CN111768623A - Intelligent traffic dispersion method based on deep learning and related device - Google Patents
Intelligent traffic dispersion method based on deep learning and related device Download PDFInfo
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
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Abstract
The embodiment of the application provides an intelligent traffic dispersion method based on deep learning and a related device, which are applied to electronic equipment, wherein the method comprises the following steps: acquiring congestion state information of a target running road on which a target vehicle runs; determining vehicle merging traffic information of a related driving road according to the congestion state information, wherein the related road comprises a road connected with the target driving road; determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road; and pushing the target traffic dispersion method to the target vehicle. The intelligence of the traffic dispersion method in determining can be improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent traffic dispersion method based on deep learning and a related device.
Background
When the traffic is congested, the traffic police usually conducts traffic dispersion to the site or conducts self dispersion through a driver driving the vehicle, and although the traffic congestion can be effectively relieved through modes such as traffic dispersion through the traffic police, the intelligence is lower when traffic dispersion is conducted due to the fact that the traffic police needs to be accessed.
Disclosure of Invention
The embodiment of the application provides an intelligent traffic grooming method and a related device based on deep learning, and convenience in modifying attribute information of a model component can be improved.
A first aspect of an embodiment of the present application provides an intelligent traffic grooming method based on deep learning, which is applied to an electronic device, and the method includes:
acquiring congestion state information of a target running road on which a target vehicle runs;
determining vehicle merging traffic information of a related driving road according to the congestion state information, wherein the related road comprises a road connected with the target driving road;
determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road;
and pushing the target traffic dispersion method to the target vehicle.
With reference to the first aspect, in one possible implementation manner, the determining, according to the congestion state information, vehicle merging traffic information related to a traveling road includes:
determining the number of the jammed vehicles on the target running road according to the jam state information;
determining the congestion level of the target running road according to the number of the congested vehicles;
determining a first number of vehicles converging into the associated driving road according to the congestion level;
acquiring a driving route of a congested vehicle on the congested road;
determining a second number of vehicles converging into the associated driving road according to the driving route of the congested vehicle;
and determining vehicle merging traffic information of the associated driving road according to the first quantity and the second quantity.
With reference to the first aspect, in one possible implementation manner, the determining, according to the congestion state information, vehicle merging traffic information related to a traveling road includes:
determining a third number of vehicles merging into the associated driving road according to the congestion state information;
acquiring current vehicle passing information of the associated driving road;
determining a fourth number of vehicles converging into the associated driving road according to the current vehicle passing information of the associated driving road;
and determining vehicle convergence traffic information of the associated driving road according to the third quantity and the fourth quantity.
With reference to the first aspect, in one possible implementation manner, the method further includes:
acquiring a target image of the target driving road;
determining a reference vehicle of the target running road according to the target image, wherein the reference vehicle is a lane changing vehicle;
acquiring lane change information of the reference vehicle and a target distance between the reference vehicle and the target vehicle;
determining a lane change risk level of the reference vehicle according to the lane change information and the target distance;
and if the lane change risk level is higher than a preset risk level, sending alarm information to the target vehicle.
With reference to the first aspect, in one possible implementation manner, the method further includes:
acquiring vehicle information of a target vehicle, wherein the vehicle information comprises vehicle appearance information;
determining a driving route of the target vehicle according to the vehicle appearance information and the target traffic dispersion method;
and pushing the driving route to the target vehicle.
A second aspect of the embodiments of the present application provides an intelligent traffic grooming device based on deep learning, which is applied to an electronic device, and the device includes:
an acquisition unit configured to acquire congestion state information of a target travel road on which a target vehicle travels;
a first determination unit, configured to determine vehicle convergence traffic information of a related travel road according to the congestion state information, where the related road includes a road connected to the target travel road;
the second determining unit is used for determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road;
and the pushing unit is used for pushing the target traffic dispersion method to the target vehicle.
With reference to the second aspect, in one possible implementation manner, the first determining unit is configured to:
determining the number of the jammed vehicles on the target running road according to the jam state information;
determining the congestion level of the target running road according to the number of the congested vehicles;
determining a first number of vehicles converging into the associated driving road according to the congestion level;
acquiring a driving route of a congested vehicle on the congested road;
determining a second number of vehicles converging into the associated driving road according to the driving route of the congested vehicle;
and determining vehicle merging traffic information of the associated driving road according to the first quantity and the second quantity.
With reference to the second aspect, in one possible implementation manner, the first determining unit is configured to:
determining a third number of vehicles merging into the associated driving road according to the congestion state information;
acquiring current vehicle passing information of the associated driving road;
determining a fourth number of vehicles converging into the associated driving road according to the current vehicle passing information of the associated driving road;
and determining vehicle convergence traffic information of the associated driving road according to the third quantity and the fourth quantity.
With reference to the second aspect, in one possible implementation manner, the apparatus is further configured to:
acquiring a target image of the target driving road;
determining a reference vehicle of the target running road according to the target image, wherein the reference vehicle is a lane changing vehicle;
acquiring lane change information of the reference vehicle and a target distance between the reference vehicle and the target vehicle;
determining a lane change risk level of the reference vehicle according to the lane change information and the target distance;
and if the lane change risk level is higher than a preset risk level, sending alarm information to the target vehicle.
With reference to the second aspect, in one possible implementation manner, the apparatus is further configured to:
acquiring vehicle information of a target vehicle, wherein the vehicle information comprises vehicle appearance information;
determining a driving route of the target vehicle according to the vehicle appearance information and the target traffic dispersion method;
and pushing the driving route to the target vehicle.
A third aspect of the embodiments of the present application provides a terminal, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiments of the present application.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program makes a computer perform part or all of the steps as described in the first aspect of embodiments of the present application.
A fifth aspect of embodiments of the present application provides a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has at least the following beneficial effects:
the method comprises the steps of obtaining congestion state information of a target driving road where a target vehicle drives, determining vehicle convergence traffic information of a related driving road according to the congestion state information, wherein the related road comprises a road connected with the target driving road, determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the related driving road, pushing the target traffic dispersion method to the target vehicle, and compared with the existing scheme, performing traffic dispersion in an artificial mode, determining the traffic dispersion method through the intelligent traffic dispersion model, and improving intelligence during traffic dispersion.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of an intelligent traffic grooming method based on deep learning according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another intelligent traffic grooming method based on deep learning according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another intelligent traffic grooming method based on deep learning according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an intelligent traffic grooming device based on deep learning according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the 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 application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic flow chart of an intelligent traffic grooming method based on deep learning according to an embodiment of the present disclosure. As shown in fig. 1, the method is applied to an electronic device, and the method includes steps 101-104 as follows:
101. the congestion state information of a target traveling road on which the target vehicle travels is acquired.
The target vehicle may be a vehicle driven by a user of the electronic device, and the vehicle travels on a target travel road, and the target travel road may be in a traffic jam state or a non-traffic jam state. The target driving road may be any one of driving roads.
The manner of acquiring congestion status information may be: the congestion state information is acquired through the traffic system, and the congestion state information can be understood as the condition of traffic congestion, which can be reflected as the number of vehicles with traffic congestion and the traffic flow of traffic running, and specifically can be as follows: taking the traffic flow as an example, the larger the traffic flow is, the more congested the traffic is, and the smaller the traffic flow is, the smoother the traffic is. The examples are given for illustrative purposes only and are not intended to be limiting.
The manner of acquiring congestion status information may also be: the method comprises the steps of obtaining the vehicle passing number of a target running road in a preset time period through an infrared sensor, and determining congestion state information according to the vehicle passing number. In the preset time period, the more vehicles pass, the larger the traffic flow is, and the more traffic jam is; if the number of passing vehicles is less, the traffic flow is smaller, and the traffic is smoother. Of course, the traffic flow can also be analyzed according to the image of the driving road by acquiring the image of the target driving road, so as to obtain the traffic congestion state information. The congestion status information may be reflected by the traffic volume.
102. And determining vehicle merging traffic information of related traveling roads according to the congestion state information, wherein the related roads comprise roads connected with the target traveling road.
The associated driving road includes a road which is connected with the target driving road, and it is understood that the associated driving road is directly connected with the target driving road, and the vehicle on the target road can directly drive to the associated driving road, for example, if there are 3 roads at an intersection, the three roads are mutually associated driving roads.
The method for determining the vehicle entry traffic information can be as follows: and determining the congestion level of the vehicles on the target form road according to the number of the congested vehicles, and determining the vehicle convergence traffic information of the associated running road according to the congestion level. The method can also comprise the following steps: and determining vehicle merging traffic information of the associated driving road according to the vehicle traffic information of the associated driving road.
103. And determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road.
The intelligent traffic dispersion model is a pre-trained model, and the model training process can be as follows: training sample data, wherein the training comprises forward training and reverse training, when the model training is converged, the training is ended to obtain an intelligent traffic grooming model, and the sample data comprises: vehicle convergence traffic information and a traffic dispersion method. The traffic dispersion method may be a driving route of the target vehicle, or the like.
104. And pushing the target traffic dispersion method to the target vehicle.
When the traffic grooming method is pushed to the target vehicle, the traffic grooming method can be pushed to the target vehicle through the 5G network, and certainly can be pushed to the target vehicle through other networks.
In one possible implementation, a possible method for determining information on vehicle merging traffic information associated with a driving road according to the congestion status information includes steps a1-a6, which are as follows:
a1, determining the number of the jammed vehicles on the target running road according to the jam state information;
a2, determining the congestion level of the target running road according to the number of the congested vehicles;
a3, determining a first number of vehicles converging into the related driving road according to the congestion level;
a4, acquiring a driving route of a congested vehicle on the congested road;
a5, determining a second number of vehicles converging into the associated driving road according to the driving route of the jammed vehicle;
a6, determining vehicle merging traffic information of the related driving road according to the first quantity and the second quantity.
According to the congestion state information, the number of congested vehicles on the target running road can be obtained, and the number of congested vehicles can be determined according to the link between the congestion state information and the vehicle running.
The higher the number of congested vehicles, the higher the congestion level of the target travel road, and the lower the number of congested vehicles and the lower the congestion level of the target travel road. The higher the congestion level, the higher the first number of vehicles merging into the associated travel road, and the lower the congestion level, the lower the first number of vehicles merging into the associated travel road. The number of the associated driving roads may be plural, and if there are plural associated driving roads, it is necessary to determine the association level of the associated driving roads, and the association level may be determined by a distance from an intersection of the associated driving roads to a start point of the target driving road, and the closer the distance is, the higher the association level is, and the farther the distance is, the lower the association level is. Of course, the association level may also be determined by the distance and the road level of the associated road, and specifically may be: normalizing the distance to obtain a normalized value; and mapping the road grade to obtain a mapping processing value, and determining the associated grade according to the sum of the normalization processing value and the mapping processing value. In the mapping process, the road rank is mapped to a value between (0,1), and the higher the road rank is, the larger the mapping value is, and the lower the road rank is, the lower the mapping value is. In the normalization process, the distance is normalized to a value between (0,1), and the larger the distance, the smaller the value, and the smaller the distance, the larger the value. The higher the association level, the larger the first number, and the lower the association level, the smaller the first number.
The method for acquiring the driving route of the congested vehicle may be to acquire the driving route by sending a route acquisition request to the congested vehicle. Of course, the driving route may be acquired by the navigation system, or may be acquired in other manners.
The associated driving road to be driven into by each congested vehicle can be determined according to the driving route. And determining the second quantity according to the associated driving road to which each jammed vehicle needs to drive.
The method for determining the vehicle entry traffic information according to the first number and the second number may be: and if the first number is larger than the second number, determining the average value of the first number and the second number as the passing number of the vehicles which are merged into the passing information. If the first number is smaller than the second number, the second number may be determined as the number of vehicles passing through the vehicle into the pass information.
In this example, the first number of vehicles merging into the associated running road is determined according to the number of congested vehicles, the second number of vehicles merging into the associated running road is determined according to the running route of the congested vehicles, and the vehicle merging traffic information of the associated running road is determined according to the first number and the second number, so that the accuracy of determining the vehicle merging traffic information can be improved.
In one possible implementation, a possible method for determining information on vehicle merging traffic information associated with a driving road according to the congestion status information includes steps B1-B4, which are as follows:
b1, determining a third number of vehicles converging into the associated driving road according to the congestion state information;
b2, acquiring the current vehicle passing information of the associated driving road;
b3, determining a fourth number of vehicles merging into the related driving road according to the current vehicle traffic information of the related driving road;
and B4, determining vehicle merging traffic information of the associated driving road according to the third quantity and the fourth quantity.
The manner of determining the third number according to the congestion status information may refer to the manner of determining the first number in the foregoing embodiment, and details are not repeated here.
The vehicle passing information of the associated running road is obtained, the image of the associated running road can be obtained through the camera, the vehicle characteristic extraction is carried out on the image to obtain the vehicle information, the vehicle passing information is obtained according to the vehicle information, and the method for carrying out the vehicle characteristic extraction on the image can adopt the characteristic extraction algorithm and other modes in the existing scheme to carry out the extraction.
The method for determining the fourth quantity according to the vehicle passing information may be: and determining the maximum number of vehicles which can be converged into the associated running road according to the vehicle passing information. The fourth number is determined based on the determination of the maximum number. Because each associated driving road has the maximum passing number during smooth passing, the maximum numerical value of vehicles which can be converged can be determined according to vehicle passing information, the numerical value of the preset proportion of the maximum numerical value is determined as the fourth number, and the preset proportion is set through experience values or historical data.
The average value of the third number and the fourth number may be determined as the number of vehicle passages of the vehicle into the passage information, if the fourth number is greater than the third number, the third number may be determined as the number of vehicle passages of the vehicle into the passage information, and if the fourth number is less than the third number, the fourth number may be determined as the number of vehicle passages of the vehicle into the passage information.
In this example, the third quantity is determined by the congestion state information, the fourth quantity is determined by associating the current vehicle traffic information of the traveling road, and the vehicle merging traffic information is determined by the third quantity and the fourth quantity, so that the vehicle merging traffic information can be determined according to the condition of the associated traveling road, and the accuracy of determining the vehicle merging traffic information is improved.
In a possible implementation manner, when a lane change vehicle occurs in a congested road section, the risk level may also be evaluated, and a risk warning may be performed, specifically, the following method may be adopted:
c1, acquiring a target image of the target driving road;
c2, determining a reference vehicle of the target driving road according to the target image, wherein the reference vehicle is a lane changing vehicle;
c3, obtaining lane change information of the reference vehicle and a target distance between the reference vehicle and the target vehicle;
c4, determining the lane change risk level of the reference vehicle according to the lane change information and the target distance;
and C5, if the lane change risk level is higher than a preset risk level, sending warning information to the target vehicle.
The target image can be acquired through a camera and other devices, the camera can be distributed on two sides of a road on which the target runs, or can be distributed on a street lamp, the camera can communicate with the server through a wireless network and send the acquired image to the server, and the electronic device can acquire the acquired image from the server, namely, the target image and the like can be acquired.
Of course, the cameras can also be distributed on the target vehicle and sent to the electronic equipment and the like through the vehicle-mounted system.
The reference vehicle may be determined by the driving route of the reference vehicle, or by a signal light of the vehicle, for example, when the vehicle turns on a turn signal, the vehicle is determined to be a lane-changing vehicle. Lane change information may be determined according to a driving route of the vehicle and a signal light, and the lane change information may be, for example, that a target driving lane includes A, B, C, D, 4 lanes, and a reference vehicle changes a lane from an a lane to a B lane, and the lane change information may be a to B, etc.
The distance between the reference vehicle and the target vehicle may be acquired through the target image. Specifically, the target distance may be determined by a distance calculation formula or the like.
The lane change information is lane change to a lane where the target vehicle travels, and the closer the target distance is, the higher the lane change risk level is, and the farther the target distance is, the lower the lane change risk level is. If the reference vehicle runs in parallel with the target vehicle and changes lanes to the lane in which the target vehicle runs, the risk level is the highest.
Of course, the lane change risk level may also be determined according to the association degree between the lane change information and the target vehicle and the target distance, where the higher the association degree is, the smaller the target distance is, the higher the lane change risk level is, and the lower the association degree is, and the larger the target distance is, the lower the lane change risk level is. The association degree may be determined according to an association degree between the lane change information and the lane on which the target vehicle travels, the closer the lane change information and the lane on which the target vehicle travels, the higher the association degree, and the farther the lane change information and the lane on which the target vehicle travels, the lower the association degree. The distance between the lane change information and the lane on which the target vehicle travels can be understood as the higher the degree of association when the lane change direction is close to the lane on which the target vehicle travels, and the lower the degree of association when the lane change direction is far away from the lane on which the target vehicle travels.
The preset risk level is set by empirical values or historical data. The warning information may be preset information that informs the target vehicle of a higher lane change risk level. And is not particularly limited herein.
In this example, when the lane change risk level of the reference vehicle is high, the target vehicle is notified, and the safety of the target vehicle during traveling can be improved.
In one possible implementation, the following method may be further included:
d1, acquiring vehicle information of the target vehicle, wherein the vehicle information comprises vehicle appearance information;
d2, determining the driving route of the target vehicle according to the vehicle appearance information and the target traffic dispersion method;
d3, pushing the driving route to the target vehicle.
The vehicle appearance information includes the length and width of the vehicle body, and the like. According to the appearance information and the target traffic dispersion method, the method for determining the driving route can be as follows: determining lane change information of a target vehicle according to a target traffic dispersion method; and determining a driving route according to the length, the width and the lane change information of the vehicle. The travel route is a travel route on a target travel road, and for example, where to change a lane, where to enter a related travel lane, and the like.
The driving route can be pushed to the target vehicle through a 5G network and the like.
In this example, the driving route of the target vehicle can be pushed to the target vehicle, so that convenience in driving of the target vehicle is improved.
In one possible implementation manner, before the target traffic grooming method is pushed to the target vehicle, the method may further include the following steps:
e1, acquiring a first fingerprint image;
e2, dividing the first fingerprint image into a plurality of areas;
e3, determining the distribution density of the feature points of each of the multiple regions to obtain a feature point distribution density set, wherein each region corresponds to one feature point distribution density;
e4, determining a target mean value and a target mean square error corresponding to the feature point distribution density set;
e5, determining a target image enhancement algorithm corresponding to the target average value according to the mapping relation between the preset average value and the image enhancement algorithm;
e6, determining a target fine tuning coefficient corresponding to the target mean square error according to a mapping relation between a preset mean square error and the fine tuning coefficient;
e7, adjusting the algorithm control parameters of the target image enhancement algorithm according to the target fine adjustment coefficients to obtain target algorithm control parameters;
e8, carrying out image enhancement processing on the first fingerprint image according to the target algorithm control parameter and the target image enhancement algorithm to obtain a second fingerprint image;
e9, matching the second fingerprint image with a preset fingerprint template;
e10, when the second fingerprint image is successfully matched with the preset fingerprint template, executing the step of pushing the target traffic dispersion method to the target vehicle.
In the embodiment of the application, the preset fingerprint template can be pre-stored in the electronic device. In specific implementation, the electronic device may acquire the first fingerprint image, and further, the first fingerprint image may be divided into a plurality of regions, the size of each region in the plurality of regions is within a preset area range, the size of each region in the plurality of regions may be the same or different, and the preset area range may be set by a user or default to a system.
Further, the electronic device may determine a feature point distribution density of each of the plurality of regions to obtain a feature point distribution density set, where the feature point distribution density set includes a plurality of feature point distribution densities, and each region corresponds to one feature point distribution density, that is, the number of feature points of each of the plurality of regions and a corresponding region area may be determined, and a ratio between the number of feature points and the corresponding region area is used as the feature point distribution density. The electronic device may determine a target average value and a target mean square error corresponding to the feature point distribution density set, that is, the target average value is the total number of feature points/the number of regions corresponding to the feature point distribution density set, and may determine the target mean square error corresponding to the feature point distribution density set based on the target average value and the feature point distribution density set.
In addition, in this embodiment of the application, the image enhancement algorithm may be at least one of the following: histogram equalization, wavelet transformation, gray stretching, Retinex algorithm, etc., without limitation. Each image enhancement algorithm corresponds to an algorithm control parameter, and the algorithm control algorithm is used for controlling the image enhancement degree. The electronic device may pre-store a mapping relationship between a preset average value and an image enhancement algorithm, and a mapping relationship between a preset mean square error and a fine tuning coefficient. The average value reflects the overall characteristics of the image, and the mean square error reflects the relevance between the regions, so that the corresponding image enhancement algorithm and the corresponding algorithm control parameters can be selected by combining the overall characteristics and the regional relevance of the image, and the image enhancement efficiency is favorably improved, namely the quality of the fingerprint image is improved.
Furthermore, the electronic device may determine a target image enhancement algorithm corresponding to the target average value according to a mapping relationship between a preset average value and an image enhancement algorithm, and may determine a target fine-tuning coefficient corresponding to the target mean-square error according to a mapping relationship between a preset mean-square error and a fine-tuning coefficient, and then, the electronic device may adjust an algorithm control parameter of the target image enhancement algorithm according to the target fine-tuning coefficient to obtain a target algorithm control parameter, and perform an image enhancement process on the first fingerprint image according to the target algorithm control parameter and the target image enhancement algorithm to obtain a second fingerprint image, and further, since the second fingerprint image has been subjected to the image enhancement process, the electronic device may match the second fingerprint image with the preset fingerprint template, and perform the step of obtaining the data transmission request when the second fingerprint image is successfully matched with the preset fingerprint template, otherwise, the user can be prompted to continue inputting the fingerprint image, and therefore the fingerprint identification efficiency can be improved.
Referring to fig. 2, fig. 2 is a schematic flow chart of another intelligent traffic grooming method based on deep learning according to an embodiment of the present application. As shown in fig. 2, the method applied to the electronic device includes step 201 and step 209, which are as follows:
201. acquiring congestion state information of a target running road on which a target vehicle runs;
202. determining the number of the jammed vehicles on the target running road according to the jam state information;
203. determining the congestion level of the target running road according to the number of the congested vehicles;
204. determining a first number of vehicles converging into the associated driving road according to the congestion level;
wherein the associated road includes a road that meets the target travel road.
205. Acquiring a driving route of a congested vehicle on the congested road;
206. determining a second number of vehicles converging into the associated driving road according to the driving route of the congested vehicle;
207. determining vehicle convergence traffic information of the associated driving road according to the first quantity and the second quantity;
208. determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road;
209. and pushing the target traffic dispersion method to the target vehicle.
In this example, the first number of vehicles merging into the associated running road is determined according to the number of congested vehicles, the second number of vehicles merging into the associated running road is determined according to the running route of the congested vehicles, and the vehicle merging traffic information of the associated running road is determined according to the first number and the second number, so that the accuracy of determining the vehicle merging traffic information can be improved.
Referring to fig. 3, fig. 3 is a schematic flow chart of another intelligent traffic grooming method based on deep learning according to an embodiment of the present application. As shown in fig. 3, the method is applied to an electronic device, and includes steps 301 and 309, which are as follows:
301. acquiring congestion state information of a target running road on which a target vehicle runs;
302. determining vehicle merging traffic information of a related driving road according to the congestion state information, wherein the related road comprises a road connected with the target driving road;
303. determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road;
304. pushing the target traffic dispersion method to the target vehicle;
305. acquiring a target image of the target driving road;
306. determining a reference vehicle of the target running road according to the target image, wherein the reference vehicle is a lane changing vehicle;
307. acquiring lane change information of the reference vehicle and a target distance between the reference vehicle and the target vehicle;
308. determining a lane change risk level of the reference vehicle according to the lane change information and the target distance;
309. and if the lane change risk level is higher than a preset risk level, sending alarm information to the target vehicle.
In this example, when the lane change risk level of the reference vehicle is high, the target vehicle is notified, and the safety of the target vehicle during traveling can be improved.
In accordance with the foregoing embodiments, please refer to fig. 4, where fig. 4 is a schematic structural diagram of a terminal provided in an embodiment of the present application, and as shown in the figure, the terminal includes a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for performing the following steps;
acquiring congestion state information of a target running road on which a target vehicle runs;
determining vehicle merging traffic information of a related driving road according to the congestion state information, wherein the related road comprises a road connected with the target driving road;
determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road;
and pushing the target traffic dispersion method to the target vehicle.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the terminal includes corresponding hardware structures and/or software modules for performing the respective functions in order to implement the above-described functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the terminal may be divided into the functional units according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
In accordance with the above, please refer to fig. 5, and fig. 5 is a schematic structural diagram of an intelligent traffic grooming device based on deep learning according to an embodiment of the present application. As shown in fig. 5, the intelligent traffic dispersion device based on deep learning is applied to an electronic device, and the device includes:
an obtaining unit 501, configured to obtain congestion state information of a target traveling road on which a target vehicle travels;
a first determining unit 502, configured to determine vehicle convergence traffic information of a related travel road according to the congestion state information, where the related road includes a road connected to the target travel road;
a second determining unit 503, configured to determine a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle merging traffic information of the associated driving road;
a pushing unit 504, configured to push the target traffic grooming method to the target vehicle.
In one possible implementation manner, the first determining unit 502 is configured to:
determining the number of the jammed vehicles on the target running road according to the jam state information;
determining the congestion level of the target running road according to the number of the congested vehicles;
determining a first number of vehicles converging into the associated driving road according to the congestion level;
acquiring a driving route of a congested vehicle on the congested road;
determining a second number of vehicles converging into the associated driving road according to the driving route of the congested vehicle;
and determining vehicle merging traffic information of the associated driving road according to the first quantity and the second quantity.
In one possible implementation manner, the first determining unit 502 is configured to:
determining a third number of vehicles merging into the associated driving road according to the congestion state information;
acquiring current vehicle passing information of the associated driving road;
determining a fourth number of vehicles converging into the associated driving road according to the current vehicle passing information of the associated driving road;
and determining vehicle convergence traffic information of the associated driving road according to the third quantity and the fourth quantity.
In one possible implementation, the apparatus is further configured to:
acquiring a target image of the target driving road;
determining a reference vehicle of the target running road according to the target image, wherein the reference vehicle is a lane changing vehicle;
acquiring lane change information of the reference vehicle and a target distance between the reference vehicle and the target vehicle;
determining a lane change risk level of the reference vehicle according to the lane change information and the target distance;
and if the lane change risk level is higher than a preset risk level, sending alarm information to the target vehicle.
In one possible implementation, the apparatus is further configured to:
acquiring vehicle information of a target vehicle, wherein the vehicle information comprises vehicle appearance information;
determining a driving route of the target vehicle according to the vehicle appearance information and the target traffic dispersion method;
and pushing the driving route to the target vehicle.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the intelligent deep learning based traffic grooming methods described in the above method embodiments.
Embodiments of the present application further provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to execute some or all of the steps of any one of the intelligent traffic grooming methods based on deep learning as described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several 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 described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash memory disks, read-only memory, random access memory, magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An intelligent traffic grooming method based on deep learning is applied to electronic equipment, and the method comprises the following steps:
acquiring congestion state information of a target running road on which a target vehicle runs;
determining vehicle merging traffic information of a related driving road according to the congestion state information, wherein the related road comprises a road connected with the target driving road;
determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road;
and pushing the target traffic dispersion method to the target vehicle.
2. The method of claim 1, wherein determining vehicle merge traffic information associated with a travel route based on the congestion status information comprises:
determining the number of the jammed vehicles on the target running road according to the jam state information;
determining the congestion level of the target running road according to the number of the congested vehicles;
determining a first number of vehicles converging into the associated driving road according to the congestion level;
acquiring a driving route of a congested vehicle on the congested road;
determining a second number of vehicles converging into the associated driving road according to the driving route of the congested vehicle;
and determining vehicle merging traffic information of the associated driving road according to the first quantity and the second quantity.
3. The method of claim 1, wherein determining vehicle merge traffic information associated with a travel route based on the congestion status information comprises:
determining a third number of vehicles merging into the associated driving road according to the congestion state information;
acquiring current vehicle passing information of the associated driving road;
determining a fourth number of vehicles converging into the associated driving road according to the current vehicle passing information of the associated driving road;
and determining vehicle convergence traffic information of the associated driving road according to the third quantity and the fourth quantity.
4. The method according to any one of claims 1-3, further comprising:
acquiring a target image of the target driving road;
determining a reference vehicle of the target running road according to the target image, wherein the reference vehicle is a lane changing vehicle;
acquiring lane change information of the reference vehicle and a target distance between the reference vehicle and the target vehicle;
determining a lane change risk level of the reference vehicle according to the lane change information and the target distance;
and if the lane change risk level is higher than a preset risk level, sending alarm information to the target vehicle.
5. The method according to any one of claims 1-4, further comprising:
acquiring vehicle information of a target vehicle, wherein the vehicle information comprises vehicle appearance information;
determining a driving route of the target vehicle according to the vehicle appearance information and the target traffic dispersion method;
and pushing the driving route to the target vehicle.
6. The utility model provides an intelligent traffic is dredged device based on deep learning which is characterized in that, is applied to electronic equipment, the device includes:
an acquisition unit configured to acquire congestion state information of a target travel road on which a target vehicle travels;
a first determination unit, configured to determine vehicle convergence traffic information of a related travel road according to the congestion state information, where the related road includes a road connected to the target travel road;
the second determining unit is used for determining a target traffic dispersion method through an intelligent traffic dispersion model according to the vehicle convergence traffic information of the associated driving road;
and the pushing unit is used for pushing the target traffic dispersion method to the target vehicle.
7. The apparatus of claim 6, wherein the first determining unit is configured to:
determining the number of the jammed vehicles on the target running road according to the jam state information;
determining the congestion level of the target running road according to the number of the congested vehicles;
determining a first number of vehicles converging into the associated driving road according to the congestion level;
acquiring a driving route of a congested vehicle on the congested road;
determining a second number of vehicles converging into the associated driving road according to the driving route of the congested vehicle;
and determining vehicle merging traffic information of the associated driving road according to the first quantity and the second quantity.
8. The apparatus of claim 6, wherein the first determining unit is configured to:
determining a third number of vehicles merging into the associated driving road according to the congestion state information;
acquiring current vehicle passing information of the associated driving road;
determining a fourth number of vehicles converging into the associated driving road according to the current vehicle passing information of the associated driving road;
and determining vehicle convergence traffic information of the associated driving road according to the third quantity and the fourth quantity.
9. A terminal, comprising a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-5.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to carry out the method according to any one of claims 1-5.
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