computers, dedicated terminals, etc., and realize the services at the user end through the combination with the user information system software.

[0063] The service platform is configured as an API server or other servers used to establish communication between the management platform and the user platform to implement corresponding functions; the physical entities of the service platform include various servers.

[0064] The management platform is configured to perform at least one of device operation status monitoring and management, data monitoring and management, device parameter management, and life cycle management; the management platform is the operation and coordination platform of the Internet of Things, and can include various management sub-platforms, and different management sub-platforms perform different management services; the physical entities of the management platform include various servers.

[0065] The sensing network platform is configured to perform at least one of network management, instruction management, device status management, data protocol management, data parsing, data classification, data transmission monitoring, and data transmission security management. The sensing network platform provides functions such as communication transmission, parsing, identification, and classification of data, avoiding the direct aggregation of data from various object platforms in the management platform, resulting in redundant data in the management platform and low data processing efficiency; the physical entities of the object platform include various gateways, edge computing devices, etc.

[0066] The object platform is configured to perform specific production work such as production control, detection, and metering; the physical entities of the production object include various production devices, sensors, etc.

[0067] Figure 6 It is a block diagram of an electronic device for a warehouse path planning method based on the industrial Internet of Things shown according to an exemplary embodiment. As Figure 6 shown, the electronic device 700 may include: a processor 701, a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an I / O interface 704 (input / output interface), and a communication component 705.

[0068] Among them, the processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned warehouse path planning method based on the industrial Internet of Things. The memory 702 is used to store various types of data to support the operation of the electronic device 700. These data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact data, received and sent messages, pictures, audio, video, and so on. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. The multimedia component 703 may include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal can be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0069] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the above-mentioned warehouse path planning method based on the industrial Internet of Things.

[0070] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When the program instructions are executed by a processor, the steps of the above-mentioned warehouse path planning method based on the industrial Internet of Things are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned warehouse path planning method based on the industrial Internet of Things.

[0071] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned warehouse path planning method based on the industrial Internet of Things when executed by the programmable device.

[0072] The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings. However, the present disclosure is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0073] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure does not separately describe various possible combination methods.

[0074] Furthermore, any combination can be made between various different embodiments of the present disclosure as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. A warehousing path planning method based on the industrial Internet of Things, characterized in that, Including: Obtain the ongoing warehousing tasks and the currently pending warehousing tasks, obtain the first priority weight and the first path information of the ongoing warehousing tasks, obtain the standard moving speed, the second priority weight and the second path information of the currently pending warehousing tasks, obtain multiple necessary nodes according to the second path information, and obtain a preset warehousing path sequence composed of all the necessary nodes; Obtain the currently pending warehousing tasks corresponding to the first priority weight higher than the second priority weight as valid warehousing tasks, and obtain the predicted occupancy rate of each necessary node according to the preset warehousing path sequence, the standard moving speed and the first path information of the valid warehousing tasks; Based on the first index model and the standard moving speed, obtain the path cost index between adjacent necessary nodes in the preset warehousing path sequence, and construct a path model for obtaining the total path cost index based on the second index model, multiple path cost indexes and the predicted occupancy rate; Obtain the lowest total path cost index according to the path model, and obtain the warehousing path sequence corresponding to the lowest total path cost index.

2. The warehousing path planning method based on industrial Internet of Things according to claim 1, wherein The obtaining the predicted occupancy rate of each necessary node according to the preset warehousing path sequence, the standard moving speed and the first path information of the valid warehousing tasks includes: Obtain adjacent necessary nodes in the preset warehousing path sequence; Obtain the total remaining occupancy time of each necessary node in the first path information of multiple valid warehousing tasks; Obtain the predicted occupancy rate of each necessary node according to the standard moving speed, adjacent necessary nodes and the total remaining occupancy time corresponding to each necessary node.

3. The warehousing path planning method based on industrial Internet of Things according to claim 2, wherein The obtaining the predicted occupancy rate of each necessary node according to the standard moving speed, the first necessary node, the last necessary node and the total remaining occupancy time corresponding to each necessary node is expressed as: ; wherein, is the predicted occupancy rate of the i-th necessary node, is the total remaining occupancy time of the i-th necessary node in the first path information of multiple effective warehousing tasks, is the position of the j-th necessary node in the x-axis direction, is the position of the (j - 1)-th necessary node in the x-axis direction, is the position of the j-th necessary node in the y-axis direction, is the position of the (j - 1)-th necessary node in the y-axis direction, is the number of necessary nodes, is the standard moving speed.

4. The warehousing path planning method based on industrial Internet of Things according to claim 1, wherein The first index model in the obtaining the path cost index between adjacent necessary nodes in the preset warehousing path sequence based on the first index model and the standard moving speed is expressed as: ; wherein, is the path cost metric between the i-th adjacent necessary nodes in the preset storage path sequence, is the position of the i-th necessary node in the x-axis direction, is the position of the (i - 1)-th necessary node in the x-axis direction, is the position of the i-th necessary node in the y-axis direction, is the position of the (i - 1)-th necessary node in the y-axis direction, is the standard moving speed.

5. The warehousing path planning method based on the industrial Internet of Things according to claim 1, wherein, The second index model in the constructing a path model for obtaining the total path cost index based on the second index model, multiple path cost indexes and the predicted occupancy rate is expressed as: ; wherein, is the total path cost index, is the number of mandatory nodes, is the path cost index between the i-th adjacent mandatory nodes in the preset warehousing path sequence, is the predicted occupancy rate of the i-th mandatory node.

6. The warehousing path planning method based on industrial Internet of Things according to claim 1, characterized in that The obtaining the standard moving speed, the second priority weight and the second path information of the currently pending warehousing tasks includes: Obtain the processing weight, the maximum processing time and the processing type of the currently pending warehousing tasks; Obtain the second priority weight and the standard moving speed according to the processing weight, the maximum processing time and the processing type.

7. A warehousing path planning system based on the industrial Internet of Things, characterized in that, The system includes a management platform, a sensing network platform and an object platform that are sequentially communicatively connected. The management platform includes: An obtaining module, configured to obtain the ongoing warehousing tasks and the currently pending warehousing tasks, obtain the first priority weight and the first path information of the ongoing warehousing tasks, obtain the standard moving speed, the second priority weight and the second path information of the currently pending warehousing tasks, obtain multiple necessary nodes according to the second path information, and obtain a preset warehousing path sequence composed of all the necessary nodes; A data processing module, configured to obtain a current to-be-processed warehousing task corresponding to a first priority weight higher than a second priority weight as a valid warehousing task, and obtain the predicted occupancy rate of each necessary node according to a preset warehousing path sequence, a standard moving speed, and the first path information of the valid warehousing task; A model calculation module, configured to obtain a path cost index between adjacent necessary nodes in a preset warehousing path sequence based on a first index model and a standard moving speed, and construct a path model for obtaining a total path cost index based on a second index model, multiple path cost indexes, and a predicted occupancy rate; An analysis module, configured to obtain a lowest total path cost index according to the path model, and obtain a warehousing path sequence corresponding to the lowest total path cost index.

8. The warehousing path planning system based on the industrial Internet of Things according to claim 7, characterized in that, The data processing module is further configured to: Obtain the first necessary node and the last necessary node in the preset warehousing path sequence; Obtain the total remaining occupancy time of each necessary node in the first path information of multiple valid warehousing tasks; Obtain the predicted occupancy rate of each necessary node according to the standard moving speed, the first necessary node, the last necessary node, and the total remaining occupancy time corresponding to each necessary node.

9. An electronic device, characterized in that, Including: A memory, on which a computer program is stored; A processor, configured to execute the computer program in the memory to implement the warehousing path planning method based on industrial Internet of Things according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the warehousing path planning method based on industrial Internet of Things according to any one of claims 1 to 6.

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