Method for managing in-warehouse and out-warehouse data of protective clothing warehouse

Abstract

The invention relates to the field of data processing, in particular to a protective clothing warehouse in-out data management method, which comprises the following steps: acquiring real-time position information of a plurality of transport vehicles, and selecting any transport vehicle as a target vehicle; calculating the optimal search step size of the path planning corresponding to the target vehicle, and obtaining all paths in the path planning based on the optimal search step size; calculating the moving cost of a path section formed between the nodes in each path to obtain the total moving cost of the corresponding path; correcting each total movement cost to obtain a corrected total cost of the corresponding path; and selecting an optimal transportation path of the target vehicle based on all the corrected total costs. According to the invention, a reasonable and efficient transport path with low collision risk and low traffic jam risk is planned for the transport vehicle, the management efficiency of the in-out data of the protective clothing warehouse is improved, the operation cost is reduced, and the overall service quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a method for managing inbound and outbound 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 continues to grow. When public health events occur, the supply and storage of protective clothing become particularly critical. In order to improve the operating efficiency and service level of protective clothing warehouses, supply chain management has been optimized, and automated warehousing systems have also been widely adopted. These measures make the storage, distribution and management of protective clothing more efficient and accurate, so as to better meet the needs. In large protective clothing warehouses, the storage and retrieval efficiency of goods is crucial. By using transport vehicle planning algorithms, the driving path of forklifts or other handling equipment can be optimized, invalid movements can be reduced, and operational efficiency can be improved.

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

[0004] However, the above patent application documents did not fully consider the real-time traffic conditions and shared information when multiple transport vehicles are transporting at the same time, resulting in traffic congestion in a multi-transport vehicle environment, which in turn reduced the efficiency of transport vehicle in and out of the warehouse management. Summary of the invention

[0005] In order to solve the above-mentioned technical problem of traffic congestion in an environment with multiple transport vehicles, which in turn reduces the efficiency of transport vehicle entry and exit management, the present invention provides the following technical solution.

[0006] A method for managing inbound and outbound data of a protective clothing warehouse, comprising: Obtain the real-time location information of multiple transport vehicles and select any transport vehicle as the target vehicle; Calculate the optimal search step length of the path planning corresponding to the target vehicle, and obtain 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 target vehicle at the current node and the adjacent obstacles of the target vehicle along the search direction, and positively correlated with the safety factor of the target vehicle at the current node; Calculate the movement cost of the path segments formed between nodes in each path to obtain the total movement cost of the corresponding path; Each of the total movement costs is corrected to obtain a corrected total cost of the corresponding path; the corrected total cost is: ; In the formula, is the total cost of the correction, is the integration factor, For the The movement cost of each path segment, is the total number of path segments of the current path; Based on all the modified total costs, the optimal transportation path for the target vehicle is selected.

[0007] The present invention dynamically adjusts the search step size of the target vehicle during path search according to factors such as the obstacle density of the target vehicle, the distance to adjacent obstacles and the safety factor to improve the accuracy of the path, further uses the optimal search step size for path planning, generates all possible paths for the target vehicle, and corrects the total moving cost of each path, so that in a dynamically changing traffic environment, the system can be helped to evaluate and adjust the path in real time to adapt to changes in traffic conditions, ensure that the target vehicle is always traveling on the optimal path, and thus improve the transportation efficiency of the protective clothing transport vehicle.

[0008] Preferably, based on all the corrected total costs, selecting the optimal transportation path for the target vehicle comprises: All paths are sorted according to the corrected total cost, and the path with the lowest corrected total cost is selected as the optimal transportation path.

[0009] Preferably, based on all the corrected total costs, selecting the optimal transportation path for the target vehicle further comprises: For the selected optimal transport path, if the movement cost of any path segment in the path is abnormal, the driving speed of the target vehicle is adjusted to reduce the movement cost of the path segment; the abnormal movement cost means that the movement cost of the path segment exceeds the preset benchmark cost.

[0010] The optimal transportation route is selected based on the corrected total cost. Through real-time monitoring, the driving speed is adjusted when necessary to deal with abnormal situations. This enables the system to respond more flexibly to changes in traffic conditions, avoid unnecessary delays, and ensure that the protective clothing is delivered on time.

[0011] Preferably, the process of obtaining the obstacle density of the current node includes: Take the current node as the center and the preset length as the radius to obtain the corresponding target area and calculate the area of ​​the target area; Calculate the area occupied by all static obstacles and dynamic obstacles in the area, and then calculate the static obstacle density and dynamic obstacle density; 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 is taken as the obstacle density of the current node.

[0012] The product or weighted sum of static obstacle density and dynamic obstacle density is used as the obstacle density of the current node. This comprehensive evaluation method can simultaneously consider the impact of static and dynamic obstacles. Static obstacles are usually fixed, while dynamic obstacles may change over time, so this comprehensive evaluation can better reflect the actual situation.

[0013] Preferably, based on the current node, all transport vehicles within a set distance threshold from the target vehicle are marked, and the safety factor satisfies the relationship: ; In the formula, is the safety factor, is the distance between the current node target vehicle and the neighboring obstacles of the target vehicle along the search direction, For the The distance between the marked transport vehicle and the target vehicle, For the The angle between the vector direction of the marked transport vehicle pointing to the target vehicle and the moving direction of the marked transport vehicle, To mark the total number of transport vehicles, To take the minimum value.

[0014] By comprehensively calculating the distance between the target vehicle and the adjacent transport vehicles and the angle 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.

[0015] Preferably, the optimal search step size satisfies the relationship: ; In the formula, is the optimal search step length, is the distance between the current node target vehicle and the neighboring obstacles of the target vehicle along the search direction, is the obstacle density of the current node, is the safety factor, To take the minimum value.

[0016] Preferably, the movement cost satisfies the relationship: ; In the formula, is the moving cost, is the Euclidean distance between nodes, is the congestion fluctuation of the path segment formed between nodes, In the intersection area The vertical distance from the transport vehicle to the path segment, is the total number of transport vehicles in the intersection area, To take the minimum value; if the obstacle density at the end of the current path is The density of obstacles relative to the starting point The difference is less than 0, ,on the contrary ; The intersection area is the intersection part of the target areas of the two nodes.

[0017] The calculation of the mobility cost takes into account multiple factors such as distance, congestion, impact of transport vehicles and density of obstacles, providing a comprehensive evaluation indicator for the algorithm. By calculating the mobility cost of different path segments, the algorithm can select the optimal path to achieve a balance between transportation efficiency, safety and cost.

[0018] Preferably, the integration factor satisfies the relationship: ; In the formula, is the integration factor, The degree of deviation of the current path relative to the reference line is the line connecting the starting point and the end point of the current path. is the average of the deviation degrees of all paths with the same starting point and end point as the current path, The maximum vertical distance from all corner points of the current path to the reference line. For all paths with the same start and end points as the current path The maximum value of the sum.

[0019] By calculation , which can reflect the stability of the current path and calculate Can reflect the complexity of the path, combining path stability and complexity, integrating factors It can realize comprehensive evaluation of the advantages and disadvantages of paths.

[0020] The beneficial effects of the present invention are: The present invention improves the search step size and movement cost calculation method of the RRT* algorithm, and realizes planning of reasonable and efficient transportation routes with low collision risk and low traffic congestion risk for transportation vehicles when a dynamic map and multiple transportation vehicles are working simultaneously, which helps to improve the efficiency of data management of inbound and outbound transportation of protective clothing warehouses, reduce operating costs, and improve the overall service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] 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 accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a method flow chart of steps S1 to S5 in a method for managing inbound and outbound data of a protective clothing warehouse in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The application scenario of the present invention is: using the improved RRT* algorithm to plan the path of a protective clothing transport vehicle.

[0024] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Reference Figure 1 A method for managing inbound and outbound data of a protective clothing warehouse includes steps S1 to S5, which are specifically as follows: S1: Obtain the real-time location information of multiple transport vehicles and select any transport vehicle as the target vehicle.

[0026] In an embodiment of the present invention, multiple transport vehicles obtain their own real-time position information through built-in position sensors (such as visual sensors, etc.), and this position information is used to update the global map or local map, so that each transport vehicle can obtain the latest information about the surrounding environment to avoid obstacles and plan paths.

[0027] In addition, the transport vehicles also share information such as their location, speed and direction of movement through wireless communication technologies (such as Bluetooth).

[0028] For the sake of analysis, one of the transport vehicles is taken as the target vehicle, which will use the shared information and updated maps to analyze and plan its own path.

[0029] S2: Calculate the optimal search step length of the path planning corresponding to the target vehicle, and obtain all paths in the path planning based on the optimal search step length.

[0030] In an embodiment of the present invention, when using the RRT* algorithm for path planning, the search step size determines the speed at which the algorithm explores new spatial areas. If the step size is too small, the algorithm may be too cautious during the exploration process, resulting in slow exploration speed and low efficiency. If the step size is too large, the algorithm may skip some important areas and miss some possible paths. The step size also affects the quality of the path. If the step size is too small, the path may be too tortuous, and if the step size is too large, the path may be too direct, may hit obstacles or may not be smooth enough. Therefore, by dynamically adjusting the search step size, it is possible to flexibly respond to different warehouse layouts and transportation task requirements and optimize the path planning effect.

[0031] In path planning, the location of the target transport vehicle is represented as a series of nodes. The current location of the transport vehicle during the path planning process is taken as the current node.

[0032] First, by calculating the obstacle density of the current node, the target transport vehicle can take into account the impact of static and dynamic obstacles when planning the path. 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, transportation efficiency and safety can be improved.

[0033] Then the obstacle density of the current node is calculated as follows: Take the current node as the center and the 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); Calculate the area occupied by all static obstacles (in this embodiment of the present invention, warehouse shelves, equipment, etc.) and dynamic obstacles (in this embodiment of the present invention, transport vehicles) in the area, and then calculate the static obstacle density and dynamic obstacle density; The product of the static obstacle density and the dynamic obstacle density is taken as the obstacle density of the current node.

[0034] Then the obstacle density of the current node satisfies the relationship:

[0035] 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 region, 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.

[0036] The higher the obstacle density, the more crowded the node is and the more difficult it is to pass through.

[0037] Among them, transport vehicles are dynamic obstacles that are constantly moving in the warehouse. Their positions and numbers will change over time. , the impact of transport vehicles on the traffic capacity of a specific area can be quantified, and the degree of congestion or traffic difficulty in the area can be evaluated.

[0038] In another embodiment, another calculation method is provided, as follows: The process of obtaining the obstacle density of the current node includes: Take the current node as the center and the preset length as the radius to obtain the corresponding target area and calculate the area of ​​the target area; Calculate the area occupied by all static obstacles and dynamic obstacles in the area, and then calculate the static obstacle density and dynamic obstacle density; The weighted sum of the static obstacle density and the dynamic obstacle density is taken as the obstacle density of the current node.

[0039] Then the obstacle density of the current node satisfies the relationship:

[0040] is the obstacle density of the current node, is the static obstacle density, is the dynamic obstacle density, is the area of ​​the target region, 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, , All are weights.

[0041] in, , The value can be obtained based on multiple tests. By adjusting the weights, the contribution of static and dynamic obstacle densities to the final obstacle density can be controlled, making the method more flexible and adjustable.

[0042] Secondly, the target vehicle starts from the current node and plans to move in the direction of the target vehicle's search direction, and calculates the straight-line distance from the target vehicle starting from the current node along the search direction to the nearest obstacle. In path planning, this distance can help the algorithm decide whether to bypass the obstacle or whether it can pass directly.

[0043] Then, in order to evaluate the safety of the target vehicle at its current location, based on the current node, all transport vehicles within a set distance threshold from the target vehicle (in spacious areas with fewer obstacles, the threshold can be appropriately increased; in narrow or obstacle-dense areas, the threshold needs to be reduced accordingly) are marked, and the safety factor of the target vehicle is calculated to satisfy the relationship: ; In the formula, is the safety factor, is the distance between the current node target vehicle and the neighboring obstacles of the target vehicle along the search direction, For the The distance between the marked transport vehicle and the target vehicle, For the The angle between the vector direction of the marked transport vehicle pointing to the target vehicle and the moving direction of the marked transport vehicle, To mark the total number of transport vehicles, To take the minimum value.

[0044] 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.

[0045] Finally, considering the obstacle density of the current node calculated above, the distance between the target vehicle at the current node and the neighboring obstacles along the search direction, and the safety factor of the target vehicle at the current node, an optimal search step length is obtained, which satisfies the following relationship:

[0046] In the formula, is the optimal search step length, is the distance between the current node target vehicle and the neighboring obstacles of the target vehicle along the search direction, is the obstacle density of the current node, is the safety factor, To take the minimum value.

[0047] in, Reflects the range of space around the current node where the target vehicle can move safely. A higher value means there is more space for the vehicle to move, so longer steps can be taken to quickly approach the target or optimize the path; a higher obstacle density This means that the environment around the current node is more complex and requires more sophisticated obstacle avoidance, so the search step should be shorter; a higher safety factor It indicates that the current location 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 a shorter step size to ensure the safety and feasibility of the path.

[0048] in, It is used to ensure that the search step size does not become impractical due to an excessively large safety factor. By taking the minimum value, the step size can be limited to a reasonable range to prevent the algorithm from being too aggressive or conservative.

[0049] 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.

[0050] In the RRT* (Rapidly-exploring Random Tree Star) algorithm, the movement cost (or path cost) is a core concept that directly affects the optimization goal of the algorithm, which 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 produce a lower cost path, the path will be rewired to continuously optimize the path. Among them, the path determined between two nodes is taken as a path segment, and the path of the target vehicle from the starting point to the end point contains multiple such path segments.

[0051] Path length is the most intuitive factor in the calculation of mobility cost. Generally speaking, the shorter the path, the lower the mobility cost. However, in the present invention, the degree of change in congestion risk and path collision risk are also considered to more comprehensively evaluate the mobility cost and provide more accurate and valuable information to decision makers.

[0052] Then the moving cost of the target vehicle's path segment satisfies the relationship:

[0053] In the formula, is the moving cost, is the Euclidean distance between nodes, is the congestion fluctuation of the path segment formed between nodes, In the intersection area The vertical distance from the transport vehicle to the path segment, is the total number of transport vehicles in the intersection area, To take the minimum value; if the obstacle density at the end of the current path is The density of obstacles relative to the starting point The difference is less than 0, ,on the contrary ; The intersection area is the intersection part of the target areas of the two nodes.

[0054] in, is the path collision risk, when The larger the value, the farther the distance between the transport vehicles in the surrounding area of ​​the path and the current path is, the safer the current path is, and the risk of path collision is higher. The smaller the path cost The smaller; when The smaller the value, the closer the distance between the transport vehicles in the surrounding area of ​​the path and the current path is, the less safe the current path is, and the higher the risk of path collision. The larger the path cost, The bigger.

[0055] In another embodiment, another formula for calculating the moving cost is provided, namely:

[0056] In the formula, is the moving cost, is the Euclidean distance between nodes, is the congestion fluctuation of the path segment formed between nodes, In the intersection area The vertical distance from the transport vehicle to the path segment, is the total number of transport vehicles in the intersection area, To take the minimum value; if the obstacle density at the end of the current path is The density of obstacles relative to the starting point The difference is less than 0, ,on the contrary ; The intersection area is the intersection part of the target areas of the two nodes, is the path collision risk; , They are all weight factors, and the values ​​of weight factors can be adjusted according to actual needs.

[0057] Through the above series of calculation methods, the movement costs of all path segments of the target vehicle can be obtained in the same way, and then the total movement cost of the corresponding path of the target vehicle can be accumulated. Similarly, the total movement cost of all optional paths of the target vehicle can be obtained.

[0058] S4: Correcting each of the total movement costs to obtain a corrected total cost of the corresponding path.

[0059] In an ideal situation (i.e., without obstacles or obstructions such as warehouse shelves), the shortest path from the starting point to the target point is a straight line directly connecting the two points. However, in real environments, warehouses are usually filled with shelves, equipment, and other obstacles that block the direct path and force the transporter to take a detour. Despite this, we still hope that the transporter can reduce detours as much as possible to reduce movement costs and improve transportation efficiency.

[0060] To achieve this goal, when planning the transport route, we should not only consider the total length of the route, but also the degree of deviation of the route from the ideal straight line. The greater the deviation, the longer the distance the transport vehicle needs to travel, so the moving cost should also increase accordingly.

[0061] Then the corrected total cost of the transport vehicle satisfies the relationship:

[0062]

[0063] In the formula, is the total cost of the correction, is the integration factor, For the The movement cost of each path segment, is the total number of path segments of the current path, The degree of deviation of the current path relative to the reference line is the line connecting the starting point and the end point of the current path. is the average of the deviation degrees of all paths with the same starting point and end point as the current path, The maximum vertical distance from all corner points of the current path to the reference line. For all paths with the same start and end points as the current path The maximum value of the sum.

[0064] By quantifying the degree of path deviation and the maximum deviation and incorporating them into the calculation of the movement cost, the cost in the actual transportation process can be more accurately reflected, thereby helping to optimize path planning and improve transportation efficiency.

[0065] In another embodiment, the modified total cost of the transport vehicle also satisfies the relationship:

[0066]

[0067] In the formula, is the total cost of the correction, is the integration factor, For the The movement cost of each path segment, is the total number of path segments of the current path, The degree of deviation of the current path relative to the reference line is the line connecting the starting point and the end point of the current path. is the average of the deviation degrees of all paths with the same starting point and end point as the current path, The maximum vertical distance from all corner points of the current path to the reference line. For all paths with the same start and end points as the current path The maximum value of the sum, is the path length of the transport vehicle, , They are all weight coefficients. Implementers can adjust the values ​​of weight coefficients based on multiple tests or historical data.

[0068] Introducing a path length , which can reflect the complexity of the path to a certain extent. Although the path length itself is not directly equivalent to complexity, longer paths often contain more complex elements such as turns and intersections, which may increase the difficulty and cost of driving.

[0069] S5: Based on all the corrected total costs, select the best transportation path for the target vehicle.

[0070] After the corrected total cost of all the paths of the target vehicle is obtained in the above step S4, all the paths are sorted according to the corrected total cost, and the path with the lowest corrected total cost is selected as the optimal transportation path.

[0071] Furthermore, for the selected optimal transportation path, if the movement cost of any path segment in the path is abnormal, the driving speed of the target vehicle is adjusted to reduce the movement cost of the path segment; the abnormal movement cost means that the movement cost of the path segment exceeds the preset benchmark cost (which can be analyzed and set by collecting transportation cost data under the same or similar conditions over a period of time in the past).

[0072] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0073] Although this specification has shown and described a number of 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 conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for managing inbound and outbound data of a protective clothing warehouse, characterized in that: include: Obtain the real-time location information of multiple transport vehicles and select any transport vehicle as the target vehicle; Calculate the optimal search step length of the path planning corresponding to the target vehicle, and obtain 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 target vehicle at the current node and the adjacent obstacles of the target vehicle along the search direction, and positively correlated with the safety factor of the target vehicle at the current node; Calculate the movement cost of the path segments formed between nodes in each path to obtain the total movement cost of the corresponding path; Each of the total movement costs is corrected to obtain a corrected total cost of the corresponding path; the corrected total cost is: ; In the formula, is the total cost of the correction, is the integration factor, For the The movement cost of each path segment, is the total number of path segments of the current path; Based on all the modified total costs, the optimal transportation path for the target vehicle is selected.

2. A method for managing inbound and outbound data of a protective clothing warehouse according to claim 1, characterized in that: Based on all the modified total costs, the optimal transportation path for the target vehicle is selected including: All paths are sorted according to the corrected total cost, and the path with the lowest corrected total cost is selected as the optimal transportation path.

3. A method for managing inbound and outbound data of a protective clothing warehouse according to claim 2, characterized in that: Based on all the modified total costs, selecting the optimal transportation path for the target vehicle also includes: For the selected optimal transport path, if the movement cost of any path segment in the path is abnormal, the driving speed of the target vehicle is adjusted to reduce the movement cost of the path segment; the abnormal movement cost means that the movement cost of the path segment exceeds the preset benchmark cost.

4. A method for managing 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: Take the current node as the center and the preset length as the radius to obtain the corresponding target area and calculate the area of ​​the target area; Calculate the area occupied by all static obstacles and dynamic obstacles in the area, and then calculate the static obstacle density and dynamic obstacle density; 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 is taken as the obstacle density of the current node.

5. A method for managing inbound and outbound data of a protective clothing warehouse according to claim 4, characterized in that: Based on the current node, all transport vehicles within the set distance threshold from the target vehicle are marked, and the safety factor satisfies the relationship: ; In the formula, is the safety factor, is the distance between the current node target vehicle and the neighboring obstacles of the target vehicle along the search direction, For the The distance between the marked transport vehicle and the target vehicle, For the The angle between the vector direction of the marked transport vehicle pointing to the target vehicle and the moving direction of the marked transport vehicle, To mark the total number of transport vehicles, To take the minimum value.

6. A method for managing inbound and outbound data of a protective clothing warehouse according to claim 5, characterized in that: The optimal search step size satisfies the relationship: ; In the formula, is the optimal search step length, is the distance between the current node target vehicle and the neighboring obstacles of the target vehicle along the search direction, is the obstacle density of the current node, is the safety factor, To take the minimum value.

7. A method for managing inbound and outbound data of a protective clothing warehouse according to claim 6, characterized in that: The movement cost satisfies the relationship: ; In the formula, is the moving cost, is the Euclidean distance between nodes, is the congestion fluctuation of the path segment formed between nodes, In the intersection area The vertical distance from the transport vehicle to the path segment, is the total number of transport vehicles in the intersection area, To take the minimum value; if the obstacle density at the end of the current path is The density of obstacles relative to the starting point The difference is less than 0, ,on the contrary ; The intersection area is the intersection part of the target areas of the two nodes.

8. A method for managing inbound and outbound data of a protective clothing warehouse according to claim 7, characterized in that: The integration factor satisfies the relationship: ; In the formula, is the integration factor, The degree of deviation of the current path relative to the reference line is the line connecting the starting point and the end point of the current path. is the average of the deviation degrees of all paths with the same starting point and end point as the current path, The maximum vertical distance from all corner points of the current path to the reference line. For all paths with the same start and end points as the current path The maximum value of the sum.

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