A method for managing inbound and outbound data of a protective clothing warehouse

Abstract

The present invention relates to the field of data processing, and particularly to a method for managing inbound and outbound data of a protective clothing warehouse. The method includes: obtaining the real-time position information of multiple transport vehicles, and selecting any one of the transport vehicles as the target vehicle; calculating the optimal search step length of the path planning corresponding to the target vehicle, and obtaining all paths in the path planning based on the optimal search step length; calculating the movement cost of the path segments formed between nodes in each path to obtain the total movement cost of the corresponding path; correcting each of the total movement costs to obtain the corrected total cost of the corresponding path; and selecting the best transport path of the target vehicle based on all the corrected total costs. The present invention plans a reasonable and efficient transport path with low collision risk and low traffic congestion risk for the transport vehicle, which helps to improve the efficiency of managing inbound and outbound data of the protective clothing warehouse, reduce the operating cost, and improve the overall service quality.

Description

Technical Field

[0001] The present invention relates to the field of data processing. More specifically, the present invention relates to a method for managing the incoming and outgoing data of a protective clothing warehouse. Background Art

[0002] With the development of industrialization and the frequent occurrence of public health events, the demand for protective clothing has been continuously increasing. During public health events, the supply and storage of protective clothing become particularly crucial. To improve the operation efficiency and service level of protective clothing warehouses, supply chain management has been optimized, and automated warehousing systems have been widely adopted. These measures make the storage, distribution, and management of protective clothing more efficient and accurate, thus better meeting the demand. In large protective clothing warehouses, the storage and retrieval efficiency of goods is crucial. By using a transport vehicle planning algorithm, the driving paths of forklifts or other handling equipment can be optimized, reducing ineffective movements and improving operation efficiency.

[0003] The prior art, such as the patent application document with the publication number CN111207767A, discloses a transport vehicle planning algorithm improved based on the RRT algorithm. This transport vehicle planning algorithm initially determines whether the direct connection from the starting node to the target node is feasible. If not, it generates a guiding root node to assist in path planning. Then, it gradually expands the path using the method of multiple growing trees until all the trees are merged into one tree, thereby finding the path from the starting node to the target node. This method combines the guiding root node and the multi-tree expansion strategy, and can effectively bypass obstacles and find the optimal path.

[0004] However, the above patent application document does not fully consider the real-time traffic conditions and shared information when multiple transport vehicles are transporting simultaneously, resulting in traffic congestion in a multi-transport vehicle environment, and further reducing the management efficiency of transport vehicles for incoming and outgoing warehouses. Summary of the Invention

[0005] To solve the above technical problem of traffic congestion occurring in a multi-transport vehicle environment and further reducing the management efficiency of transport vehicles for incoming and outgoing warehouses, the present invention provides the following technical solutions.

[0006] A method for managing the incoming and outgoing data of a protective clothing warehouse, comprising:

[0007] Obtaining the real-time position information of multiple transport vehicles, and selecting any one of the transport vehicles as the target vehicle;

[0008] Calculating the optimal search step length for the path planning corresponding to the target vehicle, and obtaining all paths in the path planning based on the optimal search step length; the optimal search step length is negatively correlated with the obstacle density of the current node in the path planning, positively correlated with the distance between the current node target vehicle and the adjacent obstacles along the search direction of the target vehicle, and positively correlated with the safety factor of the current node target vehicle;

[0009] Calculate the movement cost of the path segments formed between nodes in each path to obtain the total movement cost of the corresponding path;

[0010] Correct each of the total movement costs to obtain the corrected total cost of the corresponding path; the corrected total cost is: ; where, is the corrected total cost, is the integration factor, is the th movement cost of a path segment, is the total number of path segments of the current path;

[0011] Based on all the corrected total costs, select the best transportation path for the target vehicle.

[0012] In the present invention, by considering factors such as the obstacle density, the distance to adjacent obstacles, and the safety factor of the target vehicle, the search step size during path search of the target vehicle is dynamically adjusted to improve the accuracy of the path. Further, the optimal search step size is used for path planning to generate all possible paths of the target vehicle, and the total movement cost of each path is corrected, so that in a dynamically changing traffic environment, the system can help to evaluate and adjust the path in real time to adapt to changes in traffic conditions, ensure that the target vehicle always travels on the best path, and thus improve the transportation efficiency of the protective clothing transport vehicle.

[0013] Preferably, selecting the best transportation path for the target vehicle based on all the corrected total costs includes:

[0014] Sort all paths according to the corrected total cost, and select the path with the lowest corrected total cost as the best transportation path.

[0015] Preferably, selecting the best transportation path for the target vehicle based on all the corrected total costs further includes:

[0016] For the selected best transportation path, if the movement cost of any path segment in this path is abnormal, adjust the driving speed of the target vehicle to reduce the movement cost of the path segment; the abnormal movement cost means that the movement cost of this path segment exceeds a preset reference cost.

[0017] Selecting the best transportation path based on the corrected total cost, through real-time monitoring, adjusting the driving speed when necessary to cope with abnormal situations, enabling the system to more flexibly respond to changes in traffic conditions, avoiding unnecessary delays, and ensuring that the protective clothing is delivered on time.

[0018] Preferably, the process of obtaining the obstacle density of the current node includes:

[0019] Taking the current node as the center and a preset length as the radius, obtain the corresponding target area and calculate the area of the target area;

[0020] Calculate the occupied areas of all static obstacles and dynamic obstacles within the area, and then calculate the static obstacle density and dynamic obstacle density;

[0021] Take the product of the static obstacle density and the dynamic obstacle density or the weighted sum of the static obstacle density and the dynamic obstacle density as the obstacle density of the current node.

[0022] Taking the product or weighted sum of the static obstacle density and the dynamic obstacle density as the obstacle density of the current node, this comprehensive evaluation method can consider the impacts of both static and dynamic obstacles simultaneously. Static obstacles are usually fixed, while dynamic obstacles may change over time. Therefore, this comprehensive evaluation can better reflect the actual situation.

[0023] Preferably, based on the current node, mark all transport vehicles within a set distance threshold from the target vehicle. Then, the safety factor satisfies the relational expression:

[0024] ; In the formula, is the safety factor, is the distance between the target vehicle at the current node and the adjacent obstacle along the search direction of the target vehicle, is the th distance between the marked transport vehicle and the target vehicle, is the th included angle between the vector direction from the marked transport vehicle to the target vehicle and the moving direction of the marked transport vehicle, is the total number of marked transport vehicles, is to take the minimum value.

[0025] By comprehensively calculating the distance between the target vehicle and the adjacent transport vehicles and the included angles between the moving directions of these transport vehicles and the target vehicle, the algorithm can more accurately determine whether the moving directions of the adjacent transport vehicles may pose a threat to the target vehicle.

[0026] Preferably, the optimal search step size satisfies the relational expression:

[0027] ; In the formula, is the optimal search step size, is the distance between the target vehicle at the current node and the adjacent obstacle along the search direction of the target vehicle, is the obstacle density of the current node, is the safety factor, is to take the minimum value.

[0028] Preferably, the moving cost satisfies the following relationship:

[0029] ; where is the moving cost, is the Euclidean distance between nodes, is the congestion fluctuation of the path segment formed between nodes, is the vertical distance from the th transport vehicle in the intersection area to this path segment, is the total number of transport vehicles in the intersection area, is to take the minimum value; where, if the obstacle density at the end point of the current path and the obstacle density at the starting point have a difference less than 0, otherwise

[0030] The calculation of the moving cost comprehensively considers various factors such as distance, congestion, the influence of transport vehicles, and obstacle density, providing a comprehensive evaluation index for the algorithm. By calculating the moving cost of different path segments, the algorithm can select the optimal path to achieve a balance among transport efficiency, safety, and cost.

[0031] Preferably, the integration factor satisfies the following relationship:

[0032] ; where is the integration factor, is the degree of deviation of the current path relative to the reference line, with the line connecting the starting point and the end point of the current path as the reference line, is the average value of the degrees of deviation of all paths with the same starting point and end point as the current path, is the maximum value of the vertical distances from all corner points of the current path based on the reference line to the reference line, is the maximum value of the sum of in all paths with the same starting point and end point as the current path.

[0033] By calculating , the stability of the current path can be reflected, and calculating can reflect the complexity of the path. Combining the path stability and complexity, the integration factor can achieve a comprehensive evaluation of the quality of the path.

[0034] The beneficial effects of the present invention are:

[0035] By improving the calculation methods of the search step size and movement cost of the RRT* algorithm, the present invention realizes the planning of a reasonable and efficient transportation route with low collision risk and low traffic congestion risk for transport vehicles under the condition of a dynamic map and multiple transport vehicles working simultaneously, which helps to improve the management efficiency of the inbound and outbound data of the protective clothing warehouse, reduce the operation cost, and improve the overall service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of illustration and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0037] Figure 1 is a flowchart of the method from step S1 to step S5 in a method for managing inbound and outbound data of a protective clothing warehouse according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] The application scenario of the present invention is: using the improved RRT* algorithm to perform path planning for protective clothing transport vehicles.

[0040] The following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0041] Refer to Figure 1 , a method for managing inbound and outbound data of a protective clothing warehouse includes steps S1 - S5, specifically as follows:

[0042] S1: Obtain the real - time position information of multiple transport vehicles, and select any one transport vehicle as the target vehicle.

[0043] In the embodiments of the present invention, multiple transport vehicles obtain their own real - time position information through built - in position sensors (such as vision sensors, etc.). These position information are used to update the global map or local map, so that each transport vehicle can obtain the latest information of the surrounding environment to avoid obstacles and perform path planning.

[0044] In addition, the transport vehicles also share information such as their positions, speeds and moving directions with each other through wireless communication technologies (such as Bluetooth, etc.).

[0045] For ease of analysis, taking one of the transport vehicles as the target vehicle as an example, the target vehicle will analyze and plan its own path using the shared information and the updated map.

[0046] S2: Calculate the optimal search step length for the path planning corresponding to the target vehicle, and obtain all paths in the path planning based on the optimal search step length.

[0047] In the embodiment of the present invention, when using the RRT* algorithm for path planning, the search step length therein determines the speed at which the algorithm explores new spatial regions. If the step length is too small, the algorithm may be too cautious during the exploration process, resulting in slow exploration speed and low efficiency. If the step length is too large, the algorithm may skip some important regions and miss some possible paths. The step length also affects the quality of the path. A too small step length may result in a tortuous path, while a too large step length may result in a too direct path, which may hit obstacles or not be smooth enough. Therefore, by dynamically adjusting the search step length, it is possible to flexibly respond to different warehouse layouts and transportation task requirements and optimize the path planning effect.

[0048] In path planning, the position of the target transport vehicle is represented as a series of nodes. The position where the transport vehicle is currently located during the path planning process is used as the current node.

[0049] First, by calculating the obstacle density of the current node, it helps the target transport vehicle consider the influence of static and dynamic obstacles during path planning. The higher the obstacle density, the more obstacles there are around the current node, and the target transport vehicle needs to be more careful when planning the path to avoid collisions and congestion. In this way, the transportation efficiency and safety can be improved.

[0050] Then the calculation method of the obstacle density of the current node is as follows:

[0051] Taking the current node as the center and a preset length as the radius to obtain the corresponding target area, and calculate the area of the target area (the reference value of the area is 30 square meters, and the implementer can adjust it according to the size and layout of the specific warehouse);

[0052] Calculate the occupied area of all static obstacles (in the embodiment of the present invention, referring to warehouse shelves, equipment, etc.) and the occupied area of dynamic obstacles (in the embodiment of the present invention, referring to transport vehicles) in the area, and then calculate the static obstacle density and the dynamic obstacle density;

[0053] Take the product of the static obstacle density and the dynamic obstacle density as the obstacle density of the current node.

[0054] Then the obstacle density of the current node satisfies the relational expression:

[0055]

[0056] In the formula, is the obstacle density of the current node, is the static obstacle density, is the dynamic obstacle density, is the area of the target area, is the area occupied by all static obstacles in the target area, is the area occupied by each transport vehicle in the target area, is the number of transport vehicles in the target area.

[0057] The higher the obstacle density, the more crowded the node is and the greater the difficulty of passing through.

[0058] Among them, as dynamic obstacles, the transport vehicles move continuously in the warehouse, and their positions and quantities change over time. By calculating , the impact of transport vehicles on the passing ability of a specific area can be quantified, and then the degree of congestion or passing difficulty of this area can be evaluated.

[0059] In another embodiment, another calculation method is provided as follows:

[0060] The process of obtaining the obstacle density of the current node includes:

[0061] Taking the current node as the center and a preset length as the radius to obtain the corresponding target area, and calculating the area of the target area;

[0062] Calculating the area occupied by all static obstacles and the area occupied by dynamic obstacles in the area, and then calculating the static obstacle density and the dynamic obstacle density;

[0063] Taking the weighted sum of the static obstacle density and the dynamic obstacle density as the obstacle density of the current node.

[0064] Then the obstacle density of the current node satisfies the relational expression:

[0065]

[0066] is the obstacle density of the current node, is the static obstacle density, is the dynamic obstacle density, is the area of the target area, is the area occupied by all static obstacles in the target area, is the area occupied by each transport vehicle in the target area, is the number of transport vehicles in the target area, , are both weights.

[0067] Among them, , Numerical values can be obtained based on multiple experiments. By adjusting the weights, the contributions of static and dynamic obstacle densities to the final obstacle density are controlled, making the method more flexible and adjustable.

[0068] Secondly, the target vehicle starts from the current node, and the planned forward direction is the search direction of the target vehicle. Calculate the straight-line distance from the current node of the target vehicle along the search direction to the nearest obstacle. In path planning, this distance can help the algorithm determine whether to bypass the obstacle or whether it can pass directly.

[0069] Then, to evaluate the safety of the target vehicle at the current position, based on the current node, all transport vehicles within a set distance threshold from the target vehicle (the threshold can be appropriately increased in a spacious area with fewer obstacles; while in a narrow or obstacle-dense area, the threshold needs to be correspondingly decreased) are marked, and the safety factor of the target vehicle is calculated. The satisfaction relationship is:

[0070] ; where is the safety factor, is the distance between the target vehicle at the current node and the neighboring obstacle along the search direction of the target vehicle, is the th distance between the marked transport vehicle and the target vehicle, is the th angle between the vector direction from the marked transport vehicle to the target vehicle and the moving direction of the marked transport vehicle, is the total number of marked transport vehicles, is to take the minimum value.

[0071] Selecting the minimum value as the safety factor of the target vehicle can reflect the impact on safety of the one with the most unfavorable relative position and direction to the target vehicle among all the marked transport vehicles.

[0072] Finally, comprehensively considering the obstacle density at the current node, the distance between the target vehicle at the current node and the neighboring obstacle along the search direction, and the safety factor of the target vehicle at the current node calculated above, an optimal search step size is obtained, that is, the satisfaction relationship is as follows:

[0073]

[0074] where is the optimal search step size, is the distance between the target vehicle at the current node and the neighboring obstacle along the search direction, is the obstacle density at the current node, is the safety factor, is to take the minimum value.

[0075] Among them, reflects the spatial range within which the target vehicle can move safely around the current node. A larger value means there is more space for the vehicle to move, so a longer step size can be adopted to quickly approach the target or optimize the path; a higher obstacle density means the environment around the current node is more complex and obstacles need to be avoided more precisely, so the search step size should be shorter; a higher safety factor indicates that the current position is relatively safe, and the search step size can be appropriately increased to speed up the path planning. On the contrary, a lower safety factor may require shortening the step size to ensure the safety and feasibility of the path.

[0076] Among them, is used to ensure that the search step size does not become unrealistic due to an overly large safety factor. By taking the minimum value, the step size can be restricted within a reasonable range, avoiding the algorithm from being too aggressive or conservative.

[0077] S3: Calculate the movement cost of the path segments formed between nodes in each path to obtain the total movement cost of the corresponding path.

[0078] In the RRT* (Rapidly-exploring Random Tree Star) algorithm, the movement cost (or path cost) is a core concept, which directly affects the optimization goal of the algorithm, that is, to find the path with the lowest cost. The optimal path is found by randomly expanding the search tree and optimizing the path cost. In each iteration, the algorithm tries to connect the newly generated node with its parent node and neighboring nodes. If the combination of the new node and a certain node can generate a lower-cost path, path rewiring will be performed to continuously optimize the path. Among them, the determined path between two nodes is regarded as a path segment, and the path of the target vehicle from the starting point to the ending point contains multiple such path segments.

[0079] The path length is the most intuitive factor in the calculation of the movement cost. Generally speaking, the shorter the path, the lower the movement cost. However, in the present invention, the change degree of the congestion risk and the path collision risk also need to be considered to more comprehensively evaluate the movement cost and provide more accurate and valuable information for the decision maker.

[0080] Then the movement cost of the path segment of the target vehicle satisfies the relational expression as:

[0081]

[0082] In the formula, is the said movement cost, is the Euclidean distance between nodes, is the congestion fluctuation of the path segment formed between nodes, is the vertical distance from the th transport vehicle in the intersection area to this path segment, is the total number of transport vehicles in the intersection area, is to take the minimum value; among them, if the obstacle density at the end of the current path and the obstacle density at the starting point has a difference less than 0, , otherwise ; the intersection area is the intersection part of the target areas of two nodes.

[0083] Among them, is the path collision risk. When is larger, it means that the distances from the transport vehicles in the surrounding area of the path to the current path are all relatively far, and the current path is safer, and the path collision risk is smaller, and the path cost is smaller; when is smaller, it means that the distances from the transport vehicles in the surrounding area of the path to the current path are all relatively close, and the current path is less safe, and the path collision risk is larger, and the path cost is larger.

[0084] In another embodiment, another formula for calculating the movement cost is provided, that is:

[0085]

[0086] In the formula, is the movement cost, is the Euclidean distance between nodes, is the congestion fluctuation of the path segment formed between nodes, is the vertical distance from the th transport vehicle in the intersection area to this path segment, is the total number of transport vehicles in the intersection area, is to take the minimum value; among them, if the obstacle density at the end of the current path and the obstacle density at the starting point has a difference less than 0, , otherwise ; the intersection area is the intersection part of the target areas of two nodes, is the path collision risk; , are both weight factors, and the values of the weight factors can be adjusted according to actual needs.

[0087] Through the above series of calculation methods, similarly, the moving costs of all path segments of the target vehicle can be obtained, and then the total moving cost of the corresponding path of the target vehicle can be obtained by accumulation. Similarly, the total moving costs of all alternative paths of the target vehicle can be obtained.

[0088] S4: Correct each of the total moving costs to obtain the corrected total cost of the corresponding path.

[0089] In an ideal situation (i.e., without obstacles such as warehouse shelves or other hindrances), the shortest path from the starting point to the target point is a straight line directly connecting these two points. However, in the actual environment, the warehouse is usually filled with shelves, equipment, and other obstacles that block the direct path, forcing the transport vehicle to detour. Nevertheless, we still hope that the transport vehicle can minimize the detour to reduce the moving cost and improve the transport efficiency.

[0090] To achieve this goal, when planning the transport path, not only the total length of the path needs to be considered, but also the degree of deviation of the path from the ideal straight path. The greater the degree of deviation, the longer the distance the transport vehicle needs to detour, and thus the moving cost should also increase accordingly.

[0091] Then the corrected total cost of the transport vehicle satisfies the relational expression:

[0092]

[0093]

[0094] In the formula, is the corrected total cost, is the integration factor, is the moving cost of the th path segment, is the total number of path segments of the current path, is the degree of deviation of the current path relative to the reference line with the line connecting the starting point and the ending point of the current path as the reference line, is the mean value of the degrees of deviation of all paths with the same starting point and ending point as the current path, is the maximum value of the perpendicular distances from all corner points of the current path based on the reference line to the reference line, is the maximum value of the sum of

[0095] among all paths with the same starting point and ending point as the current path.

[0096] In another embodiment, the corrected total cost of the transport vehicle further satisfies the relational expression:

[0097]

[0098]

[0099] Wherein, is the corrected total cost, is the integration factor, is the moving cost of the th path segment, is the total number of path segments of the current path, is the deviation degree of the current path relative to the reference line with the line connecting the starting point and the ending point of the current path as the reference line, is the average value of the deviation degrees of all paths with the same starting point and ending point as the current path, is the maximum value of the vertical distances from all corner points of the current path based on the reference line to the reference line, Among all paths with the same starting point and ending point as the current path is the maximum value of the sum, is the path length of the transport vehicle, and are both weight coefficients, and the implementer can adjust the values of the weight coefficients according to multiple tests or historical data.

[0100] Introducing a path length , to a certain extent, can reflect the complexity of the path. Although the path length itself is not directly equivalent to complexity, a longer path often contains more complex elements such as more turns, intersections, etc., and these elements may increase the difficulty and cost of driving.

[0101] S5: Based on all the corrected total costs, select the best transport path for the target vehicle.

[0102] After obtaining the corrected total costs of all paths of the target vehicle in the above step S4, sort all paths according to the corrected total costs, and select the path with the lowest corrected total cost as the best transport path.

[0103] Furthermore, for the selected best transport path, if the moving cost of any path segment in this path is abnormal, adjust the driving speed of the target vehicle to reduce the moving cost of the path segment; the abnormal moving cost means that the moving cost of this path segment exceeds the preset benchmark cost (which can be set by analyzing the collected transport cost data under the same or similar conditions in the past period).

[0104] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three or more, etc., unless otherwise specifically defined.

[0105] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in the practice of the present invention.

Claims

1. A method for managing the inbound and outbound data of a protective clothing warehouse, characterized in that, Including: Obtaining the real-time position information of multiple transport vehicles and selecting any one of the transport vehicles as the target vehicle; Calculating the optimal search step length of the path planning corresponding to the target vehicle and obtaining all paths in the path planning based on the optimal search step length; the optimal search step length satisfies the relational expression: ; wherein, is the optimal search step size, is the distance between the target vehicle at the current node and the neighboring obstacle along the search direction, is the obstacle density of the current node, is the safety factor, is to take the minimum value; Calculating the movement cost of the path segments formed between nodes in each path to obtain the total movement cost of the corresponding path; Based on the current node, marking all transport vehicles within a set distance threshold from the target vehicle, then the safety factor satisfies the relational expression: ; where, is the distance between the th marker transport vehicle and the target vehicle, is the angle between the vector direction of the th marker transport vehicle pointing to the target vehicle and the moving direction of this marker transport vehicle, is the total number of marker transport vehicles; Amend each of the total movement costs to obtain the corrected total cost for the corresponding path; the corrected total cost is: ; where is the corrected total cost,[[]] is the integration factor,[[]] is the movement cost of the th path segment,[[]] is the total number of path segments of the current path; The movement cost satisfies the relational expression: ; In the formula, is the moving cost, is the Euclidean distance between nodes, is the congestion fluctuation of the path segment formed between nodes, is the vertical distance from the th transport vehicle in the intersection area to this path segment, is the total number of transport vehicles in the intersection area; among them, if the obstacle density at the end point of the current path differs from the obstacle density at the starting point by less than 0, ; otherwise ; The intersection area is the intersection part of the target areas of two nodes; The integration factor satisfies the relational expression: ; wherein, is the degree of offset of the current path relative to the reference line, with the line connecting the start and end points of the current path as the reference line, is the mean value of the degrees of offset of all paths having the same start and end points as the current path, is the maximum value of the perpendicular distances from all corner points of the current path based on the reference line to the reference line, among all paths having the same start and end points as the current path is the maximum value of the sum; Based on all the corrected total costs, selecting the best transport path for the target vehicle.

2. The method for managing the inbound and outbound data of a protective clothing warehouse according to claim 1, wherein Based on all the corrected total costs, selecting the best transport path for the target vehicle includes: Sorting all paths according to the corrected total cost and selecting the path with the lowest corrected total cost as the best transport path.

3. The method for managing the inbound and outbound data of a protective clothing warehouse according to claim 2, wherein, Based on all the corrected total costs, selecting the best transport path for the target vehicle further includes: For the selected best transport path, if the movement cost of any path segment in the path is abnormal, adjusting the driving speed of the target vehicle to reduce the movement cost of the path segment; the movement cost being abnormal means that the movement cost of the path segment exceeds a preset benchmark cost.

4. The method for managing the inbound and outbound data of a protective clothing warehouse according to claim 3, characterized in that, The process of obtaining the obstacle density of the current node includes: Taking the current node as the center and a preset length as the radius to obtain the corresponding target area and calculating the area of the target area; Calculating the occupied area of all static obstacles and the occupied area of dynamic obstacles in the area, and then calculating the static obstacle density and the dynamic obstacle density; Taking the product of the static obstacle density and the dynamic obstacle density or the weighted sum of the static obstacle density and the dynamic obstacle density as the obstacle density of the current node.

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