CN113570096A - Intelligent trip method and device based on big data - Google Patents
Intelligent trip method and device based on big data Download PDFInfo
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Abstract
The invention discloses an intelligent travel method and device based on big data, which are applied to an intelligent travel planning system, wherein the method comprises the following steps: obtaining information of a place of departure; obtaining destination information, thereby obtaining first route information; acquiring first departure time and first influence information; inputting the first route information and the first influence information into a first training model so as to obtain first road prediction information, wherein the first road prediction information is time for predicting the start of congestion of a road and time-consuming information corresponding to the time for the road to reach a destination; sending suggested departure time and predicted arrival time to a user according to the first road prediction information; and after a reply instruction is given by the user, determining a first travel plan. The technical problems that the planning mode of the current travel route is single and the travel route can not be accurately planned by combining multiple influence factors in the prior art are solved.
Description
Technical Field
The invention relates to the field of big data, in particular to a big data-based intelligent travel method and device.
Background
Big data is a product of the high-tech era, and the strategic significance of big data technology is not to master huge data information, but to perform specialized processing on the meaningful data. With the rapid development of big data technology, big data application has been integrated into various industries. The intelligent travel is also called intelligent transportation, and the traffic condition is sensed in real time by means of advanced technologies and concepts such as mobile internet, cloud computing, big data and internet of things, so that convenience is provided for social life, and the urban operation efficiency is improved.
In the process of implementing the technical scheme of the invention in the embodiment of the present application, the inventor of the present application finds that the above-mentioned technology has at least the following technical problems:
at present, a travel route is single in planning mode, and the travel route cannot be accurately planned by combining multiple influence factors.
Disclosure of Invention
By providing the method and the device for intelligent travel based on big data, the technical problems that in the prior art, the planning mode of the current travel route is single, and the travel route cannot be accurately planned by combining multiple influence factors are solved, and the technical purpose of accurately planning the travel route and the travel time by combining multiple factors such as real-time traffic states and weather based on machine learning is achieved.
The embodiment of the application provides an intelligent travel method based on big data, which is applied to an intelligent travel planning system, wherein the method comprises the following steps: obtaining information of a place of departure; obtaining destination information; obtaining first route information according to the departure place information and the destination information; obtaining a first departure time; acquiring first influence information according to the first departure time; inputting the first route information and the first influence information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the first route information, the first influence information, and identification information for identifying predicted congestion time and corresponding arrival time; obtaining first output information of the first training model, wherein the first output information comprises first road prediction information, and the first road prediction information is time for predicting that a road starts to be congested and time-consuming information corresponding to the time for the road to reach a destination; sending first guide information according to the first road prediction information, wherein the first guide information is used for sending suggested departure time and predicted arrival time to a user; acquiring first reply information according to the first guide information, wherein the first reply information is a reply instruction given by a user; and determining a first trip plan according to the first reply information.
On the other hand, this application still provides a device of intelligence trip based on big data, wherein, the device includes: a first obtaining unit for obtaining origin information; a second obtaining unit configured to obtain destination information; a third obtaining unit, configured to obtain first route information according to the departure place information and the destination information; a fourth obtaining unit configured to obtain a first departure time; a fifth obtaining unit, configured to obtain first influence information according to the first departure time; a first input unit, configured to input the first route information and the first influence information into a first training model, where the first training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the first route information, the first influence information, and identification information for identifying predicted congestion time and corresponding arrival time; a sixth obtaining unit, configured to obtain first output information of the first training model, where the first output information includes first road prediction information, and the first road prediction information is time for predicting a road to start to be congested and time-consuming information of the predicted road to reach a destination; the first sending unit is used for sending first guide information according to the first road prediction information, and the first guide information is used for sending suggested departure time and predicted arrival time to a user; a seventh obtaining unit, configured to obtain first reply information according to the first guidance information, where the first reply information is a reply instruction given by a user; and determining a first trip plan according to the first reply information.
On the other hand, an embodiment of the present application further provides a device for intelligent travel based on big data, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the steps of the method according to the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the real-time influence information on the trip of the user is obtained based on a big data information processing technology, the first departure information is obtained, the first road prediction information is obtained by inputting the first departure information and the first influence information into a training model, and the data is processed based on the way that the training model can continuously learn and obtain experience, so that the road congestion time and the trip time are accurately predicted, and the technical purpose of accurately planning the trip route and the trip time by combining multiple factors such as real-time traffic state, weather and the like is realized.
The foregoing is a summary of the present disclosure, and embodiments of the present disclosure are described below to make the technical means of the present disclosure more clearly understood.
Drawings
Fig. 1 is a schematic flow chart of a big data-based intelligent travel method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an intelligent trip device based on big data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a first input unit 16, a sixth obtaining unit 17, a first transmitting unit 18, a seventh obtaining unit 19, an eighth obtaining unit 20, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.
Detailed Description
By providing the method and the device for intelligent travel based on big data, the technical problems that in the prior art, the planning mode of the current travel route is single, and the travel route cannot be accurately planned by combining multiple influence factors are solved, and the technical purpose of accurately planning the travel route and the travel time by combining multiple factors such as real-time traffic states and weather based on machine learning is achieved. Hereinafter, example embodiments of the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Big data is a product of the high-tech era, and the strategic significance of big data technology is not to master huge data information, but to perform specialized processing on the meaningful data. With the rapid development of big data technology, big data application has been integrated into various industries. The intelligent travel is also called intelligent transportation, and the traffic condition is sensed in real time by means of advanced technologies and concepts such as mobile internet, cloud computing, big data and internet of things, so that convenience is provided for social life, and the urban operation efficiency is improved. The technical problems that the existing travel route is single in planning mode and can not be accurately planned by combining multiple influence factors exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides an intelligent travel method based on big data, which is applied to an intelligent travel planning system, wherein the method comprises the following steps: obtaining information of a place of departure; obtaining destination information; obtaining first route information according to the departure place information and the destination information; obtaining a first departure time; acquiring first influence information according to the first departure time; inputting the first route information and the first influence information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the first route information, the first influence information, and identification information for identifying predicted congestion time and corresponding arrival time; obtaining first output information of the first training model, wherein the first output information comprises first road prediction information, and the first road prediction information is time for predicting that a road starts to be congested and time-consuming information corresponding to the time for the road to reach a destination; sending first guide information according to the first road prediction information, wherein the first guide information is used for sending suggested departure time and predicted arrival time to a user; acquiring first reply information according to the first guide information, wherein the first reply information is a reply instruction given by a user; and determining a first trip plan according to the first reply information.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a big data-based intelligent travel method, which is applied to an intelligent travel planning system, where the method includes:
step S100: obtaining information of a place of departure;
step S200: obtaining destination information;
step S300: obtaining first route information according to the departure place information and the destination information;
specifically, the user inputs the departure place information and the destination information into the intelligent travel planning system through voice input and character input, the system identifies the input information through semantic identification, keyword extraction and the like, and the input information is matched with place information in a database of the system, so that the accurate departure place information and the accurate destination information are obtained. And planning a route through a mobile phone navigation system and a global positioning system of a user mobile terminal according to the departure place information and the destination information, thereby obtaining the first route information.
Step S400: obtaining a first departure time;
specifically, the first departure time is obtained by inputting information to the intelligent travel planning system by the user through manual selection or voice input, so that a route is planned more accurately in real time.
Step S500: acquiring first influence information according to the first departure time;
specifically, the first influence information includes external information influencing the trip of the user, such as municipal road information, real-time weather information, city limit information, road traffic condition information, and the like. The intelligent travel planning system acquires influence information of the user travel time, namely the first influence information, from platforms such as the internet, a traffic department, a weather forecast system and the like through a big data information processing technology.
Step S600: inputting the first route information and the first influence information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the first route information, the first influence information, and identification information for identifying predicted congestion time and corresponding arrival time;
step S700: obtaining first output information of the first training model, wherein the first output information comprises first road prediction information, and the first road prediction information is time for predicting that a road starts to be congested and time-consuming information corresponding to the time for the road to reach a destination;
specifically, the training model is a machine learning model, and the machine learning model can continuously learn through a large amount of data, further continuously modify the model, and finally obtain satisfactory experience to process other data. The machine model is obtained by training a plurality of groups of training data, and the process of training the training model by the training data is essentially the process of supervised learning. Each set of training data in the plurality of sets of training data comprises: the first route information, the first impact information, and identification information for identifying a predicted congestion time and a corresponding arrival time. Under the condition of obtaining the first route information and the first influence information, the machine learning model outputs the first road prediction information, the first road prediction information output by the machine learning model is verified through identification information for identifying predicted congestion time and corresponding arrival time, if the output first road prediction information is consistent with the identification information for identifying predicted congestion time and corresponding arrival time, the data supervised learning is finished, and then the next group of data supervised learning is carried out; and if the output first road prediction information is inconsistent with the predicted congestion time and the identification information of the corresponding arrival time, adjusting the machine learning model by the machine learning model, and performing supervised learning of the next group of data after the machine learning model reaches the expected accuracy. The machine learning model is continuously corrected and optimized through training data, the accuracy of the machine learning model for processing the data is improved through the process of supervised learning, and then the first road prediction information, namely the time for predicting the road to start to be congested and the time-consuming information corresponding to the time for reaching a destination are more accurate, so that a foundation is laid for realizing the accuracy of road planning.
Step S800: sending first guide information according to the first road prediction information, wherein the first guide information is used for sending suggested departure time and predicted arrival time to a user;
specifically, the first road prediction information includes time for predicting the start of road congestion and time-consuming information corresponding to the time for the road to reach a destination, the intelligent travel planning system estimates travel time and estimated arrival time of the user by analyzing the first road prediction information, obtains the recommended departure time and the recommended travel mode according to the time-consuming information, sends the first guidance information to the user, and the user performs selection and confirmation.
Step S900: acquiring first reply information according to the first guide information, wherein the first reply information is a reply instruction given by a user;
step S1000: and determining a first trip plan according to the first reply information.
Specifically, after the system sends the first guidance information to the user, the user performs confirmation and selection of information such as a travel mode and travel time according to the first guidance information, so as to obtain the first reply information, and the intelligent travel planning system determines the first travel plan according to the reply information of the user.
Further, the embodiment S200 of the present application further includes:
step S201: obtaining first destination input information;
step S202: obtaining first user information according to the sent first target input information;
step S203: acquiring a first user history record according to first user information, wherein the first user history record is an associated address information database of a first user acquired through big data;
step S204: obtaining first address matching information according to the first destination input information and the first user history record;
step S205: and obtaining the destination information according to the first address matching information.
Specifically, the intelligent travel planning system obtains the first user history record including the associated address data information of the first user by obtaining purchase record information, browsing record information, and the like in the user mobile terminal based on a big data information processing technology after obtaining destination information input by the user. For example, shopping mall address information is obtained by obtaining shopping bill information of the user, and dining address information, viewing address information and the like of the user are obtained through consumption records of a group purchase platform of the user. And then matching the destination information input by the user with the first user history record so as to obtain accurate destination information.
Further, step S300 in the embodiment of the present application further includes:
step S301: obtaining first historical driving information according to the departure place information and the destination information;
step S302: obtaining a first driving frequency according to the first historical driving information;
step S303: judging whether the first running frequency meets a first preset condition or not;
step S304: and when the first route information is satisfied, obtaining the first route information according to the first historical driving information.
Specifically, after determining the departure information and the destination information, the intelligent travel planning system obtains first travel frequency information of the route of the user by comparing and analyzing travel record information of the user, and determines whether the first travel frequency information meets the first predetermined condition, if so, the route is a common route of the user, and may be an address of a school, an organization, and the like, and then performs a preferred travel route on the common route of the user, so as to obtain the first route information.
Further, step S304 in the embodiment of the present application further includes:
step S3041: obtaining a first historical driving time according to the first historical driving information;
step S3042: judging whether the first historical travel time meets a second preset condition or not;
step S3043: when the first road information is not satisfied, obtaining first road information according to the first historical travel time;
step S3044: judging whether the first road information meets a trip condition;
step S3045: when the recommended driving route is not met, obtaining a recommended driving route;
step S3046: and obtaining the first route information according to the recommended driving route.
Specifically, the intelligent travel planning system obtains the common time of the user when the user travels on the first route information after determining that the first route information is the common route of the user, obtains the road condition information in the first route information based on a big data information processing technology if the first route information is determined to be the common route within a certain time threshold, and determines whether the first route information meets travel conditions, and replans the travel route for the user if the first route information does not meet the travel conditions, such as road construction and the like.
Further, step S500 in the embodiment of the present application further includes:
step S501: obtaining first user information;
step S502: acquiring license plate information of a first user according to the first user information;
step S503: obtaining the location information of a first user according to the first user information, wherein the location information of the first user is local vehicle management information;
step S504: acquiring license plate information of a first user according to the first departure time and the information of the location of the first user, and acquiring first driving management information;
step S505: and obtaining the first influence information according to the first running management information.
Specifically, the intelligent travel planning system obtains license plate information of the user and local vehicle management information of a city where the user is located based on a big data information processing technology, so as to determine influence information of vehicle travel of the user, for example, determine whether the vehicle of the user meets a local number limit policy.
Further, step S1000 in the embodiment of the present application further includes:
step S1001: and obtaining first reminding information according to the first trip plan.
Specifically, the first reminding information is that after the travel plan of the user is determined, the intelligent travel planning system reminds the user before the travel time arrives by setting an alarm clock and the like according to preset travel time information, and automatically navigates according to the first travel plan in the travel process of the user.
Further, step S600 in the embodiment of the present application further includes:
step S601: obtaining first training data and second training data in the multiple groups of training data until Nth training data;
step S602: generating first identification codes according to first training data, wherein the first identification codes correspond to the first training data one to one;
step S603: generating a second identification code according to the second training data and the first identification code, and generating an Nth identification code according to the Nth training data and the (N-1) th identification code by analogy, wherein N is a natural number greater than 1;
step S604: and copying and saving the training data and the identification codes on the M pieces of electronic equipment.
Specifically, in order to ensure the safety of the training data storage, a first identification code is generated according to the first training data, wherein the first identification code and the first training data are in one-to-one correspondence; and generating a second identification code … according to the second training data and the first identification code, and so on, using the first training data and the first identification code as a first storage unit, using the second training data and the second identification code as a second storage unit …, and so on, and obtaining N storage units in total. The identification code information is used as main body identification information, and the identification information of the main body is used for distinguishing from other main bodies. When the training data needs to be called, after each next node receives the data stored by the previous node, the data is verified through a common identification mechanism and then stored, and each storage unit is connected in series through a Hash technology, so that the training data is not easy to lose and damage, the safety and the accuracy of the training data information are improved through a data information processing technology based on a block chain, the accuracy of calling the training data information by an identification code is ensured, and the accuracy of a training model is further improved.
Further, step S205 in the embodiment of the present application further includes:
step S2051: obtaining a second confirmation instruction, and sending the destination information to the user for confirmation;
step S2052: after the user confirms, judging whether the destination information is a restaurant or not;
step S2053: if the destination information is a restaurant, obtaining dining information of the restaurant;
step S2054: judging whether the dining information of the restaurant meets a third preset condition or not;
step S2055: and if the dining information of the restaurant meets the third preset condition, obtaining a first preset instruction.
Specifically, if the destination is obtained through the first address matching information, the destination information is address information that the user has gone to, the user confirms the destination address information, if the destination is a restaurant, the predetermined amount and the passenger flow information of the restaurant are obtained based on a big data information processing technology, whether the restaurant can have meals at the arrival time of the user is judged, and if yes, the system performs advance reservation for the user, so that the intelligence of route planning is further improved.
To sum up, the method for intelligent travel based on big data provided by the embodiment of the application has the following technical effects:
the real-time influence information on the trip of the user is obtained based on a big data information processing technology, the first departure information is obtained, the first road prediction information is obtained by inputting the first departure information and the first influence information into a training model, and the data is processed based on the way that the training model can continuously learn and obtain experience, so that the road congestion time and the trip time are accurately predicted, and the technical purpose of accurately planning the trip route and the trip time by combining multiple factors such as real-time traffic state, weather and the like is realized.
Example two
Based on the same inventive concept as the method for intelligent trip based on big data in the foregoing embodiment, the present invention further provides a device for intelligent trip based on big data, as shown in fig. 2, the device includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining departure place information;
a second obtaining unit 12, the second obtaining unit 12 being configured to obtain destination information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain first route information according to the departure place information and the destination information;
a fourth obtaining unit 14, wherein the fourth obtaining unit 14 is configured to obtain the first departure time;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to obtain first influence information according to the first departure time;
a first input unit 16, where the first input unit 16 is configured to input the first route information and the first influence information into a first training model, where the first training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the first route information, the first influence information, and identification information for identifying predicted congestion time and corresponding arrival time;
a sixth obtaining unit 17, where the sixth obtaining unit 17 is configured to obtain first output information of the first training model, where the first output information includes first road prediction information, and the first road prediction information is time for predicting that a road starts to be congested and time consumption information corresponding to the time for the road to reach a destination;
a first sending unit 18, where the first sending unit 18 is configured to send first guidance information according to the first road prediction information, where the first guidance information is used to send a proposed departure time and an expected arrival time to a user;
a seventh obtaining unit 19, where the seventh obtaining unit 19 is configured to obtain first reply information according to the first guidance information, where the first reply information is a reply instruction given by a user;
an eighth obtaining unit 20, where the eighth obtaining unit 20 is configured to determine a first trip plan according to the first reply information.
Further, the apparatus further comprises:
a ninth obtaining unit configured to obtain first destination input information;
a tenth obtaining unit, configured to obtain first user information according to the sent first destination input information;
an eleventh obtaining unit, configured to obtain a first user history record according to first user information, where the first user history record is an associated address information database of a first user obtained through big data;
a twelfth obtaining unit, configured to obtain first address matching information according to the first destination input information and the first user history;
a thirteenth obtaining unit configured to obtain the destination information according to the first address matching information.
Further, the apparatus further comprises:
a fourteenth obtaining unit configured to obtain first history traveling information based on the departure point information and the destination information;
a fifteenth obtaining unit configured to obtain a first travel frequency from the first history travel information;
a first judging unit configured to judge whether the first travel frequency satisfies a first predetermined condition;
a sixteenth obtaining unit configured to obtain the first route information according to the first history traveling information when satisfied.
Further, the apparatus further comprises:
a seventeenth obtaining unit configured to obtain a first historical travel time according to the first historical travel information;
a second determination unit configured to determine whether the first historical travel time satisfies a second predetermined condition;
an eighteenth obtaining unit configured to obtain first road information according to the first history travel time when not satisfied;
a third judging unit, configured to judge whether the first road information meets a trip condition;
a nineteenth obtaining unit that obtains the recommended travel route when not satisfied;
a twentieth obtaining unit configured to obtain the first route information according to the recommended travel route.
Further, the apparatus further comprises:
a twenty-first obtaining unit, configured to obtain first user information;
a twenty-second obtaining unit, configured to obtain license plate information of the first user according to the first user information;
a twenty-third obtaining unit, configured to obtain information of a location of a first user according to the first user information, where the information of the location of the first user is local vehicle management information;
a twenty-fourth obtaining unit, configured to obtain license plate information of the first user according to the first departure time and the location information of the first user, and obtain first driving management information;
a twenty-fifth obtaining unit configured to obtain the first influence information according to the first travel management information.
Further, the apparatus further comprises:
a twenty-sixth obtaining unit, configured to obtain first reminding information according to the first trip plan.
Further, the apparatus further comprises:
a twenty-seventh obtaining unit, configured to obtain first training data, second training data, and up to nth training data in the multiple sets of training data;
a twenty-eighth obtaining unit, configured to generate a first identification code according to first training data, where the first identification code corresponds to the first training data one to one;
a twenty-ninth obtaining unit, configured to generate a second identification code according to the second training data and the first identification code, and generate an nth identification code according to the nth training data and an nth-1 identification code by analogy, where N is a natural number greater than 1;
the first storage unit is used for copying and storing the training data and the identification codes on the M pieces of electronic equipment.
Various changes and specific examples of the method for intelligent big-data-based travel in the first embodiment of fig. 1 are also applicable to the device for intelligent big-data-based travel in the present embodiment, and through the foregoing detailed description of the method for intelligent big-data-based travel, a person skilled in the art can clearly know the device for intelligent big-data-based travel in the present embodiment, so for the brevity of the description, detailed descriptions are omitted here.
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the big data based intelligent trip method in the foregoing embodiments, the present invention further provides a big data based intelligent trip apparatus, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the methods of the big data based intelligent trip method described above.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the application provides an intelligent trip method based on big data, wherein the method comprises the following steps: obtaining information of a place of departure; obtaining destination information; obtaining first route information according to the departure place information and the destination information; obtaining a first departure time; acquiring first influence information according to the first departure time; inputting the first route information and the first influence information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the first route information, the first influence information, and identification information for identifying predicted congestion time and corresponding arrival time; obtaining first output information of the first training model, wherein the first output information comprises first road prediction information, and the first road prediction information is time for predicting that a road starts to be congested and time-consuming information corresponding to the time for the road to reach a destination; sending first guide information according to the first road prediction information, wherein the first guide information is used for sending suggested departure time and predicted arrival time to a user; acquiring first reply information according to the first guide information, wherein the first reply information is a reply instruction given by a user; and determining a first trip plan according to the first reply information.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, 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 has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. An intelligent travel method based on big data is applied to an intelligent travel planning system, wherein the method comprises the following steps:
obtaining information of a place of departure;
obtaining destination information;
obtaining first route information according to the departure place information and the destination information;
obtaining a first departure time;
acquiring first influence information according to the first departure time;
inputting the first route information and the first influence information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the first route information, the first influence information, and identification information for identifying predicted congestion time and corresponding arrival time;
obtaining first output information of the first training model, wherein the first output information comprises first road prediction information, and the first road prediction information is time for predicting that a road starts to be congested and time-consuming information corresponding to the time for the road to reach a destination;
sending first guide information according to the first road prediction information, wherein the first guide information is used for sending suggested departure time and predicted arrival time to a user;
acquiring first reply information according to the first guide information, wherein the first reply information is a reply instruction given by a user;
and determining a first trip plan according to the first reply information.
2. The method of claim 1, wherein the obtaining destination information comprises:
obtaining first destination input information;
obtaining first user information according to the sent first target input information;
acquiring a first user history record according to first user information, wherein the first user history record is an associated address information database of a first user acquired through big data;
obtaining first address matching information according to the first destination input information and the first user history record;
and obtaining the destination information according to the first address matching information.
3. The method of claim 1, wherein the obtaining first route information according to the departure place information and the destination information comprises:
obtaining first historical driving information according to the departure place information and the destination information;
obtaining a first driving frequency according to the first historical driving information;
judging whether the first running frequency meets a first preset condition or not;
and when the first route information is satisfied, obtaining the first route information according to the first historical driving information.
4. The method according to claim 3, wherein the obtaining the first route information from the first historical travel information when satisfied includes:
obtaining a first historical driving time according to the first historical driving information;
judging whether the first historical travel time meets a second preset condition or not;
when the first road information is not satisfied, obtaining first road information according to the first historical travel time;
judging whether the first road information meets a trip condition;
when the recommended driving route is not met, obtaining a recommended driving route;
and obtaining the first route information according to the recommended driving route.
5. The method of claim 1, wherein the method comprises:
obtaining first user information;
acquiring license plate information of a first user according to the first user information;
obtaining the location information of a first user according to the first user information, wherein the location information of the first user is local vehicle management information;
acquiring license plate information of a first user according to the first departure time and the information of the location of the first user, and acquiring first driving management information;
and obtaining the first influence information according to the first running management information.
6. The method of claim 1, wherein said determining a first travel plan based on said first reply information comprises:
and obtaining first reminding information according to the first trip plan.
7. The method of claim 1, wherein said inputting the first route information, the first impact information into a first training model comprises:
obtaining first training data and second training data in the multiple groups of training data until Nth training data;
generating first identification codes according to first training data, wherein the first identification codes correspond to the first training data one to one;
generating a second identification code according to the second training data and the first identification code, and generating an Nth identification code according to the Nth training data and the (N-1) th identification code by analogy, wherein N is a natural number greater than 1;
and copying and saving the training data and the identification codes on the M pieces of electronic equipment.
8. An apparatus for intelligent trip based on big data, wherein the apparatus comprises:
a first obtaining unit for obtaining origin information;
a second obtaining unit configured to obtain destination information;
a third obtaining unit, configured to obtain first route information according to the departure place information and the destination information;
a fourth obtaining unit configured to obtain a first departure time;
a fifth obtaining unit, configured to obtain first influence information according to the first departure time;
a first input unit, configured to input the first route information and the first influence information into a first training model, where the first training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the first route information, the first influence information, and identification information for identifying predicted congestion time and corresponding arrival time;
a sixth obtaining unit, configured to obtain first output information of the first training model, where the first output information includes first road prediction information, and the first road prediction information is time for predicting a road to start to be congested and time-consuming information of the predicted road to reach a destination;
the first sending unit is used for sending first guide information according to the first road prediction information, and the first guide information is used for sending suggested departure time and predicted arrival time to a user;
a seventh obtaining unit, configured to obtain first reply information according to the first guidance information, where the first reply information is a reply instruction given by a user;
an eighth obtaining unit, configured to determine a first trip plan according to the first reply information.
9. An apparatus for big data based intelligent travel, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the program.
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| CN202110890045.0A CN113570096A (en) | 2021-08-04 | 2021-08-04 | Intelligent trip method and device based on big data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114677839A (en) * | 2022-05-29 | 2022-06-28 | 深圳市鸿华通交通设施工程有限公司 | Customized travel intelligent transportation system based on big data and control method thereof |
| CN116772877A (en) * | 2023-03-21 | 2023-09-19 | 纬创软件（北京）有限公司 | Method, system, device and medium for predicting endurance mileage of new energy automobile |
-
2021
- 2021-08-04 CN CN202110890045.0A patent/CN113570096A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114677839A (en) * | 2022-05-29 | 2022-06-28 | 深圳市鸿华通交通设施工程有限公司 | Customized travel intelligent transportation system based on big data and control method thereof |
| CN116772877A (en) * | 2023-03-21 | 2023-09-19 | 纬创软件（北京）有限公司 | Method, system, device and medium for predicting endurance mileage of new energy automobile |
| CN116772877B (en) * | 2023-03-21 | 2024-05-28 | 纬创软件（北京）有限公司 | Method, system, device and medium for predicting endurance mileage of new energy automobile |
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