CN111815946A

CN111815946A - Method and device for determining abnormal road section, storage medium and electronic equipment

Info

Publication number: CN111815946A
Application number: CN202010307581.9A
Authority: CN
Inventors: 刘勇; 张彦龙; 任化伟
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2020-04-17
Filing date: 2020-04-17
Publication date: 2020-10-23

Abstract

The embodiment of the disclosure discloses a method, a device, a storage medium and an electronic device for determining an abnormal road section, wherein the method comprises the following steps: determining a plurality of road sections which are sequentially connected according to the planned track, and acquiring flow data of each road section within preset time; determining candidate abnormal road sections based on the flow data of each road section; determining the degree of abnormality of the candidate abnormal road section, and determining the abnormal road section based on the degree of abnormality. According to the method and the device, the candidate abnormal road sections in the road network can be quickly positioned and the abnormal degree of the candidate abnormal road sections can be marked according to the actual travel behaviors of the user and the traffic data which changes through topology and the like, so that the user can obtain the abnormal road section information of the road network, the user can select the route more accurately, and the traffic efficiency of the traffic network is improved.

Description

Method and device for determining abnormal road section, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of traffic information processing technologies, and in particular, to a method and an apparatus for determining an abnormal road segment, a storage medium, and an electronic device.

Background

The analysis and identification of the traffic reported information are an important means for solving the map road network problem, after the driver end and the passenger end report the traffic reported information to the map background through the mobile phone App, the road network problem (such as construction, road sealing, traffic regulation and the like) which is used as a temporary event needs to be processed, and because the traffic reported information quantity of each day is very large, the traffic problem which the driver and the passenger meet at that time can not be determined by the manual analysis and identification mode, and the data modification on the map aiming at the problem can not be carried out, so that other people can not meet the same problem.

The analysis of the traffic reported information is still in a blank stage at the present stage, and most people can use a method of supervised learning classification or identify an abnormal road section by simply monitoring the change abnormality of the traffic flow. However, if a supervised learning method is used, the labeling cost is often too high (generally, the labeling is guessed by browsing a track, so that certain precision is lost, and the labeling requires a lot of reference data, so that the labeling difficulty is increased), and if the flow monitoring method is used for judging, the fluctuation is often large on short-interval flow, for example, 10min interval flow, so that the overall accuracy is not high.

Disclosure of Invention

In view of this, the present disclosure provides a method, an apparatus, a storage medium, and an electronic device for determining an abnormal road segment, so as to solve the problem in the prior art that the abnormal road segment cannot be determined by analyzing the reported traffic information effectively and accurately.

In one aspect, an embodiment of the present disclosure provides a method for determining an abnormal road segment, including the following steps: determining a plurality of road sections which are sequentially connected according to the planned track, and acquiring flow data of each road section within preset time; determining candidate abnormal road sections based on the flow data of each road section; determining the degree of abnormality of the candidate abnormal road section, and determining the abnormal road section based on the degree of abnormality.

In some embodiments, said determining a candidate abnormal road segment based on the flow data of each said road segment comprises: dividing the traffic data according to a preset time interval to construct a topological traffic matrix; processing the topological traffic matrix according to a first preset mode to obtain a classification matrix and a characteristic value of the classification matrix; and determining the candidate abnormal road sections based on the characteristic values of the classification matrix.

In some embodiments, the determining the candidate abnormal road segment based on the feature values of the classification matrix includes: according to the sequence of the road sections, dividing the classification matrix according to a second preset mode, and determining a plurality of intermediate matrixes and corresponding characteristic values; and determining the candidate abnormal road section based on the intermediate matrix with the maximum characteristic value.

In some embodiments, the determining the degree of abnormality of the candidate abnormal section, the determining the abnormal section based on the degree of abnormality, includes: dividing the flow data of the candidate abnormal road section into a normal flow data set and an abnormal flow data set; respectively determining a normal flow average value and an abnormal flow average value in the normal flow data set and the abnormal flow data set; determining an abnormality degree based on the normal flow average value and the abnormal flow average value; and judging whether the abnormality degree is greater than a preset threshold value or not, and determining an abnormal road section under the condition that the abnormality degree is greater than the preset threshold value.

In another aspect, an embodiment of the present disclosure provides an apparatus for determining an abnormal road segment, including: the acquisition module is used for determining a plurality of road sections which are sequentially connected according to the planned track and acquiring flow data of each road section in preset time; a first determination module for determining candidate abnormal road segments based on the flow data of each road segment; and the second determination module is used for determining the abnormality degree of the candidate abnormal road section and determining the abnormal road section based on the abnormality degree.

In some embodiments, the first determining module comprises: the construction unit is used for dividing the traffic data according to a preset time interval and constructing a topological traffic matrix; the acquisition unit is used for processing the topological traffic matrix according to a first preset mode and acquiring a classification matrix and a characteristic value of the classification matrix; a first determination unit for determining the candidate abnormal section based on the eigenvalue of the classification matrix.

In some embodiments, the first determination unit comprises: a first determining subunit, configured to divide the classification matrix according to a second predetermined manner according to the sequence of the road segments, and determine a plurality of intermediate matrices and corresponding feature values; a second determining subunit, configured to determine the candidate abnormal road segment based on the intermediate matrix with the largest feature value.

In some embodiments, the second determining module comprises: a dividing unit, configured to divide the traffic data of the candidate abnormal section into a normal traffic data set and an abnormal traffic data set; a second determination unit configured to determine a normal flow average value and an abnormal flow average value in the normal flow data set and the abnormal flow data set, respectively; a third determination unit for determining an abnormality degree based on the normal flow rate average value and the abnormal flow rate average value; and the reporting unit is used for judging whether the abnormality degree is greater than a preset threshold value or not, and determining an abnormal road section under the condition that the abnormality degree is greater than the preset threshold value.

In another aspect, an embodiment of the present disclosure provides a storage medium storing a computer program, where the computer program is executed by a processor to implement the steps of the method in any one of the above technical solutions.

In another aspect, an embodiment of the present disclosure provides an electronic device, which at least includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method in any one of the above technical solutions when executing the computer program on the memory.

The method comprises the steps of obtaining flow data of a plurality of road sections which are connected in sequence in a planned track within preset time. The candidate abnormal road sections are determined based on the flow data of each road section, and the abnormal road sections are determined by determining the abnormal degree of the candidate abnormal road sections, so that the candidate abnormal road sections in the road network can be quickly positioned and the abnormal degree of the candidate abnormal road sections can be marked according to the actual travel behaviors of the user and the flow data which changes through topology and the like without any mark amount, the user can obtain the abnormal road section information of the road network, the selection of the user for the route is more accurate, and the traffic efficiency of the traffic network is improved.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of a determination method provided in a first embodiment of the present disclosure;

fig. 2 is a schematic diagram illustrating a dividing manner of a planned trajectory in a first embodiment of the present disclosure;

fig. 3 is a flowchart of a determination method according to a first embodiment of the disclosure;

fig. 4 is a schematic diagram of a topological traffic matrix in a first embodiment of the present disclosure;

FIG. 5 is a schematic diagram of a classification matrix according to a first embodiment of the disclosure;

fig. 6 is a flowchart of a determination method according to a first embodiment of the disclosure;

fig. 7 is a flowchart of a determination method according to a first embodiment of the disclosure;

FIG. 8 is a schematic diagram of determining a cut point according to a first embodiment of the disclosure;

fig. 9 is a block diagram of a determining apparatus according to a second embodiment of the present disclosure;

fig. 10 is a block diagram of an electronic device according to a fourth embodiment of the present disclosure.

Reference numerals:

10-an acquisition module; 20-a first determination module; 30-a second determination module.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described below clearly and completely with reference to the accompanying drawings of the embodiments of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are within the scope of protection of the disclosure.

Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.

To maintain the following description of the embodiments of the present disclosure clear and concise, a detailed description of known functions and known components have been omitted from the present disclosure.

A first aspect of the present disclosure relates to a method for determining an abnormal road segment, which can be used for a user to request a car booking or a car calling service by using car booking and car calling software, so that, when a road network problem serving as a temporary event, such as construction, road closure, traffic regulation and the like, is encountered during a process of taking a car to reach a destination, the abnormal road segment causing the temporary event can be effectively avoided, and the method for determining the abnormal road segment is suitable for a platform or a system for vehicle management to detect and operate the road network traffic condition in real time, as shown in fig. 1, in a specific manner, see the following steps:

s101, determining a plurality of road sections which are sequentially connected according to the planned track, and acquiring flow data of each road section in preset time.

In this step, after a user inputs a starting point position a and an end point position B using a navigation App in a mobile terminal such as a mobile phone or a car booking or calling App with a navigation function, a navigation module in the mobile terminal is called to generate and output a planning track by using a navigation track algorithm, where the starting point position a may be a current position of the user, or a position dragged by the user on a map or determined by inputting, the end point position B may be a position input by the user, or a position dragged by the user on the map and determined, and the navigation track algorithm may be any navigation track algorithm in the prior art. The planned trajectory obtained through calculation is a continuous trajectory from the starting point position a to the end point position B, the continuous trajectory being formed by a plurality of road segments connected in sequence, and the user can reach the end point position B from the starting point position a based on the planned trajectory.

However, after the user starts from the departure point position a, the actual track completed by actually walking, riding or driving may be different from the planned track for some reasons, for example, the user may have to select and reach the destination point position B through other road segments due to the fact that a road block, traffic control, and the like occur in a plurality of road segments of the planned track, and thus, the actual track and the planned track may partially overlap and partially do not overlap. As shown in fig. 2, the planned trajectory m generated by the mobile terminal is from a departure point position a to a destination point position B, and includes a plurality of consecutive road segments such as link0, link1, link2, and the actual trajectory n actually completed by the user may have a partial road segment coinciding with a road segment in the planned trajectory m, but a yaw situation may occur in a certain road segment due to road closure, traffic control, and the like, and a yaw position is formed at a road segment link x, where the yaw position refers to a position where a divergence occurs between the actual trajectory and the planned trajectory of the user, so as to generate a yaw segment plan, and the yaw plan refers to a portion left in the original planned trajectory and not completed after the user generates the yaw position. And the user starts to select other road sections from link x to reach the end position B, and the situation that the road sections are partially not overlapped with the planned track occurs.

Since the planned trajectory is set as a combination of a plurality of continuous road segments in the forming and outputting process, in order to accurately determine which road segment the yaw position link x appears on, so as to enable more users to know the road condition in advance by prompting on App of the mobile terminal, and enable the mobile terminal to consider the conditions of road closure and traffic control when planning the route, it is necessary to acquire flow data of each road segment in the planned trajectory within a predetermined time, where the flow data may be obtained from data of a large number of users moving or traveling from a departure point position a to a destination point B within a predetermined time range, where a and B may be a departure point position and a destination point position of the user's movement, or may be two passing positions or process positions in the route that the user desires to complete, where the flow data may be real data, such as the number of people actually passing through each road segment or the number of vehicles, or normalized data.

And S102, determining candidate abnormal road sections based on the flow data of each road section.

After the step S101 is performed, a plurality of road segments connected in sequence are determined according to the planned trajectory of the user, and the flow data of each road segment in the predetermined time range is obtained, it is considered that if a certain road segment in the planned trajectory fails to pass due to reasons such as road closure, traffic control, and the like, the flow data of the road segment further changes greatly, and therefore, based on the flow data, by analyzing the change of the flow data, the yaw position existing in the planned trajectory and the candidate abnormal road segment in the planned trajectory, that is, the road segment in which the conditions such as road closure, traffic control, and the like may exist can be determined based on the change degree of the flow data, so that the latest traffic and road segment information can be timely and accurately obtained, so that the navigation module can reasonably plan the trajectory and the user can select the optimal route. As shown in fig. 3, the method specifically includes the following steps:

s201, dividing the traffic data according to a preset time interval, and constructing a topological traffic matrix.

After acquiring the flow rate data of each link within the predetermined time range through the step S101, in this step, the flow data of each road section is divided according to a preset time interval, wherein the time interval can be set according to requirements, the traffic data for each road segment may be divided, for example, at intervals of every 10 minutes, to construct a topological traffic matrix, which may be shown in figure 4, wherein the vertical coordinate direction of the topological flow matrix is the sequence number of the road sections in the planning track, for example, link0, link1, link2 … … are arranged in this order from the starting point position a to the end point position B, the abscissa is the time at 10 minute intervals, the data corresponding to each abscissa and ordinate is the flow data on the road section corresponding to the ordinate between the time and the previous time.

S202, processing the topological traffic matrix according to a first preset mode, and acquiring a classification matrix and a characteristic value of the classification matrix.

After the topological traffic matrix is constructed through step S201, the traffic data of each road segment in different time intervals can be displayed, in order to more obviously see the change of the traffic data of different road sections along with the time so as to obviously distinguish the positions of the candidate abnormal road sections and the moments when the candidate abnormal road sections appear, the traffic data in the topological traffic matrix is classified and converted according to a first preset mode, to perform a preliminary screening on the flow data, for example, a value above a certain threshold may be converted into 1, a value below a certain threshold may be converted into 0, to obtain a classification matrix, for example, the threshold may be set to 0, thus converting traffic data above 0 to 1, the remaining traffic data is converted to 0, and thus, the above-described topological traffic matrix can be converted to a classification matrix, as shown in fig. 5, of course, the first predetermined manner is not limited thereto, and other manners capable of classifying and filtering data may be adopted.

After the classification matrix is obtained in a first predetermined manner, an eigenvalue of the classification matrix is further obtained, where the eigenvalue is used to obtain an abnormal degree between different road segments existing in the flow data of different road segments at a certain time interval, where the eigenvalue may be represented by an entropy value, and the entropy value may be used to measure a confusion degree of original distribution of data, where the smaller the entropy value, the lower the confusion degree, where the entropy value is calculated as follows:

based on the classification matrix and the traffic data therein, it can be known that the different road segments have 77 traffic values in different time intervals, and 22 of the traffic values are converted into 1 and 55 traffic values are converted into 0 by performing classification conversion according to a first predetermined manner, so that the entropy value of the classification matrix can be calculated by:

and S203, determining candidate abnormal road sections based on the characteristic values of the classification matrix.

Considering that the classification matrix is a further process and simplification for the topological traffic matrix, the basic attribute of each traffic data can be obviously obtained by the classification matrix, and furthermore, the eigenvalue has the effect that by acquiring the degree of abnormality between different road sections existing in the traffic data of different road sections at certain time intervals, the range of the candidate abnormal road section can be quickly determined by determining the eigenvalue of the classification matrix, as shown in fig. 6, specifically including the following steps:

s301, dividing the classification matrix according to a second preset mode according to the sequence of the road sections, and determining a plurality of intermediate matrices and corresponding characteristic values.

Through the step S202, after the topological traffic matrix is processed according to the first predetermined manner, and the classification matrix and the eigenvalue of the classification matrix are obtained, the number of the road segments related to the classification matrix is often large, the data volume of the traffic data is very large, the traffic data change of each road segment in a predetermined time is complex, and in order to be able to more accurately confirm the road segment in which an abnormal condition occurs, in this step, the classification matrix may be divided according to the number sequence of the road segments and according to the second predetermined manner to obtain a plurality of intermediate matrices, and then a plurality of intermediate matrices and corresponding eigenvalues are determined, so that the huge classification matrix is gradually reduced to each intermediate matrix, and a candidate abnormal road segment is accurately determined through the eigenvalues of different intermediate matrices.

The second predetermined manner may be that, first, the classification matrix is divided into a plurality of classification matrices according to the link number sequence of link0, link1, link2.. said.. in the planned trajectory, for example, the traffic data of link0-link2 forms a first matrix, the traffic data of link3-link5 forms a second matrix, and the traffic data of link6-link8 forms a third matrix, where all the data in the first matrix may be 1, the data in the second matrix may have 1 and 0, and all the data in the third matrix may be 0, so that the situation of the candidate abnormal road segment may occur in the road segment related to the second matrix, and generally, the finer the classification matrix is divided in the second predetermined manner, the more accurately the candidate abnormal road segment can be obtained. Therefore, the range of road section screening can be reduced by dividing the classification matrix, even under the condition that the number of road sections is large or the flow data change is complex, the analysis of the candidate abnormal road sections can be rapidly concentrated into a plurality of road sections, and the accuracy and the timeliness of the candidate abnormal road section confirmation are improved.

After the classification matrixes are divided according to a second preset mode and a plurality of intermediate matrixes are determined, in order to quickly find the intermediate matrixes possibly having the candidate abnormal road sections, the eigenvalue corresponding to each intermediate matrix is respectively calculated, wherein the eigenvalue can be an entropy value, and the intermediate matrixes having the candidate abnormal road sections can be quickly determined by analyzing the eigenvalue of each intermediate matrix.

S302, determining candidate abnormal road sections based on the intermediate matrix with the maximum characteristic value.

After the eigenvalue of each intermediate matrix is obtained in step S301, in this step, the eigenvalues of the intermediate matrices are compared or sorted, and when entropy is used as the eigenvalue, and considering that entropy can represent the degree of confusion of each intermediate matrix, the degree of confusion of each intermediate matrix can be determined by comparing the entropy of each intermediate matrix, and the degree of confusion, that is, the intermediate matrix with the largest entropy can represent that the degree of change of traffic data of the related road segment is large, that is, it means that a candidate abnormal road segment will appear in the road segment related to the intermediate matrix, so that a candidate abnormal road segment can be determined.

And S103, determining the degree of abnormality of the candidate abnormal road section, and determining the abnormal road section based on the degree of abnormality.

After determining the candidate abnormal road segments based on the flow data of each road segment through step S102, in this step, the abnormality degree of each candidate abnormal road segment is determined to further determine the abnormal road segment based on the abnormality degree of each candidate abnormal road segment, where the abnormal road segment may be the abnormal road segment that needs to be reported to the navigation module finally, the abnormal road segment that is reported finally will be considered when the navigation module of the mobile terminal performs track planning, and the user will be prompted or displayed on the App of the mobile terminal for the user to know and for the user to select a suitable other road segment based on the information of the abnormal road segment. Specifically, as shown in fig. 7, the method includes the following steps:

s401, dividing the flow data of the candidate abnormal road section into a normal flow data set and an abnormal flow data set.

The traffic data in the candidate abnormal road section has a large change degree along with the change of time, and certainly, the candidate abnormal road section can be normally used for users to pass through within a certain time, and the traffic data cannot pass through due to reasons such as road closing, traffic control and the like after a certain time, so that the traffic data has a large change after a certain time. In order to determine the time when the abnormal condition occurs in the candidate abnormal road segment and the degree of the abnormal condition, the one-dimensional matrix represented by the traffic data of the candidate abnormal road segment is entropy-cut or divided to obtain an optimal cut point so as to divide the traffic data into normal traffic data and abnormal traffic data, and thus to divide the normal traffic data set and the abnormal traffic data set into a specific one, after the traffic data of the determined candidate abnormal road segment is extracted, there may be several traffic data located earlier on the left side, and after a certain time, there is an abnormal condition on the right side, so that the traffic data greater than 0 on the left side is divided into the normal traffic data set, and the other traffic data on the right side is divided into the abnormal traffic data set, for example, according to the determined candidate abnormal road segment (link X), the flow rates are listed as 33,27,22,19,29,12,20,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1, and the one-dimensional matrix is also entropy sliced to obtain the optimal cut points, as shown in fig. 8, i.e., the optimal cut points are between 3 and 0.

S402, respectively determining a normal flow average value and an abnormal flow average value in the normal flow data set and the abnormal flow data set.

After the traffic data of the candidate abnormal section is divided into the normal traffic data set and the abnormal traffic data set, the normal traffic average value of the normal traffic data set and the abnormal traffic average value of the abnormal traffic data set are determined, respectively, through step S401. For example, mean (left) mean (33,27,22,19,29,12,20,3) 20.625; mean (right) mean (0,0,0,0,0,0,0,0,0,0, 1) which is an abnormal flow average value in the abnormal flow data set is 0.071.

And S403, determining the abnormal degree based on the normal flow average value and the abnormal flow average value.

After the normal flow average value and the abnormal flow average value in the normal flow data set and the abnormal flow data set are determined through step S402, the abnormality degree of the candidate abnormal section is obtained and determined through calculation, and the abnormality degree is determined through the normal flow average value and the abnormal flow average value, and is specifically determined through the following formula:

degree of abnormality is

Therefore, the degree of abnormality of the candidate abnormal section determined in the present embodiment is calculated as follows:

the degree of abnormality is 20.625/(20.625+0.071+1) ═ 0.95

S404, judging whether the abnormality degree is larger than a preset threshold value or not, and determining an abnormal road section under the condition that the abnormality degree is larger than the preset threshold value.

The degree of abnormality of the candidate abnormal road segment obtained in step S403 may be determined based on a comparison between the degree of abnormality and a preset threshold, that is, the candidate abnormal road segment is determined as an abnormal road segment after the degree of abnormality of the candidate abnormal road segment reaches a certain degree, so as to be reported to, for example, a navigation module. Specifically, when the degree of abnormality of the candidate abnormal road segment is greater than the preset threshold, the abnormal road segment is determined, for example, the preset threshold may be set to 0.9, and when the degree of abnormality of the candidate abnormal road segment 0.95 is greater than the preset threshold 0.9 in this embodiment, it is necessary to determine that the road segment is the abnormal road segment, and the determined abnormal road segment is reported. The reported abnormal road sections are considered when a navigation module of the mobile terminal carries out track planning, and the user is prompted or displayed on an App of the mobile terminal so that the user can know the abnormal road sections and can select other appropriate road sections based on the information of the abnormal road sections.

In a second aspect of the present disclosure, there is provided an apparatus for determining an abnormal road segment, which can be used for a user to request a car-booking or car-calling service by using car-booking and car-calling software, so that the abnormal road segment can be effectively avoided in a process of arriving at a destination by a car, as shown in fig. 9, the apparatus includes an obtaining module 10, a first determining module 20, and a second determining module 30, which are coupled to each other, wherein:

the acquiring module 10 is configured to determine a plurality of road segments connected in sequence according to the planned trajectory, and acquire flow data of each road segment in a predetermined time.

Through the obtaining module 10, after a user inputs a starting point position a and an end point position B by using a navigation App in a mobile terminal such as a mobile phone or a car booking and calling App with a navigation function, the starting point position a may be a current position of the user, or a position dragged by the user or determined by inputting the position, and the end point position B may be a position input by the user, or a position dragged by the user on a map, and the navigation track algorithm may be any navigation track algorithm in the prior art. The planned trajectory obtained through calculation is a continuous trajectory from the starting point position a to the end point position B, the continuous trajectory being formed by a plurality of road segments connected in sequence, and the user can reach the end point position B from the starting point position a based on the planned trajectory.

The first determination module 20 determines candidate abnormal road segments based on the flow data of each road segment.

After the obtaining module 10 determines a plurality of road segments connected in sequence according to the planned track of the user and obtains the flow data of each road segment in the predetermined time range, it is considered that if a certain road segment in the planned track fails to pass due to road closure, traffic control and the like, the flow data of the road segment will further cause a large change, therefore, based on the flow data, by analyzing the change of the flow data, the yaw position existing in the planned track and the candidate abnormal road segment in the planned track, that is, the road segment which may have the conditions of road closure, traffic control and the like, can be determined based on the change degree of the flow data, so that the latest traffic and road segment information can be timely and accurately obtained, so that the navigation module can reasonably plan the track and the user can select the optimal route, specifically comprising the following parts:

and the construction unit is used for dividing the traffic data according to a preset time interval to construct a topological traffic matrix.

After the acquiring module 10 acquires the flow data of each road segment in the predetermined time range, the flow data of each road segment is first divided by the constructing unit according to the predetermined time interval, where the time interval can be set as required, the traffic data for each road segment may be divided, for example, at intervals of every 10 minutes, to construct a topological traffic matrix, which may be shown in figure 4, wherein the vertical coordinate direction of the topological flow matrix is the sequence number of the road sections in the planning track, for example, link0, link1, link2 … … are arranged in this order from the starting point position a to the end point position B, the abscissa is the time at 10 minute intervals, the data corresponding to each abscissa and ordinate is the flow data on the road section corresponding to the ordinate between the time and the previous time.

And the acquisition unit is used for processing the topological traffic matrix according to a first preset mode and acquiring a classification matrix and the characteristic value of the classification matrix.

After the topological traffic matrix is constructed by the construction unit, the traffic data of each road section in different time intervals can be displayed, in order to more obviously see the change of the traffic data of different road sections along with the time so as to obviously distinguish the positions of the candidate abnormal road sections and the moments when the candidate abnormal road sections appear, the traffic data in the topological traffic matrix is classified and converted according to a first preset mode, to perform a preliminary screening on the flow data, for example, a value above a certain threshold may be converted into 1, a value below a certain threshold may be converted into 0, to obtain a classification matrix, for example, the threshold may be set to 0, thus converting traffic data above 0 to 1, the remaining traffic data is converted to 0, and thus, the above-described topological traffic matrix can be converted to a classification matrix, as shown in fig. 5, of course, the first predetermined manner is not limited thereto, and other manners capable of classifying and filtering data may be adopted.

a first determination unit for determining a candidate abnormal road segment based on the eigenvalue of the classification matrix.

Considering that the classification matrix is a further process and simplification of the topological traffic matrix, therefore, the basic attribute of each traffic data can be obviously obtained by the classification matrix, and furthermore, the eigenvalue has a role in that by acquiring the degree of abnormality between different road segments existing in the traffic data of different road segments at certain time intervals, the range of the candidate abnormal road segment can be quickly determined by determining the eigenvalue of the classification matrix, and the first determination unit specifically includes the following parts:

and the first determining subunit is used for dividing the classification matrix according to a second preset mode according to the sequence of the road sections and determining a plurality of intermediate matrixes and corresponding characteristic values.

The topological traffic matrix is processed according to a first preset mode through the acquisition unit, after the classification matrix and the characteristic values of the classification matrix are acquired, the number of road sections related to the classification matrix is large, the data volume of traffic data is huge, the traffic data of each road section in preset time changes more complexly, in order to accurately confirm the road sections with abnormal conditions, the classification matrix can be divided according to the number sequence of the road sections and a second preset mode through the first determination unit to obtain a plurality of intermediate matrixes, then the plurality of intermediate matrixes and the corresponding characteristic values are determined, so that the huge classification matrix is gradually reduced to each intermediate matrix, and candidate abnormal road sections are accurately determined through the characteristic values of different intermediate matrixes.

And a second determining subunit, configured to determine a candidate abnormal road segment based on the intermediate matrix with the largest eigenvalue.

After the eigenvalue of each intermediate matrix is obtained by the first determining unit, the eigenvalues of the intermediate matrices are compared or sequenced by the second determining unit, when the entropy value is adopted as the eigenvalue, and the degree of confusion of each intermediate matrix can be determined by comparing the entropy value of each intermediate matrix, wherein the degree of confusion, namely the intermediate matrix with the largest entropy value, can represent that the degree of change of flow data of the related road section is larger, namely the candidate abnormal road section can appear in the road section related to the intermediate matrix, so that the candidate abnormal road section can be determined.

And a second determination module 30 for determining the degree of abnormality of the candidate abnormal section, and determining the abnormal section based on the degree of abnormality.

After determining the candidate abnormal road segments based on the flow data of each road segment by the first determining module 20, in this step, the abnormal degree of each candidate abnormal road segment is determined to further determine the abnormal road segment based on the abnormal degree of each candidate abnormal road segment, where the abnormal road segment may be the abnormal road segment that needs to be reported to the navigation module finally, the abnormal road segment that is reported finally will be considered when the navigation module of the mobile terminal performs track planning, and the user will be prompted or displayed on the App of the mobile terminal for the user to know and for the user to select an appropriate other road segment based on the information of the abnormal road segment. Specifically, the following parts are included:

and the dividing unit is used for dividing the traffic data of the candidate abnormal road section into a normal traffic data set and an abnormal traffic data set.

A second determination unit for determining a normal flow average value and an abnormal flow average value in the normal flow data set and the abnormal flow data set, respectively.

And after dividing the flow data of the candidate abnormal road section into a normal flow data set and an abnormal flow data set through a dividing unit, respectively determining a normal flow average value of the normal flow data set and an abnormal flow average value of the abnormal flow data set. For example, mean (left) mean (33,27,22,19,29,12,20,3) 20.625; mean (right) mean (0,0,0,0,0,0,0,0,0,0, 1) which is an abnormal flow average value in the abnormal flow data set is 0.071.

A third determination unit for determining the degree of abnormality based on the normal flow rate average value and the abnormal flow rate average value.

After the average value of the normal flow and the average value of the abnormal flow in the normal flow data set and the abnormal flow data set are determined by the second determination unit, the abnormality degree of the candidate abnormal road section is obtained and determined through calculation, and the abnormality degree is determined through the average value of the normal flow and the average value of the abnormal flow, and is specifically determined through the following formula:

degree of abnormality is

the degree of abnormality is 20.625/(20.625+0.071+1) ═ 0.95

And the reporting unit is used for judging whether the abnormality degree is greater than a preset threshold value or not, and determining an abnormal road section under the condition that the abnormality degree is greater than the preset threshold value.

The degree of abnormality of the candidate abnormal section is obtained based on the third determination unit, and the abnormal section can be determined based on the comparison between the degree of abnormality and a preset threshold, that is, the abnormal section needs to be reported to, for example, a navigation module after the degree of abnormality of the candidate abnormal section reaches a certain degree. Specifically, when the degree of abnormality of the candidate abnormal road segment is greater than the preset threshold, the abnormal road segment is determined, for example, the preset threshold may be set to 0.9, and when the degree of abnormality of the candidate abnormal road segment 0.95 is greater than the preset threshold 0.9 in this embodiment, the road segment is determined to be an abnormal road segment, and the determined abnormal road segment is reported. The reported abnormal road sections are considered when a navigation module of the mobile terminal carries out track planning, and the user is prompted or displayed on an App of the mobile terminal so that the user can know the abnormal road sections and can select other appropriate road sections based on the information of the abnormal road sections.

A third aspect of the present disclosure provides a storage medium, which is a computer-readable medium storing a computer program, which when executed by a processor implements the method provided by any embodiment of the present disclosure, including the following steps S11 to S13:

s11, determining a plurality of road sections which are connected in sequence according to the planned track, and acquiring flow data of each road section in preset time;

s12, determining candidate abnormal road sections based on the flow data of each road section;

and S13, determining the degree of abnormality of the candidate abnormal road section, and determining the abnormal road section based on the degree of abnormality.

When the computer program is executed by the processor to determine the candidate abnormal road section based on the flow data of each road section, the processor specifically executes the following steps: dividing the traffic data according to a preset time interval to construct a topological traffic matrix; processing the topological traffic matrix according to a first preset mode to obtain a classification matrix and a characteristic value of the classification matrix; and determining the candidate abnormal road sections based on the characteristic values of the classification matrix.

When the computer program is executed by the processor to determine the candidate abnormal road section based on the characteristic value of the classification matrix, the processor specifically executes the following steps: according to the sequence of the road sections, dividing the classification matrix according to a second preset mode, and determining a plurality of intermediate matrixes and corresponding characteristic values; and determining the candidate abnormal road section based on the intermediate matrix with the maximum characteristic value.

The computer program is executed by the processor to determine the degree of abnormality of the candidate abnormal road section, and when the abnormal road section is determined based on the degree of abnormality, the processor specifically executes the following steps: dividing the flow data of the candidate abnormal road section into a normal flow data set and an abnormal flow data set; respectively determining a normal flow average value and an abnormal flow average value in the normal flow data set and the abnormal flow data set; determining an abnormality degree based on the normal flow average value and the abnormal flow average value; and judging whether the abnormality degree is greater than a preset threshold value or not, and determining an abnormal road section under the condition that the abnormality degree is greater than the preset threshold value.

A fourth aspect of the present disclosure provides an electronic device, a schematic structural diagram of which may be as shown in fig. 10, and the electronic device at least includes a memory 901 and a processor 902, where the memory 901 stores a computer program, and the processor 902, when executing the computer program on the memory 901, implements the method provided in any embodiment of the present disclosure. Illustratively, the electronic device computer program steps are as follows S21-S23:

s21, determining a plurality of road sections which are connected in sequence according to the planned track, and acquiring flow data of each road section in preset time;

s22, determining candidate abnormal road sections based on the flow data of each road section;

and S23, determining the degree of abnormality of the candidate abnormal road section, and determining the abnormal road section based on the degree of abnormality.

The processor, when executing the determination of the candidate abnormal road segment based on the flow data of each of the road segments stored on the memory, further executes the following computer program: dividing the traffic data according to a preset time interval to construct a topological traffic matrix; processing the topological traffic matrix according to a first preset mode to obtain a classification matrix and a characteristic value of the classification matrix; and determining the candidate abnormal road sections based on the characteristic values of the classification matrix.

When the processor determines the candidate abnormal road section based on the eigenvalue of the classification matrix stored in the execution memory, the following computer program is specifically executed: according to the sequence of the road sections, dividing the classification matrix according to a second preset mode, and determining a plurality of intermediate matrixes and corresponding characteristic values; and determining the candidate abnormal road section based on the intermediate matrix with the maximum characteristic value.

The processor executes the computer program which is stored in the execution memory and used for determining the degree of abnormality of the candidate abnormal road section, and when determining the abnormal road section based on the degree of abnormality, the processor specifically executes the following computer program: dividing the flow data of the candidate abnormal road section into a normal flow data set and an abnormal flow data set; respectively determining a normal flow average value and an abnormal flow average value in the normal flow data set and the abnormal flow data set; determining an abnormality degree based on the normal flow average value and the abnormal flow average value; and judging whether the abnormality degree is greater than a preset threshold value or not, and determining an abnormal road section under the condition that the abnormality degree is greater than the preset threshold value.

The method comprises the steps of obtaining flow data of a plurality of road sections which are connected in sequence in a planned track within preset time. The candidate abnormal road sections are determined based on the flow data of each road section, the candidate abnormal road sections needing to be reported are determined by determining the abnormal degree of the candidate abnormal road sections, and therefore, the candidate abnormal road sections in the road network can be quickly located and the abnormal degree of the candidate abnormal road sections can be marked according to the actual travel behaviors of the user and the flow data which changes through topology and the like without any mark amount, the user can obtain the abnormal road section information of the road network, the selection of the user for the route is more accurate, and the traffic efficiency of the traffic network is improved.

In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The storage medium may be included in the electronic device; or may exist separately without being assembled into the electronic device.

The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.

Alternatively, the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It should be noted that the storage media described above in this disclosure can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any storage medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

While the present disclosure has been described in detail with reference to the embodiments, the present disclosure is not limited to the specific embodiments, and those skilled in the art can make various modifications and alterations based on the concept of the present disclosure, and the modifications and alterations should fall within the scope of the present disclosure as claimed.

Claims

1. A method for determining an abnormal section, comprising the steps of:

determining a plurality of road sections which are sequentially connected according to the planned track, and acquiring flow data of each road section within preset time;

determining candidate abnormal road sections based on the flow data of each road section;

determining the degree of abnormality of the candidate abnormal road section, and determining the abnormal road section based on the degree of abnormality.

2. The determination method according to claim 1, wherein the determining a candidate abnormal segment based on the flow data of each segment comprises:

dividing the traffic data according to a preset time interval to construct a topological traffic matrix;

processing the topological traffic matrix according to a first preset mode to obtain a classification matrix and a characteristic value of the classification matrix;

and determining the candidate abnormal road sections based on the characteristic values of the classification matrix.

3. The determination method according to claim 2, wherein the determining the candidate abnormal segment based on the eigenvalue of the classification matrix includes:

according to the sequence of the road sections, dividing the classification matrix according to a second preset mode, and determining a plurality of intermediate matrixes and corresponding characteristic values;

and determining the candidate abnormal road section based on the intermediate matrix with the maximum characteristic value.

4. The determination method according to claim 1, wherein the determining the degree of abnormality of the candidate abnormal section, and determining an abnormal section based on the degree of abnormality, includes:

dividing the flow data of the candidate abnormal road section into a normal flow data set and an abnormal flow data set;

respectively determining a normal flow average value and an abnormal flow average value in the normal flow data set and the abnormal flow data set;

determining an abnormality degree based on the normal flow average value and the abnormal flow average value;

and judging whether the abnormality degree is greater than a preset threshold value or not, and determining an abnormal road section under the condition that the abnormality degree is greater than the preset threshold value.

5. An apparatus for determining an abnormal section, comprising:

the acquisition module is used for determining a plurality of road sections which are sequentially connected according to the planned track and acquiring flow data of each road section in preset time;

a first determination module for determining candidate abnormal road segments based on the flow data of each road segment;

and the second determination module is used for determining the abnormality degree of the candidate abnormal road section and determining the abnormal road section based on the abnormality degree.

6. The apparatus according to claim 5, wherein the first determining means comprises:

the construction unit is used for dividing the traffic data according to a preset time interval and constructing a topological traffic matrix;

the acquisition unit is used for processing the topological traffic matrix according to a first preset mode and acquiring a classification matrix and a characteristic value of the classification matrix;

a first determination unit for determining the candidate abnormal section based on the eigenvalue of the classification matrix.

7. The determination apparatus according to claim 6, wherein the first determination unit includes:

a first determining subunit, configured to divide the classification matrix according to a second predetermined manner according to the sequence of the road segments, and determine a plurality of intermediate matrices and corresponding feature values;

a second determining subunit, configured to determine the candidate abnormal road segment based on the intermediate matrix with the largest feature value.

8. The apparatus according to claim 5, wherein the second determining module comprises:

a dividing unit, configured to divide the traffic data of the candidate abnormal section into a normal traffic data set and an abnormal traffic data set;

a second determination unit configured to determine a normal flow average value and an abnormal flow average value in the normal flow data set and the abnormal flow data set, respectively;

a third determination unit for determining an abnormality degree based on the normal flow rate average value and the abnormal flow rate average value;

9. A storage medium storing a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 4 when executed by a processor.

10. An electronic device comprising at least a memory, a processor, the memory having a computer program stored thereon, wherein the processor, when executing the computer program on the memory, is adapted to carry out the steps of the method of any of claims 1 to 4.