CN114863670B - Method and device for early warning of taxi gathering condition - Google Patents
Method and device for early warning of taxi gathering condition Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a device for early warning of taxi gathering conditions, wherein the method comprises the steps of judging whether taxi gathering exists in a target area or not, identifying environment data of the target area when the taxi gathering exists in the target area, wherein the environment data comprises building type data contained in the target area and/or traffic jam state data of the target area, judging whether traffic jam exists in the target area or not according to the traffic jam state data when the building type data is not contained in a preset building type data set, and giving a taxi gathering condition warning when the traffic jam does not exist in the target area. The taxi gathering condition early warning method can be combined with the environmental data of taxis in the target area to more accurately identify whether the reason of taxi group gathering is passenger reception or the taxi gathering condition, and further can improve the accuracy of early warning the taxi gathering condition.
Description
Technical Field
The invention relates to the field of taxi monitoring, in particular to a method and a device for early warning taxi gathering conditions.
Background
The taxi is used as an important carrier for bearing urban traffic, normal and effective operation not only relieves urban traffic pressure, but also is beneficial to stabilizing the society and promoting the development of harmonious connotation society. In recent years, taxi group strike events initiated against solicitations such as "network taxi appointment" occur successively in partial cities throughout the country. However, when the gathering condition occurs in the taxi industry, the market order of the taxi industry is influenced, traffic jam is easily caused, the urban image is damaged, and serious impact is brought to urban management.
At present, whether the taxis are gathered in groups or not can be simply judged through positioning of the taxis, and then an alarm program is started when the taxis are gathered in groups, however, the gathering of the taxis in groups does not mean that the taxis are gathered, and therefore a large error exists in an existing taxi aggregation state early warning method.
Disclosure of Invention
The embodiment of the invention provides a method and a device for early warning of taxi aggregation conditions, and aims to solve the problem that the existing taxi aggregation does not mean that taxis are aggregated, so that the existing taxi aggregation condition early warning method has large errors.
A method of warning of taxi gathering conditions, comprising:
judging whether taxi group aggregation exists in the target area;
when taxi population aggregation exists in a target area, identifying environment data of the target area, wherein the environment data comprises building type data contained in the target area and/or traffic jam state data of the target area;
when the building type data is not contained in a preset building type data set, judging whether the target area has traffic jam according to the traffic jam state data;
and when no traffic jam exists in the target area, sending out a taxi gathering condition warning.
Optionally, the issuing a taxi gathering condition warning when there is no traffic jam in the target area includes:
when the traffic jam does not exist in the target area, receiving taxi taking state data of the target area at the current moment, which is sent by a third-party server, wherein the taxi taking state data is used for judging whether more users need to take taxi in the target area;
and sending a taxi gathering condition warning when more users do not need to drive the taxi in the target area.
Optionally, the identifying the environmental data of the target area includes:
and receiving building type data contained in a target area at the current moment and/or traffic jam state data of the target area, which are sent by a third-party server.
Optionally, the identifying the environmental data of the target area includes:
receiving image data shot by a taxi in a target area at the current moment;
building type data and/or traffic congestion status data for the target area in the image data is identified.
Optionally, the issuing a taxi gathering condition warning when there is no traffic jam in the target area includes:
when no traffic jam exists in the target area, receiving voice information of a taxi driver acquired by a taxi in the target area at the current moment;
identifying whether a target keyword exists in the voice information or not;
and when the target keyword exists in the voice information, sending a taxi gathering condition warning.
Optionally, the determining whether there is a taxi population aggregation in the target area includes:
obtaining state information of a taxi in the target area, wherein the state information comprises the moving speed of the taxi and positioning information of the taxi;
and judging whether taxi group aggregation exists in the target area or not according to the positioning information of the taxies of which the moving speed is less than the speed threshold value in the target area.
Optionally, the determining whether there is a taxi population aggregation in the target area includes:
acquiring state information of a taxi in the target area and uploading time of the state information;
and judging whether taxi group aggregation exists in the target area or not according to the state information of the taxies in the target area within preset time.
Optionally, the determining whether a taxi group aggregation exists in the target area according to the state information of the taxis in the target area within the preset time includes:
according to the uploading time of the state information of the taxis in the target area, repeatedly uploading the state information is eliminated, and the state information of the taxis in the target area after elimination is generated;
and judging whether taxi colony aggregation exists in the target area or not according to the state information of the taxies in the target area after the elimination within the preset time.
Optionally, the determining whether there is a taxi population aggregation in the target area includes:
calculating the distance between taxis of which the moving speed is lower than the preset speed in the target area according to the positioning information of the taxis of which the moving speed is lower than the preset speed in the target area;
judging whether a taxi group aggregation exists in the target area or not according to the distance between taxis in the target area, wherein the moving speed of each taxi is less than the preset speed;
the following relation exists between the distance between the first taxi and the second taxi, of which the moving speed is smaller than the preset speed, in the target area:
the method comprises the following steps of obtaining Lat1, lng2, lng1, lng2, R and S, wherein Lat1 is a latitude value of a first taxi in a target area, lat2 is a latitude value of a second taxi in the target area, lng1 is a longitude value of the first taxi in the target area, lng2 is a longitude value of the second taxi in the target area, a is a transverse distance between the first taxi and the second taxi in the target area, b is a longitudinal distance between the first taxi and the second taxi in the target area, R is an earth radius, and S is a distance between the first taxi and the second taxi.
An apparatus for warning of a taxi gathering condition, comprising:
the taxi group aggregation judging module is used for judging whether taxi group aggregation exists in the target area;
the taxi traffic information acquisition system comprises an environment data identification module, a taxi information acquisition module and a taxi information acquisition module, wherein the environment data identification module is used for identifying environment data of a target area when taxi population aggregation exists in the target area, and the environment data comprises building type data contained in the target area and/or traffic jam state data of the target area;
the traffic jam judging module is used for judging whether traffic jam exists in the target area according to the traffic jam state data when the building type data is not contained in a preset building type data set;
and the warning module is used for sending out a taxi gathering condition warning when the traffic jam does not exist in the target area.
The invention has the following advantages:
in the invention, whether taxi crowd aggregation exists in the target area or not is judged, when the taxi crowd aggregation exists in the target area, environment data of the target area are identified, wherein the environment data comprise building type data contained in the target area and/or traffic jam state data of the target area, when the building type data are not contained in a preset building type data set, whether traffic jam exists in the target area or not is judged according to the traffic jam state data, and when the traffic jam does not exist in the target area, a taxi aggregation state warning is sent out. The taxi gathering condition early warning method can be combined with the environmental data of taxis in the target area to more accurately identify whether the reason of taxi group gathering is passenger reception or the taxi gathering condition, and further can improve the accuracy of early warning the taxi gathering condition.
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In order to more clearly illustrate the technical solution of the present invention, the drawings required to be used in the description of the present invention will be briefly introduced below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings may be obtained according to the drawings without inventive labor.
FIG. 1 is a diagram illustrating an unsupervised learning raw data set provided by the present invention;
FIG. 2 is a flow chart illustrating the steps of a method for warning a taxi gathering condition according to the present invention;
FIG. 3 is a flow chart illustrating steps of yet another method of warning of taxi gathering conditions provided by the present invention;
fig. 4 shows a schematic structural diagram of a device for warning a taxi gathering condition provided by the invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The taxi is used as an important carrier for bearing urban traffic, normal and effective operation not only relieves urban traffic pressure, but also is beneficial to stabilizing the society and promoting the development of harmonious connotation society. In recent years, taxi group strike events initiated against solicitations such as "network taxi appointment" occur in succession in parts of cities across the country. However, when the gathering condition occurs in the taxi industry, the market order of the taxi industry is affected, traffic jam is easily caused, the urban image is damaged, and serious impact is brought to urban management. Therefore, it is important to improve the emergency handling capability for the sudden aggregation situation in the taxi industry, to prevent and control the occurrence of the emergency to the maximum extent, and to reduce the social negative effect caused by the emergency. According to the method and the system, an unsupervised learning algorithm is adopted to monitor, predict and early warn the taxi gathering condition, the technological level and the guidance capability of dealing with the sudden gathering condition disposal in the taxi industry are improved, and reference is provided for relevant departments to make preventive and solution work in advance.
Unsupervised Learning (Unsupervised Learning) is a branch of Machine Learning (Machine Learning). The training samples in unsupervised learning are Label-free (Label) and are mainly used for discovering the internal structure of the samples, and the aim is to disclose the intrinsic properties and rules of the data by performing density estimation and anomaly detection on the Label-free training samples. The main methods of unsupervised learning include clustering, dimensionality reduction, visualization and the like. Aiming at the prediction and early warning of taxi gathering conditions, a clustering method is adopted for discussion.
Clustering is to divide a data set into different classes or clusters according to a certain criterion (e.g. distance criterion), so that the similarity of data objects in the same cluster is as large as possible, and the difference of data objects not in the same cluster is also as large as possible. After clustering, the data of the same class are gathered together as much as possible, and different data are separated as much as possible.
Currently, the main clustering algorithms can be divided into the following categories:
| clustering algorithm classes | Classical algorithm |
| Partition-based method | k-means、k-modes、PCM |
| Hierarchy-based approach | CURE、SBAC、BIRCH |
| Density-based method | DBSCAN、OPTICS、FDC |
| Grid-based method | STING、WaveCluster、CLIQUE |
| Model-based method | COBWEB、SOM |
The choice of clustering algorithm for a particular application depends on the type of data, the purpose of the clustering. In the prediction and early warning of the taxi gathering condition, a density-based method DBSCAN algorithm is adopted for clustering so as to achieve the prediction purpose.
The DBSCAN algorithm, a density-based clustering method with noise, is an algorithm that describes how close a sample set is based on a set of neighborhoods. It has several important concepts:
core point: a point with a number exceeding MinPts within the neighborhood distance e is then the core point.
Boundary points are as follows: the number of less than MinPts is contained within the neighborhood distance e and is within the domain of the core point.
Noise points: any point that is not a core point or a boundary point.
Where the parameter (e, minPts) is used to describe how tightly the samples of the neighborhood are distributed. E describes the neighborhood distance threshold for a certain sample, and MinPts describes the threshold for the number of samples in the neighborhood for which the distance of a certain sample is e. As for the algorithm principle and steps, which are not described herein, a small sample is used for simulation verification in Python, wherein the sample data is random points in 15 × 15 two-dimensional space, and there are 19 groups in total.
As in fig. 1, our goal is to find a set with more than 3 points aggregated. In the simulation process, the parameter epsilon is set to be 2, 2.5 and 3 respectively, the parameter MinPts is set to be constant 3, and the simulation result is as follows:
wherein, the graph A is the distribution of the original data set, the other three graphs are the clustering results under different parameters, the dots with different colors represent different clustering sets, and the black X represents a noise point. As can be seen from the results in the figure, given a constant value of MinPts, when ∈ is too small (∈ = 2), clustering is substantially impossible; when the E is moderate (E = 2.5), the clustering effect is the best, the data can be clustered into three clusters, and the noise point can be reasonably found out; when ∈ is too large (∈ = 3), although clustering is also possible, the clustering effect is not obvious. Simulation experiments show that values of epsilon and MinPts influence the effect of the algorithm, so that in practical application, parameters need to be reasonably valued and properly optimized according to real conditions.
Referring to fig. 2, a flow chart illustrating steps of a method for early warning of a taxi gathering condition according to the present invention is shown, the method comprising the steps of:
in the embodiment of the application, whether taxi group aggregation exists in the target area can be judged through DBSCAN cluster analysis, in the practical process, the parameter belonging to the neighborhood in the DBSCAN cluster analysis can be set as the distance between vehicles, the unit is meter, the parameter MinPts is set as the minimum aggregated vehicle number, when GPS coordinates of taxi groups in the target area are uploaded, the GPS coordinates are added into a data set, DBSCAN cluster analysis is executed, and whether taxi group aggregation exists in the target area is further judged. And if the number of clusters generated by the clustering result is greater than 0, indicating that the taxis are likely to be aggregated, and judging whether the taxi population aggregation exists in the target area through DBSCAN clustering analysis according to the positioning information of the taxis with the moving speed less than the preset speed in the target area in the concrete implementation process.
In an embodiment of the present application, the step 201 includes:
step S11, obtaining state information of the taxi in the target area, wherein the state information comprises the moving speed of the taxi and the positioning information of the taxi;
in the specific implementation process, the positioning information of the taxi can be acquired in real time through the GPS equipment and the communication device, or the positioning information of the taxi can be acquired in real time through a third-party server, for example, the positioning information of the taxi is acquired in real time through a server corresponding to navigation software, and the moving speed of the taxi is calculated according to the change time and the distance of the positioning information of the taxi.
And S12, judging whether a taxi group aggregation exists in the target area or not according to the positioning information of the taxies of which the moving speed is less than the preset speed in the target area.
Specifically, whether a plurality of taxis are gathered and parked in the target area is judged according to the positioning information of the taxis with the moving speed less than 0 in the target area, and then the condition that the taxi groups are gathered is judged.
In an embodiment of the present application, the substep S12 includes:
step S121, calculating the distance between taxis with the movement speed less than the preset speed in the target area according to the positioning information of the taxis with the movement speed less than the preset speed in the target area;
step S122, judging whether a taxi group aggregation exists in the target area according to the distance between taxis in the target area, wherein the moving speed of each taxi is less than the preset speed;
the following relation exists between the distance between the first taxi and the second taxi, of which the moving speed is smaller than the preset speed, in the target area:
the taxi tracking method comprises the steps that Lat1 is a latitude value of a first taxi in a target area, lat2 is a latitude value of a second taxi in the target area, lng1 is a longitude value of the first taxi in the target area, lng2 is a longitude value of the second taxi in the target area, a is a transverse distance between the first taxi and the second taxi in the target area, b is a longitudinal distance between the first taxi and the second taxi in the target area, R is the radius of the earth, and S is a distance between the first taxi and the second taxi.
In the simulation, the value of the belonged to the neighborhood is the two-dimensional Euclidean distance, but in the real GPS position distance, because the earth is a near sphere, the longitude and latitude distance of the GPS needs to be projected to be the two-dimensional plane distance, the distance between taxis can be calculated more accurately by the method, and the judgment result can be more accurate.
In an embodiment of the present application, the step 201 includes:
step S21, obtaining the state information of the taxis in the target area and the uploading time of the state information of the taxis in the target area;
the status information may be location information of a taxi.
And S22, judging whether taxi group aggregation exists in the target area or not according to the state information of the taxies in the target area within preset time.
Whether taxi group aggregation exists or not can be judged through the positioning information of the taxies, however, the positioning information of the taxies can change at any time because the taxies are units which are likely to move, and in order to ensure the accuracy of the final judgment result, the uploading time of the state information of each taxi is ensured to be short. For example, the preset time may be 10 seconds, and it may be determined whether a taxi group aggregation exists in the target area within 10 seconds, and the status information of taxis that do not belong to the target area within 10 seconds is removed.
In an embodiment of the present application, the substep S22 includes:
the substep S221 is that the repeatedly uploaded state information is removed according to the uploading time of the state information of the taxis in the target area, and the state information of the taxis in the target area after removal is generated;
and a substep S222, judging whether a taxi group aggregation exists in the target area according to the state information of the taxis in the target area after the elimination within the preset time.
In the embodiment of the application, the repeatedly uploaded state information should be eliminated, and the accuracy of the judgment result is further ensured.
To facilitate understanding of the above embodiments, the following will be illustrated by a specific example, which is not intended to limit the present application:
and step 1, data cleaning.
Because the real-time uploaded data of the taxis are non-static data and the intervals of the reported data of different taxis are different, the real-time data must be cleaned firstly, the data quality is improved, and the cleaned real-time data are as follows:
1) Data with speed greater than a designated speed value V;
2) Repeatedly uploading data by the same vehicle;
3) Data N minutes ago exceeding the current time;
and 2, setting parameters.
The parameter belongs to the neighborhood value and is the distance between vehicles, the unit is meter, and the parameter MinPts is the minimum number of the aggregated vehicles. In the above simulation, the value belonging to the neighborhood is a two-dimensional euclidean distance, but in the real GPS position distance, because the earth is a near sphere, the GPS longitude and latitude distance needs to be projected as a two-dimensional plane distance, and the formula is as follows:
and 3, analyzing data.
And when a new GPS coordinate is uploaded, adding the new GPS coordinate into the data set, and executing DBSCAN cluster analysis. If the number of clusters generated by the clustering result is greater than 0, the situation that the clusters of the taxi are likely to be clustered is indicated, but further analysis is needed to be carried out under various scenes for determining and recording, and the specific analysis rule is as follows:
no other aggregation is carried out on the current aggregation vehicles within K kilometers
Aggregation duration of the same aggregation region > N minutes
Forming N-minute vehicle list snapshots
After the three steps, the real-time monitoring of the taxi gathering can be realized. In order to verify the prediction function of the taxi in the real environment, the real-time GPS position of 2000 taxies at a certain moment is collected, the minimum aggregation vehicle value of the parameter MinPts is set to be a small value intentionally, minPts =10, and E =20 m, namely whether aggregation occurs or not under the condition that whether more than 10 vehicles exist at the moment and each vehicle does not exceed 20 m is monitored. After monitoring, 1 aggregation cluster is generated as a result, the position is near an arrival hall in the international airport, the condition that passengers such as taxis are queued at the position is shown, and meanwhile, the accuracy and the real-time performance of the algorithm are verified. Similarly, if the parameters are adjusted to be optimal, whether the taxi at any position has a group gathering event or not can be monitored, and the purposes of prediction and early warning are achieved.
where the building type data is used to refer to the types of all buildings included in the target area, the types of buildings such as hotels, restaurants, tolls, and airports, and the building type data is "0" when no building is present in the target area.
Traffic congestion status data is used to refer to the traffic congestion status of a target area, such as congested and uncongested.
In the embodiment of the application, the environment data may further include burst state data, where the burst state data is used to refer to a burst state of the target area, such as a road closure, a traffic police, a vehicle inspection, a man-made lane damage, and an important disaster caused by a natural factor in the target area.
In an embodiment of the present application, step 202 includes:
and receiving building type data contained in a target area at the current moment and/or traffic jam state data of the target area, which are sent by a third-party server.
In a specific implementation process, the third-party server identifies the environment data of the target area at the current moment, and further can receive the environment data of the target area at the current moment, which is sent by the third-party server, for example, when a hotel exists in the target area at the current moment and traffic jam exists, the third-party server can receive traffic jam state data and building type data sent by navigation software, wherein the traffic jam state data is 'traffic jam in the target area at the current moment', and the building type data is 'hotel'.
in a specific implementation process, a building type data set may be preset, for example, the building type data set is "hotel, restaurant, toll booth and airport", if the building type data set is "hotel", the building type data set is included in the preset building type data set, and if the building type data set is "house", the building type data set is not included in the preset building type data set.
When the building type data is not contained in a preset building type data set, identifying the specific content of the traffic jam state data, if the specific content of the traffic jam state data is jam, determining that traffic jam exists in the target area, and if the specific content of the traffic jam state data is not jam, determining that the traffic jam does not exist in the target area, and further judging whether the traffic jam exists in the target area according to the traffic jam state data;
and 204, sending a taxi gathering condition warning when the traffic jam does not exist in the target area.
When no traffic jam exists in the target area, taxi aggregation may occur in the target area due to taxi aggregation, and a taxi aggregation warning is sent.
In an embodiment of the present application, the step 204 includes:
step S21, when the traffic jam does not exist in the target area, receiving taxi taking state data of the target area at the current moment, which is sent by a third-party server, wherein the taxi taking state data is used for judging whether more users need to take a taxi in the target area;
the third-party server can be a server corresponding to taxi taking software or a server corresponding to navigation software.
The taxi taking state data can be the number of taxi taking orders in the target area;
in a specific implementation process, when a taxi group aggregation exists in a target area, the taxi taking state data sent by a third-party server is acquired, when no traffic jam exists in the target area, the traffic jam state data is received, when the traffic jam state data shows that the traffic jam exists in the target area, the taxi taking state data of the target area at the current moment sent by the third-party server is received, whether the preset taxi taking state data and the number of taxis in the target area at the current moment accord with a preset relation or not is judged, and the preset relation can be that: the taxi taking state data is less than or equal to three quarters of the taxi taking state data, when the preset taxi taking state data and the number of taxis in the target area at the current time are in a preset relation, it is judged that more users do not need to take a taxi in the target area, when the preset taxi taking state data and the number of taxis in the target area at the current time are not in the preset relation, more users need to take a taxi, for example, the number of taxi taking orders in the target area is 10, the number of taxis in the target area at the current time is 20, the number of taxi taking orders is 10 to one half of the number of taxis in the target area at the previous time, namely, the number of taxi taking orders is 10 to less than or equal to three quarters of the number of taxis in the target area at the current time, and therefore it is judged that more users need to take a taxi in the target area, for example, when the number threshold of taxi taking orders is 5 and the number of taxi taking state data in the target area is 6, the number of taxi taking orders is 5 to more than or more than 6, and therefore, three times of taxi taking a number of taxis in the target area need to take a taxi.
In the embodiment of the application, if the taxi taking state data meeting the preset relationship is less than or equal to three-fourths of the taxi taking state data, the taxi taking requirement is far less than the number of taxis at the current moment, and the gathering of too many taxis cannot be attributed to the reason that the taxi is taken by the user, so that the possibility of taxi gathering condition may exist.
And S22, when more users do not exist in the target area and need to drive the taxi, giving a taxi gathering condition warning.
In the embodiment of the application, whether more users need to take a taxi in the target area is judged by receiving the third-party server, and then when more users do not need to take the taxi in the target area, a taxi gathering condition warning is sent out, so that the environmental data of the target area can be accurately identified, and the calculation amount of data analysis can be reduced due to the fact that the taxi taking state data sent by the third-party server is used for judging.
In another embodiment of the present application, when the building type data is not included in the preset building type data set and there is no traffic jam in the target area, the emergency state data sent by the third-party server may be received, if there is no emergency state data or the content of the received emergency state data is "none", the target area may not have an emergency state and a taxi gathering condition warning may be issued, and when the received emergency state data is "closing a road, checking a taxi by a traffic police, damaging a man-made lane, or an important disaster due to a natural factor", the target area may have an emergency state and a taxi gathering condition warning may not be issued.
In the invention, by judging whether taxi crowd aggregation exists in the target area, when the taxi crowd aggregation exists in the target area, the environment data of the target area is identified, wherein the environment data comprises building type data contained in the target area and/or traffic jam state data of the target area, when the building type data is not contained in a preset building type data set, whether traffic jam exists in the target area is judged according to the traffic jam state data, and when the traffic jam does not exist in the target area, a taxi aggregation state warning is sent out. The taxi gathering condition early warning method can be combined with the environmental data of taxis in the target area to more accurately identify whether the reason of taxi group gathering is passenger reception or the taxi gathering condition, and further can improve the accuracy of early warning the taxi gathering condition.
Referring to fig. 3, a flow chart illustrating steps of still another method for warning a taxi gathering condition according to the present invention is shown, the method comprising the steps of:
the image data can be image data shot by a taxi driving recorder or image data shot by any camera on a taxi;
when a taxi group aggregation exists in a target area, building type data and/or traffic jam state data contained in the target area can be received through a third-party server, however, in the specific implementation process, because the position of the target area is far away, and the building type data and/or the traffic jam state data of the target area are not recorded in the third-party server, when the taxi group aggregation exists in the target area and the third-party server does not provide the environmental data of the target area at the current moment, or the environmental data of the target area at the current moment provided by the third-party server is empty data, the position of the target area is far away, at the moment, the judgment can be carried out through the image data of the target area at the current moment, specifically, image data shot by taxis in the target area at the current moment is obtained, the image data is uploaded, and the image data shot by taxis in the target area at the current moment is received.
after receiving the image data, performing image recognition on the image data, identifying building type data in the image data, specifically, identifying signboard contents of a building in the image data, and further determining a building type of the building through the signboard contents, where the signboard contents identifying the building may be a keyword in the signboard contents, for example, when the signboard contents of the building are "XX hotel," since the signboard contents include the keyword "hotel," the building type of the building is identified as hotel. Further, since there may be no sign on the building in a special scene, such as a farmer music scene, the image data may be compared with image data stored in the database in advance, image data in the database having a similarity with the image data exceeding a preset percentage (such as ninety percent) may be identified, and the building type of the image data in the identified database may be used as the building type of the image data, for example, when it is identified that the similarity between the image data and the image data of the farmer music scene in the database exceeds ninety percent, the building type of the image data may be identified as farmer music.
For a private vehicle, the third-party server may not be able to record, so that the traffic jam state may not be accurately determined by the third-party server, after receiving the image data, image recognition may be performed on the image data, traffic jam state data of a target area may be recognized, and the traffic jam state data may be judged by using changes of the vehicles in the relative background and/or foreground of the taxi in the recognized adjacent image.
It can be understood that in the prior art, a map software company (a company corresponding to a third-party server) cooperates with a taxi company, a GPS is installed in each taxi for positioning, and the speed of the taxi is calculated according to the acquired position data; the map software company can also cooperate with the owner of the private vehicle using the map software, allows the position data of the private vehicle to be acquired in real time when the map software is opened on the private vehicle, calculates the speed of the private vehicle by using the position data, and uses the common data of the private vehicle and the taxi to judge the road jam condition. For example, according to the taxi and the private vehicle on a road as the current vehicles on the road, the road congestion condition is judged according to the number of the current vehicles and the vehicle speed. For example, if the number of vehicles currently on a certain road is greater than the congestion threshold (e.g., 30 vehicles) and the vehicle speeds are all lower than a certain speed (e.g., 15 KM/H), it is determined that the road is in the traffic congestion state. Therefore, when a sufficient number of private vehicles do not use the map software, the map software cannot acquire the position data of the private vehicles on the road segment, and only can acquire the position data and the number of taxis provided on the road segment, and the third-party server judges that the traffic is not in a congestion state at the moment. Obviously, the result of the judgment according to the traffic jam state data sent by the third-party server is as follows: and no traffic jam exists in the target area. And actually, the target area has traffic jam, and at the moment, misjudgment is carried out to further send out a taxi gathering condition warning.
In order to solve the above problem, the method further comprises:
and step A, when the received environment data of the target area at the current moment provided by the third-party server is empty data, and when the traffic jam does not exist according to the received traffic jam state data of the target area at the current moment sent by the third-party server, and when taxi colony aggregation exists in the target area, receiving image data shot by taxis in the target area at the current moment.
And B, identifying the type of the vehicle on the current road according to the image data, and counting the number of other vehicles (such as private vehicles) which are not taxis.
It should be noted that the same private vehicle may exist in the image data captured by the image capturing devices on different taxis on a section of congestion reason, and when counting the number, it is necessary to exclude the situation that the same private vehicle is counted many times, that is, one or two private vehicles count for one time, and count the number of other vehicles (for example, private vehicles) that are not taxis in the image data of all taxis on the road.
And C, adding the number of other vehicles which are not taxis on the current road in the target area and the number of the taxis to obtain the total number of the current vehicles.
Step D, if the total number of the current vehicles is larger than the jam threshold value, no taxi gathering condition warning is sent out; and if the total number of the current vehicles is not greater than the jam threshold value, sending a taxi gathering condition warning.
It can be understood that the third-party server acquires the total number of the current vehicles on the current road corresponding to the target area through the elevator software, and when the third-party server judges that the total number of the current vehicles is greater than the congestion threshold and the speed is lower than a certain speed, the traffic congestion state data can be marked as the traffic congestion state. The taxi company can obtain the size of the congestion threshold from the third-party server, and the congestion threshold is used for correcting the traffic congestion state at a remote road section, so that misjudgment of the taxi aggregation condition is avoided.
the voice information of the taxi drivers can be acquired through the interphones among the taxi drivers and can also be acquired through any pickup equipment on the taxi, and the method and the device are not limited.
Because a taxi driver may inform other taxi drivers in a telephone or interphone mode before the taxi gathering condition is carried out, in order to judge whether the taxi gathering condition warning needs to be carried out more accurately, when the traffic jam does not exist in a target area and the taxi group gathering exists, voice information of the taxi driver is obtained through any sound pickup device and is uploaded, after the voice information of the taxi driver is received, the voice information of the taxi driver is further judged, when the traffic jam exists in the target area, the voice information of the taxi driver cannot be obtained through any sound pickup device, and the judgment and warning of the taxi gathering condition are guaranteed due to the privacy of the taxi driver.
In another embodiment of the application, the voice information of the taxi driver can be intercepted through a satellite or a sound pickup device pre-installed on a road, and the application does not limit the mode of intercepting the voice information of the taxi driver.
the target keywords may be "counterfeited", "not dried", and "stopped".
After the voice information is received, voice recognition can be carried out on the voice information, the voice information is further converted into character information, and after the voice information is converted into the character information, whether a target keyword is contained in the character information or not can be recognized, for example, whether the keyword contained in the character information is 'irrelevant' is recognized.
In the embodiment of the application, whether taxi group aggregation exists in a target area or not is judged, when the taxi group aggregation exists in the target area, image data shot by taxies in the target area at the current moment are received, building type data in the image data and/or traffic jam state data of the target area are/is identified, when the building type data do not include a preset building type data set, whether traffic jam exists in the target area or not is judged according to the traffic jam state data, when the traffic jam does not exist in the target area, voice information of taxi drivers obtained in the target area at the current moment is received, whether target keywords exist in the voice information is identified, when the target keywords exist in the voice information, a taxi aggregation state warning is sent, taxi aggregation state warning in a remote area is achieved, the taxi aggregation state can be further judged through a voice identification mode, and taxi aggregation state warning accuracy is improved.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.
Referring to fig. 4, a schematic structural diagram of an apparatus for warning a taxi gathering condition according to an embodiment of the present application is shown, where the apparatus includes:
the taxi group aggregation judging module 401 is configured to judge whether a taxi group aggregation exists in the target area;
the environment data identification module 402 is configured to identify environment data of a target area when a taxi population aggregation exists in the target area, where the environment data includes building type data included in the target area and/or traffic congestion state data of the target area;
a traffic jam determining module 403, configured to determine whether a traffic jam exists in the target area according to the traffic jam status data when the building type data is not included in a preset building type data set;
and a warning module 404, configured to send a taxi gathering condition warning when there is no traffic jam in the target area.
In an embodiment of the present application, the warning module 404 includes:
the third-party server taxi taking requirement judging submodule is used for receiving taxi taking state data of a target area at the current moment, which are sent by a third-party server, when the target area has no traffic jam, and the taxi taking state data is used for judging whether more users in the target area need to take a taxi or not;
and the taxi gathering condition warning submodule is used for sending a taxi gathering condition warning when more users in the target area do not need to drive the taxi.
In an embodiment of the application, the environment data identification module 402 includes:
and the third-party server data receiving submodule is used for receiving the building type data contained in the target area at the current moment and/or the traffic jam state data of the target area, which are sent by the third-party server.
In an embodiment of the present application, the environment data identification module 402 includes:
the image data receiving submodule is used for receiving image data shot by the taxi in the target area at the current moment;
and the image data identification submodule is used for identifying the building type data in the image data and/or the traffic jam state data of the target area.
In an embodiment of the present application, the warning module 404 includes:
the voice information receiving module is used for receiving the voice information of the taxi driver, which is acquired by the taxi in the target area at the current moment, when the traffic jam does not exist in the target area;
identifying whether a target keyword exists in the voice information;
and sending a taxi gathering condition warning when the target keyword exists in the voice information.
In an embodiment of the present application, the taxi group aggregation determining module 401 includes:
the state information acquisition submodule is used for acquiring state information of the taxi in the target area, wherein the state information comprises the moving speed of the taxi and the positioning information of the taxi;
and the threshold judgment submodule is used for judging whether taxi group aggregation exists in the target area or not according to the positioning information of the taxies of which the moving speed is less than the speed threshold in the target area.
In an embodiment of the present application, the taxi group aggregation determining module 401 includes:
the state information uploading time obtaining submodule is used for obtaining the state information of the taxies in the target area and the uploading time of the state information;
and the state information judgment submodule is used for judging whether taxi group aggregation exists in the target area or not according to the state information of the taxies in the target area within the preset time.
In an embodiment of the present application, the status information determining sub-module includes:
the repeated uploading and rejecting unit is used for rejecting the repeatedly uploaded state information according to the uploading time of the state information of the taxis in the target area and generating the rejected state information of the taxis in the target area;
and the post-rejection judging unit is used for judging whether the taxi group aggregation exists in the target area or not according to the state information of the taxies in the target area after the rejection within the preset time.
In an embodiment of the present application, the taxi group aggregation determining module 401 includes:
the distance calculation submodule is used for calculating the distance between taxis of which the moving speed is lower than the preset speed in the target area according to the positioning information of the taxis of which the moving speed is lower than the preset speed in the target area;
the distance judgment sub-module is used for judging whether a taxi group aggregation exists in the target area or not according to the distance between taxis in the target area, wherein the moving speed of each taxi is less than the preset speed;
the following relationship exists between the distance between the first taxi and the distance between the second taxi, of which the moving speed is less than the preset speed, of the target area:
the taxi tracking method comprises the steps that Lat1 is a latitude value of a first taxi in a target area, lat2 is a latitude value of a second taxi in the target area, lng1 is a longitude value of the first taxi in the target area, lng2 is a longitude value of the second taxi in the target area, a is a transverse distance between the first taxi and the second taxi in the target area, b is a longitudinal distance between the first taxi and the second taxi in the target area, R is the radius of the earth, and S is a distance between the first taxi and the second taxi.
For the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The embodiment of the invention also provides a device, which comprises at least one processor and a memory which is used for being connected with the at least one processor in a communication way; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aforementioned method of warning of a taxi cab gathering condition.
The memory, as a non-transitory computer-readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer-executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk memory, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located from the control processor, and these remote memories may be connected to the power transmission circuit across the smart identification device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Embodiments of the present invention further provide a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, which are executed by one or more control processors, for example, by a control processor, and may cause the one or more control processors to perform a method for warning a taxi aggregation condition in the above-described method embodiments, for example, perform the above-described method steps S201 to S204 in fig. 2.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are included in the scope of the present invention defined by the claims.
Claims (10)
1. A method for early warning of taxi gathering condition is characterized by comprising the following steps:
judging whether taxi group aggregation exists in the target area;
when taxi population aggregation exists in a target area, identifying environment data of the target area, wherein the environment data comprises building type data contained in the target area and/or traffic jam state data of the target area;
when the building type data are not contained in a preset building type data set, judging whether traffic jam exists in the target area or not according to the traffic jam state data;
and when no traffic jam exists in the target area, sending out a taxi gathering condition warning.
2. The method for warning of taxi aggregation condition according to claim 1, wherein the issuing of a taxi aggregation condition warning when there is no traffic congestion in the target area comprises:
when no traffic jam exists in the target area, receiving taxi taking state data of the target area at the current moment, which are sent by a third-party server, wherein the taxi taking state data are used for judging whether more users need to take a taxi in the target area;
and when no more users need to drive the taxi in the target area, giving out a taxi gathering condition warning.
3. The method for warning of a taxi cab aggregation condition according to claim 1, wherein the identifying environmental data of the target area includes:
and receiving building type data contained in a target area at the current moment and/or traffic jam state data of the target area, which are sent by a third-party server.
4. The method for warning of a taxi cab aggregation condition according to claim 1, wherein the identifying environmental data of the target area includes:
receiving image data shot by a taxi in a target area at the current moment;
building type data and/or traffic congestion status data for the target area in the image data is identified.
5. The method for warning about taxi gathering condition according to claim 4, wherein the step of issuing a taxi gathering condition warning when there is no traffic jam in the target area comprises:
when no traffic jam exists in the target area, receiving voice information of a taxi driver acquired by a taxi in the target area at the current moment;
identifying whether a target keyword exists in the voice information;
and sending a taxi gathering condition warning when the target keyword exists in the voice information.
6. The method for warning taxi gathering condition according to claim 1, wherein the step of judging whether the taxi group gathering exists in the target area comprises the following steps:
acquiring state information of the taxis in the target area, wherein the state information comprises the moving speed of the taxis and the positioning information of the taxis;
and judging whether taxi group aggregation exists in the target area or not according to the positioning information of the taxies of which the moving speed is less than the speed threshold value in the target area.
7. The method for warning of taxi aggregation condition according to claim 1, wherein the determining whether there is taxi population aggregation in the target area comprises:
acquiring state information of a taxi in the target area and uploading time of the state information;
and judging whether taxi group aggregation exists in the target area or not according to the state information of the taxies in the target area within the preset time.
8. The method for early warning of taxi aggregation condition according to claim 7, wherein the step of judging whether taxi population aggregation exists in the target area according to the taxi state information in the target area within a preset time comprises the following steps:
according to the uploading time of the state information of the taxis in the target area, repeatedly uploading the state information is eliminated, and the state information of the taxis in the target area after elimination is generated;
and judging whether taxi group aggregation exists in the target area or not according to the state information of the taxies in the target area after the taxies are removed within the preset time.
9. The method for warning taxi gathering condition according to claim 1, wherein the judging whether the taxi group gathering exists in the target area comprises the following steps:
calculating the distance between taxis of which the moving speed is lower than the preset speed in the target area according to the positioning information of the taxis of which the moving speed is lower than the preset speed in the target area;
judging whether taxi colony aggregation exists in the target area or not according to the distance between taxis in the target area, wherein the moving speed of each taxi is less than the preset speed;
the following relation exists between the distance between the first taxi and the second taxi, of which the moving speed is smaller than the preset speed, in the target area:
the method comprises the following steps of obtaining Lat1, lng2, lng1, lng2, R and S, wherein Lat1 is a latitude value of a first taxi in a target area, lat2 is a latitude value of a second taxi in the target area, lng1 is a longitude value of the first taxi in the target area, lng2 is a longitude value of the second taxi in the target area, a is a transverse distance between the first taxi and the second taxi in the target area, b is a longitudinal distance between the first taxi and the second taxi in the target area, R is an earth radius, and S is a distance between the first taxi and the second taxi.
10. A device for early warning of taxi gathering condition, comprising:
the taxi group aggregation judging module is used for judging whether taxi group aggregation exists in the target area;
the taxi traffic information acquisition system comprises an environment data identification module, a taxi information acquisition module and a taxi information acquisition module, wherein the environment data identification module is used for identifying environment data of a target area when taxi population aggregation exists in the target area, and the environment data comprises building type data contained in the target area and/or traffic jam state data of the target area;
the traffic jam judging module is used for judging whether the target area has traffic jam according to the traffic jam state data when the building type data is not contained in a preset building type data set;
and the warning module is used for sending out a taxi gathering condition warning when the traffic jam does not exist in the target area.
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