CN108401025B - Intelligent transportation software data sharing method based on big data - Google Patents
Intelligent transportation software data sharing method based on big data Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
Abstract
The invention provides an intelligent transportation software data sharing method based on big data, which can provide real-time positioning service for intelligent transportation software in other platforms according to the self positioning function of the intelligent transportation software in the platform when a certain server fails, thereby making up the interruption of the positioning service caused by the failure of the server in the platform and realizing the self-rescue of the positioning service in the platform; furthermore, when a certain intelligent traffic software fails to provide real-time positioning service due to the fault of a server provided by a single developer, the real-time positioning data of the intelligent traffic software in the servers provided by other developers can be acquired, so that the traffic information is ensured not to be influenced by the stability of the single software platform, and the real-time performance and the accuracy of the traffic information acquisition are higher.
Description
Technical Field
The invention relates to the technical field of big data cloud computing and vehicle networking, in particular to an intelligent transportation software data sharing method based on big data.
Background
The rapid increase of the automobile holding capacity brings convenience to people in all aspects, and simultaneously causes the frequent urban road congestion phenomenon. Road congestion has a number of negative effects. The most important solutions are two, one is the traditional way of increasing the number of roads and controlling the increase of vehicles, and the other is evacuation. Under the current situation, although the modes of expanding roads, controlling the number of vehicles to increase and the like can relieve traffic pressure, the utilization of land resources is limited, and the mode cannot solve the congestion phenomenon from the sustainable development perspective. To dredge traffic, advanced technologies are required and existing road traffic infrastructure is constantly perfected. The most direct and effective method in the grooming mode belongs to the application of ITS (Intelligent Transport System). The existing traffic data acquisition and road condition estimation still have the following defects: the road needs to be additionally provided with basic hardware facilities, is generally fixedly installed at the intersection of an expressway, an express way and a main road, and realizes traffic monitoring on key nodes, but because hardware equipment only partially covers, traffic information acquisition has a blind area, and the road condition of a specific road section cannot be monitored in real time; the floating car is required to be provided with special equipment, is used in a large scale and has overhigh cost, and meanwhile, the floating car is used as one component of traffic participants, and the acquired information cannot cover all roads in a city; at present, no unified method exists for real-time road condition estimation, and the estimation effect is not ideal. With the wide application of mobile terminals such as mobile phones and vehicle-mounted terminals, the use of mobile terminals to connect with vehicles to collect information such as data is gaining increasing attention. Compared with the traditional acquisition technology, the mobile terminal has many advantages, such as short construction time, low cost, wide coverage area, accurate data and strong timeliness. When the vehicle runs On a road, the mobile terminal collects data such as vehicle speed and the like through an OBD-II (On-Board Diagnostic-II) interface, so that the accurate and reliable vehicle instantaneous speed and the like can be directly measured. At present, a mobile terminal is widely applied at home and abroad as an important traffic information acquisition device, and has become a great research direction in the ITS field.
In order to solve the above problems, document CN106169243A can implement user activation authentication, online update, vehicle type configuration, whole vehicle diagnosis, data stream reading and vehicle positioning functions through data acquisition software, and adopts a modular development architecture, so that the data acquisition system is suitable for different brands of vehicle types, and the system has good compatibility and universality; the vehicle can be positioned to a specific road section by adopting a map matching algorithm, and the coordinates and the driving direction of a GPS positioning point of the vehicle are determined; the route speed of the road section can be accurately estimated by adopting an SVM algorithm, and the dynamic information of the vehicles on the road can be efficiently obtained; the road section travel speed estimated by the SVM is served to the public society, the simple and visual traffic conditions of the whole city and the hot road section are provided for the public society, and the functions of vehicle real-time positioning, historical travel playback and the like are realized.
Firstly, although the method can improve the positioning accuracy, the positioning is greatly influenced by the stability of the platform system due to the adoption of a single technical platform, and when the stability of a server of the platform is influenced, the problem that a user cannot accurately acquire road condition information in real time still exists; secondly, the method is a unidirectional information transmission method, and when the user side has error output due to an emergency, the information feedback of the user side cannot be obtained in time, so that the user experience is affected.
Disclosure of Invention
The invention provides an intelligent transportation software data sharing method based on big data, which comprises the following steps:
s1, the first server acquires the real-time position of the networked vehicle connected with the first server in real time, and sends the real-time position of the networked vehicle to intelligent transportation software in a plurality of user mobile terminals connected with the first server; any one of the users starts the intelligent transportation software and then sends the real-time position information of the mobile terminal to the first server; the first server stores real-time location information of the networked vehicle and real-time location information of the mobile terminal of the user; if the first server cannot acquire the position information of the networked vehicles within the preset time or the intelligent transportation software feedback of any one user cannot acquire the position information of the networked vehicles, the step S2 is entered;
s2, the first server calculating an estimated real-time location of the networked vehicle based on the saved real-time location information of the networked vehicle and the real-time location information of the mobile terminal of the user; sending the estimated real-time position information of the networked vehicles to the intelligent transportation software which cannot acquire the position information of the networked vehicles; detecting whether the intelligent transportation software of any one user feeds back the position information of the networked vehicles, and if so, entering the step S3; if the feedback does not exist, ending the operation;
s3, the first server acquires the third-party server recorded with the real-time position information of the networked vehicles through a big data server, and establishes communication connection with the third-party server corresponding to the third-party intelligent transportation software; the first server sends a request for obtaining the real-time position information of the networked vehicles, the third-party server feeds the real-time position information of the networked vehicles back to the first server after carrying out security verification on the first server, and the first server sends the real-time position information of the networked vehicles to the intelligent transportation software of all the users.
As a preferred embodiment, the first server calculates the estimated real-time location of the networked vehicle based on the saved real-time location information of the networked vehicle and the real-time location information of the mobile terminal of the user, specifically including:
the first server compares the stored historical real-time position information of the networked vehicle in the latest preset time period with the historical real-time position information of the mobile terminal of the user in the latest preset time period, calculates a historical real-time distance difference value between the networked vehicle and the mobile terminal of the user according to the comparison result, and takes the current real-time position information of the mobile terminal of the user as the estimated real-time position of the networked vehicle if the historical real-time distance difference value is smaller than a preset historical distance threshold value; and if the historical real-time distance difference is larger than or equal to a preset historical distance threshold value, the estimation fails, and error information of the estimation failure is sent to the first server.
As a preferred embodiment, the first server calculates the estimated real-time location of the networked vehicle based on the saved real-time location information of the networked vehicle and the real-time location information of the mobile terminal of the user, specifically including:
the first server compares the stored historical real-time position information of the networked vehicles in the latest preset time period with the historical real-time position information of the mobile terminals of a plurality of users in the latest preset time period, calculates the historical real-time distance difference between the networked vehicles and the mobile terminals of the users according to a plurality of comparison results, and takes the current real-time position information of the mobile terminals of the users as the estimated real-time position of the networked vehicles if the historical real-time distance difference is smaller than a preset historical distance threshold value and the number of the historical real-time distance difference is larger than or equal to a set value; and if the historical real-time distance difference is smaller than the preset historical distance threshold, the estimation fails, and error information of the estimation failure is sent to the first server.
As a preferred embodiment, after sending the error information of estimation failure to the first server, the method further includes:
the big data server detects other third-party servers recorded with the real-time position information of the networked vehicles and establishes communication connection with the other third-party servers; the first server sends a request for obtaining the real-time position information of the networked vehicles to the other third-party servers, the other third-party servers feed the real-time position information of the networked vehicles back to the first server after performing security verification on the first server, and the first server sends the real-time position information of the networked vehicles to the intelligent transportation software of all the users.
As a preferred embodiment, the method further comprises:
the big data server establishes communication connection with a third-party server corresponding to a plurality of third-party intelligent transportation software; establishing a score for the third-party server feeding back the real-time position information of the networked vehicles, and setting an information feedback priority order of the third-party server feeding back the real-time position information of the networked vehicles based on the score; and the third-party server feeds back the real-time position information of the networked vehicles to the first server in sequence based on the priority order.
As a preferred embodiment, the method further comprises:
the big data server establishes communication connection with the first server and the plurality of third-party servers; the first server stores the real-time location information and vehicle number information of the networked vehicles belonging to the first server, and the third-party server stores the real-time location information and vehicle number information of the networked vehicles belonging to the third-party server; the big data server stores unified vehicle number information of the networked vehicles belonging to the first server and unified vehicle number information of the networked vehicles belonging to the third-party servers at the same time; the unified vehicle number information is different from the vehicle number information.
As a preferred embodiment, the big data server stores unified vehicle number information of the networked vehicles belonging to the first server and unified vehicle number information of the networked vehicles belonging to a plurality of the third-party servers at the same time, and further includes:
the big data server stores unified vehicle number information of the networked vehicles belonging to the first server and vehicle number information of the networked vehicles of the first server in a first server vehicle number data table, and establishes a corresponding relation between the unified vehicle number information and the vehicle number information in the first server vehicle number data table;
the big data server stores unified vehicle number information of the networked vehicles belonging to the third-party server and vehicle number information of the networked vehicles of the third-party server in a third-party server vehicle number data table, and establishes a corresponding relation between the unified vehicle number information and the vehicle number information in the third-party server vehicle number data table; the number of the third-party servers is multiple.
As a preferred embodiment, the method further comprises:
the first server sends the vehicle number information of the networked vehicles to the big data server, and the big data server inquires corresponding uniform vehicle number information in the first server vehicle number data table according to the vehicle number information; inquiring the corresponding third-party server according to the uniform vehicle number information, and inquiring the vehicle number information corresponding to the uniform vehicle number information in a vehicle number data table of the third-party server; and after the third-party server carries out safety verification on the big data server, the networking vehicle real-time position information corresponding to the vehicle number information is fed back to the big data server, the big data server feeds back the networking vehicle real-time position information to the first server, and the first server sends the networking vehicle real-time position information to the intelligent transportation software of all the users.
A user terminal comprising a processor, wherein the processor performs the method of any of the above embodiments.
The invention provides an intelligent transportation software data sharing method based on big data, which can provide real-time positioning service for intelligent transportation software in other platforms according to the self positioning function of the intelligent transportation software in the platform when a certain server fails, thereby making up the interruption of the positioning service caused by the failure of the server in the platform and realizing the self-rescue of the positioning service in the platform; furthermore, when a certain intelligent traffic software fails to provide real-time positioning service due to the fault of a server provided by a single developer, the real-time positioning data of the intelligent traffic software in the servers provided by other developers can be acquired, so that the traffic information is ensured not to be influenced by the stability of the single software platform, and the real-time performance and the accuracy of the traffic information acquisition are higher.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the embodiments and the drawings used in the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an embodiment of a big data-based intelligent transportation software data sharing method according to the present invention.
Detailed Description
The embodiments of the present invention are further described below with reference to the drawings.
The first embodiment is as follows:
as shown in fig. 1, the present invention provides a method for sharing data of intelligent transportation software based on big data, wherein the method comprises the following steps:
s1, the first server acquires the real-time position of the networked vehicle connected with the first server in real time, and sends the real-time position of the networked vehicle to intelligent transportation software in a plurality of user mobile terminals connected with the first server; any one of the users starts the intelligent transportation software and then sends the real-time position information of the mobile terminal to the first server; the first server stores real-time location information of the networked vehicle and real-time location information of the mobile terminal of the user; if the first server cannot acquire the position information of the networked vehicles within the preset time or the intelligent transportation software feedback of any one user cannot acquire the position information of the networked vehicles, the step S2 is entered;
it should be noted that the networked vehicles may be public transportation vehicles or shared transportation vehicles, and are not limited herein. The first servers are servers belonging to the same development and operation platform, so that the first servers are not limited to one; in addition, each first server is connected with and serves a plurality of intelligent transportation software belonging to the development operation platform; after the user starts the intelligent transportation software, the intelligent transportation software is authorized to acquire the real-time position information of the mobile terminal of the user, at the moment, the transportation means sends the real-time position of the user to the related first server, the user shares the real-time position of the transportation means taken by the user, and meanwhile, the real-time positions of other transportation means in the development operation platform can be acquired through the intelligent transportation software, so that the real-time sharing of the position information is realized. Further, if the first server cannot acquire the position information of the vehicle in real time due to a failure of the relevant first server or a failure of the position transmitting device of the vehicle, a subsequent operation is required to achieve accurate acquisition of the real-time position information of the vehicle.
S2, the first server calculating an estimated real-time location of the networked vehicle based on the saved real-time location information of the networked vehicle and the real-time location information of the mobile terminal of the user; sending the estimated real-time position information of the networked vehicles to the intelligent transportation software which cannot acquire the position information of the networked vehicles; detecting whether the intelligent transportation software of any one user feeds back the position information of the networked vehicles, and if so, entering the step S3; if the feedback does not exist, ending the operation;
it should be noted that, before the fault occurs, the first server already stores the historical location information of the networked vehicle a and the user M/N, and calculates the estimated real-time location of the networked vehicle according to the historical location information; for example, after the estimation, if the distance between the networked vehicle a and the user M is kept within 2 meters in a certain period of time, it is determined that the user M takes the networked vehicle a, and at this time, the real-time location of the user M is used as the real-time location of the networked vehicle a, and the real-time location information is sent to the user N, who cannot obtain the location information of the networked vehicle through the first server in normal operation, so that the user N can obtain the real-time location information of the networked vehicle. Therefore, the method can provide real-time positioning service for other intelligent transportation software in the platform according to the self positioning function of the intelligent transportation software in the platform when a certain server fails, so that the interruption of the positioning service caused by the failure of the server in the platform is compensated, and the self-rescue of the positioning service in the platform is realized.
S3, the first server acquires the third-party server recorded with the real-time position information of the networked vehicles through a big data server, and establishes communication connection with the third-party server corresponding to the third-party intelligent transportation software; the first server sends a request for obtaining the real-time position information of the networked vehicles, the third-party server feeds the real-time position information of the networked vehicles back to the first server after carrying out security verification on the first server, and the first server sends the real-time position information of the networked vehicles to the intelligent transportation software of all the users.
It should be noted that, in addition to the above location service self-rescue in the same development and operation platform, the present invention further provides a location service sharing function outside the development and operation platform on this basis. At the moment, the big data server is used as a bridge for connecting a development operation platform where the first server is located and a development operation platform where the third-party server is located, and the positioning information of the two platforms is shared; the third-party server carries out safety verification on the first server, so that the safety of a development operation platform where the third-party server is located is ensured; at this time, the big data server is only responsible for establishing an association relationship for the two development operation platforms, and does not participate in the transmission of the positioning information.
As a preferred embodiment, the first server calculates the estimated real-time location of the networked vehicle based on the saved real-time location information of the networked vehicle and the real-time location information of the mobile terminal of the user, specifically including:
the first server compares the stored historical real-time position information of the networked vehicle in the latest preset time period with the historical real-time position information of the mobile terminal of the user in the latest preset time period, calculates a historical real-time distance difference value between the networked vehicle and the mobile terminal of the user according to the comparison result, and takes the current real-time position information of the mobile terminal of the user as the estimated real-time position of the networked vehicle if the historical real-time distance difference value is smaller than a preset historical distance threshold value; and if the historical real-time distance difference is larger than or equal to a preset historical distance threshold value, the estimation fails, and error information of the estimation failure is sent to the first server. It should be noted that, for example, the last preset time period may be 30 minutes, and the preset historical distance threshold may be 2 meters, where the historical distance threshold should be guaranteed to be within the effective length of the networked vehicle; i.e., determines that the user is a passenger of the networked vehicle. In addition, when the passenger turns off the intelligent transportation software, the passenger is far away from the networked vehicle, and the real-time position information of the passenger is not acquired at the moment.
As a preferred embodiment, the first server calculates the estimated real-time location of the networked vehicle based on the saved real-time location information of the networked vehicle and the real-time location information of the mobile terminal of the user, specifically including:
the first server compares the stored historical real-time position information of the networked vehicles in the latest preset time period with the historical real-time position information of the mobile terminals of a plurality of users in the latest preset time period, calculates the historical real-time distance difference between the networked vehicles and the mobile terminals of the users according to a plurality of comparison results, and takes the current real-time position information of the mobile terminals of the users as the estimated real-time position of the networked vehicles if the historical real-time distance difference is smaller than a preset historical distance threshold value and the number of the historical real-time distance difference is larger than or equal to a set value; and if the historical real-time distance difference is smaller than the preset historical distance threshold, the estimation fails, and error information of the estimation failure is sent to the first server. It should be noted that, in this embodiment, historical real-time positions of a plurality of users are collected and compared with historical real-time position information of the networked vehicles, respectively, so as to increase the accuracy of the above determination; for example, if the historical real-time distance difference between the networked vehicle and the mobile terminal of the user is less than the preset historical distance threshold by 2 meters and is 5, and is greater than or equal to the set value 3, the current real-time position information of the mobile terminal of 5 users less than the preset historical distance threshold by 2 meters is used as the estimated real-time position of the networked vehicle, so that the accuracy of the estimated real-time position is improved. If the historical real-time distance difference value between the networked vehicle and the mobile terminal of the user is smaller than the preset historical distance threshold value by 2 meters, and is smaller than the set value by 3, the estimation fails, the current real-time position information of the mobile terminal of 2 users smaller than the preset historical distance threshold value by 2 meters does not have reference significance, and error information of the estimation failure is sent to the first server.
As a preferred embodiment, after sending the error information of estimation failure to the first server, the method further includes:
the big data server detects other third-party servers recorded with the real-time position information of the networked vehicles and establishes communication connection with the other third-party servers; the first server sends a request for obtaining the real-time position information of the networked vehicles to the other third-party servers, the other third-party servers feed the real-time position information of the networked vehicles back to the first server after performing security verification on the first server, and the first server sends the real-time position information of the networked vehicles to the intelligent transportation software of all the users. It should be noted that, in the foregoing embodiment, since effective real-time location information of the networked vehicle is not obtained, at this time, the present embodiment further searches the real-time location information of the networked vehicle from another third-party server by using the big data server, and further provides the real-time location information of the networked vehicle for the first server; this is merely an example, and in addition, different third party servers may be searched multiple times until a third party server storing the real-time location information of the networked vehicle is found; in this way, the efficiency and accuracy of the networked vehicle real-time location information may be greatly improved.
As a preferred embodiment, the method further comprises:
the big data server establishes communication connection with a third-party server corresponding to a plurality of third-party intelligent transportation software; establishing a score for the third-party server feeding back the real-time position information of the networked vehicles, and setting an information feedback priority order of the third-party server feeding back the real-time position information of the networked vehicles based on the score; and the third-party server feeds back the real-time position information of the networked vehicles to the first server in sequence based on the priority order. It should be noted that the big data server establishes a scoring mechanism for each third-party server, and adds points for accurately improving the third-party servers providing the real-time location information of the networked vehicles; and then setting the priority order of each third-party server according to the integral, so that the networking vehicle real-time position information can be fed back at the highest speed by the third-party server with the best performance, the feedback speed and the feedback efficiency of the networking vehicle real-time position information are improved, and the user experience is further improved.
As a preferred embodiment, the method further comprises:
the big data server establishes communication connection with the first server and the plurality of third-party servers; the first server stores the real-time location information and vehicle number information of the networked vehicles belonging to the first server, and the third-party server stores the real-time location information and vehicle number information of the networked vehicles belonging to the third-party server; the big data server stores unified vehicle number information of the networked vehicles belonging to the first server and unified vehicle number information of the networked vehicles belonging to the third-party servers at the same time; the unified vehicle number information is different from the vehicle number information. It should be noted that the unified vehicle number information can effectively avoid the problem that the first server and the third-party server cannot accurately identify the networked vehicles due to inconsistent encoding modes, and improve the efficiency of information interaction; in addition, in order to ensure the safety of information interaction, a first server sets a first safety encryption mode for the vehicle number information of the first server, a third-party server sets a second safety encryption mode for the vehicle number information of the third-party server, and the big data server is provided with a first safety decryption mode and a second safety decryption mode which are authorized by the first safety encryption mode and the second safety encryption mode; therefore, bidirectional security isolation of the first server and the third-party server can be achieved, and data security of each platform is further guaranteed. In addition, similar encryption and decryption methods as above may be performed on the real-time location information of the networked vehicles of the first server and the third-party server, which are not described herein again.
As a preferred embodiment, the big data server stores unified vehicle number information of the networked vehicles belonging to the first server and unified vehicle number information of the networked vehicles belonging to a plurality of the third-party servers at the same time, and further includes:
the big data server stores unified vehicle number information of the networked vehicles belonging to the first server and vehicle number information of the networked vehicles of the first server in a first server vehicle number data table, and establishes a corresponding relation between the unified vehicle number information and the vehicle number information in the first server vehicle number data table; for example, a first server vehicle number data table (table 1) is established as follows:
| vehicle number of first server networked vehicle | Unifying vehicle numbering |
| A001 | 010001 |
| A073 | 010082 |
| A256 | 010519 |
It should be noted that the big data server establishes a unified corresponding vehicle number for the vehicle numbers of the networked vehicles stored by the servers of different platforms, thereby realizing unified naming and management of the numbers and seamless connection of information.
The big data server stores unified vehicle number information of the networked vehicles belonging to the third-party server and vehicle number information of the networked vehicles of the third-party server in a third-party server vehicle number data table, and establishes a corresponding relation between the unified vehicle number information and the vehicle number information in the third-party server vehicle number data table; the number of the third-party servers is multiple. For example, the following third party server vehicle number data table (table 2) is established:
| vehicle number of third party server M networked vehicle | Unifying vehicle numbering |
| B062 | 010001 |
| B295 | 010082 |
| B501 | 012108 |
It should be noted that the big data server establishes a unified corresponding vehicle number for the vehicle numbers of the networked vehicles stored by the servers of different platforms, thereby realizing unified naming and management of the numbers and seamless connection of information.
As a preferred embodiment, the method further comprises: the first server sends the vehicle number information of the networked vehicles to the big data server, and the big data server inquires corresponding uniform vehicle number information in the first server vehicle number data table according to the vehicle number information; inquiring the corresponding third-party server according to the uniform vehicle number information, and inquiring the vehicle number information corresponding to the uniform vehicle number information in a vehicle number data table of the third-party server; and after the third-party server carries out safety verification on the big data server, the networking vehicle real-time position information corresponding to the vehicle number information is fed back to the big data server, the big data server feeds back the networking vehicle real-time position information to the first server, and the first server sends the networking vehicle real-time position information to the intelligent transportation software of all the users.
It should be noted that, continuing with the embodiment of table 1 and table 2, for example, the first server sends the vehicle number information of the networked vehicles to the big data server, and the big data server queries the corresponding unified vehicle number information 010082 in the first server vehicle number data table according to the vehicle number information a 073; inquiring the corresponding third party server M according to the uniform vehicle number information 010082, and inquiring the vehicle number information B295 corresponding to the uniform vehicle number information 010082 in a vehicle number data table of the third party server M; and after the third-party server M carries out security verification on the big data server, feeding the networking vehicle real-time position information corresponding to the vehicle number information B295 back to the big data server, feeding the networking vehicle real-time position information back to the first server by the big data server, and sending the networking vehicle real-time position information to the intelligent transportation software of all the users by the first server.
Example two:
a user terminal comprising a processor, wherein the processor performs the method of any of the above embodiments.
The invention provides an intelligent transportation software data sharing method based on big data, which can provide real-time positioning service for intelligent transportation software in other platforms according to the self positioning function of the intelligent transportation software in the platform when a certain server fails, thereby making up the interruption of the positioning service caused by the failure of the server in the platform and realizing the self-rescue of the positioning service in the platform; furthermore, when a certain intelligent traffic software fails to provide real-time positioning service due to the fault of a server provided by a single developer, the real-time positioning data of the intelligent traffic software in the servers provided by other developers can be acquired, so that the traffic information is ensured not to be influenced by the stability of the single software platform, and the real-time performance and the accuracy of the traffic information acquisition are higher.
It will be understood by those within the art that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, 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, 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 methods specified in the block or blocks of the block diagrams and/or flowchart block or blocks.
Those of skill in the art will appreciate that various operations, methods, steps in the processes, acts, or solutions discussed in the present application may be alternated, modified, combined, or deleted. Further, various operations, methods, steps in the flows, which have been discussed in the present application, may be interchanged, modified, rearranged, decomposed, combined, or eliminated. Further, steps, measures, schemes in the various operations, methods, procedures disclosed in the prior art and the present invention can also be alternated, changed, rearranged, decomposed, combined, or deleted.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. A big data-based intelligent transportation software data sharing method is characterized by comprising the following steps:
s1, the first server acquires the real-time position of the networked vehicle connected with the first server in real time, and sends the real-time position of the networked vehicle to intelligent transportation software in a plurality of user mobile terminals connected with the first server; any one of the users starts the intelligent transportation software and then sends the real-time position information of the mobile terminal to the first server; the first server stores real-time location information of the networked vehicle and real-time location information of the mobile terminal of the user; if the first server cannot acquire the position information of the networked vehicles within the preset time or the intelligent transportation software feedback of any one user cannot acquire the position information of the networked vehicles, the step S2 is entered;
s2, the first server calculating an estimated real-time location of the networked vehicle based on the saved real-time location information of the networked vehicle and the real-time location information of the mobile terminal of the user; sending the estimated real-time position information of the networked vehicles to the intelligent transportation software which cannot acquire the position information of the networked vehicles; detecting whether the intelligent transportation software of any one user feeds back the position information of the networked vehicles, and if so, entering the step S3; if the feedback does not exist, ending the operation;
s3, the first server acquires the third-party server recorded with the real-time position information of the networked vehicles through a big data server, and establishes communication connection with the third-party server corresponding to the third-party intelligent transportation software; the first server sends a request for obtaining the real-time position information of the networked vehicles, the third-party server feeds the real-time position information of the networked vehicles back to the first server after carrying out security verification on the first server, and the first server sends the real-time position information of the networked vehicles to the intelligent transportation software of all the users;
the first server calculates an estimated real-time location of the networked vehicle based on the saved real-time location information of the networked vehicle and the real-time location information of the mobile terminal of the user, and specifically includes:
the first server compares the stored historical real-time position information of the networked vehicle in the latest preset time period with the historical real-time position information of the mobile terminal of the user in the latest preset time period, calculates a historical real-time distance difference value between the networked vehicle and the mobile terminal of the user according to the comparison result, and takes the current real-time position information of the mobile terminal of the user as the estimated real-time position of the networked vehicle if the historical real-time distance difference value is smaller than a preset historical distance threshold value; if the historical real-time distance difference value is larger than or equal to a preset historical distance threshold value, the estimation fails, and error information of the estimation failure is sent to the first server;
or the first server compares the stored historical real-time position information of the networked vehicle in the latest preset time period with the historical real-time position information of the mobile terminals of the users in the latest preset time period, calculates the historical real-time distance difference between the networked vehicle and the mobile terminals of the users according to a plurality of comparison results, and takes the current real-time position information of the mobile terminals of the users as the estimated real-time position of the networked vehicle if the historical real-time distance difference is smaller than a preset historical distance threshold value and the number of the historical real-time distance difference is larger than or equal to a set value; and if the historical real-time distance difference is smaller than the preset historical distance threshold, the estimation fails, and error information of the estimation failure is sent to the first server.
2. The method of claim 1, wherein after sending the error message of the estimation failure to the first server, further comprising:
the big data server detects other third-party servers recorded with the real-time position information of the networked vehicles and establishes communication connection with the other third-party servers; the first server sends a request for obtaining the real-time position information of the networked vehicles to the other third-party servers, the other third-party servers feed the real-time position information of the networked vehicles back to the first server after performing security verification on the first server, and the first server sends the real-time position information of the networked vehicles to the intelligent transportation software of all the users.
3. The method of claim 1, further comprising:
the big data server establishes communication connection with a third-party server corresponding to a plurality of third-party intelligent transportation software; establishing a score for the third-party server feeding back the real-time position information of the networked vehicles, and setting an information feedback priority order of the third-party server feeding back the real-time position information of the networked vehicles based on the score; and the third-party server feeds back the real-time position information of the networked vehicles to the first server in sequence based on the priority order.
4. The method of claim 3, further comprising:
the big data server establishes communication connection with the first server and the plurality of third-party servers; the first server stores the real-time location information and vehicle number information of the networked vehicles belonging to the first server, and the third-party server stores the real-time location information and vehicle number information of the networked vehicles belonging to the third-party server; the big data server stores unified vehicle number information of the networked vehicles belonging to the first server and unified vehicle number information of the networked vehicles belonging to the third-party servers at the same time; the unified vehicle number information is different from the vehicle number information.
5. The method of claim 4, wherein the big data server stores both unified vehicle number information for the networked vehicles that are attributed to the first server and unified vehicle number information for the networked vehicles that are attributed to a plurality of the third party servers, further comprising:
the big data server stores unified vehicle number information of the networked vehicles belonging to the first server and vehicle number information of the networked vehicles of the first server in a first server vehicle number data table, and establishes a corresponding relation between the unified vehicle number information and the vehicle number information in the first server vehicle number data table;
the big data server stores unified vehicle number information of the networked vehicles belonging to the third-party server and vehicle number information of the networked vehicles of the third-party server in a third-party server vehicle number data table, and establishes a corresponding relation between the unified vehicle number information and the vehicle number information in the third-party server vehicle number data table; the number of the third-party servers is multiple.
6. The method of claim 5, further comprising:
the first server sends the vehicle number information of the networked vehicles to the big data server, and the big data server inquires corresponding uniform vehicle number information in the first server vehicle number data table according to the vehicle number information; inquiring the corresponding third-party server according to the uniform vehicle number information, and inquiring the vehicle number information corresponding to the uniform vehicle number information in a vehicle number data table of the third-party server; and after the third-party server carries out safety verification on the big data server, the networking vehicle real-time position information corresponding to the vehicle number information is fed back to the big data server, the big data server feeds back the networking vehicle real-time position information to the first server, and the first server sends the networking vehicle real-time position information to the intelligent transportation software of all the users.
7. A user terminal comprising a processor, characterized in that the processor performs the method of any of claims 1-6.
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