CN116258290A - Wisdom trip system based on big data - Google Patents

Abstract

The invention discloses an intelligent travel system based on big data, which comprises a data processing terminal, a cloud computing platform, an intelligent traffic module, a travel planning module, a real-time updating module and a GPS positioning module. This wisdom travel system based on big data, in the route planning of going out, the more comprehensive line analysis that realizes starting point to destination, select the shortest optimizing line of time simultaneously, and at the in-process of going out, utilize real-time update module and GPS orientation module to monitor concrete prediction optimizing line and the peripheral highway section condition information of the highway section that is traveling, thereby predict the emergency on the line in advance, and switch to the shortest optimizing line of time when dodging the emergency, then realized the intelligent trip operation after the big data analysis, the effectual efficiency of having improved the trip, and compared the time of going out has been practiced thrift in ordinary trip.

Description

A smart travel system based on big data

Technical Field

The present invention relates to the field of big data technology, and in particular to a smart travel system based on big data.

Background Art

Big data is a product of the high-tech era. The strategic significance of big data technology lies not in mastering huge amounts of data information, but in professionally processing these meaningful data. With the rapid development of big data technology, big data applications have been integrated into all walks of life. Smart travel, also known as intelligent transportation, uses advanced technologies and concepts such as mobile Internet, cloud computing, big data, and the Internet of Things to achieve real-time perception of traffic conditions, thereby providing convenience for social life and improving urban operation efficiency.

The invention with patent publication number CN113570096A discloses a method and device for intelligent travel based on big data, which is applied to an intelligent travel planning system, wherein the method includes: obtaining departure information; obtaining destination information, thereby obtaining first route information; obtaining first departure time and first impact information; inputting the first route information and the first impact information into a first training model, thereby obtaining first road prediction information, wherein the first road prediction information is the time when the road starts to be congested and the corresponding time information of reaching the destination; according to the first road prediction information, sending a recommended departure time and an estimated arrival time to the user; after the user gives a reply instruction, determining the first travel plan. The method solves the technical problem that the current travel route planning method in the prior art is relatively single and cannot combine multiple influencing factors to plan the travel route more accurately.

Based on the above, the travel route is planned more accurately by combining multiple influencing factors, so as to achieve an ideal route planning trip. However, the following problems still exist in the process of travel:

As time goes by after the trip, other factors change on the planned route from the departure point to the destination, making it impossible to make advance predictions. In addition, it is impossible to update and feedback data in real time during the trip, such as traffic jams caused by accidents, damaged traffic lights, and a sharp increase in traffic during rush hours. As a result, the actual time for driving the planned route is longer than the expected time for the optimal planned route, which affects the time of travel arrangements.

To this end, the present invention provides a smart travel system based on big data.

Summary of the invention

In view of the shortcomings of the prior art, the present invention provides a smart travel system based on big data, which solves the problem that the existing smart travel system cannot make advance predictions due to changes in other factors in the planned route, and since it is impossible to realize real-time update and feedback operations of data during the travel process, the actual driving time of the planned route is longer than the expected preferred planned route time, thereby affecting the travel arrangement time.

To achieve the above objectives, the present invention is implemented through the following technical solutions: A smart travel system based on big data, comprising:

The data processing terminal is used to collect images of various information data in the travel route, and pre-process the collected data to convert the image collected data into digital data, and then transmit the processed data to the cloud computing platform. The various information data include road section traffic information, route information, parking information and weather information;

The cloud computing platform is used to further analyze and calculate the information pre-processed by the data processing terminal, and compare the travel time of multiple route segments within the same starting point according to the calculated data, until the road segment information with the shortest time at each starting point is selected for combination, and the deeply processed data is transmitted to the intelligent transportation module and the travel planning module to realize the use of data;

The intelligent traffic module combines traffic information to sort out the traffic conditions on the route, including the number of traffic lights and the switching time of the corresponding route, the traffic jam caused by the volume of traffic on the road section, and the traffic accidents on the road section;

The travel planning module plans the specific predicted optimized route for the trip based on the user's starting point input and the optimized road section information calculated by the cloud computing platform, in combination with the parking information at the destination and the weather information along the way. At the same time, the data is converted and transmitted to the vehicle for voice broadcast via the car computer.

Preferably, it also includes a real-time update module and a GPS positioning module. The real-time update module monitors the specific predicted optimized route and the surrounding road conditions of the driving section during the travel of the specific predicted optimized route, and the positioning information of the driving route is displayed on the vehicle computer through the GPS positioning module. At the same time, the information monitored by the real-time update module is transmitted to the cloud computing platform, the intelligent transportation module and the travel planning module, and calculations and comparisons are performed based on the changes in information during the driving process, so as to optimize the travel route of the next section.

Preferably, the specific method of preprocessing of the data processing terminal is:

The satellite sensor collects images of the city's ground information and transmits the collected data to the data conversion module to convert the image information into digital information;

The converted data is then analyzed and preliminarily screened, and the screened information is summarized in the model building module to form a traffic route construction model for the city, and the preprocessed information is transmitted to the cloud computing platform.

Preferably, the specific implementation method of the cloud computing platform for reprocessing the preprocessed data is:

S1, the urban traffic route construction model formed by extraction;

S2, converting the image data into corresponding actual digital data, the cloud computing platform also includes a data analysis module, a data calculation module and a data comparison module;

The data is analyzed by the data analysis module, and the data calculation module is used to calculate the data to obtain data segments of multiple different road sections, and finally the data comparison module is used to select data segments with the same starting point for comparison to obtain the data segment with the shortest time;

S3. The obtained data segments are transmitted to the intelligent transportation module and the travel planning module to realize the planning operation of optimizing the route.

Preferably, the operation method for forming a travel route with the shortest time for data processing in S2 is:

S2-1. Input the current starting point and the destination location.

S2-2, dividing the total road section from the starting point to the destination into multiple single segments P ₁ , P ₂ , P ₃ , ..., P _n according to the form of from the starting point to the traffic light, between each traffic light and the traffic light, and from the traffic light to the destination;

接着将多个因素片段的行驶时间进行对应的计算得到

S2-3, and the way to reach the starting point to the end point of a single segment of _P1 is further divided into multiple factor segments

Then the travel time of multiple factor segments is calculated accordingly to obtain

S2-4, compare the time of multiple factor segments in a single segment, select the shortest time factor segment in each single segment, and finally obtain the shortest travel route, which is transmitted to the travel planning module to realize the predicted travel route broadcast;

Preferably, the travel time of the vehicle on different roads is taken into account, and the travel time of the traffic lights on each road on the route, as well as the arrival time and parking time of the parking space, to calculate the travel planning section with the shortest travel time, and finally select the planned route with the shortest travel time. The calculation process in S2-1 to S2-4 is described by the following formula:

The time calculation formula for removing the traffic jam section from a single segment after combination is:

Where S _pn is the distance of a factor segment in a single segment, m is the number of congested vehicles in the factor segment, S _i is the average length and interval length of congested vehicles in the factor segment, and V ₁ is the speed during normal driving;

The time calculation formula for the combined traffic jam section is:

Where V ₂ is the speed when there is a vehicle ahead and the vehicle is at the traffic light;

The formula for calculating the parking time after arriving at the destination is:

Where S _q is the length of the road section during parking, and V ₃ is the driving speed during parking;

Combining the above formulas, the total travel time formula is ①+②+③:

Preferably, during the driving process, when driving in a single segment, by monitoring the road conditions of the current driving section, the road conditions of other factor segments in the single segment and the road conditions of all factor segments to be entered next, when an emergency is predicted, by avoiding the emergency section, the time taken for the road conditions of other factor segments is compared, and a second optimized route is selected and transmitted to the travel planning module for replacement.

Preferably, the specific manner in which the data comparison module performs data comparison is:

并采用逐级比对法实现数据的比对操作；Select one of the individual segments and calculate the time required for the multiple factor segments it contains to obtain the time required for each factor segment:

And the step-by-step comparison method is used to realize the data comparison operation;

和

进行比对，

和

进行比对，以此类推，两者相比较时间较少的一个继续与下一个对比，直至得到最短时间的因素片段；Among them will

and

Make a comparison,

and

Compare them, and so on, the one with the shorter time will be compared with the next one, until the shortest time factor segment is obtained;

Preferably, the comparison operation is applicable to the comparison process of the real-time update module after the route monitoring, so as to achieve the travel by always maintaining the shortest time route in the road section after the update.

Beneficial Effects

The present invention provides a smart travel system based on big data. Compared with the prior art, it has the following beneficial effects:

(1) The smart travel system based on big data collects and preliminarily processes data through a data processing terminal, and then reprocesses the data through a cloud computing platform. The converted data is segmented into data segments of multiple different sections, and then further subdivided into multiple factor segments. In this way, in the travel route planning, a more comprehensive route analysis from the starting point to the destination is realized, and the shortest optimized route is selected at the same time. In the process of travel, the real-time update module and the GPS positioning module are used to monitor the specific predicted optimized route and the surrounding road conditions of the driving section, so as to predict the emergency situation on the route in advance, and switch to the shortest optimized route while avoiding the emergency situation. Then, the smart travel operation based on big data analysis is realized, which effectively improves the travel efficiency and saves the travel time compared with the usual travel.

(2) The smart travel system based on big data, through the provision of intelligent traffic module and travel planning module, sorts out the traffic conditions in the travel route, including the number of traffic lights and the conversion time of the corresponding route, the traffic jam caused by the volume of traffic in the road section, and makes advance planning, and at the same time plans the specific predicted optimized route of the travel route, and at the same time transmits the data to the driving vehicle through the vehicle computer for voice broadcast, thereby achieving safe and convenient operation based on vehicle travel, and can timely avoid congestion and waiting at traffic lights, making the travel process smoother, thereby improving the comfort of people's travel mood.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of a smart travel system based on big data according to the present invention.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Please refer to Figure 1, the present invention provides two technical solutions:

Embodiment 1

A smart travel system based on big data, including a data processing terminal, a cloud computing platform, an intelligent transportation module, a travel planning module, a real-time update module and a GPS positioning module;

The output end of the data processing terminal is electrically connected to the input end of the cloud computing platform, the cloud computing platform is electrically connected to the intelligent transportation module and the travel planning module in a two-way manner, the output end of the cloud computing platform is connected to the input end of the real-time update module, the real-time update module is electrically connected to the GPS positioning module in a two-way manner, and the output end of the real-time update module is electrically connected to the input end of the intelligent transportation module and the travel planning module;

The data processing terminal is used to collect images of various information data in the travel route, and pre-process the collected data to convert the image collected data into digital data, and then transmit the processed data to the cloud computing platform. The various information data include road section traffic information, route information, parking information and weather information;

The data processing terminal also includes satellite sensors, image collectors, and weather prediction modules;

And the satellite sensors are used to detect and transmit traffic information and route information;

The image collector is used to collect images of building information, road information, and parking space information in the city, and the corresponding size of the side end;

The weather forecast module is used to obtain information about environmental changes in the area where the vehicle will be traveling in the future and transmit it to the travel planning module;

The cloud computing platform is used to further analyze and calculate the information pre-processed by the data processing terminal, and compare the travel time of multiple route segments within the same starting point according to the calculated data, until the road segment information with the shortest time at each starting point is selected for combination, and the deeply processed data is transmitted to the intelligent transportation module and the travel planning module to realize the use of data;

The intelligent traffic module combines traffic information to sort out the traffic conditions on the route, including the number of traffic lights and the switching time of the corresponding route, the traffic jam caused by the volume of traffic on the road section, and the traffic accidents on the road section;

The travel planning module plans the specific predicted optimized route for the trip based on the user's starting point input and the optimized road section information calculated by the cloud computing platform, in combination with the parking information at the destination and the weather information along the way. At the same time, the data is converted and transmitted to the vehicle for voice broadcast via the car computer.

It also includes a real-time update module and a GPS positioning module. The real-time update module monitors the specific predicted optimized route and the surrounding road conditions of the driving section during the travel process of the specific predicted optimized route, and the positioning information of the driving route is displayed on the vehicle computer through the GPS positioning module. At the same time, the information monitored by the real-time update module is transmitted to the cloud computing platform, the intelligent transportation module and the travel planning module, and calculations and comparisons are performed based on the changes in information during the driving process, so as to optimize the travel route of the next section.

The data is collected and preliminarily processed through the data processing terminal, and then reprocessed through the cloud computing platform. The data is converted and segmented into data segments of multiple different sections, and then further subdivided into multiple factor segments, so that in the travel route planning, a more comprehensive route analysis from the starting point to the destination can be achieved, and the optimized route with the shortest time can be selected. In the process of travel, the real-time update module and the GPS positioning module are used to monitor the specific predicted optimized route and the surrounding road conditions of the driving section, so as to predict emergencies on the route in advance, and switch to the optimized route with the shortest time while avoiding emergencies, thereby realizing intelligent travel operations based on big data analysis, effectively improving travel efficiency, and saving travel time compared to ordinary travel.

In the embodiment of the present invention, the specific method of preprocessing of the data processing terminal is:

The satellite sensor collects images of the city's ground information and transmits the collected data to the data conversion module to convert the image information into digital information;

The converted data is then analyzed and preliminarily screened, and the screened information is summarized in the model building module to form a traffic route construction model for the city, and the preprocessed information is transmitted to the cloud computing platform.

In the embodiment of the present invention, the specific implementation method of reprocessing the preprocessed data by the cloud computing platform is as follows:

S1, the urban traffic route construction model formed by extraction;

S2, converting the image data into corresponding actual digital data, the cloud computing platform also includes a data analysis module, a data calculation module and a data comparison module;

The data is analyzed by the data analysis module, and the data calculation module is used to calculate the data to obtain data segments of multiple different road sections. Finally, the data comparison module is used to select data segments with the same starting point for comparison to obtain the data segment with the shortest time.

S3. The obtained data segments are transmitted to the intelligent transportation module and the travel planning module to realize the planning operation of optimizing the route.

In the embodiment of the present invention, the operation method for forming the travel route with the shortest time for data processing in S2 is:

S2-1. Input the current starting point and the destination.

S2-2, dividing the total road section from the starting point to the destination into multiple single segments P ₁ , P ₂ , P ₃ , ..., P _n according to the form of from the starting point to the traffic light, between each traffic light and the traffic light, and from the traffic light to the destination;

接着将多个因素片段的行驶时间进行对应的计算得到

S2-3, and the way to reach the starting point to the end point of a single segment of _P1 is further divided into multiple factor segments

Then the travel time of multiple factor segments is calculated accordingly to obtain

S2-4, compare the time of multiple factor segments in a single segment, select the shortest time factor segment in each single segment, and finally obtain the shortest travel route, which is transmitted to the travel planning module to realize the predicted travel route broadcast;

In the embodiment of the present invention, the travel time of the vehicle on different roads is taken into account, and the travel time of the traffic lights on each road on the route, as well as the arrival time and parking time of the parking space, the travel planning section with the shortest travel time is calculated, and finally the planned route with the shortest travel time is selected. The calculation process in S2-1 to S2-4 is described by the following formula:

The time calculation formula for removing the traffic jam section from a single segment after combination is:

Where S _pn is the distance of a factor segment in a single segment, m is the number of congested vehicles in the factor segment, S _i is the average length and interval length of congested vehicles in the factor segment, and V ₁ is the speed during normal driving;

The time calculation formula for the combined traffic jam section is:

Where V ₂ is the speed when there is a vehicle ahead and the vehicle is at the traffic light;

The formula for calculating the parking time after arriving at the destination is:

Where S _q is the length of the road section during parking, and V ₃ is the driving speed during parking;

Combining the above formulas, the total travel time formula is ①+②+③:

In an embodiment of the present invention, during the driving process, when driving in a single segment, by monitoring the road conditions of the current driving section, the road conditions of other factor segments in the single segment and the road conditions of all factor segments to be entered next, when an emergency is predicted, the time taken for the road conditions of other factor segments is compared after avoiding the emergency section, and a second optimized route is selected and transmitted to the travel planning module for replacement.

In the embodiment of the present invention, the specific manner in which the data comparison module performs data comparison is:

并采用逐级比对法实现数据的比对操作；Select one of the individual segments and calculate the time required for the multiple factor segments it contains to obtain the time required for each factor segment:

And the step-by-step comparison method is used to realize the data comparison operation;

和

进行比对，

和

进行比对，以此类推，两者相比较时间较少的一个继续与下一个对比，直至得到最短时间的因素片段；Among them will

and

Make a comparison,

and

Compare them, and so on, the one with the shorter time will be compared with the next one, until the shortest time factor segment is obtained;

Embodiment 2

In the specific implementation process, compared with the first embodiment, the specific difference of this embodiment is that the comparison operation is applicable to the comparison process of the real-time update module after the route monitoring, so as to achieve the shortest time route in the road section to travel after the update.

experiment

The same starting point and destination are selected, and a travel experiment is carried out using two identical vehicles and people. One of them adopts the route planning of the travel system in this embodiment, and the other drives normally autonomously. Finally, the time data taken by the two vehicles to reach the same destination from the same starting point are shown in the following table:

Experimental vehicle 1 (route planning) Experimental vehicle 2 (free route) Short distance scene time/s 196s 215s Long distance scene time/h 1.4h 2h

It can be seen from the data in the table that the measured time for short-distance routes is relatively close, while the time for long-distance routes varies greatly. Therefore, users can use the smart travel system based on big data to travel in a shorter time.

Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims (9)

1. A smart travel system based on big data, characterized in that: comprising: The data processing terminal is used to collect images of various information data in the travel route, perform preprocessing operations on the collected data, convert the image collected data into digital data, and then transmit the processed data to In the cloud computing platform, and all kinds of information data include road section traffic information, route information, parking information and weather information; The cloud computing platform is used to further analyze and calculate the information preprocessed by the data processing terminal, and at the same time compare the travel time of multiple route segments within the same starting point according to the calculated data, until each starting point is selected. Combine the road section information with the shortest time, and transmit the deeply processed data to the intelligent transportation module and travel planning module to realize the use and operation of the data; The intelligent traffic module combines traffic information to sort out the traffic situation in the travel route. The traffic situation includes the number of traffic lights and the conversion time of the corresponding line, the traffic jam caused by the traffic volume in the road section, and the traffic accident situation in the road section; The travel planning module, according to the user's starting point input, and through the optimized road section information calculated by the cloud computing platform, and in conjunction with the parking information of the destination and the weather information during the road section, plans a specific forecast for the travel route Optimize the line, and at the same time, transmit the data to the vehicle through the vehicle-machine conversion for voice broadcast. 2. A kind of intelligent travel system based on big data according to claim 1, it is characterized in that: also comprise real-time update module and GPS positioning module, described real-time update module is based on the travel process of concrete prediction optimization route to this A specific prediction and optimization route and the condition information of the surrounding road sections of the driving section are monitored, and the driving distance positioning information is displayed on the vehicle through the GPS positioning module, and the information monitored by the real-time update module is transmitted to the cloud computing platform, intelligent transportation Module and travel planning module, and calculate and compare according to the information changes in the driving process, so as to optimize the travel route of the next road section. 3. A kind of smart travel system based on big data according to claim 1, characterized in that: the specific preprocessing method of the data processing terminal is: Collect images of the city's ground information through satellite sensors, and transmit the collected data to the data conversion module to convert image information into digital information; Then the converted data is analyzed and preliminarily screened, and the screened information is summarized in the model building module to form a city traffic route structure model, and the preprocessed information is transmitted to the cloud computing platform. 4. A kind of smart travel system based on big data according to claim 2, characterized in that: the specific implementation of the reprocessing of the preprocessed data by the cloud computing platform is as follows: S1, the urban traffic route structure model formed by extraction; S2, converting the image data into corresponding actual digital data, the cloud computing platform also includes a data analysis module, a data calculation module and a data comparison module; And use the data analysis module to analyze the data, calculate the data through the data calculation module to obtain multiple data segments of different road sections, and finally use the data comparison module to select the data segments at the same starting point for comparison to obtain the data segment with the shortest time; S3. The obtained data fragments are transmitted to the intelligent transportation module and the travel planning module to combine to realize the planning operation of the optimized route. 5. A kind of smart travel system based on big data according to claim 4, characterized in that: the travel route operating method with the shortest formation time for data processing in the described S2 is: S2-1. Input the current starting point and the upcoming destination; S2-2. Divide the total road section between the starting point and the destination into multiple single segments P ₁ , P ₂ , P ₃ , .. ., P _n ; S2-3, and the way of arriving at the starting point to the end point of a single segment of P ₁ is divided into multiple factor segments P ₁ ¹ , P ₁ ² , P ₁ ³ , ..., P ₁ ⁿ again, and then multiple factors Carry out corresponding calculations on the travel time of the segments to obtain T ₁ ¹ , T ₁ ² , T ₁ ³ , ..., T ₁ ⁿ ; S2-4. Perform time comparison of multiple factor segments in a single segment, select the shortest time-consuming factor segment in each single segment, and finally obtain the travel route with the shortest time, and transmit it to the travel planning module to realize the predicted travel route broadcast. 6. A smart travel system based on big data according to claim 5, characterized in that: for the passing time of vehicles on different roads, comprehensively consider the passing time of the traffic lights of each road on the route and the arrival of the parking space and parking time, calculate the travel planning section with the least time, and finally select the planning route with the shortest time, and the calculation process in S2-1 to S2-4 is described by the following formula: The formula for calculating the time for removing traffic jams from a single segment after combination:

Among them, S _pn is the distance of a factor segment in a certain single segment, m is the number of congested vehicles in this factor segment, S _i is the average vehicle length and intermittent length of the congested vehicles in this factor segment, V ₁ is normal driving the speed of the process; The formula for calculating the time of the traffic jam section after the combination:

Among them, V ₂ is the speed during the driving process when there is a vehicle in front and the traffic light is passing; The formula for calculating the parking time after arriving at the destination:

Among them, S _q is the length of the road section when parking, and V ₃ is the driving speed in the process of parking; Combining the above formulas, the total travel time formula is ①+②+③:

7. A kind of intelligent travel system based on big data according to claim 5, characterized in that: in the process of driving, when driving in a single segment, by monitoring the road condition of the current driving section, in a single segment Other factor segment road conditions and all the factor segment road conditions entered next, when predicting the unexpected situation, compare the time spent by other factor segment road conditions after avoiding the sudden situation road section, and select the second optimal route And transmit it to the travel planning module for replacement. 8. A kind of smart travel system based on big data according to claim 5, characterized in that: the specific way for the data comparison module to perform data comparison is: Select one of the single fragments, and calculate the time of the multiple factor fragments it contains to obtain the time required for each factor fragment as T ₁ ¹ , T ₁ ² , T ₁ ³ , ..., T ₁ ⁿ , and Use the step-by-step comparison method to realize the comparison operation of data; Among them, T ₁ ¹ is compared with T ₁ ² , T ₁ ³ is compared with T ₁ ⁴ , and so on, and the one with the shorter comparison time between the two continues to compare with the next one until the factor fragment with the shortest time is obtained . 9. A kind of smart travel system based on big data according to claim 8, characterized in that: and the comparison operation is suitable for the comparison process of the real-time update module after the route monitoring, and always maintains the road section after the update. The shortest time route to travel.

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