CN116258290A - Wisdom trip system based on big data - Google Patents
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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
Technical Field
The invention relates to the technical field of big data, in particular to an intelligent travel system based on big data.
Background
Big data are products of the high-tech era, strategic significance of big data technology is not to master huge data information, specialized processing is performed on the data containing significance, along with rapid development of big data technology, big data application is integrated into various industries, intelligent travel is also called intelligent traffic, real-time perception of traffic conditions is achieved by means of advanced technologies and concepts such as mobile internet, cloud computing, big data and Internet of things, convenience is provided for social life, and urban operation efficiency is improved.
The invention discloses a method and a device for intelligent travel based on big data, which are applied to an intelligent travel planning system, wherein the method comprises the following steps: obtaining departure place information; 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 starting congestion time of a road and corresponding time-consuming information for reaching a destination; according to the first road prediction information, sending suggested departure time and expected arrival time to a user; after a reply instruction is given by the user, determining a first travel plan. The travel route planning method and device solve the technical problems that the current travel route planning method in the prior art is single, and the travel route cannot be planned more accurately by combining multiple influence factors.
Based on the combination of the above-mentioned factors, the travel route is planned more accurately, so that an idealized travel route is planned, but the following problems are also existed in the travel process:
the method has the advantages that the method is capable of predicting the planned route from the departure point to the destination in advance due to the fact that other factors are changed along with the time delay after traveling, and the problem that the time of actually traveling the planned route is longer than the time of expected optimal planned route and the travel arrangement time is influenced is solved because the factors such as accident traffic jam, traffic light damage, rapid increase of traffic flow in the time period of going to and off duty and the like cannot be updated and fed back in real time during traveling.
Therefore, the invention provides an intelligent travel system based on big data.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the intelligent travel system based on big data, which solves the problems that the existing intelligent travel system planning route has other factors, so that the prediction cannot be performed in advance, and the real-time updating feedback operation of the data in the travel process cannot be realized, so that the time of actually traveling the planning route is longer than the expected optimal planning route, and the travel arrangement time is influenced.
In order to achieve the above purpose, the invention is realized by the following technical scheme: an intelligent travel system based on big data, comprising:
the data processing terminal is used for carrying out image acquisition on various information data in the travel route, carrying out preprocessing operation on the acquired data, converting the image acquired data into digital data, and then transmitting the processed data into the cloud computing platform, wherein the various information data comprise road section traffic information, route information, parking information and weather information;
the cloud computing platform is used for further analyzing and computing the information preprocessed by the data processing terminal, comparing travel time of a plurality of route segments in the same starting point according to the computed data until the shortest road segment information used on each starting point is selected for combination, and transmitting the data subjected to the deep processing into the intelligent traffic module and the travel planning module to realize the use operation of the data;
the intelligent traffic module is used for combing out traffic conditions in the travel route by combining traffic information, wherein the traffic conditions comprise traffic light number, corresponding route travel conversion time, traffic jam conditions caused by traffic flow in a road section and traffic accident conditions in the road section;
and the trip planning module is used for planning a specific prediction optimization line of the trip route according to the starting point input when a user uses the trip planning module, optimizing road section information calculated through the cloud computing platform and the parking information of a destination and weather information in the road section process, and transmitting data to a travelling crane through vehicle-to-machine conversion for voice broadcasting.
Preferably, the system further comprises a real-time updating module and a GPS positioning module, wherein the real-time updating module monitors the situation information of the specific prediction optimizing line and the peripheral road section of the running road section in the traveling process of the specific prediction optimizing line, the running path positioning information is displayed on the vehicle machine through the GPS positioning module, meanwhile, the information monitored by the real-time updating module is transmitted to the cloud computing platform, the intelligent transportation module and the traveling planning module, and calculation comparison is carried out according to the information change in the traveling process, so that the traveling line of the next road section is optimized.
Preferably, the preprocessing specific mode of the data processing terminal is as follows:
the method comprises the steps that image acquisition is carried out on ground information of a city through a satellite sensor, and acquired data are transmitted to a data conversion module to realize that the image information is changed into digital information;
and then, the converted data is analyzed and subjected to preliminary screening, and the screened information is summarized in a model building module to form a traffic route construction model of the city, and the preprocessed information is transmitted into a cloud computing platform.
Preferably, the specific implementation mode of reprocessing the data to be preprocessed by the cloud computing platform is as follows:
s1, extracting a formed urban traffic route construction model;
s2, converting the image data into corresponding actual digital data, wherein the cloud computing platform further comprises a data analysis module, a data computing module and a data comparison module;
analyzing the data by using a data analysis module, calculating the data by using a data calculation module to obtain a plurality of data fragments of different road sections, and finally selecting the data fragments with the same starting point by using a data comparison module to compare the data fragments to obtain the data fragment with the shortest time;
and S3, transmitting the obtained data fragments to an intelligent traffic module and a travel planning module to realize the planning operation of optimizing the route.
Preferably, the travel route operation method with the shortest forming time for the data processing in S2 is as follows:
s2-1, inputting a starting point where the current position is located and a destination position to be obtained;
s2-2, starting point is to be reachedThe total road section between the destination is divided into a plurality of single fragments P according to the form of starting point to traffic light, each traffic light and the traffic light, and the traffic light to the destination 1 、P 2 、P 3 、...、P n ；
S2-3, and P 1 The arrival mode from the starting point to the end point of the single segment is divided into a plurality of factor segments again
Then, the running time of the plurality of factor segments is correspondingly calculated to obtain
S2-4, performing time comparison on a plurality of factor segments in the single segments, selecting the shortest time-consuming factor segment in each single segment, finally obtaining a travel route with the shortest time, and transmitting the travel route to a travel planning module to realize predicted travel route broadcasting;
preferably, for the traffic time of the vehicle on different roads, the traffic time of the traffic lights of each road on the road and the arrival parking space and the parking time are comprehensively considered, the travel planning road section with the least time is calculated, the planning route with the shortest time is finally selected, and the calculation process in S2-1 to S2-4 is described by the following formula:
the calculation formula of the time for removing the traffic jam road section of the single segment after combination is as follows:
wherein S is pn For the distance of a factor segment in a single segment, m is the number of congestion traffic in the factor segment, S i For average length and intermittent length of congestion in the factor segment, V 1 Is the speed during normal driving;
the time calculation formula of the combined traffic jam road section comprises the following steps:
wherein V is 2 Is the speed in the driving process when the vehicle exists in front and is positioned when the traffic light passes through;
the calculation formula of the parking time after reaching the destination is as follows:
wherein S is q For the length of the road section when parking, V 3 Is the driving speed in the parking process;
the above formulas are combined, and the total time formula of travel is (1) + (2) + (3):
preferably, during running, the road condition of the current running road section, the road condition of other factor segments in the single segment and the road condition of all factor segments entering next are monitored, and when an emergency is predicted, the time of the road condition of the other factor segments is compared after the emergency road section is avoided, and a second optimized route is selected and transmitted to the travel planning module for replacement.
Preferably, the specific way for the data comparison module to compare data is as follows:
selecting one single segment, and calculating the time of the multiple factor segments contained in the single segment to obtain the time required by each factor segment to be
A step-by-step comparison method is adopted to realize the comparison operation of the data;
wherein will be
And->
Performing comparison, and adding->
And->
Comparing, and so on, wherein one of the two parts with smaller comparison time is continuously compared with the next part until the factor fragment with the shortest time is obtained;
preferably, the comparison operation is suitable for the comparison process of the real-time updating module after route monitoring, and the shortest time route in the road section is always kept for traveling after updating.
Advantageous effects
The invention provides an intelligent travel system based on big data. Compared with the prior art, the method has the following beneficial effects:
(1) The intelligent travel system based on the big data collects and primarily processes the data through the data processing terminal, reprocesss the data through the cloud computing platform, segments the data into data segments of different road sections after the data are converted, subdivides the data into a plurality of factor segments again, so that in the travel route planning, the route analysis from a starting point to a destination is realized more comprehensively, meanwhile, the optimal route with the shortest time is selected, in the travel process, the real-time updating module and the GPS positioning module are utilized to monitor the specific prediction optimal route and the peripheral road section condition information of the road section which is being traveled, so that the emergency on the route is predicted in advance, the optimal route with the shortest time is switched to while the emergency is avoided, the travel efficiency is effectively improved, and compared with the travel time which is saved in the usual travel process, the intelligent travel operation based on the big data analysis is realized.
(2) This wisdom travel system based on big data, through being provided with intelligent transportation module and trip planning module, comb out the traffic conditions in the travel route, traffic conditions include traffic lights number and the switching time that corresponding circuit was gone, traffic flow size in the highway section causes the traffic jam condition, and plan in advance, plan out the concrete prediction optimization circuit of this trip route simultaneously, carry out voice broadcast with data through the conversion of car machine transmission to the driving simultaneously, thereby realized going on a journey based on the vehicle safety and convenient operation, avoid crowding and traffic lights waiting that simultaneously can be timely, make the process of going out more smooth and easy, with this smoothness that has improved personnel's trip mood.
Drawings
Fig. 1 is a schematic structural diagram of an intelligent travel system based on big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides two technical schemes:
example 1
An intelligent travel system based on big data 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;
the output end of the data processing terminal is electrically connected with the input end of the cloud computing platform, the cloud computing platform is electrically connected with the intelligent transportation module and the travel planning module in a bidirectional manner, the output end of the cloud computing platform is connected with the input end of the real-time updating module, the real-time updating module is electrically connected with the GPS positioning module in a bidirectional manner, and the output end of the real-time updating module is electrically connected with the input ends of the intelligent transportation module and the travel planning module;
the data processing terminal is used for carrying out image acquisition on various information data in the travel route, carrying out preprocessing operation on the acquired data, converting the image acquired data into digital data, and then transmitting the processed data into the cloud computing platform, wherein the various information data comprise road section traffic information, route information, parking information and weather information;
the data processing terminal also comprises a satellite sensor, an image collector and a weather prediction module;
the satellite sensor is used for detecting and transmitting traffic information and route information;
the image collector is used for collecting images of building information, road information and parking space information in the city, and the side ends have corresponding sizes;
the weather prediction module is used for acquiring environmental change information of an area where the vehicle runs in future time and transmitting the environmental change information to the travel planning module;
the cloud computing platform is used for further analyzing and computing the information preprocessed by the data processing terminal, comparing travel time of a plurality of route segments in the same starting point according to the computed data until the shortest road segment information used on each starting point is selected for combination, and transmitting the data subjected to the deep processing into the intelligent traffic module and the travel planning module to realize the use operation of the data;
the intelligent traffic module is used for combing out traffic conditions in the travel route by combining traffic information, wherein the traffic conditions comprise traffic light number, corresponding route travel conversion time, traffic jam conditions caused by traffic flow in a road section and traffic accident conditions in the road section;
and the trip planning module is used for planning a specific prediction optimization line of the trip route according to the starting point input when a user uses the trip planning module, optimizing road section information calculated through the cloud computing platform and the parking information of a destination and weather information in the road section process, and transmitting data to a travelling crane through vehicle-to-machine conversion for voice broadcasting.
The system comprises a cloud computing platform, an intelligent traffic module and a travel planning module, wherein the cloud computing platform is used for computing and comparing information monitored by the real-time updating module, and the travel route of the next road section is optimized.
The data is collected and primarily processed through the data processing terminal, the data are reprocessed through the cloud computing platform, the data are segmented into data segments of different road sections after being converted, and the data are subdivided into factor segments again, so that in route planning of travel, route analysis from a starting point to a destination is more comprehensively realized, meanwhile, an optimized route with shortest time is selected, in the process of travel, the real-time updating module and the GPS positioning module are utilized to monitor the situation information of the specific predicted optimized route and the peripheral road section of the road section which is traveling, so that emergency on the route is predicted in advance, the emergency is avoided, and meanwhile, the data are switched to the optimized route with shortest time, intelligent travel operation based on big data analysis is realized, travel efficiency is effectively improved, and travel time is saved compared with ordinary travel.
In the embodiment of the invention, the preprocessing specific mode of the data processing terminal is as follows:
the method comprises the steps that image acquisition is carried out on ground information of a city through a satellite sensor, and acquired data are transmitted to a data conversion module to realize that the image information is changed into digital information;
and then, the converted data is analyzed and subjected to preliminary screening, and the screened information is summarized in a model building module to form a traffic route construction model of the city, and the preprocessed information is transmitted into a cloud computing platform.
In the embodiment of the invention, the specific implementation mode of reprocessing the data to be preprocessed by the cloud computing platform is as follows:
s1, extracting a formed urban traffic route construction model;
s2, converting the image data into corresponding actual digital data, wherein the cloud computing platform further comprises a data analysis module, a data computing module and a data comparison module;
analyzing the data by using a data analysis module, calculating the data by using a data calculation module to obtain a plurality of data fragments of different road sections, and finally selecting the data fragments with the same starting point by using a data comparison module to compare the data fragments to obtain the data fragment with the shortest time;
and S3, transmitting the obtained data fragments to an intelligent traffic module and a travel planning module to realize the planning operation of optimizing the route.
In the embodiment of the present invention, the travel route operation method with the shortest forming time for data processing in S2 is as follows:
s2-1, inputting a starting point where the current position is located and a destination position to be obtained;
s2-2, dividing the total road section from the starting point to the destination into a plurality of single fragments P according to the form of the starting point to the traffic light, the traffic lights and the form of the traffic lights to the destination 1 、P 2 、P 3 、...、P n ；
S2-3, and P 1 The arrival mode from the starting point to the end point of the single segment is divided into a plurality of factor segments again
Then, the running time of the plurality of factor segments is correspondingly calculated to obtain
S2-4, performing time comparison on a plurality of factor segments in the single segments, selecting the shortest time-consuming factor segment in each single segment, finally obtaining a travel route with the shortest time, and transmitting the travel route to a travel planning module to realize predicted travel route broadcasting;
in the embodiment of the invention, aiming at the passing time of vehicles on different roads, the passing time of traffic lights of all roads on the roads and the arrival parking space and the parking time are comprehensively considered, the travel planning road section with the least time is calculated, the planning road section with the shortest time is finally selected, and the calculation process in S2-1 to S2-4 is described by the following formula:
the calculation formula of the time for removing the traffic jam road section of the single segment after combination is as follows:
wherein S is pn For the distance of a factor segment in a single segment, m is the number of congestion traffic in the factor segment, S i For average length and intermittent length of congestion in the factor segment, V 1 Is the speed during normal driving;
the time calculation formula of the combined traffic jam road section comprises the following steps:
wherein V is 2 Is the speed in the driving process when the vehicle exists in front and is positioned when the traffic light passes through;
the calculation formula of the parking time after reaching the destination is as follows:
wherein S is q For the length of the road section when parking, V 3 Is the driving speed in the parking process;
the above formulas are combined, and the total time formula of travel is (1) + (2) + (3):
in the embodiment of the invention, during running, the road condition of the current running road section, the road condition of other factor segments in the single segment and the road condition of all factor segments entering next are monitored, and when an emergency situation is predicted, the time used for the road condition of the other factor segments is compared after the emergency road section is avoided, and a second optimized route is selected and transmitted to the travel planning module for replacement.
In the embodiment of the invention, the specific mode of the data comparison module for data comparison is as follows:
selecting one single segment, and calculating the time of the multiple factor segments contained in the single segment to obtain the time required by each factor segment to be
A step-by-step comparison method is adopted to realize the comparison operation of the data;
wherein will be
And->
Performing comparison, and adding->
And->
Comparing, and so on, wherein one of the two parts with smaller comparison time is continuously compared with the next part until the factor fragment with the shortest time is obtained;
example two
In the specific implementation process, compared with the first embodiment, the specific difference is that the comparison operation is suitable for the comparison process of the real-time updating module after route monitoring, and the shortest time route in the road section is always kept for traveling after updating.
Experiment
The same starting point and destination are selected, and travel experiments of driving are carried out by using the same two vehicles and personnel, wherein one of the travel experiments adopts the route planning of the travel system in the embodiment, the other travel experiment is carried out by autonomous normal running, and finally the time data of the travel system and the travel experiment reach the same destination at the same starting point are shown in the following table:
| experimental vehicle I (planning route) | Experimental vehicle II (free route) | |
| Time/s for short distance scene | 196s | 215s |
| Time/h for long distance scene | 1.4h | 2h |
As can be seen from the data in the table, the time spent measured under the short-distance route is closer, and the time spent of the long-distance route is more different, so that the time spent for traveling by using the intelligent traveling system with big data is shorter.
And all that is not described in detail in this specification is well known to those skilled in the art.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. An wisdom trip system based on big data, its characterized in that: comprising the following steps:
the data processing terminal is used for carrying out image acquisition on various information data in the travel route, carrying out preprocessing operation on the acquired data, converting the image acquired data into digital data, and then transmitting the processed data into the cloud computing platform, wherein the various information data comprise road section traffic information, route information, parking information and weather information;
the cloud computing platform is used for further analyzing and computing the information preprocessed by the data processing terminal, comparing travel time of a plurality of route segments in the same starting point according to the computed data until the shortest road segment information used on each starting point is selected for combination, and transmitting the data subjected to the deep processing into the intelligent traffic module and the travel planning module to realize the use operation of the data;
the intelligent traffic module is used for combing out traffic conditions in the travel route by combining traffic information, wherein the traffic conditions comprise traffic light number, corresponding route travel conversion time, traffic jam conditions caused by traffic flow in a road section and traffic accident conditions in the road section;
and the trip planning module is used for planning a specific prediction optimization line of the trip route according to the starting point input when a user uses the trip planning module, optimizing road section information calculated through the cloud computing platform and the parking information of a destination and weather information in the road section process, and transmitting data to a travelling crane through vehicle-to-machine conversion for voice broadcasting.
2. The big data based intelligent travel system of claim 1, wherein: the system comprises a cloud computing platform, an intelligent transportation module and a travel planning module, wherein the cloud computing platform is used for computing and comparing information monitored by the real-time updating module, and the travel route of the next road section is optimized.
3. The big data based intelligent travel system of claim 1, wherein: the preprocessing method of the data processing terminal comprises the following specific steps:
the method comprises the steps that image acquisition is carried out on ground information of a city through a satellite sensor, and acquired data are transmitted to a data conversion module to realize that the image information is changed into digital information;
and then, the converted data is analyzed and subjected to preliminary screening, and the screened information is summarized in a model building module to form a traffic route construction model of the city, and the preprocessed information is transmitted into a cloud computing platform.
4. The big data based intelligent travel system of claim 2, wherein: the specific implementation mode of reprocessing the data to be preprocessed by the cloud computing platform is as follows:
s1, extracting a formed urban traffic route construction model;
s2, converting the image data into corresponding actual digital data, wherein the cloud computing platform further comprises a data analysis module, a data computing module and a data comparison module;
analyzing the data by using a data analysis module, calculating the data by using a data calculation module to obtain a plurality of data fragments of different road sections, and finally selecting the data fragments with the same starting point by using a data comparison module to compare the data fragments to obtain the data fragment with the shortest time;
and S3, transmitting the obtained data fragments to an intelligent traffic module and a travel planning module to realize the planning operation of optimizing the route.
5. The big data based intelligent travel system of claim 4, wherein: the travel route operation method with the shortest forming time for data processing in the S2 comprises the following steps:
s2-1, inputting a starting point where the current position is located and a destination position to be obtained;
s2-2, dividing the total road section from the starting point to the destination into a plurality of single fragments P according to the form of the starting point to the traffic light, the traffic lights and the form of the traffic lights to the destination 1 、P 2 、P 3 、...、P n ；
S2-3, and P 1 The arrival mode from the starting point to the end point of the single segment is divided into a plurality of factor segments P again 1 1 、P 1 2 、P 1 3 、...、P 1 n Then, the running time of the plurality of factor segments is correspondingly calculated to obtain T 1 1 、T 1 2 、T 1 3 、...、T 1 n ；
S2-4, comparing the time of the plurality of factor segments in the single segments, selecting the shortest time-consuming factor segment in each single segment, finally obtaining the travel route with the shortest time, and transmitting the travel route to the travel planning module to realize the predicted travel route broadcasting.
6. The big data based intelligent travel system of claim 5, wherein: aiming at the traffic time of vehicles on different roads, and comprehensively considering the traffic time of traffic lights of all roads on the roads and the arrival parking space and parking time, calculating the travel planning road section with the least time, and finally selecting the planning road with the shortest time, wherein the calculation process in S2-1 to S2-4 is described by the following formula:
the calculation formula of the time for removing the traffic jam road section of the single segment after combination is as follows:
wherein S is pn For the distance of a factor segment in a single segment, m is the number of congestion traffic in the factor segment, S i For average length and intermittent length of congestion in the factor segment, V 1 Is the speed during normal driving;
the time calculation formula of the combined traffic jam road section comprises the following steps:
wherein V is 2 Is the speed in the driving process when the vehicle exists in front and is positioned when the traffic light passes through;
the calculation formula of the parking time after reaching the destination is as follows:
wherein S is q For the length of the road section when parking, V 3 Is the driving speed in the parking process;
the above formulas are combined, and the total time formula of travel is (1) + (2) + (3):
7. the big data based intelligent travel system of claim 5, wherein: and in the running process, when the vehicle runs in a single segment, the road condition of the current running road section, the road conditions of other factor segments in the single segment and the road conditions of all factor segments entering next are monitored, and when an emergency situation is predicted, the time used for the road conditions of the other factor segments is compared after the emergency road section is avoided, a second optimized route is selected and transmitted to the travel planning module for replacement.
8. The big data based intelligent travel system of claim 5, wherein: the data comparison module performs data comparison in the following specific modes:
selecting one single segment, and calculating the time of the multiple factor segments contained in the single segment to obtain the time required by each factor segment as T 1 1 、T 1 2 、T 1 3 、...、T 1 n And a step-by-step comparison method is adopted to realize the comparison operation of the data;
wherein T is to 1 1 And T 1 2 Comparing, T 1 3 And T 1 4 And comparing, and so on, wherein one of the two parts with smaller comparison time is continuously compared with the next part until the factor fragment with the shortest time is obtained.
9. The big data based intelligent travel system of claim 8, wherein: the comparison operation is suitable for the comparison process of the real-time updating module after route monitoring, and the shortest time route in the road section is always kept for traveling after updating.
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| CN117152961A (en) * | 2023-09-20 | 2023-12-01 | 深圳市孪生云计算技术有限公司 | Wisdom road condition monitoring display system based on data analysis |
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| CN116433144A (en) * | 2023-06-15 | 2023-07-14 | 广州一链通互联网科技有限公司 | Route planning method for logistics transportation based on multi-mode intermodal transportation |
| CN116433144B (en) * | 2023-06-15 | 2023-09-15 | 广州一链通互联网科技有限公司 | Route planning method for logistics transportation based on multi-mode intermodal transportation |
| CN117152961A (en) * | 2023-09-20 | 2023-12-01 | 深圳市孪生云计算技术有限公司 | Wisdom road condition monitoring display system based on data analysis |
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