CN114234995A - Navigation method, navigation device, electronic equipment and storage medium - Google Patents
Navigation method, navigation device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a navigation method, which comprises the following steps: acquiring a preliminary selectable path from a current position to a target position, wherein the preliminary selectable path comprises a plurality of selectable paths; performing fusion calculation on the first source data and the second source data to obtain road condition information corresponding to the preliminary selectable path; screening the preliminary selectable path according to the road condition information to obtain a candidate navigation path; and determining a target navigation path from the current position to the target position according to the alternative navigation path, and navigating based on the target navigation path. Road condition information is obtained through fusion processing of multi-dimensional data of different types in two data sources, and a target navigation path can be screened out twice from multiple primary selectable paths based on the road condition information and user requirements for optimal navigation, so that the technical problem that the data sources are single and incomplete is solved, errors and delay of the road condition information are reduced, and the real-time performance and accuracy of user navigation driving are improved.
Description
Technical Field
The present application relates to the field of vehicle navigation, and in particular, to a navigation method, an apparatus, an electronic device, and a storage medium.
Background
The most common existing Navigation terminal is a smart phone, which receives a GPS (Global Positioning Navigation System) signal and target position information, calculates a Navigation path by combining road condition information provided by a third-party map service provider, and displays the Navigation path on a smart phone screen, thereby implementing a Navigation function. However, the road condition information data source of the third-party map service provider only comes from the mobile phone signaling data of the mobile phone terminal of the user, and the data source is single and incomplete; meanwhile, the navigation path is directly determined only by calculating the road condition information through the mobile phone terminal, and certain errors and delays exist.
Disclosure of Invention
In view of this, an embodiment of the present invention provides a navigation method, an apparatus, an electronic device, and a storage medium, which utilize multi-source traffic information and data to solve the technical problem of single and incomplete data source, thereby reducing errors and delays of the traffic information and improving real-time performance and accuracy of user navigation.
In a first aspect, an embodiment of the present application provides a navigation method, where the method includes: acquiring a preliminary selectable path from a current position to a target position, wherein the preliminary selectable path comprises a plurality of selectable paths; performing fusion calculation on the first source data and the second source data to obtain road condition information corresponding to the preliminary selectable path; wherein the first source data comprises real-time traffic data and the second source data comprises historical traffic data; screening the preliminary selectable path according to the road condition information to obtain a candidate navigation path; and determining a target navigation path from the current position to the target position according to the alternative navigation path, and navigating based on the target navigation path. The first source data comprises real-time vehicle data of road testing equipment, and the second source data comprises construction accidents, weather and road line type data.
In the implementation process, the road condition information of a plurality of primary optional paths is obtained by fusing multi-dimensional data such as real-time traffic data and historical traffic data of different types in two data sources, and the target navigation path can be screened out for optimal navigation from the plurality of primary optional paths through secondary screening based on the road condition information and user requirements, so that the technical problem of single and incomplete data sources is solved, errors and delay of the road condition information are reduced, and the real-time performance and accuracy of user navigation driving are improved.
With reference to the first aspect, an embodiment of the present application provides a first possible implementation manner of the first aspect, where: the first source data is from message middleware, and the real-time traffic data comprises vehicle data recorded by the road test equipment in real time; the second source data is from a database, and the historical traffic data includes construction accident, weather and road linetype data.
In the implementation process, the first source data is used for collecting real-time traffic data which is captured or recorded in real time from drive test equipment such as a gate and a toll station through an RMQ message queue of Kafka message middleware; the second source data is used for collecting historical traffic data such as construction accidents, weather and road linetypes and the like received through a basic information database of the vehicle-mounted terminal. The data of the card ports and the toll stations of the expressway are accessed in real time in a message queue mode, and the integrity and the real-time performance of the data of the accessed vehicles are improved, so that the accuracy and the real-time performance of the road condition information are improved.
With reference to the first aspect, an embodiment of the present application provides a second possible implementation manner of the first aspect, where: the performing fusion calculation on the first source data and the second source data to obtain the traffic information corresponding to the preliminary selectable path includes: performing fusion calculation on the first source data and the second source data to obtain real-time road condition data corresponding to the preliminary selectable path; based on the principle of an attention network model of the abnormal map, performing fusion calculation on the real-time road condition data and the second source data to obtain future road condition data corresponding to the preliminary selectable path; and determining the road condition information corresponding to the preliminary optional path according to the real-time road condition data and the future road condition data.
In the implementation process, the real-time traffic information may include traffic data, speed data, and congestion data of the vehicle on the road segment corresponding to the preliminary selectable path; the heterogeneous graph network multi-source data fusion analysis model of the scheme is established based on the heterogeneous graph attention network model principle for learning, and is predicted based on real-time road condition information, and future road condition information corresponding to the primary optional path is predicted. The special-pattern attention neural network algorithm with the characteristics of standardization, reusability and high usability enables system developers and users to concentrate on business processes and business customization related to pattern neural models, less effort is spent on system components such as models and training, and flexible modes of multiple model access can be provided.
With reference to the first aspect, an embodiment of the present application provides a third possible implementation manner of the first aspect, where: the performing fusion calculation on the first source data and the second source data to obtain the real-time road condition data corresponding to the preliminary selectable path includes: preprocessing the first source data to obtain intermediate real-time data; and performing fusion calculation on the intermediate real-time data and the second source data to obtain the real-time road condition data corresponding to the preliminary optional path.
In the implementation process, intermediate real-time data such as time, license plate and vehicle speed are obtained after preprocessing such as data cleaning and vehicle speed calculation is carried out on the first source data through the easy flex real-time computing platform. The intermediate real-time data is combined with the second source data for fusion analysis, and the real-time road condition is obtained through big data batch processing calculation and big data stream processing calculation, so that the data processing efficiency is improved, and the accuracy and the real-time performance of obtaining road condition information are improved.
With reference to the first aspect, an embodiment of the present application provides a fourth possible implementation manner of the first aspect, where: the performing fusion calculation on the real-time road condition data and the second source data based on the attention network model with the special-pattern structure to obtain future road condition data corresponding to the preliminary selectable path includes: training the real-time road condition data and the second source data based on a heteromorphic image attention network model principle to obtain a multi-source data fusion analysis model; and identifying the multi-source data fusion analysis model to obtain future road condition data corresponding to the primary optional path.
In the implementation process, a multi-source data fusion analysis model is established based on the attention network model of the different composition, the future road condition is calculated and predicted by adopting a deep learning algorithm, and the integrity and the accuracy of acquiring the road condition information are improved.
With reference to the first aspect, an embodiment of the present application provides a fourth possible implementation manner of the first aspect, where: the screening the preliminary selectable path according to the road condition information to obtain an alternative navigation path includes: calculating based on the road condition information to obtain the travel time of the preliminary optional path; sorting the preliminary selectable paths according to the travel time of the corresponding preliminary selectable paths; and selecting one or more selectable paths with shorter travel time in the preliminary selectable paths as the alternative navigation paths.
In the implementation process, the travel time is calculated based on the road condition information, the travel time of the primary selectable path is numerically sequenced, and then a plurality of optimal alternative navigation paths are screened out, so that the most suitable selectable paths in certain scenes can be avoided being excluded, and the optimality of selecting the target navigation path is improved.
With reference to the first aspect, an embodiment of the present application provides a fifth possible implementation manner of the first aspect, where: calculating the travel speed based on the road condition information to obtain the travel time of the preliminary optional path, wherein the calculation comprises the following steps: dividing the total road section of the preliminary selectable path into a plurality of sub road sections; calculating the travel time of the preliminary selectable path on the sub-road sections based on the road condition information to obtain a plurality of sub-travel times corresponding to the preliminary selectable path; and summing the plurality of sub-travel times to obtain the travel time of the preliminary selectable path.
In the implementation process, the secondary screening can be implemented according to the road traffic jam condition and the latest road condition in the road condition information through the calculation and comparison of the travel time, so that the determined navigation path is the optimal path, and the real-time and accurate requirements of user driving can be met.
In a second aspect, an embodiment of the present application provides a navigation device, including: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a preliminary optional path from a current position to a target position, and the preliminary optional path comprises a plurality of optional paths; the fusion module is used for performing fusion calculation on the first source data and the second source data to obtain the road condition information corresponding to the preliminary selectable path; the screening module is used for screening the preliminary selectable path according to the road condition information to obtain an alternative navigation path; and the navigation module is used for determining a navigation path from the current position to the target position according to the alternative navigation path and navigating based on the navigation path.
In the implementation process, the obtaining module may be configured to obtain a preliminary selectable path from the current location to the target location, where the preliminary selectable path includes multiple selectable paths; the fusion module can be used for performing fusion calculation on the first source data and the second source data to obtain road condition information corresponding to the preliminary selectable path; the screening module can be used for screening the primary selectable path according to the road condition information to obtain an alternative navigation path; and the navigation module can be used for determining a navigation path from the current position to the target position according to the alternative navigation path and navigating based on the navigation path.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory storing machine-readable instructions executable by the processor, the machine-readable instructions being executable by the processor to perform the steps of the method described above when the electronic device is run.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, performs the steps of the method described above.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a first navigation method provided in an embodiment of the present application;
FIG. 2 is a flow chart of a second navigation method provided by an embodiment of the present application;
FIG. 3 is a flow chart of a third navigation method provided by the embodiment of the present application;
fig. 4 is a functional block diagram of a navigation device according to an embodiment of the present application; and
fig. 5 is a block diagram of an electronic device according to an embodiment of the present application.
Icon: 210-an obtaining module; 220-a fusion module; 230-a screening module; 240-a navigation module; 300-an electronic device; 311-a memory; 312 — a storage controller; 313-a processor; 314-peripheral interfaces; 315-input-output unit; 316-display unit.
Detailed Description
The technical solution in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The present inventors have noted that more and more vehicle-mounted terminals are using a GPS navigation system for providing position information of a vehicle to a driver of the vehicle. Typically, GPS navigation systems are provided with electronic maps to provide basic data information including nearby obstacles, service stations, gas stations, or other locations. However, the existing navigation system does not receive vehicle data captured or recorded in real time by road test equipment such as a gate, a toll station and the like, and further performs fusion processing on multiple source data by combining basic data, so that the congestion condition and the latest road condition of road traffic cannot be known in time during navigation, and thus the determined navigation path is not an optimal path and cannot meet the real-time and accurate requirements of user driving.
Based on the above research, embodiments of the present application provide a navigation method, an apparatus, an electronic device, and a storage medium. Through access bayonet, toll station real-time traffic data, historical traffic data such as construction accidents, meteorological information, road linetype in the database are combined, data based on multiple data sources are fused to obtain road condition information of multiple primary selectable paths, multiple superior alternative navigation paths are obtained through primary screening of the multiple primary selectable paths based on the road condition information, a target navigation path is secondarily screened out from the superior alternative navigation paths based on user demands to perform optimal navigation, the technical problems of single data source and incompleteness are solved, errors and delay of the road condition information are reduced, and the real-time performance and accuracy of user navigation driving are improved.
Referring to fig. 1, a flowchart of a navigation method according to an embodiment of the present application is shown. The specific process shown in FIG. 1 will be described in detail below. The navigation method provided by the embodiment of the application can specifically comprise the following steps:
step 100: acquiring a preliminary selectable path from a current position to a target position, wherein the preliminary selectable path comprises a plurality of selectable paths;
step 120: performing fusion calculation on the first source data and the second source data to obtain road condition information corresponding to the preliminary selectable path; wherein the first source data comprises real-time traffic data and the second source data comprises historical traffic data;
step 140: screening the preliminary selectable path according to the road condition information to obtain a candidate navigation path; and
step 160: and determining a target navigation path from the current position to the target position according to the alternative navigation path, and navigating based on the target navigation path.
Wherein, the step 100: and acquiring a preliminary optional path from the current position to the target position, wherein the preliminary optional path comprises a plurality of optional paths.
Illustratively, the vehicle-mounted terminal integrates a GPS technology, a mileage positioning technology and an automobile black box technology, can be used for modern management of transport vehicles, and comprises the following steps: traffic safety monitoring management, operation management, service quality management, intelligent centralized scheduling management, electronic stop board control management and the like. The vehicle-mounted terminal can comprise various functional modules such as an intelligent control module, an audio conversion module, a GPS positioning module, a display operation module, a voice broadcast module and a vehicle monitoring module, and intelligent driving of a vehicle is completed in cooperation among the functional modules.
A GPS positioning module carried by the vehicle-mounted terminal receives GPS satellite signals and generates information of a current position, wherein the information of the current position can specifically comprise longitude and latitude information and a moving direction of the current position; specifically, a GPS navigation system in the GPS positioning module receives data information transmitted by at least 3 satellites in GPS satellites surrounding the earth through an antenna, and determines the position coordinates through GPS satellite signals by combining an electronic map stored in a vehicle-mounted terminal so as to be matched with the position coordinates, thereby determining the exact position of the vehicle in the electronic map.
The information of the target position can comprise destination information input by a user in a voice input, text input or touch selection mode, and further, an audio conversion component in the vehicle-mounted terminal can convert the input voice message of the target position into text for processing.
In one embodiment, the intelligent control module in the vehicle-mounted terminal searches an electronic map database in the vehicle-mounted terminal according to the information of the current position and the information of the target position, and automatically generates a plurality of preliminary selectable paths. The intelligent control module may specifically include a mobile communication unit, a path generator, and a road condition generator, which are connected to each other in a communication manner. The Mobile Communication unit may connect to at least one of a GSM (Global System for Mobile Communication) network, a CDMA (Code Division Multiple Access) network, and a GPRS (General Packet Radio Service) network.
The mobile communication unit receives data of current position information and target position information of the vehicle-mounted terminal, and then sends the data to the path generator, the path generator searches an internal database, and can obtain basic road conditions such as highway network topology, road section basic information, starting and ending point information and the like by combining various data such as a map database, a historical road condition database, a user preference path database and the like, all selectable paths of the current position to the target position are automatically calculated by a dynamic planning method, and at least a plurality of different paths, namely a plurality of primary selectable paths, can be included in all selectable paths. The length of each road section and the predicted passing time under the current road condition of each road section can be known by the map database, the empirical passing time of each road section in the time range can be known by the historical road condition database, and the times of selecting each road section by the user can be known by the user preference path database.
Further, the path generator searches an internal database to automatically calculate the preliminary optional path from the current position to the target position, and a dynamic planning method of weighted calculation can be adopted. Firstly, the length of each road section and the event numerical value are weighted and calculated, so that each road section can obtain a weight value, then a shorter weighting-shorter path algorithm is used, the sum of times of selection of a user is larger under the condition that the sum of the weight values of the paths is smaller, and a plurality of shorter selectable paths in the paths with smaller sum of the weight values and larger sum of times are selected as primary selectable paths.
Step 120: performing fusion calculation on the first source data and the second source data to obtain road condition information corresponding to the preliminary selectable path; wherein the first source data comprises real-time traffic data and the second source data comprises historical traffic data.
Illustratively, the data sources of the first source data and the second source data are different, and the first source data and the second source data may include real-time traffic data obtained from real-time snapshots or records of drive test equipment such as a gate and a toll station, and may further include various historical traffic data such as construction accidents, weather and road linetype data. The gate is a gate monitoring device in a road traffic security gate monitoring system, and is used for shooting, processing and recording all motor vehicles and persons in the vehicles passing through the gate in specific places on a road, such as a toll station, a traffic inspection group, a security inspection station, a transit road and the like. A toll booth is a device for collecting toll fees for passing vehicles.
In one embodiment, a road condition generator in the vehicle-mounted terminal constructs a series of algorithm models for the first source data and the second source data to perform fusion calculation, and obtains road condition information of each selectable path from the current position to the target position. The calculated traffic information can be stored in the Mysql database, and the Mysql database can provide an open API application programming interface to support third party calls. The path generator in the vehicle-mounted terminal can call the road condition information under the corresponding path in an API (application program interface) mode, and further, after the information of the primary selectable path is input, the road condition generator calculates and returns the road condition information of the corresponding path.
The traffic information may include traffic information provided by a third-party map service provider, and may also include historical traffic information in a map database, a historical traffic database, and a user preference database. The road condition information may indicate one or more of speed limit information of a current traveling section on the selectable path, a traffic accident on the selectable path, congestion conditions of all sections on the selectable path, weather information on the selectable path, current time period information, and traffic monitoring information.
The data of the card ports and the toll stations of the expressway are accessed in real time in a message queue mode, the integrity and the real-time performance of the accessed traffic data are guaranteed, the user can obtain accurate and real-time road condition information, the information is timely broadcasted in the navigation process, and the driving is well assisted.
Step 140: and screening the preliminary selectable path according to the road condition information to obtain a selectable navigation path.
Illustratively, according to the speed limit information of the current driving road section on the selectable path, the traffic accident on the selectable path, the congestion conditions of all road sections on the selectable path, the weather information on the selectable path, the current time period information and the data in the traffic monitoring information, the vehicle-mounted terminal screens out several superior paths in a plurality of preliminary selectable paths by adopting a series of algorithm calculation on the preliminary selectable path, and takes the superior paths as alternative navigation paths.
Step 160: and determining a target navigation path from the current position to the target position according to the alternative navigation path, and navigating based on the target navigation path.
Illustratively, according to the alternative navigation paths calculated and screened in step 140, the user may further screen out an optimal path suitable for the user from the preferred alternative navigation paths on the terminal map interface based on the preference and the actual demand of the user, and use the optimal path as a target navigation path to assist driving according to the target navigation path. Specifically, the display operation module in the vehicle-mounted terminal can display the alternative navigation path on a terminal map interface, and manually touch a screen to select the target.
The vehicle monitoring module in the vehicle-mounted terminal can judge whether the vehicle deviates from the target navigation path according to the target navigation path and the vehicle-mounted GPS positioning module, if so, the target position and the current position of GPS positioning are sent to the intelligent control module again through the mobile communication unit of the vehicle-mounted terminal, the intelligent control module recalculates to generate a new target navigation path and corresponding road condition information, and navigation is restarted according to the new target navigation path.
The voice broadcast module in the vehicle-mounted terminal may generate voice broadcast information based on the target navigation path and the road condition information on the target navigation path fused and calculated in step 120, where the voice broadcast information may include a road section speed limit prompt, an intersection turning prompt, a congested road section prompt, a traffic accident prompt, a road section restriction prompt, a traffic monitoring prompt, and the like. Specifically, the vehicle-mounted terminal may generate a road section speed limit prompt based on a road section speed limit currently driven on the target navigation path, may generate a current traffic limit condition of some roads based on current time period information on the target navigation path, and may generate a turn prompt based on intersection information of the target navigation path.
In one embodiment, step 120 may include: the first source data is from message middleware, and the real-time traffic data comprises vehicle data recorded by the road test equipment in real time; the second source data is from a database, and the historical traffic data includes construction accident, weather and road linetype data.
Illustratively, message middleware is a supporting software system that provides synchronous or asynchronous, reliable message transmission for application systems in a network environment based on queue and message passing technologies, which by providing a message passing and message queue model, can extend the communication of processes in a distributed environment. The message middleware can be any one of the commonly used Kafka, RabbitMQ, RockattMQ, ActiveMQ and the like, and is used for acquiring and temporarily storing the real-time vehicle data of the road test equipment in a message queue form.
The database may include basic data such as construction accidents, weather and road linetypes in a map database, a historical road condition database and a user preference database in the vehicle-mounted terminal, and may specifically include: the current speed limit of the road section, traffic or construction accidents on the navigation path, the congestion condition of the road section, the road weather information, the current time period information, the traffic monitoring information and the like.
In one embodiment, real-time traffic data captured or recorded in real time is acquired from drive test equipment such as a gate and a toll station through an RMQ message queue of Kafka message middleware, wherein the data includes main data such as vehicle equipment ID, license plate, gate name, photographing time and the like, and the main data serves as first source data. Historical traffic data such as construction accidents, weather and road linetypes are received through a basic information database of the vehicle-mounted terminal, and the data are used as second source data.
Referring to fig. 2, a flowchart of a second navigation method according to an embodiment of the present application is shown. The specific process shown in fig. 2 will be described in detail below. In the navigation method provided in the embodiment of the present application, specifically, step 120 may include:
step 122: performing fusion calculation on the first source data and the second source data to obtain real-time road condition data corresponding to the preliminary selectable path;
step 124: based on the principle of an attention network model of the abnormal map, performing fusion calculation on the real-time road condition data and the second source data to obtain future road condition data corresponding to the preliminary selectable path;
step 126: and determining the road condition information corresponding to the preliminary optional path according to the real-time road condition data and the future road condition data. Wherein, step 122: performing fusion calculation on the first source data and the second source data to obtain real-time road condition data corresponding to the preliminary selectable path;
for example, the real-time traffic data may include traffic data, speed data, and congestion data on the road segment corresponding to the preliminary selectable path. The traffic data can be the number of vehicles obtained by taking the total number of vehicles passing through all the gates in a section of area and a section of time and then carrying out de-duplication processing on license plates of the vehicles; the vehicle speed road condition data can be a result calculated by combining the distance between the vehicle and the two checkpoints and the used time; the traffic jam road condition data can comprehensively consider the conditions of traffic flow road conditions and vehicle speed road conditions of the vehicle, and the related data of the road jam can be obtained according to the definition of the predetermined jam interval.
An intelligent control module in the vehicle terminal receives first source data containing vehicle equipment ID, license plate, bayonet name, photographing time and the like from road test equipment such as a bayonet and a toll station and the like through an RMQ message queue of Kafka message middleware, receives second source data such as construction accidents, weather and road line types and the like through a basic information database of the vehicle terminal, inputs the first source data and the second source data into a road condition generator, and sequentially and respectively fuses a plurality of primary optional paths calculated by the path generator in the step 100 to obtain corresponding real-time road condition data such as vehicle flow road condition data, vehicle speed road condition data, vehicle road condition congestion data and the like.
In one embodiment, step 122 may include step 122a and step 122 b.
Step 122 a: preprocessing the first source data to obtain intermediate real-time data;
step 122 b: and performing fusion calculation on the intermediate real-time data and the second source data to obtain the real-time road condition data corresponding to the preliminary optional path.
Wherein, step 122 a: and preprocessing the first source data to obtain intermediate real-time data.
For example, the road condition generator may perform data layering on the whole data processing process according to actual requirements, and may specifically be divided into a data operation layer, a data warehouse layer DW, and a data service layer. The data warehouse layer DW can be divided into a data detail layer DWD, a data intermediate layer DWM and a data service layer DWs from top to bottom.
The original data accessed into the Kafka message middleware in real time, namely the first source data, enters a data operation layer after ETL (Extract Transform Load) preprocessing processes such as extraction, cleaning and transmission, the DWD (data detail layer) is used for performing data cleaning and normalization operations on the data operation layer, for example, after preprocessing processes such as null data, dirty data and outlier removal, the DWD is stored in an HDFS (Hadoop Distributed File System) for data backup. Meanwhile, the backed-up first source data is subjected to data cleaning, vehicle speed calculation and other preprocessing through an easy flex real-time computing platform to obtain intermediate real-time data such as time, license plate, vehicle speed and the like.
Step 122 b: and performing fusion calculation on the intermediate real-time data and the second source data to obtain the real-time road condition data corresponding to the preliminary optional path.
Illustratively, Hive is data warehouse architecture software established on a Hadoop Distributed File System, and can analyze and manage intermediate real-time data stored in a Hadoop Distributed File System (HDFS). Further, big data processing can be divided into stream processing and batch processing, and batch processing can be to operate a large-capacity static data set in the second source data and return a result after the accounting process is completed, so that the result is used for the whole demand of the big data processing; the stream processing may be an accounting operation performed on data entering the system in real time in the second source data, and a processing result is immediately available and is updated continuously with arrival of new data.
The first source data is used as original data accessed by Kafka message middleware in real time, and intermediate real-time data is obtained after a series of preprocessing, and can be transmitted to a data detail layer DWD of Kafka again for storage, and meanwhile, the data preprocessed again can be stored in an HDFS (Hadoop Distributed File System) as a backup. The backed-up intermediate real-time data and second source data such as construction accidents, weather and road linear data from the basic information database are further subjected to fusion analysis through a Hive off-line calculation engine in the data service layer, real-time road condition data such as vehicle flow road condition data, vehicle speed road condition data and vehicle congestion road condition data are respectively obtained after the fusion analysis result is subjected to large data batch processing calculation and large data stream processing calculation, and the corresponding real-time road condition data are further obtained according to a plurality of preliminary optional paths calculated by the path generator in the step 100.
Step 124: and performing fusion calculation on the real-time road condition data and the second source data based on a heteromorphic graph attention network model principle to obtain future road condition data corresponding to the preliminary selectable path.
Illustratively, the heterogeneous graph attention network model can be built through deep learning based on a graph neural network model, wherein the graph attention network is a novel convolution graph neural network and only processes non-heterogeneous graphs of one node or connection type; the attention network of the heterogeneous graph can generally process various types of nodes, different types of nodes have different characteristics, the characteristics may fall into different characteristic spaces, the nodes form connection relations through various types of meta-paths, and the relations among the nodes in the heterogeneous graph can have different semantics according to the difference of the meta-paths.
The second source data can comprise data sets of three different types, namely construction accidents, weather and road line types, any incidence relation among the data in the three data sets can represent different attributes, namely, a heterogeneous graph network of the scheme can be established for learning based on a heterogeneous graph attention network model principle, the heterogeneous graph network of the scheme is predicted based on real-time road condition information, and future road condition information corresponding to the primary selectable path is predicted.
In one embodiment, step 124 may include:
124 a: training the real-time road condition data and the second source data based on a heteromorphic image attention network model principle to obtain a multi-source data fusion analysis model;
124 b: and identifying the multi-source data fusion analysis model to obtain future road condition data corresponding to the primary optional path.
Wherein, 124 a: training the real-time road condition data and the second source data based on a heteromorphic image attention network model principle to obtain a multi-source data fusion analysis model;
for example, the multi-source data fusion analysis model may be a graph neural network model preset in advance according to a common heterogeneous graph attention network model. In the using process of the model, if the model memory in the road condition generator has the graph neural network model meeting the requirement, the existing graph neural network model can be directly called from the model memory based on the packaged API (application program interface), so that the rapid multiplexing of the graph neural network model is realized. However, if there is no graph neural network model satisfying the requirement in the model memory, the new graph neural network model may be configured through a preset API application program interface according to the specific requirement, and the configured graph neural network model is stored in the model memory, so as to complete the actual service requirement, and provide a basis for the subsequent rapid reuse of the new graph neural network model.
And (4) combining the real-time traffic data such as the traffic data of the vehicle flow, the traffic data of the vehicle speed, the traffic data of the vehicle congestion and the like corresponding to the plurality of preliminary selectable paths calculated in the step 122 or the step 122b with the type data such as the construction accident, the meteorological information, the road line type data and the like in the second source data to construct the attention network with the different composition. In particular, a traffic dissimilarity map attention network may be generated that contains semantic information for different spatial features under different temporal features.
Further, time characteristics of the attention network of the traffic abnormality map are captured, and the time characteristics can include whether holidays are saved on the day of the week, whether minibuses exist on the day of the week, weather types in the week and the like; and capturing the spatial characteristics of the attention network of the traffic abnormal map, wherein the spatial characteristics can comprise the number of passable lanes, the positions of ramps, the number of lanes before and after the ramps are turned, the nearest construction position, the nearest accident position and the like.
Time and space characteristics are extracted from historical data before the current time and real-time data of the current time respectively based on the constructed traffic heteromorphic image attention network, the characteristics of the historical data are used as a training set, the characteristics of the real-time data are used as a test set, the traffic heteromorphic image attention network is trained, and a preliminary multi-source data fusion analysis model of the selectable path is obtained.
The abnormal graph attention neural network algorithm with the specification, reusability and high usability enables system developers and users to concentrate on business processes and business customization related to the graph neural model, less effort is spent on system components such as models and training, and a flexible mode of accessing various models can be provided.
124 b: and identifying the multi-source data fusion analysis model to obtain future road condition data corresponding to the primary optional path.
For example, a current latest traffic dissimilarity graph attention network may be further constructed from the first source data and the second source data which are obtained at present, the graph is used as an input graph of the multi-source data fusion analysis model trained in the step 124a to be calculated, a feature fusion graph which may represent a future traffic condition state is output, and different features of the future traffic condition data may be further extracted from the feature fusion graph, wherein the different features specifically include vehicle flow traffic condition data, vehicle speed traffic condition data and vehicle congestion traffic condition data of the future traffic condition on a road segment corresponding to the preliminary selectable path.
Step 126: and determining the road condition information corresponding to the preliminary optional path according to the real-time road condition data and the future road condition data.
Illustratively, the real-time traffic status data and the future traffic status data calculated in the above steps are stored in the Mysql database, and the Mysql database may provide an open API application program interface to support third party calls. The path generator can call the road condition information under the corresponding path in a mode of an API application program interface, namely after the path information is input, the road condition generator can calculate and return the road condition information of the corresponding path.
Referring to fig. 3, a flowchart of a third navigation method according to an embodiment of the present application is provided. The specific flow shown in fig. 3 will be described in detail below. In the navigation method provided in the embodiment of the present application, specifically, step 140 may include:
step 141: calculating based on the road condition information to obtain the travel time of the preliminary optional path;
step 142: sorting the preliminary selectable paths according to the travel time of the corresponding preliminary selectable paths;
step 143: and selecting one or more selectable paths with shorter travel time in the preliminary selectable paths as the alternative navigation paths.
Wherein, step 141: and calculating based on the road condition information to obtain the travel time of the preliminary optional path.
For example, after receiving data of the current position information and the target position information of the vehicle-mounted terminal, the mobile communication unit sends the data to the path generator, and the path generator searches a map database stored inside, so that the basic road conditions such as the network topology of the expressway, the road section basic information, the start and end point information and the like can be obtained. Further, all preliminary selectable paths of which the current position reaches the target position are automatically calculated through a dynamic planning method, and then the information of all the preliminary selectable paths is sent to a road condition generator through a mobile communication unit to generate road condition information of corresponding paths through fusion calculation, wherein the road condition information specifically comprises real-time road conditions and future road conditions at the current time. And finally, calculating the travel time of all the preliminary optional paths based on the road condition information of the corresponding paths.
In one embodiment, step 141 may include step 141a, step 141b, and step 141 c.
Step 141 a: dividing the total road section of the preliminary selectable path into a plurality of sub road sections;
step 141 b: calculating the travel time of the preliminary selectable path on the sub-road sections based on the road condition information to obtain a plurality of sub-travel times corresponding to the preliminary selectable path;
step 141 c: and summing the plurality of sub-travel times to obtain the travel time of the preliminary selectable path.
For example, according to the real-time and future traffic information such as the traffic data, the speed data, and the traffic congestion data of the vehicle of the preliminary selectable path, the traffic generator may divide all the road segments including the congested road segment on the preliminary selectable path into a plurality of sub-road segments.
The road condition generator is accessed to original data, namely first source data, in the Kafka message middleware in real time, the original data enters a data operation layer after the processes of extraction, cleaning and transmission preprocessing, cleaning and standardization operation of some data are carried out on the data operation layer, for example, after preprocessing such as removal of null data, dirty data and outliers, data cleaning and preprocessing such as calculation of vehicle speed are carried out through an easy flex real-time computing platform, and then middle real-time data of real-time road condition data such as time, license plate and vehicle speed are obtained.
Further, road condition information such as the vehicle speed of each sub-road section and basic road condition information such as the road section length of each sub-road section are extracted, and the road section length of each sub-road section is correspondingly divided by the vehicle speed to obtain a plurality of corresponding sub-travel times. And performing accumulation summation calculation on the plurality of calculated sub-travel times, finally obtaining the total travel time of the preliminary selectable path, and determining the total travel time as the final travel time of the preliminary selectable path.
Step 142: and sequencing the preliminary optional paths according to the travel time of the corresponding preliminary optional paths.
For example, a sorting function may be constructed for a plurality of numerical data such as the final travel time of the preliminary selectable path, and the numerical data may be input into the sorting function to be sorted once according to the magnitude of the numerical value, and may be sorted from large to small, or sorted from small to large.
Step 143: and selecting one or more selectable paths with shorter travel time in the preliminary selectable paths as the alternative navigation paths.
Illustratively, based on the principle that the travel time is short, the numerical sorting result is screened for the first time, several better selectable paths with short travel time are screened out as alternative navigation paths, and the alternative navigation paths are further used as alternative path sources for secondary screening by the user according to personal preference or actual requirements. Specifically, the user can acquire the alternative path sources from the terminal map interface, and then secondarily screen out an optimal path suitable for the user from the optimal alternative navigation paths to serve as a target navigation path, and drive assistance is performed according to the target navigation path.
Please refer to fig. 4, which is a schematic diagram of functional modules of a navigation device according to an embodiment of the present application. Each module in the navigation device in this embodiment is configured to perform each step in the above method embodiment. The navigation device includes an acquisition module 210, a fusion module 220, a filtering module 230, and a navigation module 240.
The obtaining module 210 is configured to obtain a preliminary optional path from the current location to the target location, where the preliminary optional path includes multiple optional paths;
the fusion module 220 is configured to perform fusion calculation on the first source data and the second source data to obtain road condition information corresponding to the preliminary selectable path; wherein the first source data comprises real-time traffic data and the second source data comprises historical traffic data;
a screening module 230, configured to screen the preliminary selectable path according to the road condition information to obtain an alternative navigation path; and
and a navigation module 240, configured to determine a navigation path from the current location to the target location according to the alternative navigation path, and perform navigation based on the navigation path.
The first source data is from message middleware, and the first source data comprises vehicle data recorded by the road test equipment in real time; the second source data is from a database, the second source data including construction accident, weather and road linetype data.
In a first alternative embodiment, the fusion module 220 is configured to:
performing fusion calculation on the first source data and the second source data to obtain real-time road condition data corresponding to the preliminary selectable path;
based on the principle of an attention network model of the abnormal map, performing fusion calculation on the real-time road condition data and the second source data to obtain future road condition data corresponding to the preliminary selectable path;
and determining the road condition information corresponding to the preliminary optional path according to the real-time road condition data and the future road condition data.
In a second alternative embodiment, the fusion module 220 is configured to:
preprocessing the first source data to obtain intermediate real-time data;
and performing fusion calculation on the intermediate real-time data and the second source data to obtain the real-time road condition data corresponding to the preliminary optional path.
In a third alternative embodiment, the fusion module 220 is configured to:
training the real-time road condition data and the second source data based on a heteromorphic image attention network model principle to obtain a multi-source data fusion analysis model;
and identifying the multi-source data fusion analysis model to obtain future road condition data corresponding to the primary optional path.
In a fourth alternative embodiment, the screening module 230 is configured to:
calculating based on the road condition information to obtain the travel time of the preliminary optional path;
sorting the preliminary selectable paths according to the travel time of the corresponding preliminary selectable paths;
and selecting one or more selectable paths with shorter travel time in the preliminary selectable paths as the alternative navigation paths.
In a fifth alternative embodiment, the screening module 230 is configured to:
dividing the total road section of the preliminary selectable path into a plurality of sub road sections;
calculating the travel time of the preliminary selectable path on the sub-road sections based on the road condition information to obtain a plurality of sub-travel times corresponding to the preliminary selectable path;
and summing the plurality of sub-travel times to obtain the travel time of the preliminary selectable path.
Fig. 5 is a block diagram of an electronic device. The electronic device 300 may include a memory 311, a memory controller 312, a processor 313, a peripheral interface 314, an input-output unit 315, and a display unit 316. It will be understood by those skilled in the art that the structure shown in fig. 5 is merely illustrative and is not intended to limit the structure of the electronic device 300. For example, electronic device 300 may also include more or fewer components than shown in FIG. 5, or have a different configuration than shown in FIG. 5.
The above-mentioned memory 311, memory controller 312, processor 313, peripheral interface 314, input/output unit 315 and display unit 316 are electrically connected to each other directly or indirectly to implement data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The processor 313 described above is used to execute executable modules stored in memory.
The Memory 311 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 311 is configured to store a program, and the processor 313 executes the program after receiving an execution instruction, and the method executed by the electronic device 300 defined by the process disclosed in any embodiment of the present application may be applied to the processor 313, or implemented by the processor 313.
The processor 313 may be an integrated circuit chip having signal processing capabilities. The Processor 313 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The peripheral interface 314 couples various input/output devices to the processor 313 and to the memory 311. In some embodiments, peripheral interface 314, processor 313, and memory controller 312 may be implemented in a single chip. In other examples, they may be implemented separately from the individual chips.
The input/output unit 315 is used for providing input data to a user. The input/output unit 315 may be, but is not limited to, a mouse, a keyboard, and the like.
The display unit 316 provides an interactive interface (e.g., a user interface) between the electronic device 300 and the user for reference. In this embodiment, the display unit 316 may be a liquid crystal display or a touch display. The liquid crystal display or the touch display can display the process of the program executed by the processor.
The electronic device 300 in this embodiment may be configured to perform each step in each method provided in this embodiment.
Furthermore, an embodiment of the present application also provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method in the above-mentioned method embodiment.
The computer program product of the presentation method for configuration application provided in the embodiment of the present application includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the method in the above method embodiment, which may be referred to in the above method embodiment specifically, and are not described herein again.
In summary, the following steps: the embodiment of the application provides a navigation method, a navigation device, electronic equipment and a storage medium, wherein the navigation method comprises the following steps: acquiring a preliminary selectable path from a current position to a target position, wherein the preliminary selectable path comprises a plurality of selectable paths; performing fusion calculation on the first source data and the second source data to obtain road condition information corresponding to the preliminary selectable path; screening the preliminary selectable path according to the road condition information to obtain a candidate navigation path; and determining a target navigation path from the current position to the target position according to the alternative navigation path, and navigating based on the target navigation path.
In the implementation process, real-time traffic data of a card access port and a toll station are accessed, historical traffic data such as construction accidents, weather and road linetypes in a database are combined, fusion processing is carried out on the basis of data of different types from two data sources, road condition information of a plurality of primary selectable paths is obtained, a plurality of superior alternative navigation paths are obtained by screening the plurality of primary selectable paths once on the basis of the road condition information, a target navigation path is screened out from the superior alternative navigation paths secondarily on the basis of user demands for optimal navigation, so that the technical problems of single and incomplete data sources are solved, errors and delay of the road condition information are reduced, and the real-time performance and accuracy of user navigation driving are improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the modules is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form. The functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as independent products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In this document, relational terms such as first and second, and the like may be 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.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method of navigation, the method comprising:
acquiring a preliminary selectable path from a current position to a target position, wherein the preliminary selectable path comprises a plurality of selectable paths;
performing fusion calculation on the first source data and the second source data to obtain road condition information corresponding to the preliminary selectable path; wherein the first source data comprises real-time traffic data and the second source data comprises historical traffic data;
screening the preliminary selectable path according to the road condition information to obtain a candidate navigation path; and
and determining a target navigation path from the current position to the target position according to the alternative navigation path, and navigating based on the target navigation path.
2. The method of claim 1, wherein the first source data is from message middleware, and the real-time traffic data comprises vehicle data recorded by a drive test device in real time; the second source data is from a database, and the historical traffic data includes construction accident, weather and road linetype data.
3. The method according to claim 1, wherein the performing the fusion calculation on the first source data and the second source data to obtain the traffic information corresponding to the preliminary selectable path comprises:
performing fusion calculation on the first source data and the second source data to obtain real-time road condition data corresponding to the preliminary selectable path;
based on the principle of an attention network model of the abnormal map, performing fusion calculation on the real-time road condition data and the second source data to obtain future road condition data corresponding to the preliminary selectable path;
and determining the road condition information corresponding to the preliminary optional path according to the real-time road condition data and the future road condition data.
4. The method according to claim 3, wherein the performing the fusion calculation on the first source data and the second source data to obtain the real-time traffic data corresponding to the preliminary selectable path includes:
preprocessing the first source data to obtain intermediate real-time data;
and performing fusion calculation on the intermediate real-time data and the second source data to obtain the real-time road condition data corresponding to the preliminary optional path.
5. The method according to claim 3, wherein the performing a fusion calculation on the real-time traffic data and the second source data based on the heteromorphic image attention network model to obtain future traffic data corresponding to the preliminary selectable path comprises:
training the real-time road condition data and the second source data based on a heteromorphic image attention network model principle to obtain a multi-source data fusion analysis model;
and identifying the multi-source data fusion analysis model to obtain future road condition data corresponding to the primary optional path.
6. The method as claimed in claim 1, wherein the filtering the preliminary selectable path according to the traffic information to obtain alternative navigation paths comprises:
calculating based on the road condition information to obtain the travel time of the preliminary optional path;
sorting the preliminary selectable paths according to the travel time of the corresponding preliminary selectable paths;
and selecting one or more selectable paths with shorter travel time in the preliminary selectable paths as the alternative navigation paths.
7. The method as claimed in claim 5, wherein calculating the travel speed based on the traffic information to obtain the travel time of the preliminary alternative path comprises:
dividing the total road section of the preliminary selectable path into a plurality of sub road sections;
calculating the travel time of the preliminary selectable path on the sub-road sections based on the road condition information to obtain a plurality of sub-travel times corresponding to the preliminary selectable path;
and summing the plurality of sub-travel times to obtain the travel time of the preliminary selectable path.
8. A navigation device, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a preliminary optional path from a current position to a target position, and the preliminary optional path comprises a plurality of optional paths;
the fusion module is used for performing fusion calculation on the first source data and the second source data to obtain the road condition information corresponding to the preliminary selectable path; wherein the first source data comprises real-time traffic data and the second source data comprises historical traffic data;
the screening module is used for screening the preliminary selectable path according to the road condition information to obtain an alternative navigation path; and
and the navigation module is used for determining a navigation path from the current position to the target position according to the alternative navigation path and navigating based on the navigation path.
9. An electronic device, comprising: a processor, a memory storing machine-readable instructions executable by the processor, the machine-readable instructions when executed by the processor performing the steps of the method of any of claims 1 to 7 when the electronic device is run.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117493820A (en) * | 2024-01-03 | 2024-02-02 | 中国电子工程设计院股份有限公司 | Data element processing method and device |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107144286A (en) * | 2016-03-01 | 2017-09-08 | 阿里巴巴集团控股有限公司 | Air navigation aid and device |
| CN108847042A (en) * | 2018-08-24 | 2018-11-20 | 讯飞智元信息科技有限公司 | A kind of traffic information dissemination method and device |
| CN109360419A (en) * | 2018-11-16 | 2019-02-19 | 浩鲸云计算科技股份有限公司 | A kind of calculation method of link flow alarm |
| CN110491154A (en) * | 2019-07-23 | 2019-11-22 | 同济大学 | Suggestion speed formulating method based on security risk and distance |
| CN110553656A (en) * | 2018-05-31 | 2019-12-10 | 上海博泰悦臻网络技术服务有限公司 | road condition planning method and system for vehicle machine |
| CN111540199A (en) * | 2020-04-21 | 2020-08-14 | 浙江省交通规划设计研究院有限公司 | High-speed traffic flow prediction method based on multi-mode fusion and graph attention machine mechanism |
| CN112507040A (en) * | 2020-12-21 | 2021-03-16 | 北京百度网讯科技有限公司 | Training method and device for multivariate relation generation model, electronic equipment and medium |
| CN112562337A (en) * | 2020-12-10 | 2021-03-26 | 之江实验室 | Expressway real-time traffic accident risk assessment method based on deep learning |
| CN113779429A (en) * | 2021-09-18 | 2021-12-10 | 平安国际智慧城市科技股份有限公司 | Traffic congestion situation prediction method, device, equipment and storage medium |
-
2021
- 2021-12-21 CN CN202111568485.0A patent/CN114234995A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107144286A (en) * | 2016-03-01 | 2017-09-08 | 阿里巴巴集团控股有限公司 | Air navigation aid and device |
| CN110553656A (en) * | 2018-05-31 | 2019-12-10 | 上海博泰悦臻网络技术服务有限公司 | road condition planning method and system for vehicle machine |
| CN108847042A (en) * | 2018-08-24 | 2018-11-20 | 讯飞智元信息科技有限公司 | A kind of traffic information dissemination method and device |
| CN109360419A (en) * | 2018-11-16 | 2019-02-19 | 浩鲸云计算科技股份有限公司 | A kind of calculation method of link flow alarm |
| CN110491154A (en) * | 2019-07-23 | 2019-11-22 | 同济大学 | Suggestion speed formulating method based on security risk and distance |
| CN111540199A (en) * | 2020-04-21 | 2020-08-14 | 浙江省交通规划设计研究院有限公司 | High-speed traffic flow prediction method based on multi-mode fusion and graph attention machine mechanism |
| CN112562337A (en) * | 2020-12-10 | 2021-03-26 | 之江实验室 | Expressway real-time traffic accident risk assessment method based on deep learning |
| CN112507040A (en) * | 2020-12-21 | 2021-03-16 | 北京百度网讯科技有限公司 | Training method and device for multivariate relation generation model, electronic equipment and medium |
| CN113779429A (en) * | 2021-09-18 | 2021-12-10 | 平安国际智慧城市科技股份有限公司 | Traffic congestion situation prediction method, device, equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117493820A (en) * | 2024-01-03 | 2024-02-02 | 中国电子工程设计院股份有限公司 | Data element processing method and device |
| CN117493820B (en) * | 2024-01-03 | 2024-04-02 | 中国电子工程设计院股份有限公司 | Data element processing method and device |
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