CN111739322B - Data processing method and device - Google Patents
Data processing method and device Download PDFInfo
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- CN111739322B CN111739322B CN201910385169.6A CN201910385169A CN111739322B CN 111739322 B CN111739322 B CN 111739322B CN 201910385169 A CN201910385169 A CN 201910385169A CN 111739322 B CN111739322 B CN 111739322B
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Abstract
The embodiment of the application discloses a data processing method and a device thereof, wherein the method comprises the following steps: the method comprises the steps of obtaining an actually measured road section set, a standard road section set, attribute information of the actually measured road section and attribute information of the standard road section, determining a target actually measured road section from the actually measured road section set, wherein the characteristic attribute of the target actually measured road section is associated with the characteristic attribute of the standard road section, adjusting the road condition state of the target actually measured road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actually measured road section, and determining the road condition state of the road section to be predicted according to the attribute information of the adjusted actually measured road section. According to the embodiment of the application, the prediction accuracy of the road condition state can be improved, so that a reasonable travel route can be provided for a user.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a data processing method and apparatus.
Background
Along with the continuous increase of vehicles, roads are more and more congested, and inconvenience is brought to traveling of users. Therefore, in the process that a terminal (such as a vehicle-mounted terminal) provides navigation service for a user, the road condition state of a road needs to be predicted, and then a travel route is provided for the user by combining the road condition state of the road, so that the travel time of the user is saved. At present, the accuracy of the predicted road condition state is low, so that a reasonable travel route cannot be provided for a user.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present application is to provide a data processing method and device, which can improve the prediction accuracy of the road condition state, so as to provide a reasonable travel route for a user.
In one aspect, the present application provides a data processing method, including:
acquiring an actually measured road section set, a standard road section set, attribute information of an actually measured road section and attribute information of a standard road section, wherein the actually measured road section set comprises the actually measured road section, the standard road section comprises the standard road section, and the attribute information comprises characteristic attributes and a road condition state;
determining a target measured road section from the measured road section set, wherein the characteristic attribute of the target measured road section is associated with the characteristic attribute of the standard road section;
adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section;
and determining the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section.
In another aspect, the present application provides a data processing apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an actually measured road section set, a standard road section set, attribute information of an actually measured road section and attribute information of a standard road section, the actually measured road section set comprises the actually measured road section, the standard road section comprises the standard road section, and the attribute information comprises characteristic attributes and a road condition state;
the determining module is used for determining a target actual measurement road section from the actual measurement road section set, and the characteristic attribute of the target actual measurement road section is associated with the characteristic attribute of the standard road section;
the adjusting module is used for adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section;
and the prediction module is used for determining the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section.
In yet another aspect, the present application provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor, the processor executing the program to perform the following steps: acquiring an actually measured road section set, a standard road section set, attribute information of an actually measured road section and attribute information of a standard road section, wherein the actually measured road section set comprises the actually measured road section, the standard road section comprises the standard road section, and the attribute information comprises characteristic attributes and a road condition state;
determining a target measured road section from the measured road section set, wherein the characteristic attribute of the target measured road section is associated with the characteristic attribute of the standard road section;
adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section;
and determining the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section.
In yet another aspect, the present application provides a computer storage medium having one or more instructions stored thereon, the one or more instructions adapted to be loaded by a processor and to perform the steps of:
acquiring an actually measured road section set, a standard road section set, attribute information of an actually measured road section and attribute information of a standard road section, wherein the actually measured road section set comprises the actually measured road section, the standard road section comprises the standard road section, and the attribute information comprises characteristic attributes and a road condition state;
determining a target measured road section from the measured road section set, wherein the characteristic attribute of the target measured road section is associated with the characteristic attribute of the standard road section;
adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section;
and determining the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section.
In this application, because the accuracy of the road condition state of the standard road section is higher, therefore, the road condition state of the target actual measurement road section is adjusted through the road condition state of the standard road section, the attribute information of the actual measurement road section after adjustment is obtained, and the accuracy of the road condition state of the actual measurement road section can be improved, namely, the accuracy of the attribute information of the actual measurement road section after adjustment is higher. Furthermore, the road condition state of the road section to be predicted is determined through the adjusted attribute information of the actual measurement road section, and the prediction precision of the road condition state can be improved.
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 flow chart of a data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another data processing method provided by the embodiment of the invention;
fig. 3 is a schematic flow chart illustrating a process of adjusting a road condition of a target actual measurement road section according to an embodiment of the present invention;
fig. 4 is a schematic distribution diagram of road condition states of an actually measured road section according to an embodiment of the present invention;
fig. 5 is a schematic distribution diagram of a road condition status of an adjusted actual measurement road section according to an embodiment of the present invention;
fig. 6 is a schematic distribution diagram of road condition states of another actually measured road section according to an embodiment of the present invention;
fig. 7 is a schematic distribution diagram of a road condition status of an adjusted actual measurement road section according to another embodiment of the present invention;
fig. 8 is a schematic distribution diagram of road condition states of another actually measured road section according to an embodiment of the present invention;
fig. 9 is a schematic distribution diagram of a road condition status of an adjusted actual measurement road section according to another embodiment of the present invention;
FIG. 10 is a block diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
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.
Hereinafter, some terms in the present application are explained to facilitate understanding by those skilled in the art.
(1) The actually measured road section set is a set of actually measured road sections, that is, the actually measured road section set includes a plurality of actually measured road sections, the actually measured road sections may also be referred to as reference road sections (or sample road sections), that is, the road condition state of the road section to be predicted may be determined according to the attribute information of the actually measured road sections, and the attribute information of the actually measured road sections is calculated according to the driving data of the vehicle driving on the actually measured road sections. The travel data of the vehicle may include a travel environment image, travel track data, and the like.
(2) The attribute information may include a characteristic attribute including basic physical information and timely spatial distribution information and a traffic condition state. The basic physical information includes at least one of a road segment grade, a length of a road segment, a driving speed of a vehicle on the road segment, a free flow speed, a distance of the road segment from a traffic light, a number of lanes on the road segment, or a number of vehicles on the road segment. The level of the road section may include a function level and an administration level, the function level is divided according to the purpose or the flow of the road section, and may include a first-level road, a second-level road, and the like. Administrative levels include national, provincial, and county roads, among others. The free flow speed refers to a maximum traveling speed at which the vehicle can travel on the link. The spatiotemporal distribution information may include distribution data of the road condition status of the road segment in a preset time period, for example, the spatiotemporal distribution information may include the number of vehicles determining that the road condition status of the road segment is in a smooth state, the number of vehicles determining that the road condition status is in a slow-moving state, the number of vehicles determining that the road condition status is in a congestion state, and the like in every minute in the last day. The road condition state can be used for reflecting the unblocked degree of the road section, and the road condition state can comprise a congestion state, a slow-moving state or an unblocked state, namely the congestion state means that the unblocked degree of the road section is poor, the slow-moving state means that the unblocked degree of the road section is good, and the unblocked state means that the unblocked degree of the road section is optimal.
(3) The standard road segment set refers to a set of standard road segments, that is, the standard road segment set includes one or more standard road segments, the standard road segment may refer to a road segment whose accuracy of attribute information is greater than a preset threshold, and the attribute information of the standard road segment may be obtained by screening, verifying or auditing a plurality of labeled information of the standard road segment. The labeled information may include characteristic attributes and road conditions of the standard road section, and the labeled information may be obtained by feedback of vehicles driving on the standard road section, or the labeled information is obtained by performing on-site road measurement on the standard road section, or the labeled information is obtained by labeling the standard road section according to driving data of the vehicles driving on the standard road section.
Referring to fig. 1, a schematic flow chart of a data processing method provided in an embodiment of the present application is shown, where the data processing method can be applied to an electronic device. The electronic device may be a front-end device capable of providing relevant information such as road conditions and states of a road section for a vehicle, that is, the electronic device may include a car navigation device, a smart phone, a tablet computer, or the like. Alternatively, the electronic device may be a back-end service device capable of providing relevant information such as a road condition state of a road segment for a vehicle, for example, the electronic device may be a server, and specifically may be a background server of a navigation application. The method may include the following steps S101 to S104.
S101, acquiring an actually measured road section set, a standard road section set, attribute information of the actually measured road section and attribute information of the standard road section, wherein the actually measured road section set comprises the actually measured road section, the standard road section comprises the standard road section, and the attribute information comprises characteristic attributes and a road condition state.
Generally, the accuracy of the attribute information of the standard road section is higher, but the acquisition of the attribute information of the standard road section is time-consuming and labor-consuming; the accuracy of the attribute information of the actually measured link is relatively low, but the attribute information of the actually measured link can be acquired relatively easily. In addition, the road section range covered by the standard road section set is limited, and the road section range covered by the actual measurement road section set is wider, namely, the standard road section set comprises fewer standard road sections, and the actual measurement road section set comprises more actual measurement road sections. Therefore, in order to reduce the cost of predicting the road condition, improve the efficiency of prediction, and ensure the prediction accuracy of the road condition, the electronic device may optimize the attribute information of the actual measured road section by using the attribute information of the standard road section. Firstly, the electronic device may obtain the actually measured road segment set, where the actually measured road segment set may include actually measured road segments of multiple levels, and for example, the actually measured road segment set may include national road segments, provincial road segments, and county road segments; and obtaining a set of standard road segments, which may include at least one level of standard road segments, such as a set of standard road segments including a provincial road segment. Further, the electronic device may obtain attribute information of the measured road section and attribute information of the standard road section, where the attribute information of the measured road section may be calculated by the electronic device or other devices according to driving data of a vehicle driving on the measured road section, and the attribute information of the standard road section may be obtained by the electronic device or other devices by screening, verifying, or auditing pieces of labeled information of the standard road section.
S102, determining a target actual measurement road section from the actual measurement road section set, wherein the characteristic attribute of the target actual measurement road section is associated with the characteristic attribute of the standard road section.
The electronic device can compare the characteristic attribute of each standard road section in the standard road section set with the characteristic attribute of each actually measured road section in the actually measured road section set, so that the target actually measured road section is determined from the actually measured road section set.
S103, adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section.
Because the attribute information of the actual measurement road section is obtained by calculation according to the driving data of the vehicle driving on the actual measurement road section, and the attribute information of the standard road section is obtained by screening, auditing or verifying a plurality of pieces of labeled information of the standard road section, the accuracy of the attribute information of the standard road section is higher than that of the actual measurement road section, that is, the accuracy of the road condition state of the standard road section is higher than that of the actual measurement road section. In order to improve the accuracy of the road condition state of the actually measured road section, the electronic device can adjust the road condition state of the actually measured road section according to the road condition state of the standard road section. Specifically, since the characteristic attribute of the target actual measurement road section is associated with the characteristic attribute of the standard road section, which indicates that the target actual measurement road section and the standard road section are of the same type or similar type, such as being all highway sections or being all school zone road sections, the road condition state of the target actual measurement road section is generally the same as the road condition state of the standard road section at the same time period, so that the electronic device can adjust the road condition state of the target actual measurement road section according to the road condition state of the standard road section, and obtain the attribute information of the adjusted actual measurement road section. More specifically, the road condition status of the target actual measurement road section in a certain time period can be adjusted according to the road condition status of the standard road section in the certain time period. For example, the road condition state of the target actual measurement road section at 8: 00-8: 30 in the morning of monday can be adjusted according to the road condition state of the standard road section at 8: 00-8: 30 in the morning of monday.
And S104, determining the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section.
Because the accuracy of the adjusted attribute information of the actual measurement road section is higher, the electronic device can determine the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section. Specifically, the electronic device may determine, from the actually measured road section set, a road section whose characteristic attribute matches the characteristic attribute of the road section to be predicted, and determine the road condition state of the road section to be predicted according to the adjusted road condition state corresponding to the matched road section. Or, the electronic device may perform optimization training on the prediction model according to the adjusted attribute information of the actually measured road segment, and determine the road condition state of the road segment to be predicted through the prediction model of the optimization training. Further, the electronic equipment can output the road condition state of the road section to be predicted on a user interface, so that a user can independently select a reasonable travel route; or planning a reasonable travel route for the user according to the road condition state of the road section to be predicted.
In this application, because the accuracy of the road condition state of the standard road section is higher, therefore, the road condition state of the target actual measurement road section is adjusted through the road condition state of the standard road section, the attribute information of the actual measurement road section after adjustment is obtained, and the accuracy of the road condition state of the actual measurement road section can be improved, namely, the accuracy of the attribute information of the actual measurement road section after adjustment is higher. Furthermore, the road condition state of the road section to be predicted is determined through the adjusted attribute information of the actual measurement road section, and the prediction precision of the road condition state can be improved.
Referring to fig. 2, a schematic flow chart of another data processing method provided in the embodiment of the present application is shown, where the data processing method may be applied to an electronic device, and the method may include the following steps S201 to S206.
S201, acquiring an actually measured road section set, a standard road section set, attribute information of the actually measured road section and attribute information of the standard road section, wherein the actually measured road section set comprises the actually measured road section, the standard road section comprises the standard road section, and the attribute information comprises characteristic attributes and a road condition state.
For example, assuming that the set of measured road segments includes n measured road segments, it may be represented as { A }1，A2，A3...AnThe standard road segment set comprises m standard road segments, which can be denoted as { B }1,B2,B3...BmAnd the electronic equipment can acquire the attribute information of the measured road section and the attribute information of the standard road section at the same time point. Assuming that the characteristic attributes of the standard road segment and the measured road segment each include x characteristic attribute values, the characteristic attribute of the ith measured road segment may be represented as FAi＝(f1,f2...fx) Wherein f isxRefers to the xth characteristic attribute value, one characteristic attribute value being used to identify a characteristic attribute, e.g., f1For identifying the level of the road segment. The characteristic attribute of the ith standard road segment may be represented as FBi＝(f1,f2...fx). The status of the ith actual measurement road section can be represented as labelAiThe status of the ith standard road can be represented as labelBi。
In one embodiment, the step S201 of obtaining the attribute information of the measured road segment includes the following steps S11 to S13.
And s11, acquiring the running environment image and the running track data of the vehicle running on the measured road section.
The electronic equipment can be provided with a camera device or a visual sensor, the camera device or the visual sensor is used for acquiring a running environment image of a vehicle running on an actually measured road section, the running environment image can be an image obtained by shooting the area where the vehicle is located, the running environment image can comprise a guideboard of the actually measured road section, the vehicle on the actually measured road section and the like, and if a traffic signal lamp is arranged on the actually measured road section, the running environment image can also comprise the traffic signal lamp. The electronic device may further include a Vehicle Positioning System (VPS), which may include a Global Positioning System (GPS) or a Geographic Information System (GIS), and the vehicle positioning system may acquire travel track data of the vehicle traveling on the measured road section, which may include a position corresponding to each time point during the vehicle traveling on the measured road section.
s12, determining the characteristic attribute of the measured road section according to the driving environment image.
In order to accurately identify the characteristic attribute of the actually measured road section, the electronic device may perform binarization processing on the driving environment image to obtain a binarized driving environment image, where the binarized driving environment image may be a grayscale image of the driving environment image, and further perform characteristic identification on the binarized driving environment image to obtain the characteristic attribute of the actually measured road section.
And s13, determining the road condition state of the actual measurement road section according to the driving track data.
The electronic device can determine the driving speed of the vehicle according to the driving track data, and further determine the road condition state of the actual measurement road section according to the driving speed.
In one embodiment, step s13 may include steps s 21-s 24 as follows.
And s21, determining the running speed of the vehicle according to the running track data.
And s22, if the running speed is less than the first speed threshold value, determining that the road condition of the measured road section is the congestion state.
s23, if the driving speed is greater than or equal to the first speed threshold and less than the second speed threshold, determining that the road condition of the measured road section is a slow driving state, wherein the second speed threshold is greater than the first speed threshold.
s24, if the driving speed is greater than or equal to the second speed threshold, determining the road condition status of the measured road section as a clear status.
In steps s 21-s 24, the electronic device may determine a driving speed of the vehicle according to the driving track data, and the driving speed may refer to an average driving speed of the vehicle on the measured road section. For example, the electronic device may determine the length of the measured road segment and the length of time the vehicle travels based on the travel track data, and determine the travel speed based on the length of the measured road segment and the length of time the vehicle travels. If the driving speed is less than the first speed threshold value, the vehicle is in a particularly slow moving or non-moving state, and the road condition state of the actually measured road section can be determined to be a congestion state. And if the running speed is greater than or equal to the first speed threshold and less than the second speed threshold, the vehicle is in a slow moving state, and the road condition state of the actually measured road section is determined to be a slow running state. And if the running speed is greater than or equal to the second speed threshold value, the vehicle is in a fast moving state, and the road condition state of the actually measured road section is determined to be a smooth state. The first speed threshold and the second speed threshold corresponding to different measured road sections are usually different, so the first speed threshold and the second speed threshold may be determined according to the attribute information of the measured road sections. Alternatively, the first speed threshold and the second speed threshold may be set by a user.
In one embodiment, acquiring the attribute information of the standard link in step S201 includes the following steps S31 to S33.
s31, receiving multiple pieces of labeling information of the standard road section, wherein the labeling information includes the characteristic attribute of the standard road section and the road condition state of the standard road section;
s32, acquiring the occurrence frequency of each piece of labeling information in the plurality of pieces of labeling information;
s33, determining the label information with the occurrence frequency larger than the preset frequency in the plurality of label information as the attribute information of the standard road section.
In steps s31 to s33, in order to improve the accuracy of the attribute information of the standard link, the electronic device may filter the label information of the standard link to obtain the attribute information of the standard link. Specifically, the electronic device may obtain multiple pieces of labeling information for the standard road segment, where the labeling information may be obtained by labeling the standard road segment at the same time point by multiple users. Further, the number of times of occurrence of each piece of labeling information in the plurality of pieces of labeling information is obtained, the more the number of times of occurrence of the labeling information is, the higher the accuracy of the labeling information of the standard road segment is indicated, and the less the number of times of occurrence of the labeling information is, the lower the accuracy of the labeling information of the standard road segment is indicated, so that the electronic device can determine the labeling information of which the number of times of occurrence is greater than the preset number of times in the plurality of pieces of labeling information as the attribute information of the standard road segment, that is, determine the labeling information of which the number of times of occurrence is the largest in the plurality of pieces of labeling information as the attribute information of the standard road segment.
S202, determining a target actual measurement road section from the actual measurement road section set, wherein the characteristic attribute of the target actual measurement road section is associated with the characteristic attribute of the standard road section.
In one embodiment, the association of the characteristic attribute of the target measured road segment with the characteristic attribute of the standard road segment may refer to: and matching the characteristic attribute of the target measured road section with the characteristic attribute of the standard road section. The characteristic attribute matching means that the characteristic attribute of the actual measurement road section is the same as the characteristic attribute of the standard road section or the similarity is greater than a preset similarity value, the preset similarity value can be set by electronic equipment or set by a user, and the user can adjust the size of the preset similarity value according to an application scene. For example, as shown in fig. 3, step S202 may include steps S1 and S2 as follows.
s1 traversing each standard road segment B in the set of standard road segmentsi。
s2 determining the standard road section B from the measured road section setiAn associated target measured road segment.
In steps s1 and s2, the electronic device may traverse each standard road segment B in the set of standard road segmentsi(BiRepresenting the ith standard link), i.e., each of the set of standard linksAnd comparing the characteristic attribute of each standard road section with the characteristic attribute of the actually measured road section to determine the target actually measured road section with the characteristic attribute matched with the characteristic attribute of each standard road section. For example, if the standard road section B is determined5Characteristic attribute and actually measured road section A5、A4、A6And A7Matching the characteristic attributes of the actual measurement road section A4、A5、A6And A7As a target measured link. If the standard road section B is determined15Characteristic attribute and actually measured road section A10、A9And A11Matching the characteristic attributes of the actual measurement road section A10、A9And A11As a target measured link.
In another embodiment, the association of the characteristic attribute of the target measured road segment with the characteristic attribute of the standard road segment may refer to: the characteristic attribute of the target measured road section is matched with the characteristic attribute of the standard road section, the target measured road section is a road section located in the adjacent area of the first measured road section, and the first measured road section is a measured road section of which the characteristic attribute is matched with the characteristic attribute of the standard road section. The acquisition of the attribute information of the standard link in step S202 includes the following steps S41 to S43.
s41, determining a first measured road segment from the set of measured road segments with a characteristic attribute matching the characteristic attribute of the standard road segment.
s42, obtaining a second measured road section from the measured road sections, wherein the distance between the second measured road section and the first measured road section is less than the preset distance.
s43, determining the first measured road segment and the second measured road segment as the target measured road segment.
In steps s41 to s43, the electronic device may compare the characteristic attribute of the measured road segment with the characteristic attribute of the standard road segment, to determine a first measured road segment from the set of measured road segments, where the characteristic attribute of the first measured road segment matches the characteristic attribute of the standard road segment, and obtain a second measured road segment from the set of measured road segments, where a distance between the first measured road segment and the measured road segment is less than a preset distance. The characteristic attribute of the first measured road section is matched with the characteristic attribute of the standard road section, and the first measured road section and the standard road section are of the same type or similar types; the distance between the second actually measured road section and the first actually measured road section is smaller than the preset distance, namely the second actually measured road section and the first actually measured road section are the same type or similar type of road sections. Thus, a link where both the first measured link and the second measured link are associated with a standard link is determined to be a target measured link.
S203, adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section.
For example, if the standard road section B5Characteristic attribute and actually measured road section A4、A5、A6And A7The electronic device may correlate the measured road segment a with the characteristic attributes of (b), the electronic device may then correlate the measured road segment a with the characteristic attributes of (c)4、A5、A6And A7The road condition state of the road section is adjusted to be a standard road section B5Road condition status labelB5. If the standard road section B15Characteristic attribute and actually measured road section A10、A9And A11Is correlated, the measured road section A is measured10、A9And A11The road condition state of the road section is adjusted to be a standard road section B15Road condition status labelB15。
For example, the distribution of the road condition states of the actual measurement road sections is shown in fig. 4, a circle in the drawing indicates that the road condition of the actual measurement road section is a congestion state, a triangle indicates that the road condition of the actual measurement road section is a slow-moving state, a rectangle indicates that the road condition of the actual measurement road section is an unblocked state, a five-pointed star indicates that the road condition of the standard road section is a congestion state, and the road condition of each actual measurement road section and the standard road section are the road condition of the same time slot. If it is determined that the characteristic attribute of the actual measured road segment 11 matches the characteristic attribute of the standard road segment 12 and the road condition of the standard road segment 12 is in a congestion state, the electronic device may infect (i.e., adjust) the road condition states of all the actual measured road segments in the field 13 where the actual measured road segment 11 is located to be in the congestion state, and the distribution of the road condition states of the actual measured road segments after adjustment is shown in fig. 5. Wherein, the distance between the actual measurement road section in the field 13 and the actual measurement road section 11 is less than the preset distance. As can be seen from fig. 4, before the road condition of the actual measurement road section in the field 13 is adjusted, the field 13 includes both the actual measurement road section whose road condition is the slow traveling state and the actual measurement road section whose road condition is the congestion state. That is, the actual measurement road sections in the slow driving state and the congestion state overlap each other in the field 13, and it is difficult to clearly determine the boundary between the actual measurement road sections in different road conditions. As can be seen from fig. 5, after the road condition status of the actual measurement road section in the field 13 is adjusted, the road condition statuses of the actual measurement road sections in the field 13 are all congestion states, and there are obvious boundaries between the actual measurement road section in the field 13 and the actual measurement road sections in different road condition statuses in other fields. Therefore, the adjusted attribute information of the actual measurement road section can be matched with the attribute information of the actual road section, and further, the road condition state of the road section to be predicted can be accurately determined according to the adjusted attribute information of the actual measurement road section. Therefore, the problem that the congested road section boundary is judged as the slow-moving road section by mistake can be solved by adjusting the road condition state of the actually-measured road section according to the road condition state of the standard road section.
For another example, the distribution of the road conditions of the actual measurement road sections is as shown in fig. 6, a hexagon star in the figure indicates that the road conditions of the standard road section are slow-moving conditions, the representation of the road conditions of the actual measurement road sections is the same as that in fig. 3, and the road conditions of the actual measurement road sections and the standard road section are the same as the road conditions of the same time slot. If it is determined that the characteristic attribute of the measured road section 14 matches the characteristic attribute of the standard road section 15 and the road condition status of the standard road section 15 is in the slow moving state, the electronic device may infect (i.e., adjust) the road condition statuses of all measured road sections in the field 16 where the measured road section 14 is located to be in the slow moving state, and the distribution of the road condition statuses of the measured road sections after adjustment is shown in fig. 7. Wherein the distance between the measured road section in the field 16 and the measured road section 15 is less than the preset distance. As can be seen from fig. 6, before the road condition of the actual measurement road section in the field 16 is adjusted, the field 16 includes both the actual measurement road section whose road condition is in the congestion state and the actual measurement road section whose road condition is in the slow traveling state. That is, the actual measurement road sections in the slow driving state and the congestion state overlap each other in the field 16, and it is difficult to clearly determine the boundary between the actual measurement road sections in different road conditions. As can be seen from fig. 7, after the road condition of the actual measurement road section in the field 16 is adjusted, the road condition of the actual measurement road section in the field 16 is a slow moving state, and there is an obvious boundary between the actual measurement road section in the field 16 and the actual measurement road sections in different road condition states in other fields. Therefore, the adjusted attribute information of the actual measurement road section can be matched with the attribute information of the actual road section, and further, the road condition state of the road section to be predicted can be accurately determined according to the adjusted attribute information of the actual measurement road section. Therefore, the problem that the boundary of the slow road section is judged to be the congested road section by mistake can be solved by adjusting the road condition state of the actually measured road section according to the road condition state of the standard road section.
For another example, the distribution of the road condition of the actual measurement road section is as shown in fig. 8, the seven-pointed star in the figure indicates that the road condition of the standard road section is a smooth state, the representation of the road condition of the actual measurement road section is the same as that in fig. 3, and the road condition of each of the actual measurement road section and the standard road section is the same as that of the same time slot. If it is determined that the characteristic attribute of the measured road section 17 matches the characteristic attribute of the standard road section 18 and the road condition of the standard road section 18 is in a smooth state, the electronic device may infect (i.e., adjust) the road condition states of all measured road sections in the field 19 where the measured road section 17 is located to be in a smooth state, and the distribution of the road condition states of the adjusted measured road sections is shown in fig. 9. Wherein the distance between the measured road section in the field 19 and the measured road section 17 is less than the preset distance. As can be seen from fig. 8, before the road condition of the actual measurement road section in the field 19 is adjusted, the field 19 includes the actual measurement road section in which the road condition is the congestion state, the actual measurement road section in which the road condition is the slow traveling state, and the actual measurement road section in which the road condition is the smooth state. That is, the actual measurement road sections in the slow running state, the congestion state and the smooth state in the field 16 are overlapped with each other, so that it is difficult to clearly determine the boundary between the actual measurement road sections in different road conditions. As can be seen from fig. 9, after the road condition of the actual measurement road section in the field 19 is adjusted, the road condition of the actual measurement road section in the field 19 is all smooth, and there is an obvious boundary between the actual measurement road section in the field 19 and the actual measurement road sections in different road condition states in other fields. Therefore, the adjusted attribute information of the actual measurement road section can be matched with the attribute information of the actual road section, and further, the road condition state of the road section to be predicted can be accurately determined according to the adjusted attribute information of the actual measurement road section. Therefore, the problem that the smooth road section boundary is judged to be the congested road section and/or the slow road section by mistake can be solved by adjusting the road condition state of the actually measured road section according to the road condition state of the standard road section.
In one embodiment, as shown in fig. 3, after the electronic device performs step S203, S3 may be further performed as follows.
s3, judging the standard road section BiAnd if the measured road section is the last standard road section in the standard road section set, executing the step S204, otherwise, increasing 1 by the step i to obtain the measured road section with the characteristic attribute associated with the characteristic attribute of the next standard road section.
And S204, performing optimization training on the prediction model according to the adjusted attribute information of the actual measurement road section to obtain an optimized prediction model.
The electronic device may obtain a prediction model for predicting a road condition of the road segment, where the prediction model may be a machine learning model or a deep neural network model, and further, in order to improve the prediction accuracy of the prediction model, the electronic device may perform optimization training on the prediction model according to the adjusted attribute information of the actually measured road segment to obtain an optimized prediction model. Specifically, the electronic device may perform iterative training on the prediction model by using the characteristic attribute included in the adjusted attribute information of the actual measurement road section as an input and using the road condition state included in the adjusted attribute information of the actual measurement road section as a training target, and when the output of the prediction model is in a stable state or the output road condition state of the prediction model is the same as or similar to the road condition state included in the adjusted attribute information of the actual measurement road section, it indicates that the prediction accuracy of the prediction model is high, and the iterative training may be ended to obtain the optimized prediction model.
And S205, acquiring the characteristic attribute of the road section to be predicted.
And S206, inputting the characteristic attribute of the road section to be predicted into the optimized prediction model for prediction processing to obtain the road condition state of the road section to be predicted.
In step S205 and step S206, the electronic device may determine the characteristic attribute of the road segment to be predicted through the driving environment image of the vehicle driving on the predicted road segment, and input the characteristic attribute of the road segment to be predicted into the optimized prediction model for prediction processing, so as to obtain the road condition status of the road segment to be predicted.
In this application, because the accuracy of the road condition state of the standard road section is higher, therefore, the road condition state of the target actual measurement road section is adjusted through the road condition state of the standard road section, the attribute information of the actual measurement road section after adjustment is obtained, and the accuracy of the road condition state of the actual measurement road section can be improved, namely, the accuracy of the attribute information of the actual measurement road section after adjustment is higher. Further, since the accuracy of the sample data is one of the key factors affecting the prediction accuracy of the prediction model, the optimized prediction model is obtained by performing optimization training on the prediction model by using the adjusted attribute information of the actually measured road section as the sample data of the prediction model, and the prediction accuracy of the prediction model can be improved, that is, the prediction accuracy of the optimized prediction model is higher. The road condition state of the road section to be predicted is determined by adopting the optimized prediction model, so that the accuracy of determining the road condition state of the road section can be improved.
An embodiment of the present application provides a data processing apparatus, which may be a computer program in an electronic device, for example, an application program in the electronic device, such as a navigation application program or a background service program corresponding to the navigation application program. Referring to fig. 10, the apparatus includes:
the obtaining module 110 is configured to obtain an actually measured road segment set, a standard road segment set, attribute information of an actually measured road segment, and attribute information of a standard road segment, where the actually measured road segment set includes the actually measured road segment, the standard road segment includes the standard road segment, and the attribute information includes a characteristic attribute and a road condition state.
A determining module 111, configured to determine a target measured road segment from the set of measured road segments, where a characteristic attribute of the target measured road segment is associated with a characteristic attribute of the standard road segment.
The adjusting module 112 is configured to adjust the road condition state of the target actual measurement road section according to the road condition state of the standard road section, so as to obtain the attribute information of the adjusted actual measurement road section.
And the prediction module 113 is configured to determine a road condition state of the road section to be predicted according to the adjusted attribute information of the actually measured road section.
Optionally, the obtaining module 110 is specifically configured to obtain a driving environment image and driving track data of a vehicle driving on the actually measured road section; determining the characteristic attribute of the actually measured road section according to the driving environment image; and determining the road condition state of the actually measured road section according to the driving track data.
Optionally, the obtaining module 110 is specifically configured to determine a driving speed of the vehicle according to the driving track data; if the running speed is less than a first speed threshold value, determining that the road condition state of the actually measured road section is a congestion state; if the running speed is greater than or equal to a first speed threshold and less than a second speed threshold, determining that the road condition state of the actually measured road section is a slow running state, wherein the second speed threshold is greater than the first speed threshold; and if the running speed is greater than or equal to the second speed threshold value, determining that the road condition state of the actually measured road section is a smooth state.
Optionally, the obtaining module 110 is specifically configured to receive multiple pieces of labeling information for the standard road segment, where the labeling information includes a characteristic attribute of the standard road segment and a road condition state of the standard road segment; acquiring the occurrence frequency of each piece of labeling information in the plurality of pieces of labeling information; and determining the marking information with the occurrence frequency larger than the preset frequency in the plurality of pieces of marking information as the attribute information of the standard road section.
Optionally, the determining module 111 is specifically configured to determine, from the actually measured road segment set, a first actually measured road segment whose characteristic attribute matches the characteristic attribute of the standard road segment; acquiring a second measured road section of which the distance from the first measured road section is smaller than a preset distance from the measured road section; and determining the first measured road section and the second measured road section as a target measured road section.
Optionally, the prediction module 113 specifically performs optimization training on the prediction model according to the adjusted attribute information of the actually measured road segment to obtain an optimized prediction model; acquiring the characteristic attribute of the road section to be predicted;
and inputting the characteristic attribute of the road section to be predicted into the optimized prediction model for prediction processing to obtain the road condition state of the road section to be predicted.
Optionally, the characteristic attribute includes basic physical information and timely spatial distribution information; the basic physical information comprises at least one of road section grade, length of the road section, driving speed of vehicles on the road section, free flow speed, distance between the road section and a traffic light, number of lanes on the road section or number of vehicles on the road section; the space-time distribution information comprises distribution data of road condition states of road sections in a preset time period.
In this application, because the accuracy of the road condition state of the standard road section is higher, therefore, the road condition state of the target actual measurement road section is adjusted through the road condition state of the standard road section, the attribute information of the actual measurement road section after adjustment is obtained, and the accuracy of the road condition state of the actual measurement road section can be improved, namely, the accuracy of the attribute information of the actual measurement road section after adjustment is higher. Furthermore, the road condition state of the road section to be predicted is determined through the adjusted attribute information of the actual measurement road section, and the accuracy of determining the road condition state of the road section to be predicted can be improved.
An embodiment of the present application provides an electronic device, please refer to fig. 11, where the electronic device includes: the processor 151, the user interface 152, the network interface 154, and the memory 155 are connected by a bus 153.
A user interface 152 for enabling human-computer interaction, which may include a display screen or a keyboard, among others. And a network interface 154 for communication connection with an external device. A memory 155 is coupled to the processor 151 for storing various software programs and/or sets of instructions. In particular implementations, memory 155 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid state storage devices. The memory 155 may store an operating system (hereinafter referred to simply as a system), such as an embedded operating system like ANDROID, IOS, WINDOWS, or LINUX. The memory 155 may also store a network communication program that may be used to communicate with one or more additional devices, one or more terminal devices, one or more network devices. The memory 155 may further store a user interface program, which may vividly display the content of the application program through a graphical operation interface, and receive a user's control operation of the application program through input controls such as menus, dialog boxes, and buttons. The memory 155 may also store one or more application programs.
In one embodiment, the memory 155 may be used to store one or more instructions; the processor 151 may be capable of executing a data processing method when invoking the one or more instructions, and specifically, the processor 151 invokes the one or more instructions to perform the following steps:
acquiring an actually measured road section set, a standard road section set, attribute information of an actually measured road section and attribute information of a standard road section, wherein the actually measured road section set comprises the actually measured road section, the standard road section comprises the standard road section, and the attribute information comprises characteristic attributes and a road condition state;
determining a target measured road section from the measured road section set, wherein the characteristic attribute of the target measured road section is associated with the characteristic attribute of the standard road section;
adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section;
and determining the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section.
Optionally, the processor calls an instruction to perform the following steps:
acquiring a driving environment image and driving track data of a vehicle driving on the actually measured road section;
determining the characteristic attribute of the actually measured road section according to the driving environment image;
and determining the road condition state of the actually measured road section according to the driving track data.
Optionally, the processor calls an instruction to perform the following steps:
determining the running speed of the vehicle according to the running track data;
if the running speed is less than a first speed threshold value, determining that the road condition state of the actually measured road section is a congestion state;
if the running speed is greater than or equal to a first speed threshold and less than a second speed threshold, determining that the road condition state of the actually measured road section is a slow running state, wherein the second speed threshold is greater than the first speed threshold;
and if the running speed is greater than or equal to the second speed threshold value, determining that the road condition state of the actually measured road section is a smooth state.
Optionally, the processor calls an instruction to perform the following steps:
receiving a plurality of pieces of marking information of the standard road section, wherein the marking information comprises characteristic attributes of the standard road section and road condition states of the standard road section;
acquiring the occurrence frequency of each piece of labeling information in the plurality of pieces of labeling information;
and determining the marking information with the occurrence frequency larger than the preset frequency in the plurality of pieces of marking information as the attribute information of the standard road section.
Optionally, the processor calls an instruction to perform the following steps:
determining a first measured road section with characteristic attributes matched with the characteristic attributes of the standard road section from the measured road section set;
acquiring a second measured road section of which the distance from the first measured road section is smaller than a preset distance from the measured road section;
and determining the first measured road section and the second measured road section as a target measured road section.
Optionally, the processor calls an instruction to perform the following steps:
performing optimization training on a prediction model according to the adjusted attribute information of the actually measured road section to obtain an optimized prediction model;
acquiring the characteristic attribute of the road section to be predicted;
and inputting the characteristic attribute of the road section to be predicted into the optimized prediction model for prediction processing to obtain the road condition state of the road section to be predicted.
Optionally, the characteristic attribute includes basic physical information and timely spatial distribution information; the basic physical information comprises at least one of road section grade, length of the road section, driving speed of vehicles on the road section, free flow speed, distance between the road section and a traffic light, number of lanes on the road section or number of vehicles on the road section; the space-time distribution information comprises distribution data of road condition states of road sections in a preset time period.
In this application, because the accuracy of the road condition state of the standard road section is higher, therefore, the road condition state of the target actual measurement road section is adjusted through the road condition state of the standard road section, the attribute information of the actual measurement road section after adjustment is obtained, and the accuracy of the road condition state of the actual measurement road section can be improved, namely, the accuracy of the attribute information of the actual measurement road section after adjustment is higher. Furthermore, the road condition state of the road section to be predicted is determined through the adjusted attribute information of the actual measurement road section, and the accuracy of determining the road condition state of the road section to be predicted can be improved.
Embodiments and advantageous effects of the program for solving the problems may refer to the embodiments and advantageous effects of the data processing method described in fig. 1 and fig. 2, and repeated parts are not described again.
The above disclosure is only a few examples of the present application, and certainly should not be taken as limiting the scope of the present application, which is therefore intended to cover all modifications that are within the scope of the present application and which are equivalent to the claims.
Claims (9)
1. A method of data processing, the method comprising:
acquiring an actually measured road section set, a standard road section set, attribute information of the actually measured road section and attribute information of the standard road section, wherein the actually measured road section set comprises the actually measured road section, the standard road section set comprises the standard road section, and the attribute information comprises characteristic attributes and a road condition state;
determining a target measured road section from the measured road section set, wherein the characteristic attribute of the target measured road section is associated with the characteristic attribute of the standard road section;
adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section; the number of the road sections of the target actual measurement road section is greater than that of the standard road section;
determining the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section, wherein the determining step comprises the following steps:
performing optimization training on a prediction model according to the adjusted attribute information of the actually measured road section to obtain an optimized prediction model;
acquiring the characteristic attribute of the road section to be predicted;
and inputting the characteristic attribute of the road section to be predicted into the optimized prediction model for prediction processing to obtain the road condition state of the road section to be predicted.
2. The method of claim 1, wherein the obtaining attribute information for the measured road segment comprises:
acquiring a driving environment image and driving track data of a vehicle driving on the actually measured road section;
determining the characteristic attribute of the actually measured road section according to the driving environment image;
and determining the road condition state of the actually measured road section according to the driving track data.
3. The method of claim 2, wherein determining the road condition status of the measured road segment from the travel track data comprises:
determining the running speed of the vehicle according to the running track data;
if the running speed is less than a first speed threshold value, determining that the road condition state of the actually measured road section is a congestion state;
if the running speed is greater than or equal to a first speed threshold and less than a second speed threshold, determining that the road condition state of the actually measured road section is a slow running state, wherein the second speed threshold is greater than the first speed threshold;
and if the running speed is greater than or equal to the second speed threshold value, determining that the road condition state of the actually measured road section is a smooth state.
4. The method according to claim 1, wherein the obtaining of the attribute information of the standard road segment includes:
receiving a plurality of pieces of marking information of the standard road section, wherein the marking information comprises characteristic attributes of the standard road section and road condition states of the standard road section;
acquiring the occurrence frequency of each piece of labeling information in the plurality of pieces of labeling information;
and determining the marking information with the occurrence frequency larger than the preset frequency in the plurality of pieces of marking information as the attribute information of the standard road section.
5. The method of claim 1, wherein said determining a target measured road segment from said set of measured road segments comprises:
determining a first measured road section with characteristic attributes matched with the characteristic attributes of the standard road section from the measured road section set;
acquiring a second measured road section of which the distance from the first measured road section is smaller than a preset distance from the measured road section;
and determining the first measured road section and the second measured road section as a target measured road section.
6. The method of any one of claims 1-5, wherein the characteristic attributes include basic physical information and temporal distribution information; the basic physical information comprises at least one of road section grade, length of the road section, driving speed of vehicles on the road section, free flow speed, distance between the road section and a traffic light, number of lanes on the road section or number of vehicles on the road section; the space-time distribution information comprises distribution data of road condition states of road sections in a preset time period.
7. A data processing apparatus, characterized in that the apparatus comprises:
an obtaining module, configured to obtain an actually measured road segment set, a standard road segment set, attribute information of the actually measured road segment, and attribute information of the standard road segment, where the actually measured road segment set includes the actually measured road segment, the standard road segment set includes the standard road segment, and the attribute information includes a characteristic attribute and a road condition state
The determining module is used for determining a target actual measurement road section from the actual measurement road section set, and the characteristic attribute of the target actual measurement road section is associated with the characteristic attribute of the standard road section;
the adjusting module is used for adjusting the road condition state of the target actual measurement road section according to the road condition state of the standard road section to obtain the attribute information of the adjusted actual measurement road section; the number of the road sections of the target actual measurement road section is greater than that of the standard road section;
the prediction module is used for determining the road condition state of the road section to be predicted according to the adjusted attribute information of the actual measurement road section, and comprises the following steps:
performing optimization training on a prediction model according to the adjusted attribute information of the actually measured road section to obtain an optimized prediction model;
acquiring the characteristic attribute of the road section to be predicted;
and inputting the characteristic attribute of the road section to be predicted into the optimized prediction model for prediction processing to obtain the road condition state of the road section to be predicted.
8. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, characterized in that the processor implements the data processing method according to any of claims 1-6 when executing the program.
9. A computer storage medium having stored thereon one or more instructions adapted to be loaded by a processor and to perform a data processing method according to any of claims 1-6.
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