CN113155135B - Deep learning-based route planning method and system - Google Patents
Deep learning-based route planning method and system Download PDFInfo
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- CN113155135B CN113155135B CN202110575788.9A CN202110575788A CN113155135B CN 113155135 B CN113155135 B CN 113155135B CN 202110575788 A CN202110575788 A CN 202110575788A CN 113155135 B CN113155135 B CN 113155135B
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Abstract
The embodiment of the invention provides a route planning method and a route planning system based on deep learning, wherein a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned are formed, then complete first route planning data to be planned are synthesized, other set navigation route node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned are respectively extracted, a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned are formed, complete second route planning data to be planned are synthesized, and the first route planning data to be planned and the second route planning data to be planned are outputted in a time-sharing mode and then uploaded to a corresponding planning route library. Therefore, the route planning precision and the route planning efficiency can be improved by distinguishing the dynamic navigation initial route and the non-dynamic navigation initial route and carrying out the operation of route planning data on the basis of real time and fixation.
Description
Technical Field
The invention relates to the technical field of deep learning, in particular to a route planning method and system based on deep learning.
Background
How to operate the route planning data based on real-time and fixed, which can improve the route planning precision and the route planning efficiency, is a technical problem to be solved urgently in the field.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a route planning method and system based on deep learning, which can improve route planning accuracy and improve route planning efficiency.
According to an aspect of the embodiments of the present invention, there is provided a deep learning-based route planning method applied to a server, where the server is in communication connection with a mobile navigation device, the method including:
respectively extracting set navigation routing node information of a dynamic navigation initial route to be planned and a non-dynamic navigation initial route to be planned in the navigation request configuration information according to the navigation request configuration information reported by each mobile navigation device to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
taking the first dynamic navigation initial route to be planned as first real-time route planning data to be planned, taking the first non-dynamic navigation initial route to be planned as first fixed route planning data to be planned, synthesizing complete first route planning data to be planned, and respectively extracting other set navigation route node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned;
and taking the second dynamic navigation initial route to be planned as second real-time route planning data to be planned, taking the second non-dynamic navigation initial route to be planned as second fixed route planning data to be planned, synthesizing complete second route planning data to be planned, outputting the first route planning data to be planned and the second route planning data to be planned in a time-sharing manner, and uploading the first route planning data to be planned and the second route planning data to be planned to a corresponding route planning library.
In one possible example, the method further comprises:
and the data processing mode of extracting and combining the initial route of the to-be-planned dynamic navigation and the initial route of the to-be-planned non-dynamic navigation forms the first to-be-planned route planning data and the second to-be-planned route planning data which contain all information of the initial route of the to-be-planned non-dynamic navigation, the first to-be-planned route planning data and the second to-be-planned route planning data are uploaded to the corresponding planning route library for time sharing output, and the to-be-planned route planning data are output.
In a possible example, the step of separately extracting the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned in the navigation request configuration information to form the first dynamic navigation initial route to be planned and the first non-dynamic navigation initial route to be planned includes:
traversing and extracting the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the step of respectively extracting other navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned includes:
traversing and extracting other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
In one possible example, the step of traversing and extracting the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned includes:
respectively extracting real-time information of a dynamic navigation initial route to be planned and a non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the step of traversing and extracting other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned includes the following steps:
and respectively extracting the fixed information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
In one possible example, the step of traversing and extracting the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned includes:
respectively extracting real-time information of a dynamic navigation initial route to be planned and fixed information of a non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the step of traversing and extracting other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned includes the following steps:
respectively extracting the fixed information of the dynamic navigation initial route to be planned and the real-time information of the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
According to another aspect of the embodiments of the present invention, there is provided a deep learning-based route planning system applied to a server, the server being in communication connection with a mobile navigation device, the system including:
the extraction module is used for respectively extracting the information of the set navigation routing nodes of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned in the navigation request configuration information according to the navigation request configuration information reported by each piece of mobile navigation equipment to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the first synthesis module is used for taking the first dynamic navigation initial route to be planned as first real-time route planning data to be planned, taking the first non-dynamic navigation initial route to be planned as first fixed route planning data to be planned, synthesizing complete first route planning data to be planned, and respectively extracting other set navigation route node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned;
and the second synthesis module is used for taking the second dynamic navigation initial route to be planned as second real-time route planning data to be planned, taking the second non-dynamic navigation initial route to be planned as second fixed route planning data to be planned, synthesizing complete second route planning data to be planned, outputting the first route planning data to be planned and the second route planning data to be planned in a time-sharing manner, and uploading the first route planning data to be planned and the second route planning data to be planned to a corresponding route planning library.
According to another aspect of the embodiments of the present invention, there is provided a readable storage medium, on which a computer program is stored, which, when being executed by a processor, can perform the steps of the deep learning based route planning method described above.
Compared with the prior art, the route planning method and system based on deep learning provided by the embodiment of the invention form the first to-be-planned dynamic navigation initial route and the first to-be-planned non-dynamic navigation initial route by respectively extracting the set navigation route node information of the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route in the navigation request configuration information, then synthesize complete first to-be-planned route planning data, respectively extract other set navigation route node information of the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route, form the second to-be-planned dynamic navigation initial route and the second to-be-planned non-dynamic navigation initial route, thereby synthesize complete second to-be-planned route planning data, and output the first to-be-planned route planning data and the second to-be-planned route planning data in a time-sharing manner, and uploading the data to a corresponding planned route library. Therefore, the route planning precision and the route planning efficiency can be improved by distinguishing the dynamic navigation initial route and the non-dynamic navigation initial route and carrying out the operation of route planning data on the basis of real time and fixation.
In order to make the aforementioned objects, features and advantages of the embodiments of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 illustrates a component diagram of a server provided by an embodiment of the invention;
FIG. 2 is a flow chart of a deep learning based route planning method according to an embodiment of the present invention;
fig. 3 shows a functional block diagram of a deep learning based route planning system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by the scholars in the technical field, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component schematic of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage media 106 for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, storage medium 106 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of server 100. In one case, when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media, the server 100 may perform any of the operations of the associated instructions. The server 100 further comprises one or more drive units 108 for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, etc.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114)). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the above-described components together.
The communication unit 122 may be implemented in any manner, such as over a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, and so forth, governed by any protocol or combination of protocols.
Fig. 2 shows a flowchart of a deep learning-based route planning method provided by an embodiment of the present invention, which can be executed by the server 100 shown in fig. 1, and the detailed steps of the deep learning-based route planning method are described below.
Step S110, according to the navigation request configuration information reported by each mobile navigation device, respectively extracting the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned in the navigation request configuration information, and forming a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned.
Step S120, the first dynamic navigation initial route to be planned is used as first real-time route planning data to be planned, the first non-dynamic navigation initial route to be planned is used as first fixed route planning data to be planned, the first complete route planning data to be planned is synthesized, other set navigation route node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned is respectively extracted, and a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned are formed.
Step S130, the second dynamic navigation initial route to be planned is used as second real-time route planning data to be planned, the second non-dynamic navigation initial route to be planned is used as second fixed route planning data to be planned, the second complete route planning data to be planned is synthesized, and the first route planning data to be planned and the second route planning data to be planned are output in a time-sharing mode and then uploaded to a corresponding planning route library.
Based on the above steps, in this embodiment, the navigation request configuration information is extracted to respectively obtain the set navigation routing node information of the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route, so as to form the first to-be-planned dynamic navigation initial route and the first to-be-planned non-dynamic navigation initial route, then complete first to-be-planned route planning data is synthesized, and other set navigation routing node information of the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route is extracted respectively to form the second to-be-planned dynamic navigation initial route and the second to-be-planned non-dynamic navigation initial route, so as to synthesize complete second to-be-planned route planning data, and the first to-be-planned route planning data and the second to-be-planned route planning data are output in a time-sharing manner, and then are uploaded to the corresponding planning route library. Therefore, the route planning precision and the route planning efficiency can be improved by distinguishing the dynamic navigation initial route and the non-dynamic navigation initial route and carrying out the operation of route planning data on the basis of real time and fixation.
In a possible example, the embodiment may further perform a data processing manner of extracting and combining the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route to form the first to-be-planned route planning data and the second to-be-planned route planning data containing all information of the to-be-planned non-dynamic navigation initial route, upload the first to-be-planned route planning data and the second to-be-planned route planning data to the corresponding planning route library, and then upload the first to-be-planned route planning data and the second to-be-planned route planning data to the corresponding planning route library for time-sharing output, and output the to-be-planned route planning data.
In one possible example, for step S110, set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned may be extracted through traversal to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned.
For example, the real-time information of the dynamic navigation initial route to be planned and the real-time information of the non-dynamic navigation initial route to be planned can be respectively extracted to form the first dynamic navigation initial route to be planned and the first non-dynamic navigation initial route to be planned.
For another example, the real-time information of the dynamic navigation initial route to be planned and the fixed information of the non-dynamic navigation initial route to be planned can be respectively extracted to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned.
For step S120, other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned may be extracted through traversal, so as to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
For example, the fixed information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned can be respectively extracted to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
For another example, the fixed information of the dynamic navigation initial route to be planned and the real-time information of the non-dynamic navigation initial route to be planned can be respectively extracted to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
Fig. 3 shows a functional block diagram of a deep learning based route planning system 200 according to an embodiment of the present invention, where the functions implemented by the deep learning based route planning system 200 may correspond to the steps executed by the above method. The deep learning based route planning system 200 may be understood as the server 100, or a processor of the server 100, or may be understood as a component that is independent from the server 100 or the processor and implements the functions of the present invention under the control of the server 100, as shown in fig. 3, and the functions of the functional modules of the deep learning based route planning system 200 are described in detail below.
The extracting module 210 is configured to respectively extract, according to the navigation request configuration information reported by each mobile navigation device, set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned in the navigation request configuration information, so as to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned.
The first synthesizing module 220 is configured to use the first to-be-planned dynamic navigation initial route as first real-time to-be-planned route planning data, use the first to-be-planned non-dynamic navigation initial route as first fixed to-be-planned route planning data, synthesize complete first to-be-planned route planning data, and respectively extract other set navigation routing node information of the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route to form a second to-be-planned dynamic navigation initial route and a second to-be-planned non-dynamic navigation initial route.
The second synthesizing module 230 is configured to use the second to-be-planned dynamic navigation initial route as second real-time to-be-planned route planning data, use the second to-be-planned non-dynamic navigation initial route as second fixed to-be-planned route planning data, synthesize complete second to-be-planned route planning data, output the first to-be-planned route planning data and the second to-be-planned route planning data in a time-sharing manner, and upload the first to-be-planned route planning data and the second to-be-planned route planning data to a corresponding planning route library.
In a possible example, the extracting module 210 is further configured to, after forming the first to-be-planned route planning data and the second to-be-planned route planning data that include all information of the to-be-planned non-dynamic navigation initial route in a data processing manner of extracting and combining the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route, upload the first to-be-planned route planning data and the second to-be-planned route planning data to a corresponding planning route library, and output the to-be-planned route planning data in a time-sharing manner.
In a possible example, the extracting module 210 extracts the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned respectively to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned by:
and traversing and extracting the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned.
In a possible example, the extraction module 210 extracts other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned respectively to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned by:
traversing and extracting other navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
In one possible example, the extraction module 210 traverses and extracts the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned by:
respectively extracting real-time information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned.
In a possible example, the extraction module 210 traverses and extracts other set navigation routing node information of the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route to form a second to-be-planned dynamic navigation initial route and a second to-be-planned non-dynamic navigation initial route by:
and respectively extracting the fixed information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
In one possible example, the extraction module 210 traverses and extracts the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned by:
and respectively extracting the real-time information of the dynamic navigation initial route to be planned and the fixed information of the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned.
In a possible example, the extraction module 210 may extract the other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned in a traversal manner to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned by:
respectively extracting the fixed information of the dynamic navigation initial route to be planned and the real-time information of the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
Alternatively, all or part may be implemented by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any drawing credit or debit acknowledgement in the claims should not be construed as limiting the claim concerned.
Claims (2)
1. A deep learning-based route planning method is applied to a server which is in communication connection with a mobile navigation device, and the method comprises the following steps:
respectively extracting set navigation routing node information of a dynamic navigation initial route to be planned and a non-dynamic navigation initial route to be planned in the navigation request configuration information according to the navigation request configuration information reported by each mobile navigation device to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
taking the first dynamic navigation initial route to be planned as first real-time route planning data to be planned, taking the first non-dynamic navigation initial route to be planned as first fixed route planning data to be planned, synthesizing complete first route planning data to be planned, and respectively extracting other set navigation route node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned;
taking the second dynamic navigation initial route to be planned as second real-time route planning data to be planned, taking the second non-dynamic navigation initial route to be planned as second fixed route planning data to be planned, synthesizing complete second route planning data to be planned, outputting the first route planning data to be planned and the second route planning data to be planned in a time-sharing manner, and uploading the first route planning data to be planned and the second route planning data to be planned to a corresponding route planning library;
the method further comprises the following steps:
the data processing mode of extracting and combining the initial route of the to-be-planned dynamic navigation and the initial route of the to-be-planned non-dynamic navigation forms first to-be-planned route planning data and second to-be-planned route planning data containing all information of the initial route of the to-be-planned non-dynamic navigation, the first to-be-planned route planning data and the second to-be-planned route planning data are uploaded to the corresponding planning route base to be output in a time sharing mode, and the to-be-planned route planning data are output;
the step of respectively extracting the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned in the navigation request configuration information to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned comprises the following steps:
traversing and extracting set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the step of respectively extracting other navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned includes:
traversing and extracting other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned;
the step of traversing and extracting the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned comprises the following steps:
respectively extracting real-time information of a dynamic navigation initial route to be planned and a non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the step of traversing and extracting other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned includes the following steps:
respectively extracting fixed information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned;
respectively extracting real-time information of a dynamic navigation initial route to be planned and fixed information of a non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
respectively extracting the fixed information of the dynamic navigation initial route to be planned and the real-time information of the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
2. A deep learning based route planning system, applied to a server, the server being in communication connection with a mobile navigation device, the system comprising:
the extraction module is used for respectively extracting the information of the set navigation routing nodes of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned in the navigation request configuration information according to the navigation request configuration information reported by each piece of mobile navigation equipment to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the first synthesis module is used for taking the first to-be-planned dynamic navigation initial route as first real-time to-be-planned route planning data, taking the first to-be-planned non-dynamic navigation initial route as first fixed to-be-planned route planning data, synthesizing complete first to-be-planned route planning data, and respectively extracting other set navigation route node information of the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route to form a second to-be-planned dynamic navigation initial route and a second to-be-planned non-dynamic navigation initial route;
the second synthesis module is used for taking the second dynamic navigation initial route to be planned as second real-time route planning data to be planned, taking the second non-dynamic navigation initial route to be planned as second fixed route planning data to be planned, synthesizing complete second route planning data to be planned, outputting the first route planning data to be planned and the second route planning data to be planned in a time-sharing manner, and uploading the first route planning data to be planned and the second route planning data to be planned to a corresponding route planning library;
the extraction module is also used for forming first to-be-planned route planning data and second to-be-planned route planning data containing all information of the to-be-planned non-dynamic navigation initial route in a data processing mode of extracting and combining the to-be-planned dynamic navigation initial route and the to-be-planned non-dynamic navigation initial route, uploading the first to-be-planned route planning data and the second to-be-planned route planning data to a corresponding planning route library, outputting the to-be-planned route planning data in a time sharing mode;
the extraction module respectively extracts the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned in the following modes:
traversing and extracting set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the extraction module respectively extracts other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned in the following modes:
traversing and extracting other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned;
the extraction module traversably extracts the set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned in the following modes:
respectively extracting real-time information of a dynamic navigation initial route to be planned and a non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
the extraction module traversably extracts other set navigation routing node information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned in the following modes:
respectively extracting fixed information of the dynamic navigation initial route to be planned and the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned;
respectively extracting real-time information of a dynamic navigation initial route to be planned and fixed information of a non-dynamic navigation initial route to be planned to form a first dynamic navigation initial route to be planned and a first non-dynamic navigation initial route to be planned;
respectively extracting the fixed information of the dynamic navigation initial route to be planned and the real-time information of the non-dynamic navigation initial route to be planned to form a second dynamic navigation initial route to be planned and a second non-dynamic navigation initial route to be planned.
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