CN112860765A

CN112860765A - Tunnel information processing method, device, equipment and storage medium

Info

Publication number: CN112860765A
Application number: CN202110179751.4A
Authority: CN
Inventors: 杨宁; 孙文秀; 李友; 王亦乐; 潘羽
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-02-07
Filing date: 2021-02-07
Publication date: 2021-05-28
Anticipated expiration: 2041-02-07
Also published as: CN112860765B

Abstract

The disclosure provides a method, a device, equipment and a storage medium for processing tunnel information, and relates to the fields of tunnel information processing, navigation, big data and the like. The specific implementation scheme is as follows: and excavating tunnel information and track information passing through the tunnel, and calculating the average passing time of the tunnel in a specified time period according to the tunnel information and the track information. The embodiment of the disclosure can provide data support for tunnel navigation, which is helpful for improving accuracy of tunnel navigation and improving user experience.

Description

Tunnel information processing method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technology, and in particular, to the fields of tunnel information processing, navigation, big data, and the like.

Background

In map navigation applications, due to weak GPS (Global Positioning System) satellite signals, interference, multiple shelters, and the like, the phenomena of inaccurate navigation Positioning and altitude drift occur, and even the situation of being unable to position in individual scenes such as tunnels may occur.

In the conventional VDR (Vehicle Dead Reckoning) solution based on a mobile phone sensor, information acquired by the sensor is used for Reckoning the traveling speed and direction of a mobile phone of a user in a tunnel, and then the current position of the user is estimated and provided in navigation. However, navigation inaccuracy is still frequent for tunnel scenes, resulting in a high proportion of the user's bad experience.

Disclosure of Invention

The disclosure provides a method, a device, equipment and a storage medium for processing tunnel information.

According to an aspect of the present disclosure, there is provided a method for processing tunnel information, including:

excavating tunnel information and track information passing through the tunnel;

and calculating the average passing time length of the tunnel in a specified time period according to the tunnel information and the track information.

According to another aspect of the present disclosure, there is provided a processing apparatus of tunnel information, including:

the excavation module is used for excavating tunnel information and track information passing through the tunnel;

and the calculation module is used for calculating the average passing time of the tunnel in the specified time period according to the tunnel information and the track information.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method according to any one of the embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform a method in any of the embodiments of the present disclosure.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method in any of the embodiments of the present disclosure.

According to the technology disclosed by the invention, tunnel information and track information passing through the tunnel are mined, and the average passing time of the tunnel in a specified time period is calculated according to the tunnel information and the track information, so that data support can be provided for tunnel navigation, the accuracy of tunnel navigation is improved, and the user experience is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is a flowchart of a processing method of tunnel information according to an embodiment of the present disclosure;

fig. 2 is a flowchart of a processing method of tunnel information according to another embodiment of the present disclosure;

fig. 3 is a flowchart of a processing method of tunnel information according to another embodiment of the present disclosure;

FIG. 4 is a schematic view of a tunnel according to another embodiment of the present disclosure;

fig. 5 is a schematic diagram of a bifurcated tunnel according to an embodiment of the present disclosure;

FIG. 6 is a schematic illustration of a road segment according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of an anomaly track according to an embodiment of the present disclosure;

FIG. 8 is a schematic diagram of a trajectory through a tunnel according to an embodiment of the present disclosure;

FIG. 9 is a schematic diagram of a trajectory replacement strategy according to an embodiment of the present disclosure;

FIG. 10 is a block diagram of an overall implementation framework according to an embodiment of the present disclosure;

fig. 11 is a schematic block diagram of a processing apparatus of tunnel information according to an embodiment of the present disclosure;

fig. 12 is a schematic block diagram of a processing apparatus of tunnel information according to an embodiment of the present disclosure;

fig. 13 is a schematic block diagram of a processing apparatus of tunnel information according to an embodiment of the present disclosure;

fig. 14 is a block diagram of an electronic device for implementing a processing method of tunnel information according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a flowchart of a processing method of tunnel information according to an embodiment of the present disclosure. The method can comprise the following steps:

s11, excavating tunnel information and track information passing through the tunnel;

and S12, calculating the average passing time of the tunnel in the appointed time period according to the tunnel information and the track information.

The tunnel involved in the embodiments of the present disclosure may be of various types, such as having a single entrance and exit, or one entrance and multiple exits, or multiple entrances and one exit, or multiple entrances and multiple exits, etc., that is, some tunnels include branches, and some tunnels do not include branches. In embodiments of the present disclosure, a bifurcated tunnel may comprise a tunnel having multiple entrances or multiple exits, and a non-bifurcated tunnel may comprise a tunnel having only one entrance and one exit. A tunnel may include one or more nodes, that is, points in an electronic map that may uniquely identify a physical location. In tunneling, node information of the tunnel may be obtained, including but not limited to which nodes the tunnel is composed of, and their order, distance, and so on. In the embodiment of the present disclosure, the track may include an actual passing track of the user, and track information may be recorded during the navigation used by the user. The track information mentioned in the embodiments of the present disclosure may include track information related to a tunnel. The track information obtained by mining includes, but is not limited to, the length of the track, included nodes, a passing tunnel, and the like.

The mining related to the embodiment of the present disclosure may be performed based on road network information and a large amount of traffic tracks, and may be implemented by using different mining algorithms, such as mining based on a kd-tree (k-dimensional tree), and the like, which is not limited specifically. The big data mining-based mode enables results to be more accurate and has strong reliability.

For example, the specified time period in the embodiment of the present disclosure may be any time period in a day, and the range of the time period is not limited, and may be specifically set according to needs. For example, the specified time period is 8:00-9:00, 17:30-18:30, 18:00-19:00, or 12:00-14:00, and so on.

The average passing time of the tunnel in the embodiment of the disclosure can be output to the tunnel navigation module to provide data support. The tunnel navigation module may be understood as a functional module that provides navigation for a user, and may also be referred to as a downstream module of the present disclosure. After receiving the average passing time length, the tunnel navigation module can formulate a corresponding strategy according to the data to provide tunnel navigation service for the user, so that the accuracy of tunnel navigation is improved.

According to the method provided by the embodiment, the tunnel information and the track information passing through the tunnel are mined, and the average passing time of the tunnel in the specified time period is calculated according to the tunnel information and the track information, so that data support can be provided for tunnel navigation, the accuracy of tunnel navigation is improved, and the user experience is improved.

Fig. 2 is a flowchart of a processing method of tunnel information according to another embodiment of the present disclosure. The method can comprise the following steps:

and S21, mining tunnel information and track information passing through the tunnel.

In this embodiment, S21 is the same as S11 in the previous embodiment, and is not repeated here.

And S22, calculating the passing time of the plurality of tracks through the target tunnel according to the tunnel information and the track information.

And S23, calculating the average passing time of the target tunnel in the specified time period according to the calculated passing time of the plurality of tracks.

In the present embodiment, S22 and S23 are an exemplary manner of S12 in the previous embodiment.

In the embodiment of the present disclosure, the plurality of tracks may include all tracks passing through the target tunnel, and may also include a part of tracks passing through the target tunnel. Generally, to improve the accuracy of the subsequent calculation results, it is preferable to calculate all the trajectories passing through the target tunnel. Of course, it is also possible to choose to calculate the partial trajectory through the target tunnel in order to reduce the amount of calculation. The concrete selection can be carried out according to actual needs.

The target tunnel in the embodiment of the present disclosure generally refers to any one tunnel, and actually, the target tunnel is taken as an example for description, and in particular, in practice, each tunnel may be calculated and processed by using the same method, which is not described again.

According to the embodiment of the disclosure, the passing time of the target tunnel through the plurality of tracks is calculated first, and then the average passing time of the target tunnel in the specified time period is calculated, so that the calculation result based on the plurality of tracks is calculated, the advantages of big data are fully exerted, the accuracy of the calculation result is improved, and the obtained average passing time of the tunnel is more reliable.

Fig. 3 is a flowchart of a processing method of tunnel information according to another embodiment of the present disclosure. The method can comprise the following steps:

and S31, mining tunnel information and track information passing through the tunnel.

In this embodiment, S31 is the same as S11 in the above embodiments, and is not repeated here.

Specifically, track information penetrating a tunnel, and road section and node information included in the tunnel and the track may be mined, wherein the length of the track penetrating the tunnel is not less than the length of the tunnel.

Fig. 4 is a tunnel schematic according to another embodiment of the present disclosure. Referring to fig. 4, a tunnel is formed on the electronic map, and extends from one side of a river to the other side of the river to form a river bottom tunnel. In this embodiment, for example, after the tunnel information is obtained by mining, it may be further identified whether the tunnel is a branch tunnel. Fig. 5 is a schematic diagram of a bifurcated tunnel according to an embodiment of the present disclosure. Referring to fig. 5, one tunnel on the electronic map is a branch tunnel. Wherein, identifying whether a tunnel is a branch tunnel may be determined based on a relationship of nodes and links. Fig. 6 is a schematic illustration of a road segment according to an embodiment of the present disclosure. Referring to fig. 6, a line shown in black represents a road segment. Identifying the bifurcation tunnel according to the segment information may specifically include: and traversing each node in the road section sequence included in the tunnel, and judging whether more than one road section passes through the current node. If yes, confirming that the tunnel is a bifurcation tunnel; otherwise, the tunnel is determined to be a non-branching tunnel. After confirming the forking property of the tunnel, the forking property of the tunnel can be subsequently provided for tunnel navigation, so that reliable support is provided for the fineness of the tunnel navigation strategy. For example, the tunnel navigation may adopt a corresponding aggressive application strategy for a tunnel without bifurcation and a corresponding conservative application strategy for a tunnel without bifurcation, thereby bringing better navigation experience to the user and accordingly effectively reducing the bad experience rate of the user.

In one embodiment, S22 may include:

and S32, counting a plurality of tracks passing through the target tunnel according to the tunnel information and the track information, and calculating the passing time of each track by calculating the difference between the end point time and the starting point time of the track.

The starting point time of the track may be a time when the track passes through the entrance of the tunnel, and the ending point time may be a time when the track passes through the exit of the tunnel. The embodiment calculates the passing time of the track based on the end point time and the starting point time of the track, has simple calculation mode, is easy to realize, and fully exerts the advantages of big data by a mode of calculating based on the calculation results of a plurality of tracks, improves the accuracy of the calculation results, and obtains more reliable average passing time of the tunnel.

Further, in order to improve the calculation accuracy, avoid the influence of bad data and ensure that the data of the normal track is used as much as possible for calculation, the abnormal track can be filtered before the track passing time is calculated. The abnormal trajectory may include an untrusted trajectory, a trajectory with an abnormal transit time, and the like, wherein the untrusted trajectory may include a trajectory with a confidence level below a threshold. FIG. 7 is a schematic diagram of an abnormal trajectory according to an embodiment of the present disclosure. Referring to fig. 7, a line extending obliquely upward may represent an abnormal trajectory. Since the area of the track is a river, the track cannot be a vehicle running track, belongs to an abnormal track, and needs to be filtered. The filtering method of the abnormal track includes but is not limited to: the method for screening based on the thresholds such as the track matching confidence, the emission probability, the GPS radius and the like is not particularly limited. The abnormal passing time can be the phenomenon that the passing time is too long or too short, and if a user enters the tunnel, navigation is closed, so that the condition that the passing time is too long and even return cannot be carried out occurs. If an abnormal track is introduced, for example, an un-confidence track and a track with abnormal passage duration participate in calculation, the calculated result is likely to be faster, and the accuracy of navigation is finally affected, so that the filtering can improve the calculation precision, avoid the influence of bad data and be beneficial to improving the accuracy of navigation. Specifically, a method of screening by using a preset time interval threshold may be adopted, and is not limited specifically.

In an embodiment, the manner of calculating the trajectory passage time length at S32 may be replaced by the following manner: the length of the tunnel is calculated to be proportional to the length of the track, the difference between the end point time and the start point time of the track is calculated, and then the passing time of the track is obtained by multiplying the difference by the proportion. Illustratively, it can be expressed by the following formula:

for example, fig. 8 is a schematic diagram of a track through a tunnel according to an embodiment of the present disclosure. Referring to fig. 8, there is a trajectory through a tunnel that includes 3 segments B, C and D and nodes 2-8. The track includes, in addition to road segment B, C, D, road segment a before the tunnel and road segment E after the tunnel, and in addition to nodes 2-8,

nodes

1 and 9. It is clear from fig. 8 that the trajectory is a traversal trajectory of the tunnel. Thus, the transit time for the trajectory can be calculated as follows:

wherein the time of day_{Node 9}The time when the through trace reaches the node 9_{Node 1}Length is the time at which the traversal trajectory reaches node 1_tunnelLength is the sum of the tunnel length, i.e. the length of road section B, C, D_allIs the length of the through-track, i.e., the sum of the lengths of road segments A, B, C, D, E.

The method for calculating by considering the ratio of the tunnel length to the track length can effectively eliminate errors introduced by the head or the tail of the tunnel. And moreover, the calculation mode is carried out again based on the calculation results of the plurality of tracks, the advantages of big data are fully exerted, the accuracy of the calculation results is improved, and the obtained average passing time of the tunnel is more reliable.

In one embodiment, S23 may include S33 and S34:

s33, selecting the tracks passing through the target tunnel in the appointed time period, solving a weight mean value according to the corresponding weight of each selected track, and solving a median value for the passing time of each selected track.

For example, when the weight is given, each track may be given a corresponding weight according to the track attribute, for example, different weights may be given according to factors such as different sources of the track, different GPS radii, different confidence degrees, and the like, and the specific method is not limited. And the median value is obtained from the transit time lengths of all the selected tracks, so that the interference of individual abnormal tracks on the whole can be avoided, and the calculation precision is further improved.

In one embodiment, step S33 may further include:

in the case that the track passing through the target tunnel does not exist in the specified time period, the track passing through the target tunnel in other time periods is selected as an alternative, and the other time periods comprise other time periods of the day or other time periods of the day. The mode of selecting data of other time periods to fill up calculation can enable calculation to be more reasonable, avoid influence of extreme data on calculation, and further improve calculation accuracy.

And S34, calculating the average passing time of the target tunnel in the specified time period according to the weight average value and the median value.

In one embodiment, step S34 may specifically include:

and the average passing time length of the target tunnel in the specified time period is obtained by summing the product of the median value and a preset first coefficient and the product of the weight average value and a preset second coefficient.

The mode of calculating the average passing time of the target tunnel based on the set first coefficient and the set second coefficient reduces the interference of individual abnormal tracks to the whole, reduces the influence of bad data and further improves the accuracy and the reliability of the data.

The first coefficient and the second coefficient are empirical values, and may be set as needed, and are not limited specifically. For example, the first coefficient may be set to 0.8 and the second coefficient to 0.2, and the above calculation may be expressed by the following equation: average transit time length is median value 0.8+ weight average value 0.2.

FIG. 9 is a schematic diagram of a trajectory replacement strategy according to an embodiment of the present disclosure. Referring to fig. 9, the above steps may include, for example, at least one of:

selecting the track passing through the target tunnel in the time period on other days of the week as a substitute for the condition that the track passing through the target tunnel does not exist in the specified time period on the day;

under the condition that no track passing through the target tunnel exists in the time period on other days of the week, selecting a track passing through the target tunnel in an adjacent time period on the day as a substitute;

under the condition that no track passing through the target tunnel exists in the adjacent time period of the day, the track passing through the target tunnel in the rest time periods of the day is selected as a substitute;

and under the condition that no track passing through the target tunnel exists in the rest time periods of the day, selecting tracks passing through the target tunnel on other days of the week as substitutes.

The method for selecting various data to replace calculation gives full play to the advantages of big data, eliminates the influence of bad data, improves the accuracy of calculation and has higher precision of calculation results.

Illustratively, in the above process, if the present day is a working day, the other days of the week are other working days of the week. If the day is one of the weekends, the other days of the week are the other days of the weekend.

The above-mentioned date, week, etc. are only examples, not limitations, and may be flexibly selected according to actual requirements. For example, a month, a year, etc.

In this embodiment, the calculated average passage time length may be output to a downstream tunnel navigation module, and the average passage time length may be used for tunnel navigation. Therefore, abundant data can be provided for tunnel navigation, and the accuracy of tunnel navigation is improved.

In one embodiment, the method may further comprise: and judging whether the tunnel is a bifurcation tunnel or not according to the tunnel information. Referring to fig. 5 and its related description, after determining whether the tunnel is a branch tunnel according to the tunnel information, the branch attribute of the tunnel may also be provided for tunnel navigation, thereby improving the fineness and accuracy of the tunnel navigation policy.

In one embodiment, the method may further comprise: and converting the calculated average passing time length into an average speed. Optionally, the average speed is output to a tunnel navigation module. In the mode, the average passing time and the average speed are simultaneously used as data support to be provided for the tunnel navigation module, so that the data are richer, the reference value is higher, and a better tunnel navigation strategy is favorably formulated.

In one embodiment, the method may further include the steps of:

and comparing the average passing time of the tunnel in the specified time period with the average passing time of the tunnel in other time periods, and calculating the confidence coefficient. Specifically, the average passing time of the week and the average passing time of the last week may be compared, and the confidence may be calculated. If the week or the last week is a holiday, comparing the average passing time of the week with the average passing time of the week in the last year, calculating the confidence coefficient, and providing the calculated confidence coefficient for the tunnel navigation module. In the mode, the confidence level and the average passing time are both provided to the tunnel navigation module as data support, so that the data is richer, the reference value is higher, and a better tunnel navigation strategy is favorably formulated. The calculation mode considering holidays can enable the calculation result to be more accurate and the error to be smaller, and improves the reliability and the usability of data. Of course, other factors may also be considered when calculating the confidence, such as the number of the compared average transit time periods, whether to replace with data of other time periods, and the like, also participate in the calculation, thereby further improving the reliability of the confidence.

FIG. 10 is a block diagram of an overall implementation framework according to an embodiment of the present disclosure. Referring to fig. 10, the overall scheme is implemented on four parts basis. Firstly, excavation is carried out according to a basic road network to obtain tunnel information and attributes. Secondly, track mining is carried out after big data of the track is accessed, and track information and attributes are obtained, wherein the track information and the attributes include but are not limited to: tunnel, duration, time of day, forking, confidence, etc. Then, based on data fusion, calculating the passing time of the multiple tracks passing through the target tunnel, and calculating the average passing time of the target tunnel in a specified time period. Furthermore, self-evaluation can be carried out, confidence coefficient is calculated, and accordingly powerful data support is provided for a tunnel navigation formulation strategy, accuracy of tunnel navigation is improved, and bad user experience is reduced.

In the method provided by this embodiment, the tunnel information and the track information passing through the tunnel are mined, and the passage time of the multiple tracks passing through the target tunnel is calculated according to the tunnel information and the track information. And calculating the average passing time length of the target tunnel in the specified time period according to the passing time lengths of the plurality of tracks. Therefore, data support can be provided for tunnel navigation, the accuracy of tunnel navigation is improved, the condition of tunnel navigation yaw is effectively reduced, the problems of height drift of a car logo and too early or too late time for playing or amplifying image display caused by estimation deviation in navigation can be effectively solved, the navigation performance under severe scenes such as poor GPS signals and sensor uncertainty is improved, and the poor experience rate of tunnel navigation of a user is reduced. Compared with the tunnel navigation bad experience rate of at least 8.3% of the traditional VDR scheme, the tunnel navigation bad experience rate of the method can reach below 5.93%, is reduced by at least 28%, and greatly improves the tunnel navigation experience of a user. In addition, the calculation process can be off-line operation, and resources are not occupied in the navigation process of the user, so that the power is saved for the user.

Fig. 11 is a block diagram of a processing apparatus of tunnel information according to an embodiment of the present disclosure. The apparatus may include:

a mining module 1101 for mining tunnel information and track information passing through the tunnel;

and a calculating module 1102, configured to calculate an average transit time of the tunnel in a specified time period according to the tunnel information and the track information.

Fig. 12 is a block diagram of a processing apparatus of tunnel information according to another embodiment of the present disclosure. The apparatus of this embodiment may include the various components of the apparatus embodiments described above. In this embodiment, in one implementation, the computing module 1202 of the apparatus includes:

a first calculating unit 1202a, configured to calculate, according to the tunnel information and the trajectory information, a transit time length for the multiple trajectories to pass through the target tunnel;

and the second calculating unit 1202b is configured to calculate an average passage time length of the target tunnel in the specified time period according to the calculated passage time lengths of the plurality of tracks.

In one embodiment, the first calculation unit 1202a is specifically configured to:

counting a plurality of tracks passing through the target tunnel according to the tunnel information and the track information, and calculating the passing time of each track by calculating the difference of the endpoint time and the starting point time of the track; alternatively, the first and second electrodes may be,

according to the tunnel information and the track information, a plurality of tracks passing through the target tunnel are counted, for each track, the ratio of the length of the target tunnel to the length of the track is calculated, the difference between the end point moment and the starting point moment of the track is calculated, and then the difference is multiplied by the ratio to obtain the passing time length of the track.

In an embodiment, the second calculating unit 1202b is specifically configured to: selecting tracks passing through the target tunnel in a specified time period, solving a weight mean value according to the corresponding weight of each selected track, solving a median value of the transit time of each selected track, and calculating the average transit time of the target tunnel in the specified time period according to the weight mean value and the median value.

In an embodiment, the second calculating unit 1202b is specifically configured to: selecting tracks passing through the target tunnel in a specified time period, solving a weight mean value according to the corresponding weight of each selected track, solving a median value of the passing time of each selected track, multiplying the median value by a preset first coefficient, multiplying the weight mean value by a preset second coefficient, and then summing to obtain the average passing time of the target tunnel in the specified time period.

Further, the second calculation unit 1202b may be further configured to:

and selecting tracks passing through the target tunnel in other time periods instead of tracks passing through the target tunnel in a specified time period, wherein the other time periods comprise other time periods of the day or other date time periods. Specifically, at least one of the following may be included:

Alternatively, in the above calculation process, if the current day is a weekday, the other day of the week is another weekday of the week, and if the current day is a certain day on a weekend, the other day of the week is another day on the weekend.

Further, the first calculation unit 1202a is further configured to: before the passing time of the plurality of tracks through the target tunnel is calculated, the tracks with the confidence coefficient lower than the threshold value and the tracks with abnormal passing time are filtered.

Fig. 13 is a block diagram of a processing apparatus of tunnel information according to another embodiment of the present disclosure. The apparatus of this embodiment may include the various components of the apparatus embodiments described above. In this embodiment, as shown in fig. 13, the digging module 1301 may include:

the mining unit 1301a is configured to mine tunnel information and trajectory information passing through the tunnel, and determine whether the tunnel is a branch tunnel according to the tunnel information.

Further, in one embodiment, the calculation module may be further configured to: converting the average passing time length into an average speed; and/or comparing the average passing time of the tunnel in the specified time period with the average passing time of the tunnel in other time periods, and calculating the confidence coefficient. Specifically, the confidence may be calculated by comparing the average passing time of the week with the average passing time of the last week, and if the week or the last week is a holiday, the confidence may be calculated by comparing the average passing time of the week with the average passing time of the week in the last year. Accordingly, the calculated average speed and/or confidence may also be provided to the tunnel navigation module for tunnel navigation.

The functions of each unit, module or sub-module in each application detection device in the embodiments of the present disclosure may refer to the corresponding description in the application detection method, and are not described herein again.

According to the device provided by the embodiment of the disclosure, the average passage time of the tunnel in the specified time period is calculated according to the tunnel information and the track information by excavating the tunnel information and the track information passing through the tunnel, so that data support can be provided for tunnel navigation, the accuracy of tunnel navigation is improved, and the user experience is improved.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 14 shows a schematic block diagram of an example electronic device 1400 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 14, the device 1400 includes a computing unit 1401 that can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)1402 or a computer program loaded from a storage unit 1408 into a Random Access Memory (RAM) 1403. In the RAM 1403, various programs and data required for the operation of the device 1400 can also be stored. The calculation unit 1401, the ROM 1402, and the RAM 1403 are connected to each other via a bus 1404. An input/output (I/O) interface 1405 is also connected to bus 1404.

Various components in device 1400 connect to I/O interface 1405, including: an input unit 1406 such as a keyboard, a mouse, or the like; an output unit 1407 such as various types of displays, speakers, and the like; a storage unit 1408 such as a magnetic disk, optical disk, or the like; and a communication unit 1409 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 1409 allows the device 1400 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 1401 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 1401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The computing unit 1401 executes the respective methods and processes described above, such as the application detection method. For example, in some embodiments, the application detection method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 1408. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1400 via ROM 1402 and/or communication unit 1409. When the computer program is loaded into the RAM 1403 and executed by the computing unit 1401, one or more steps of the application detection method described above may be performed. Alternatively, in other embodiments, the computing unit 1401 may be configured to perform the application detection method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

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.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A processing method of tunnel information comprises the following steps:

excavating tunnel information and track information passing through the tunnel;

2. The method of claim 1, wherein calculating an average transit time for the tunnel over a specified time period based on the tunnel information and trajectory information comprises:

calculating the passing time of the plurality of tracks passing through the target tunnel according to the tunnel information and the track information;

and calculating the average passing time length of the target tunnel in a specified time period according to the calculated passing time lengths of the tracks.

3. The method of claim 2, wherein calculating the transit time of the plurality of tracks through the target tunnel according to the tunnel information and the track information comprises:

and counting a plurality of tracks passing through the target tunnel according to the tunnel information and the track information, calculating the ratio of the length of the target tunnel to the length of the track for each track, calculating the difference between the end point moment and the start point moment of the track, and multiplying the difference by the ratio to obtain the passing time of the track.

4. The method of claim 2, wherein calculating an average transit time length of the target tunnel in a specified time period according to the calculated transit time lengths of the plurality of tracks comprises:

and selecting tracks passing through the tunnel in a specified time period, solving a weight mean value according to the corresponding weight of each selected track, solving a median value of the passage time of each selected track, and calculating the average passage time of the target tunnel in the specified time period according to the weight mean value and the median value.

5. The method of claim 4, wherein calculating an average transit time of the target tunnel in a specified time period according to the weight mean value and the median value comprises:

and summing the product of the median value and a preset first coefficient and the product of the weight mean value and a preset second coefficient to obtain the average passing time of the target tunnel in a specified time period.

6. The method of claim 4, further comprising:

and selecting tracks passing through the target tunnel in other time periods instead of tracks passing through the target tunnel in a specified time period, wherein the other time periods comprise other time periods of the day or other date time periods.

7. The method of claim 6, wherein for the case that no trajectory passes through the target tunnel within a specified time period, selecting trajectories that pass through the target tunnel within other time periods instead comprises at least one of:

8. The method of claim 2, further comprising:

before the passing time of the plurality of tracks through the target tunnel is calculated, the tracks with the confidence coefficient lower than the threshold value and the tracks with abnormal passing time are filtered.

9. The method of claim 1, wherein the average transit time duration is used for tunnel navigation.

10. The method of any one of claims 1 to 9, further comprising at least one of:

judging whether the tunnel is a bifurcation tunnel or not according to the tunnel information;

converting the average passing time length into an average speed;

and comparing the average passing time of the tunnel in the specified time period with the average passing time of the tunnel in other time periods, and calculating the confidence coefficient.

11. An apparatus for processing tunnel information, comprising:

12. The apparatus of claim 11, wherein the computing module comprises:

the first calculation unit is used for calculating the passing time of the plurality of tracks passing through the target tunnel according to the tunnel information and the track information;

and the second calculating unit is used for calculating the average passing time length of the target tunnel in a specified time period according to the calculated passing time lengths of the tracks.

13. The apparatus according to claim 12, wherein the first computing unit is specifically configured to:

14. The apparatus according to claim 12, wherein the second computing unit is specifically configured to: selecting tracks passing through the target tunnel in a specified time period, solving a weight mean value according to the corresponding weight of each selected track, solving a median value of the passing time of each selected track, and calculating the average passing time of the target tunnel in the specified time period according to the weight mean value and the median value.

15. The apparatus according to claim 14, wherein the second computing unit is specifically configured to: selecting tracks passing through a target tunnel in a specified time period, solving a weight mean value according to the corresponding weight of each selected track, solving a median value of the passing time of each selected track, multiplying the median value by a preset first coefficient and multiplying the weight mean value by a preset second coefficient, and then summing to obtain the average passing time of the target tunnel in the specified time period.

16. The apparatus of claim 14, wherein the second computing unit is further configured to: and selecting tracks passing through the target tunnel in other time periods instead of tracks passing through the target tunnel in a specified time period, wherein the other time periods comprise other time periods of the day or other date time periods.

17. The apparatus according to claim 16, wherein the second computing unit selects, as an alternative to the trajectory that passes through the target tunnel in a specified time period, the trajectory that passes through the target tunnel in another time period, including at least one of:

18. The apparatus of claim 12, wherein the first computing unit is further configured to: before the passing time of the plurality of tracks through the target tunnel is calculated, the tracks with the confidence coefficient lower than the threshold value and the tracks with abnormal passing time are filtered.

19. The apparatus of claim 11, wherein the average transit time duration is used for tunnel navigation.

20. The apparatus of any one of claims 11 to 19, wherein the computing module is further configured to perform at least one of:

converting the average passing time length into an average speed;

21. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

22. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-10.

23. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-10.