CN117474441B - Unmanned warehouse access path management method based on intelligent logistics big data - Google Patents
Unmanned warehouse access path management method based on intelligent logistics big data Download PDFInfo
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Abstract
The invention relates to the field of unmanned warehouse access path management, in particular to an unmanned warehouse access path management method based on intelligent logistics big data, which comprises the following steps: s1, acquiring a real-time running state of an unmanned warehouse based on intelligent logistics big data; s2, establishing an unmanned warehouse access path dynamic model by utilizing the unmanned warehouse real-time running state; s3, unmanned warehouse access path management is completed according to the unmanned warehouse access path dynamic model, the unmanned warehouse access path is verified through multi-level planning processing of the unmanned warehouse path, and the unmanned warehouse access path management is considered from two aspects, so that the whole operation of the warehouse is guaranteed to be normal, efficient planning is performed in each path processing in the warehouse, the operation efficiency of the unmanned warehouse is improved, and the cost brought by manual monitoring is reduced.
Description
Technical Field
The invention relates to the field of unmanned warehouse access path management, in particular to an unmanned warehouse access path management method based on intelligent logistics big data.
Background
Big data are widely applied to various fields of social production and life, and from the original internet application, the fields of logistics, medical treatment, finance and the like are continuously moved. In the logistics industry, various links of logistics, such as transportation, storage, loading and unloading, carrying, packaging and the like, can generate a large amount of data. Related intelligent logistics big data are generated, which means various related data generated when logistics services such as supply, demand and the like are carried out in the logistics activity process. Meanwhile, with the continuous development of automation technology, computer technology and artificial intelligence technology, the storage mode of accumulating goods on the ground in the past has been replaced, and the three-dimensional goods shelf becomes a bright spot for storage. The automatic warehouse is characterized by large storage capacity and high goods storage and taking efficiency. However, in the conventional unmanned warehouse path planning scheme, workers are still required to monitor the real-time running state of the unmanned warehouse, and in order to further improve the running efficiency of the unmanned warehouse and reduce the labor cost and the error probability, a practical efficient self-circulation monitoring scheme for the unmanned warehouse is needed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an unmanned warehouse access path management method based on intelligent logistics big data, and an efficient circulation real-time monitoring method is established by integrating all operation data in an unmanned warehouse through main monitoring planning of an unmanned warehouse path.
In order to achieve the above purpose, the invention provides an unmanned warehouse entry and exit path management method based on intelligent logistics big data, comprising the following steps:
s1, acquiring a real-time running state of an unmanned warehouse based on intelligent logistics big data;
s2, establishing an unmanned warehouse access path dynamic model by utilizing the unmanned warehouse real-time running state;
s3, completing unmanned warehouse access path management according to the unmanned warehouse access path dynamic model.
Preferably, the acquiring the real-time running state of the unmanned warehouse based on the intelligent logistics big data comprises the following steps:
acquiring real-time access path data of an unmanned warehouse based on intelligent logistics big data;
acquiring real-time access data of the unmanned warehouse by using the real-time access path data of the unmanned warehouse;
and using the real-time access path data of the unmanned warehouse and the real-time access data of the unmanned warehouse as the real-time running state of the unmanned warehouse.
Further, the obtaining the real-time access data of the unmanned warehouse by using the real-time access path data of the unmanned warehouse comprises the following steps:
acquiring the real-time running state of the unmanned aerial vehicle warehouse according to the real-time access path data of the unmanned aerial vehicle warehouse;
obtaining a real-time warehouse entry and exit flow state of the unmanned warehouse according to the real-time running state of the unmanned vehicle of the unmanned warehouse;
using the real-time warehouse entry and exit flow state of the unmanned warehouse as real-time warehouse entry and exit data of the unmanned warehouse;
the real-time running state of the unmanned vehicle comprises a use state and a non-use state.
Further, establishing the unmanned warehouse access path dynamic model by utilizing the unmanned warehouse real-time running state comprises the following steps:
s2-1, establishing an unmanned warehouse access path planning model by utilizing real-time access path data of the unmanned warehouse in real-time operation state;
s2-2, establishing an unmanned warehouse in-out flow planning model by utilizing real-time in-out warehouse data of the unmanned warehouse in-out warehouse real-time running state;
s2-3, using the unmanned warehouse access path planning model and the unmanned warehouse access flow planning model as an unmanned warehouse access path dynamic model.
Further, the establishing the unmanned warehouse access path planning model by utilizing the real-time access path data of the unmanned warehouse real-time running state comprises the following steps:
s2-1-1, using the current moment as a standard moment t of an unmanned warehouse access path planning model;
s2-1-2, judging whether path conflict exists in real-time access path data corresponding to the real-time running state of the unmanned warehouse at the standard time t, if so, carrying out coordination processing on the real-time access path data with the path conflict, otherwise, executing S2-1-3;
s2-1-3, judging whether path conflict exists in real-time access path data corresponding to the real-time running state of the unmanned warehouse at the time t+1, if so, using the time t+2 as updated time t+1, and returning to S2-1-2, otherwise, reserving the current real-time access path data as an access path planning model output of the unmanned warehouse;
the path conflict is that collision exists in unmanned vehicle operation.
Further, the coordinating processing of the real-time access path data with path conflict includes:
s2-1-2-1, respectively acquiring the corresponding in-out states of the real-time in-out path data with the path conflict as a path conflict coordination starting point;
s2-1-2-2, judging whether the access states of the real-time access path data corresponding to the path conflict coordination starting point are consistent, if so, executing S2-1-2-3, otherwise, directly executing S2-1-2-4;
s2-1-2-3, judging whether the path starting moments of the real-time access path data corresponding to the path conflict coordination starting points are consistent, if so, executing S2-1-2-4, otherwise, carrying out delay processing by utilizing the real-time access path data corresponding to the rear path starting moments, and returning to S2-1-2;
s2-1-2-4, judging whether the running paths of the real-time access path data corresponding to the path conflict coordination starting point are consistent, if so, directly executing S2-1-3, otherwise, carrying out delay processing by using the running relatively short paths, and returning to S2-1-2;
the in-out state comprises a warehouse-in state and a warehouse-out state, and the delay processing is that the path pauses and dodges.
Further, the establishing the unmanned warehouse in-out flow planning model by utilizing the real-time in-out database data of the unmanned warehouse real-time running state comprises the following steps:
s2-2-1, judging whether the number of times of coordination processing performed by the unmanned warehouse at the current moment is greater than 1, if so, executing S2-2-2, otherwise, using real-time business turn over database data of the real-time running state of the unmanned warehouse as business turn over flow coordination data to establish a business turn over flow planning model of the unmanned warehouse;
s2-2-2, judging whether the blocking time corresponding to the coordination processing of the unmanned warehouse is abnormal, if so, acquiring real-time access path data corresponding to the coordination processing of the blocking time abnormal as access flow uncoordinated data, and executing S2-2-3, otherwise, performing time sequence arrangement on the real-time access path data of the coordination processing of the unmanned warehouse to obtain access flow coordination data, and establishing an unmanned warehouse access flow planning model;
s2-2-3, carrying out integrity adjustment processing according to the non-coordinated data of the inlet and outlet flow to obtain an unmanned warehouse integrity adjustment processing result;
s2-2-4, judging whether the integrity adjustment processing result of the unmanned warehouse is normal, if so, returning to S2-2-2, otherwise, using the current moment as an updating standard moment t, and returning to S2-1-1;
the blocking time abnormality is interference of delay processing on punctuality of real-time running states of the unmanned warehouse.
Further, performing integrity adjustment processing according to the traffic uncoordinated data to obtain an unmanned warehouse integrity adjustment processing result includes:
s2-2-3-1, acquiring the operation time of the real-time access path data corresponding to the access flow uncoordinated data as access flow uncoordinated data comparison time;
s2-2-3-2, judging whether adjacent moments exist in the in-out flow uncoordinated data comparison time, if yes, executing S2-2-3-3, otherwise, enabling the integrity adjustment processing result of the unmanned warehouse to be abnormal, and executing S2-2-4;
s2-2-3-3, judging whether the inlet and outlet states of the inlet and outlet flow uncoordinated data are consistent, if so, judging that the integrity adjustment processing result of the unmanned warehouse is abnormal, and executing S2-2-4, otherwise, judging that the integrity adjustment processing result of the unmanned warehouse is normal, and executing S2-2-4;
the adjacent time is the adjacent front and rear time of each in-out flow uncoordinated data comparison time.
Further, completing the unmanned warehouse access path management according to the unmanned warehouse access path dynamic model includes:
s3-1, judging whether an unmanned warehouse access path planning model of the unmanned warehouse access path dynamic model and an unmanned warehouse access flow planning model correspond to each other, if so, executing S3-2, otherwise, returning to S2-2-1;
s3-2, judging whether the updating times of the unmanned warehouse in-out flow planning model are greater than 1, if so, S3-3, otherwise, directly completing the unmanned warehouse in-out path management;
s3-3, judging whether the operation of the unmanned warehouse is abnormal at the current moment, if so, executing S3-4, otherwise, completing the management of the access paths of the unmanned warehouse;
s3-4, judging whether the operation of the unmanned warehouse is abnormal or not corresponding to delay processing, if yes, completing the management of the access path of the unmanned warehouse, otherwise, outputting an access path planning model of the unmanned warehouse of an access path dynamic model of the unmanned warehouse, and completing the management of the access path of the unmanned warehouse;
the unmanned warehouse access path planning model and the unmanned warehouse access flow planning model correspond to each other, real-time access data of the unmanned warehouse access path planning model and access flow coordination data of the unmanned warehouse access flow planning model correspond to each other, and the update times are the execution times of S2-2-1.
Compared with the closest prior art, the invention has the following beneficial effects:
the unmanned warehouse path multistage planning processing verification is carried out, and the unmanned warehouse path multistage planning processing verification is considered from two aspects, so that the whole operation of the warehouse is guaranteed to be normal, efficient planning is carried out in each path processing in the warehouse, the operation efficiency of the unmanned warehouse is improved, the cost caused by manual monitoring is reduced, meanwhile, the scheme can be flexibly adjusted according to actual operation, and the unmanned warehouse path management duration, the output target and the like can be flexibly operated.
Drawings
Fig. 1 is a flowchart of an unmanned warehouse entry and exit path management method based on intelligent logistics big data.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of 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, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: the invention provides an unmanned warehouse entry and exit path management method based on intelligent logistics big data, which is shown in figure 1 and comprises the following steps:
s1, acquiring a real-time running state of an unmanned warehouse based on intelligent logistics big data;
s2, establishing an unmanned warehouse access path dynamic model by utilizing the unmanned warehouse real-time running state;
s3, completing unmanned warehouse access path management according to the unmanned warehouse access path dynamic model.
S1 specifically comprises:
s1-1, acquiring real-time access path data of an unmanned warehouse based on intelligent logistics big data;
s1-2, acquiring real-time in-out database data of the unmanned warehouse by using the real-time in-out path data of the unmanned warehouse;
s1-3, utilizing the real-time access path data of the unmanned warehouse and the real-time access data of the unmanned warehouse as the real-time running state of the unmanned warehouse.
S1-2 specifically comprises:
s1-2-1, acquiring the real-time running state of the unmanned aerial vehicle warehouse according to the real-time access path data of the unmanned aerial vehicle warehouse;
s1-2-2, obtaining a real-time warehouse entry and exit flow state of the unmanned warehouse according to the real-time running state of the unmanned vehicle of the unmanned warehouse;
s1-2-3, using the real-time warehouse entry and exit flow state of the unmanned warehouse as real-time warehouse entry and exit data of the unmanned warehouse;
the real-time running state of the unmanned vehicle comprises a use state and a non-use state.
S2 specifically comprises:
s2-1, establishing an unmanned warehouse access path planning model by utilizing real-time access path data of the unmanned warehouse in real-time operation state;
s2-2, establishing an unmanned warehouse in-out flow planning model by utilizing real-time in-out warehouse data of the unmanned warehouse in-out warehouse real-time running state;
s2-3, using the unmanned warehouse access path planning model and the unmanned warehouse access flow planning model as an unmanned warehouse access path dynamic model.
S2-1 specifically comprises:
s2-1-1, using the current moment as a standard moment t of an unmanned warehouse access path planning model;
s2-1-2, judging whether path conflict exists in real-time access path data corresponding to the real-time running state of the unmanned warehouse at the standard time t, if so, carrying out coordination processing on the real-time access path data with the path conflict, otherwise, executing S2-1-3;
s2-1-3, judging whether path conflict exists in real-time access path data corresponding to the real-time running state of the unmanned warehouse at the time t+1, if so, using the time t+2 as updated time t+1, and returning to S2-1-2, otherwise, reserving the current real-time access path data as an access path planning model output of the unmanned warehouse;
the path conflict is that collision exists in unmanned vehicle operation.
S2-1-2 specifically comprises:
s2-1-2-1, respectively acquiring the corresponding in-out states of the real-time in-out path data with the path conflict as a path conflict coordination starting point;
s2-1-2-2, judging whether the access states of the real-time access path data corresponding to the path conflict coordination starting point are consistent, if so, executing S2-1-2-3, otherwise, directly executing S2-1-2-4;
s2-1-2-3, judging whether the path starting moments of the real-time access path data corresponding to the path conflict coordination starting points are consistent, if so, executing S2-1-2-4, otherwise, carrying out delay processing by utilizing the real-time access path data corresponding to the rear path starting moments, and returning to S2-1-2;
s2-1-2-4, judging whether the running paths of the real-time access path data corresponding to the path conflict coordination starting point are consistent, if so, directly executing S2-1-3, otherwise, carrying out delay processing by using the running relatively short paths, and returning to S2-1-2;
the in-out state comprises a warehouse-in state and a warehouse-out state, and the delay processing is that the path pauses and dodges.
In this embodiment, an unmanned warehouse entry and exit path management method based on intelligent logistics big data, wherein the path conflict coordination starting point is used as an abstract concept, and the entry and exit state is used as a step node, so that a subsequent processing scheme is led out.
S2-2 specifically comprises:
s2-2-1, judging whether the number of times of coordination processing performed by the unmanned warehouse at the current moment is greater than 1, if so, executing S2-2-2, otherwise, using real-time business turn over database data of the real-time running state of the unmanned warehouse as business turn over flow coordination data to establish a business turn over flow planning model of the unmanned warehouse;
s2-2-2, judging whether the blocking time corresponding to the coordination processing of the unmanned warehouse is abnormal, if so, acquiring real-time access path data corresponding to the coordination processing of the blocking time abnormal as access flow uncoordinated data, and executing S2-2-3, otherwise, performing time sequence arrangement on the real-time access path data of the coordination processing of the unmanned warehouse to obtain access flow coordination data, and establishing an unmanned warehouse access flow planning model;
s2-2-3, carrying out integrity adjustment processing according to the non-coordinated data of the inlet and outlet flow to obtain an unmanned warehouse integrity adjustment processing result;
s2-2-4, judging whether the integrity adjustment processing result of the unmanned warehouse is normal, if so, returning to S2-2-2, otherwise, using the current moment as an updating standard moment t, and returning to S2-1-1;
the blocking time abnormality is interference of delay processing on punctuality of real-time running states of the unmanned warehouse.
S2-2-3 specifically comprises:
s2-2-3-1, acquiring the operation time of the real-time access path data corresponding to the access flow uncoordinated data as access flow uncoordinated data comparison time;
s2-2-3-2, judging whether adjacent moments exist in the in-out flow uncoordinated data comparison time, if yes, executing S2-2-3-3, otherwise, enabling the integrity adjustment processing result of the unmanned warehouse to be abnormal, and executing S2-2-4;
s2-2-3-3, judging whether the inlet and outlet states of the inlet and outlet flow uncoordinated data are consistent, if so, judging that the integrity adjustment processing result of the unmanned warehouse is abnormal, and executing S2-2-4, otherwise, judging that the integrity adjustment processing result of the unmanned warehouse is normal, and executing S2-2-4;
the adjacent time is the adjacent front and rear time of each in-out flow uncoordinated data comparison time.
S3 specifically comprises:
s3-1, judging whether an unmanned warehouse access path planning model of the unmanned warehouse access path dynamic model and an unmanned warehouse access flow planning model correspond to each other, if so, executing S3-2, otherwise, returning to S2-2-1;
s3-2, judging whether the updating times of the unmanned warehouse in-out flow planning model are greater than 1, if so, S3-3, otherwise, directly completing the unmanned warehouse in-out path management;
s3-3, judging whether the operation of the unmanned warehouse is abnormal at the current moment, if so, executing S3-4, otherwise, completing the management of the access paths of the unmanned warehouse;
s3-4, judging whether the operation of the unmanned warehouse is abnormal or not corresponding to delay processing, if yes, completing the management of the access path of the unmanned warehouse, otherwise, outputting an access path planning model of the unmanned warehouse of an access path dynamic model of the unmanned warehouse, and completing the management of the access path of the unmanned warehouse;
the unmanned warehouse access path planning model and the unmanned warehouse access flow planning model correspond to each other, real-time access data of the unmanned warehouse access path planning model and access flow coordination data of the unmanned warehouse access flow planning model correspond to each other, and the update times are the execution times of S2-2-1.
In the embodiment, in the unmanned warehouse entry and exit path management method based on the intelligent logistics big data, the overall design thought of S3 is to verify each path planning of the preamble and establishment of the related flow model again, and the combination of existing paths is considered in the path establishment, so that the intelligent logistics big data is used as a reference to verify the whole scheme.
In this embodiment, according to the method for managing the access paths of the unmanned warehouse based on the intelligent logistics big data, in the actual operation, the situation that the warehouse is blocked in the access of the warehouse, the goods are placed in the warehouse and the situation that the goods are out of the warehouse is wrong exists in the warehouse, so that when the final path management scheme is completed, the whole operation of the warehouse is required to be judged, and the fact that the path planning does not interfere with the actual operation is confirmed.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (2)
1. An unmanned warehouse entry and exit path management method based on intelligent logistics big data is characterized by comprising the following steps:
s1, acquiring a real-time running state of an unmanned warehouse based on intelligent logistics big data;
s1-1, acquiring real-time access path data of an unmanned warehouse based on intelligent logistics big data;
s1-2, acquiring real-time in-out database data of the unmanned warehouse by using the real-time in-out path data of the unmanned warehouse;
s1-3, using the real-time access path data of the unmanned warehouse and the real-time access data of the unmanned warehouse as the real-time running state of the unmanned warehouse;
s2, establishing an unmanned warehouse access path dynamic model by utilizing the unmanned warehouse real-time running state;
s2-1, establishing an unmanned warehouse access path planning model by utilizing real-time access path data of the unmanned warehouse in real-time operation state;
s2-1-1, using the current moment as a standard moment t of an unmanned warehouse access path planning model;
s2-1-2, judging whether path conflict exists in real-time access path data corresponding to the real-time running state of the unmanned warehouse at the standard time t, if so, carrying out coordination processing on the real-time access path data with the path conflict, otherwise, executing S2-1-3;
s2-1-2-1, respectively acquiring the corresponding in-out states of the real-time in-out path data with the path conflict as a path conflict coordination starting point;
s2-1-2-2, judging whether the access states of the real-time access path data corresponding to the path conflict coordination starting point are consistent, if so, executing S2-1-2-3, otherwise, directly executing S2-1-2-4;
s2-1-2-3, judging whether the path starting moments of the real-time access path data corresponding to the path conflict coordination starting points are consistent, if so, executing S2-1-2-4, otherwise, carrying out delay processing by utilizing the real-time access path data corresponding to the rear path starting moments, and returning to S2-1-2;
s2-1-2-4, judging whether the running paths of the real-time access path data corresponding to the path conflict coordination starting point are consistent, if so, directly executing S2-1-3, otherwise, carrying out delay processing by using the running relatively short paths, and returning to S2-1-2;
the method comprises the steps of receiving a path suspension avoidance request, wherein the entering and exiting state comprises a warehouse-in state and a warehouse-out state, and the path suspension avoidance request is processed in a delay mode;
s2-1-3, judging whether path conflict exists in real-time access path data corresponding to the real-time running state of the unmanned warehouse at the time t+1, if so, using the time t+2 as updated time t+1, and returning to S2-1-2, otherwise, reserving the current real-time access path data as an access path planning model output of the unmanned warehouse;
wherein, the path conflict is that the unmanned vehicle runs and has collision conditions;
s2-2, establishing an unmanned warehouse in-out flow planning model by utilizing real-time in-out warehouse data of the unmanned warehouse in-out warehouse real-time running state;
s2-2-1, judging whether the number of times of coordination processing performed by the unmanned warehouse at the current moment is greater than 1, if so, executing S2-2-2, otherwise, using real-time business turn over database data of the real-time running state of the unmanned warehouse as business turn over flow coordination data to establish a business turn over flow planning model of the unmanned warehouse;
s2-2-2, judging whether the blocking time corresponding to the coordination processing of the unmanned warehouse is abnormal, if so, acquiring real-time access path data corresponding to the coordination processing of the blocking time abnormal as access flow uncoordinated data, and executing S2-2-3, otherwise, performing time sequence arrangement on the real-time access path data of the coordination processing of the unmanned warehouse to obtain access flow coordination data, and establishing an unmanned warehouse access flow planning model;
s2-2-3, carrying out integrity adjustment processing according to the non-coordinated data of the inlet and outlet flow to obtain an unmanned warehouse integrity adjustment processing result;
s2-2-3-1, acquiring the operation time of the real-time access path data corresponding to the access flow uncoordinated data as access flow uncoordinated data comparison time;
s2-2-3-2, judging whether adjacent moments exist in the in-out flow uncoordinated data comparison time, if yes, executing S2-2-3-3, otherwise, enabling the integrity adjustment processing result of the unmanned warehouse to be abnormal, and executing S2-2-4;
s2-2-3-3, judging whether the inlet and outlet states of the inlet and outlet flow uncoordinated data are consistent, if so, judging that the integrity adjustment processing result of the unmanned warehouse is abnormal, and executing S2-2-4, otherwise, judging that the integrity adjustment processing result of the unmanned warehouse is normal, and executing S2-2-4;
the adjacent time is the adjacent front and rear time of each in-out flow uncoordinated data comparison time;
s2-2-4, judging whether the integrity adjustment processing result of the unmanned warehouse is normal, if so, returning to S2-2-2, otherwise, using the current moment as an updating standard moment t, and returning to S2-1-1;
wherein, the blocking time abnormality is that delay processing causes interference to punctuality of real-time running state of the unmanned warehouse;
s2-3, using the unmanned warehouse access path planning model and the unmanned warehouse access flow planning model as an unmanned warehouse access path dynamic model;
s3, completing unmanned warehouse access path management according to the unmanned warehouse access path dynamic model;
s3-1, judging whether an unmanned warehouse access path planning model of the unmanned warehouse access path dynamic model and an unmanned warehouse access flow planning model correspond to each other, if so, executing S3-2, otherwise, returning to S2-2-1;
s3-2, judging whether the updating times of the unmanned warehouse in-out flow planning model are greater than 1, if so, executing S3-3, otherwise, directly completing the unmanned warehouse in-out path management;
s3-3, judging whether the operation of the unmanned warehouse is abnormal at the current moment, if so, executing S3-4, otherwise, completing the management of the access paths of the unmanned warehouse;
s3-4, judging whether the operation of the unmanned warehouse is abnormal or not corresponding to delay processing, if yes, completing the management of the access path of the unmanned warehouse, otherwise, outputting an access path planning model of the unmanned warehouse of an access path dynamic model of the unmanned warehouse, and completing the management of the access path of the unmanned warehouse;
the unmanned warehouse access path planning model and the unmanned warehouse access flow planning model correspond to each other, real-time access data of the unmanned warehouse access path planning model and access flow coordination data of the unmanned warehouse access flow planning model correspond to each other, and the update times are the execution times of S2-2-1.
2. The unmanned warehouse entry and exit path management method based on intelligent logistics big data as claimed in claim 1, wherein the obtaining the real-time entry and exit data of the unmanned warehouse by using the real-time entry and exit path data of the unmanned warehouse comprises:
acquiring the real-time running state of the unmanned aerial vehicle warehouse according to the real-time access path data of the unmanned aerial vehicle warehouse;
obtaining a real-time warehouse entry and exit flow state of the unmanned warehouse according to the real-time running state of the unmanned vehicle of the unmanned warehouse;
using the real-time warehouse entry and exit flow state of the unmanned warehouse as real-time warehouse entry and exit data of the unmanned warehouse;
the real-time running state of the unmanned vehicle comprises a use state and a non-use state.
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