CN114205733A

CN114205733A - Method for positioning abnormal perception event of expressway user

Info

Publication number: CN114205733A
Application number: CN202010897714.2A
Authority: CN
Inventors: 杨占军; 彭陈发; 陈锋; 张士聪
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Zhejiang Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Zhejiang Co Ltd
Priority date: 2020-08-31
Filing date: 2020-08-31
Publication date: 2022-03-18
Anticipated expiration: 2040-08-31
Also published as: CN114205733B

Abstract

The embodiment of the invention relates to the field of computer data processing, and discloses a method for positioning an abnormal perception event of an expressway user, which comprises the following steps: acquiring a plurality of pieces of MR data of a user to be positioned, and determining the reporting time point of each piece of MR data and the occupation of a main cell; determining the average running speed of a user to be positioned according to MR data, determining whether the user to be positioned is a high-speed user, acquiring an abnormal event perception time point of the user to be positioned when the user to be positioned is the high-speed user, and acquiring a reporting time point which is closest to the abnormal event perception time point as a reference time point; determining a reference relative displacement according to the reference time point; determining the longitude and latitude of a reference point according to the MR data taking the reporting time point as the reference time point; and identifying the expressway grid matched with the latitude and longitude of the reference point as a reference grid identification, and determining a target grid according to the reference grid identification. The invention improves the accuracy of high-speed user abnormal perception positioning.

Description

Method for positioning abnormal perception event of expressway user

Technical Field

The embodiment of the invention relates to the technical field of computer data processing, in particular to a method, a device, equipment and a readable medium for positioning an abnormal perception event of an expressway user.

Background

With the rapid development of mobile communication technology, the usage amount of mobile terminals increases, and more terminal users have higher requirements on the signal quality of the mobile terminals in the driving process. In order to improve the signal quality of a user and improve the perception experience of the user, a private network is often arranged along a high-speed line to isolate a private network cell from a common cell, and a user who drives at a high speed basically uses the private network, so that the improvement of the quality of the private network cell is very important.

At present, the quality of a private network cell is evaluated by adopting perception information of a road mobile user, the basic method is that the user speed per hour is calculated according to design requirements such as the speed per hour of road operation and the like and according to user signaling data including road passing time, displacement information and the like, and when the user meets a basic speed per hour interval of road design, the user is considered as a road user. And for the identified road users, matching and estimating the positions of the users according to a specific rule by using MR information reported by the mobile users at regular time through a road fingerprint library, displaying the position information, positioning the abnormal perception events of the users and the like.

However, in the prior art, the location fingerprint feature index in the location fingerprint database for user location generally adopts the signal strength of the cell and the neighboring cell extracted from the MR data reported by the user, but the factors affecting the signal strength are not only related to the location of the user terminal, but also affected by many other factors, so that the situation of location deviation of the user terminal occurs, the final location result is affected, and further, the problems of low location ratio and low accuracy of abnormal events, errors in index statistics and the like are caused, thereby affecting the location of the abnormal events, and further causing the problems of low location ratio and low accuracy of the abnormal events, and the like

Disclosure of Invention

In view of the above problems, embodiments of the present invention provide a method for locating an abnormal event of an expressway user, so as to solve the problem in the prior art that the accuracy of locating an abnormal event of an expressway user is low.

According to an aspect of an embodiment of the present invention, there is provided a method for locating an abnormal event of an expressway user, the method including:

acquiring a plurality of pieces of MR data of a user to be positioned, and determining a reporting time point corresponding to each piece of MR data and occupying a main cell;

determining the average running speed of the user to be positioned according to the MR data, and determining whether the user to be positioned is a high-speed user;

when the user to be positioned is a high-speed user, acquiring abnormal event perception information of the user to be positioned, and determining an abnormal event perception time point according to the abnormal event perception information;

acquiring a reporting time point which is contained in the MR data and is closest to the abnormal event perception time point as a reference time point;

determining the reference relative displacement of the user to be positioned according to the reference time point, the abnormal event perception time point and the average running speed;

determining the longitude and latitude of the user to be positioned at the reference time point according to the MR data corresponding to the reference time point as the longitude and latitude of a reference point;

matching the longitude and latitude of the reference point with the longitude and latitude of a grid contained in a preset highway fingerprint library, and determining a highway grid identifier corresponding to the longitude and latitude of the reference point as a reference grid identifier, wherein the highway fingerprint library comprises a plurality of highway grid identifiers and the longitude and latitude of the grid corresponding to the highway grid identifier;

and determining a target grid corresponding to the abnormal event perception information according to the reference relative displacement and the reference grid identification.

In an optional mode, the expressway fingerprint database further comprises a main cell identifier and a neighbor cell identifier associated with each expressway grid identifier, a main cell RSRP value corresponding to the main cell identifier, and a neighbor cell RSRP value corresponding to the neighbor cell identifier,

after the determining the user to be located as a high-speed user, further comprising:

taking a cell currently occupied in each piece of MR data of the user to be positioned as a target main cell, and determining at least one adjacent cell corresponding to the target main cell as a target adjacent cell;

determining a target main cell identifier and an RSRP value of the target main cell, matching the target main cell with the main cell identifiers in the expressway fingerprint library, and calculating a difference value between the RSRP value of the main cell in the matched expressway fingerprint library and the RSRP value of the target main cell;

acquiring a highway grid identifier corresponding to a main cell in a highway fingerprint database with the minimum difference value as an alternative main cell grid identifier;

matching the target adjacent cell identification of the target adjacent cell with the adjacent cell identification corresponding to the alternative main cell grid identification in the highway fingerprint database;

calculating the difference value between the RSRP value of the adjacent cell in the matched expressway fingerprint library and the RSRP value of the target adjacent cell, and acquiring an expressway grid identifier corresponding to the adjacent cell in the expressway fingerprint library with the minimum difference value as an alternative adjacent cell grid identifier;

and determining a target grid identifier corresponding to the user to be positioned according to the alternative main cell grid identifier and the alternative neighbor cell grid identifier.

In an optional embodiment, after the determining the user to be located as a high-speed user, the method further includes:

In an optional embodiment, after determining the abnormal event perception time point according to the abnormal event perception information, the method further includes:

determining a primary cell occupied by the user to be positioned at the reference time point as a sensing abnormal primary cell;

matching the sensing abnormal main cell with a cell switching pair contained in a preset switching fingerprint library, and determining the cell occupied before abnormal sensing and the cell occupied after abnormal sensing according to the matched cell switching pair, wherein the switching fingerprint library comprises at least one pair of cell switching pairs consisting of a cell where switching occurs and a cell switched to, switching position longitude and latitude of the cell switching pairs and highway grid identification corresponding to the switching position longitude and latitude, wherein each target driving section comprises the cell switching pairs;

determining MR data occupying a main cell in the MR data as an occupation cell before abnormal sensing as reported data before abnormal sensing, and determining MR data occupying the main cell in the MR data as the reported data after abnormal sensing;

determining the occupation starting time point of the user to be positioned for the abnormal perception cell according to the reported data before abnormal perception, and determining the occupation ending time point of the user to be positioned for the abnormal perception cell according to the reported data after abnormal perception;

and determining a coverage grid range of the abnormal cell to be positioned, and determining a target grid corresponding to the abnormal event according to the abnormal sensing time point, the occupation starting time point, the occupation ending time point and the coverage grid range.

In an optional embodiment, the determining a target grid corresponding to the abnormal event according to the abnormal sensing time point, the occupation starting time point, the occupation ending time point, and the coverage grid range further includes:

determining a ratio of an interval duration between the abnormal sensing time point and the occupation starting time point as a numerator and an interval duration between the occupation ending time point and the occupation starting time point as a denominator as a target movement multiple;

and determining a target grid corresponding to the abnormal event according to the target movement multiple, the number of the covering grids and the grid coverage range of the abnormal cell to be positioned.

In an optional embodiment, the cell handover fingerprint database includes a plurality of cell handover pairs, an expressway identifier corresponding to each of the cell handover pairs, a cell handover pair identifier, and a handover location longitude and latitude, and the determining process of the cell handover fingerprint database further includes:

acquiring multi-time network pulling data, and determining switching information corresponding to each height road mark according to the multi-time network pulling data;

determining a cell where switching occurs and a cell of a target cell to which switching occurs as a pair of cell switching pairs according to the switching information;

determining a cell switching pair identifier and a switching position longitude and latitude corresponding to the cell switching pair;

and matching the longitude and latitude of the switching position with the longitude and latitude of the grid in the altitude road fingerprint library to obtain the corresponding expressway grid identification of the matched grid longitude and latitude, and determining the expressway grid identification, the cell switching pair identification and the switching position longitude and latitude corresponding to each expressway identification as one fingerprint in the cell switching fingerprint library.

In an optional embodiment, the determining an average traveling speed of the user to be positioned according to the MR data, and determining whether the user to be positioned is a high-speed user further includes:

determining a driving time interval and an average driving speed of the user to be positioned on each high-speed sub-road section according to the MR data, wherein the high-speed sub-road section is a road section between connecting points of each high-speed road;

determining the average running speed of the user to be positioned according to the target running road section and the running time interval;

determining whether the average running speed meets a speed threshold corresponding to the high-speed sub-road section;

determining the number of tunnels, work parameter cells and non-work parameter cells corresponding to the main cell occupied by the user to be positioned in the driving time interval as driving road characteristic information under the condition that the average driving speed does not meet the corresponding threshold value;

determining the number of tunnels, the number of work parameter cells and the number of non-work parameter cells corresponding to the target driving road section as reference information of the expressway;

and determining whether the characteristic information of the running road is matched with the reference information of the expressway, and determining the user to be positioned as the high-speed user under the condition that the characteristic information of the running road is matched with the reference information of the expressway.

In an optional embodiment, the determining, according to the reference relative displacement and the reference cell grid identifier, a target grid corresponding to the abnormal event awareness information further includes:

acquiring the length of a unit grid road corresponding to each expressway grid, and determining the number of grids passed by the displacement according to the reference relative displacement and the length of the unit grid road;

and determining a target grid corresponding to the abnormal event perception information according to the number of the displaced passing grids and the grid identifier of the reference cell.

According to another aspect of the embodiments of the present invention, there is provided an abnormal event locating apparatus for an expressway user, including:

the data acquisition module is used for acquiring a plurality of pieces of MR data of a user to be positioned within a preset time length, and determining a reporting time point corresponding to each piece of MR data and occupying a main cell;

the high-speed user determining module is used for determining the average running speed of the user to be positioned according to the MR data and determining whether the user to be positioned is a high-speed user;

the abnormal sensing time point determining module is used for acquiring the abnormal event sensing information of the user to be positioned and determining the abnormal event sensing time point according to the abnormal event sensing information;

a reference time point determining module, configured to, when the user to be positioned is a high-speed user, obtain, as a reference time point, a reporting time point that is closest to the abnormal event sensing time point among reporting time points included in a travel time interval corresponding to the user to be positioned;

the relative displacement determining module is used for determining the reference relative displacement of the user to be positioned according to the reference time point, the abnormal event perception time point and the average running speed;

a reference point longitude and latitude determining module, configured to acquire MR data with a reporting time point as the reference time point from the multiple pieces of MR data, and determine, according to the reference point MR data, a longitude and latitude of the user to be positioned at the reference time point as a reference point longitude and latitude;

the grid matching module is used for matching the longitude and latitude of the reference point with the longitude and latitude of a grid contained in a preset highway fingerprint library and determining a highway grid identifier corresponding to the longitude and latitude of the reference point as a reference grid identifier, wherein the highway fingerprint library comprises a plurality of highway grid identifiers and the longitude and latitude of the grid corresponding to the highway grid identifier;

and the grid positioning module is used for determining a target grid corresponding to the abnormal event perception information according to the reference relative displacement and the reference grid identification.

In an optional manner, the high speed user determination module is further configured to:

In an optional manner, the above-mentioned abnormality sensing time point determining module is further configured to:

In an optional embodiment, the grid positioning module is further configured to:

In an optional embodiment, the high speed subscriber determination module is further configured to:

According to another aspect of the embodiments of the present invention, there is provided an expressway user abnormal event locating apparatus including:

According to yet another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium having at least one executable instruction stored therein, the executable instruction causing an expressway user abnormal event locating apparatus/device to perform the following operations:

the executable instructions are further for:

In an alternative embodiment, the executable instructions are further operable to:

The method comprises the steps of determining a reporting time point corresponding to each piece of MR data and occupying a main cell by acquiring a plurality of pieces of MR data of a user to be positioned;

and finally, determining a target grid corresponding to the abnormal event perception information according to the reference relative displacement and the reference grid identification. The method and the system have the advantages that the problems that in the prior art, the high-speed users are only identified through speed, and the positioning proportion of the sensed abnormal events is too low and the accuracy is not enough due to MR positioning are solved, the characteristic matching of the MR data reported in the driving process of the high-speed users is carried out by introducing a high-speed road fingerprint library combining the characteristics of a high-speed road and the characteristics of a cell and a cell switching fingerprint library, and the positioning accuracy of the abnormal event sensing for the high-speed road users is improved.

The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.

Drawings

The drawings are only for purposes of illustrating embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

FIG. 1 illustrates a flow chart of a method embodiment of the present invention for locating an exceptional event for an expressway user;

FIG. 2 shows a flow diagram for determining whether a user to be located is a high speed user in one embodiment;

FIG. 3 is a flow diagram that illustrates the determination of a target grid in which a user to be located is located in one embodiment;

FIG. 4 illustrates a flow diagram for determining a target grid corresponding to anomalous event awareness information in one embodiment;

FIG. 5 is a flow diagram illustrating the determination of a target grid corresponding to anomalous event awareness information in another embodiment;

FIG. 6 illustrates a flow diagram for determining a cell switch fingerprint repository in one embodiment;

FIG. 7 illustrates a flow diagram for determining a target grid corresponding to the exception event in one embodiment;

FIG. 8 is a schematic structural diagram of an embodiment of an abnormal event locator for an expressway user according to the present invention;

fig. 9 is a schematic structural diagram illustrating an embodiment of the expressway user abnormal event locating apparatus according to the present invention.

Detailed Description

Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein.

FIG. 1 illustrates a flow chart of an embodiment of a method of the present invention for locating an exceptional event for an expressway user, the method being performed by a computer processing device. Specific computer processing devices may include notebook computers, cell phones, and the like. As shown in fig. 1, the method includes the following steps 110-180:

step 110: the method comprises the steps of obtaining a plurality of pieces of MR data of a user to be positioned within a preset time length, and determining a reporting time point corresponding to each piece of MR data and occupying a main cell.

The first user to be located may be a device to be located, and the user reports MR data at regular time, for example, reports MR data every 5 minutes. And determining the identification of the currently occupied cell according to the MR data.

Step 120: and determining the average running speed of the user to be positioned according to the MR data, and determining whether the user to be positioned is a high-speed user.

Particular step 120 may include steps 1201-1206 shown in fig. 2. Fig. 2 shows a flow diagram for determining whether a user to be located is a high speed user in one embodiment.

Step 1201: and determining the running time interval and the average running speed of the user to be positioned on each high-speed sub-road section according to the MR data, wherein the high-speed sub-road section is a road section between the connection points of each high-speed road.

Step 1202: and determining the average running speed of the user to be positioned according to the target running road section and the running time interval.

Step 1202 may also include steps 12021-12022:

step 12021: and determining the expressway connection points contained in the coverage area of the occupied main cell as the expressway connection points corresponding to the reporting time points.

Firstly, the highway connection point mainly comprises a preset entrance and exit and intercommunication on each highway, wherein the highway intercommunication is the intersection point of one highway and other roads, and is generally in the form of an overpass. For a certain target highway G318, the highway connection points included in G318 may be a gateway F1, a gateway F2, an interchange F3, a gateway F4, an interchange F5, an interchange F6, a gateway F7, and a gateway F8. And sub-sections of all the express roads can be defined through all the entrances and exits and intercommunication. A user may enter or leave a section of an expressway through various expressway connection points. Determining the highway connection points that the user passes in adjacent time periods thereby determines the road segment on which the user is traveling.

In addition, it should be noted that the coverage area of a cell is limited, generally 200 m to 500 m, and the distance between the high-speed intercommunication opening and the entrance/exit is far beyond the coverage area of the cell, so that there is no one cell covering multiple high-speed intercommunication or high-speed intersections, and therefore, the expressway connection point passed by the user can be determined by comparing the longitude and latitude of the coverage area of the main cell occupied in the current MR data of the user with the longitude and latitude of the high-speed intersection.

Step 12022: and under the condition that two adjacent reporting time points correspond to different expressway connection points, determining the expressway section between the different expressway connection points as the target driving section corresponding to the user to be positioned.

For example, a primary cell occupied by one piece of MR data reported by the user K at 16:30:00 at 7/15/2020 is cell a, and a primary cell occupied by one piece of MR data reported at 16:50:00 at 7/15/2020 is cell B at the next closest time point.

And the expressway junction point corresponding to the cell a is S1, and the expressway junction point corresponding to the cell B is S2. Therefore, the user K is considered to move from the expressway connecting point S1 to the expressway connecting point S2 at 16:30:00-16:50:00, that is, the user K is considered to cross the sub-link between the connecting point S1 and the connecting point S2, and therefore the sub-links S1-S2 are the target driving links driven by the user K.

It should be noted that, the user K may report an MR data again at 17:10:00 to indicate that the main cell occupied by the user K is C, but the expressway connection point existing within the coverage area of the cell C is still S2, so that the user K is considered to still travel on the road segment S1-S2.

Step 12023: and determining a time interval between the two adjacent reporting time points as a running time interval of the user to be positioned on the target running road section.

Correspondingly, the report time of the MR data corresponding to the cell A is 16:30:00 at 7, month and 15 days in 2020, and the report time point of the MR data corresponding to the cell B is 16:50:00 at 7, month and 15 days in 2020, so that the driving time interval on the road sections S1-S2 is 16:30:00-16:50:00 at 7, month and 15 days in 2020.

Step 12024: and determining the average running speed of the user to be positioned according to the target running road section and the running time interval.

The length of the target travel section can be acquired, the duration of the travel time interval is determined, and the ratio of the length to the duration is used as the average travel speed.

Step 1203: and determining whether the average running speed meets a speed threshold corresponding to the high-speed sub-road section.

Step 1204: and under the condition that the average running speed does not meet the speed threshold, determining the number of tunnels, work parameter cells and non-work parameter cells corresponding to the main cell occupied by the user to be positioned in the running time interval as the running road characteristic information.

Firstly, the work parameter cells and the non-work parameter cells are specified in the work parameter table in advance, the number of the work parameter/non-work parameter cells corresponding to one expressway is also constant, and whether the currently occupied main cell is the work parameter or the non-work parameter can also be determined.

Therefore, by analyzing the number of tunnels included in all main cells, the number of working parameter cells in all occupied cells, and the number of non-working parameter cells in the occupied cells corresponding to the MR data reported by the user on the whole road segment, matching is performed with the road characteristics (the number of cells and tunnels crossing each type) of the target driving road segment, and in case of successful matching, it can be considered that the user K really drives on the corresponding highway.

It should be noted that, in the statistics of the number of the working parameter cells that the user K passes through in the driving time interval, the preset highway connection point is removed, so as to eliminate the statistics of which high speed cells (i.e. cells distributed on the highway) besides the cells with the high speed obvious identifier, and in order that when the user speed per hour does not meet the high speed per hour, whether the user is a high speed user can be determined additionally according to whether the cell occupied by the user is a high speed cell.

Optionally, the number of bridges covered by each primary cell occupied during driving may be counted and compared with the number of bridges on the road section.

The speed of the high-speed user cannot reach the speed threshold value of the current driving road section due to the fact that during actual road driving, the user may have a short break in a service area of an expressway or the situation that traffic jam occurs on the expressway, so that the user is forced to decelerate.

Therefore, in order to determine whether a user occupying a cell corresponding to a certain highway section actually drives on the highway section, in addition to comparing the driving speed with a road speed threshold value according to the prior art, work parameter table parameters corresponding to the highway section, such as work parameter cells covered by the highway section, non-work parameter cells, and information of bridges, tunnels and the like passed by each cell (called as a high-speed cell) on the highway section, can be compared with corresponding information of user MR data.

Step 1205: and determining the number of tunnels, the number of work parameter cells and the number of non-work parameter cells corresponding to the target driving road section as the reference information of the expressway.

Step 1206: and determining whether the characteristic information of the running road is matched with the reference information of the expressway, and determining the user to be positioned as the high-speed user under the condition that the characteristic information of the running road is matched with the reference information of the expressway.

It should be particularly noted that, in an alternative embodiment, in order to avoid the inaccuracy of the judgment of the high-speed user with respect to the speed, when the average speed meets the speed threshold, the reference information of the expressway of the target travel road segment may be further compared with the cell parameter characteristics of the main cell occupied by the travel time interval, so as to improve the accuracy of the determination of the high-speed user.

Step 130: and acquiring abnormal event perception information of the user to be positioned, and determining an abnormal event perception time point according to the abnormal event perception information.

First, an exception event, i.e., a perceived exception event, includes, but is not limited to: VOLTE single-pass, VOLTE word swallowing, VOLTE interruption, 4G offline, A request failure, attachment failure, low download rate and the like.

The time point of the user at which the sensing abnormal event occurs is determined as the abnormal event sensing time point, and the time point can be, for example, 16:49:10 in 7/15/2020.

Step 140: and acquiring a reporting time point which is closest to the abnormal event perception time point from the reporting time points contained in the running time interval corresponding to the user to be positioned as a reference time point.

That is, if the user travels on the road segment a-B in the interval 16:30:00-16:50:00 at 7/15/2020, while in the interval 16:30:00-16:50:00 the user K may report MR data every 5 minutes, the reference time point here is determined to be 16:50:00 at 7/15/2020.

It should be particularly noted that, in an alternative embodiment, there may be just MR data reported by the user at the abnormal event perception time point, that is, the MR data reported by the user at the abnormal event perception time point is directly acquired to determine the location where the user perceives the abnormality.

Step 150: and determining the reference relative displacement of the user to be positioned according to the reference time point, the abnormal event perception time point and the average running speed.

A specific reference relative displacement refers to the distance traveled by a high-speed user between a reference point in time (there is reporting of MR data) and an exceptional perceived time (there is not necessarily reporting of MR data).

The calculation of the reference relative displacement may be a product of a difference between the reference time point and the abnormal event perception time point and an average traveling speed.

Step 160: and acquiring MR data with a reporting time point as the reference time point from the plurality of pieces of MR data as reference point MR data, and determining the longitude and latitude of the user to be positioned at the reference time point as the reference point longitude and latitude according to the reference point MR data.

That is, the longitude and latitude positions of the high-speed user at the reference time point are determined by acquiring the MR data reported by the high-speed user at the time point closest to the sensing time of the abnormal event (i.e. the aforementioned reference time point).

Step 170: and matching the longitude and latitude of the reference point with the longitude and latitude of a grid contained in a preset highway fingerprint library, and determining a highway grid identifier corresponding to the longitude and latitude of the reference point as a reference grid identifier, wherein the highway fingerprint library comprises a plurality of highway grid identifiers and the longitude and latitude of the grid corresponding to the highway grid identifier.

Firstly, the highway fingerprint database further comprises a main cell identifier and an adjacent cell identifier which are associated with each highway raster identifier, a main cell RSRP value corresponding to the main cell identifier, an adjacent cell RSRP value corresponding to the adjacent cell identifier, and the RSRP value is an electrical level value.

That is, one expressway (generally, a length of more than several kilometers) may be divided into several grids of a predetermined length, for example, every 50 meters into one grid, and considering that the coverage area of one serving cell is generally 500 meters or more, a cell may cover a plurality of different grids, and signal level values measured in different grids from different cells may differ according to distance, and thus even if the one serving cell covers the one expressway, the level values of the one serving cell in different grid positions may differ according to the distance of the one serving cell.

And one main cell corresponds to a plurality of adjacent cells, and similarly, even if the main cell is covered by the same adjacent cell, the level values of the adjacent cells measured in grids at different positions are different, so that a specific expressway grid fingerprint can be formed.

Specifically, grid location for a high-speed user may include steps 1701-1706 shown in fig. 3, where fig. 3 shows a flow chart for determining a target grid in which a user to be located is located in one embodiment.

Step 1701: and taking the currently occupied cell in each piece of MR data of the user to be positioned as a target main cell, and determining at least one adjacent cell corresponding to the target main cell as a target adjacent cell.

Step 1702: and determining a target main cell identifier and an RSRP value of the target main cell, matching the target main cell with the main cell identifiers in the expressway fingerprint library, and calculating a difference value between the RSRP value of the main cell in the matched expressway fingerprint library and the RSRP value of the target main cell.

It should be noted that, signals from a plurality of different primary cells may correspond to one grid, and the different primary cells have different level values in the grid due to their distances from the current grid, so that there may be a plurality of different grids whose primary cell identities may all match with the target primary cell identity, for example, the target primary cell is D1, and the primary cell identity corresponding to grid M1 may be D1, D2, D3, D4.

It is therefore desirable to acquire the grid corresponding to the primary cell in the highway fingerprint library that is closest to the target primary cell level value in the MR data.

Step 1703: and acquiring the expressway grid identifier corresponding to the main cell in the expressway fingerprint library with the minimum difference value as the alternative main cell grid identifier.

For example, in the case that the target primary cell is D1, and its level value is Y1, there may be 10 corresponding primary cells in the fingerprint library containing a grid of D1, and wherein RSRP values of the primary cells D1 in grid M1, grid M2, and grid M3 are all the same and closest to Y1. Thus, the alternative primary cell grids here are identified as M1, M2, M3.

Step 1704: and matching the target adjacent cell identification of the target adjacent cell with the adjacent cell identification corresponding to the alternative main cell grid identification in the expressway fingerprint database.

Specifically, target neighboring cells corresponding to the target primary cell D1 in the MR data may be N1, N2, N3, and N4, and corresponding neighboring cells in grids M1, M2, and M3 in the expressway fingerprint library are obtained, and it is found that the grid M1 includes neighboring cells N1, N2, and N3, the grid M2 includes neighboring cells N1, N2, and N4, and the grid M3 includes neighboring cells N2, and N4.

Step 1705: and calculating the difference value between the RSRP value of the adjacent cell in the matched expressway fingerprint library and the RSRP value of the target adjacent cell, and acquiring the expressway grid mark corresponding to the adjacent cell in the expressway fingerprint library with the minimum difference value as the candidate adjacent cell grid mark.

In connection with the example in step 1704, the level values of the corresponding neighboring cells matched in N1, N2, N3, N4 in grids M1, M2, M3 in the aforementioned expressway fingerprint library are calculated respectively.

Similarly, considering that the MR data generally includes a primary cell and a plurality of neighboring cells corresponding to the primary cell, a plurality of primary cells and a plurality of neighboring cells corresponding to each grid exist in the fingerprint database, and the identification and level values of the primary cell and the neighboring cells corresponding to different grids may be different. There may be two or more grids with corresponding primary cell level values being the same and all closest to the target primary cell level value in the MR data, so that the level values of the neighboring cells in the grids are further to be matched separately.

Specifically, the level values of target neighbor cells N1, N2, N3, and N4 in the MR data are compared with the corresponding neighbor cells included in grids M1, M2, and M3, respectively, and it can be found that the grids closest to the level values of target neighbor cells N1, N2, N3, and N4 are M1, M2, M3, and M2, respectively.

And further calculating the selection weight of the difference value of the level values of the matched adjacent cells according to a preset exponential function to determine the grid identifier of the alternative adjacent cell, wherein the grid corresponding to the matched adjacent cell with smaller difference value of the level values is used as the selection weight of the grid of the alternative adjacent cell to be larger.

Step 1706: and determining a target grid identifier corresponding to the user to be positioned according to the alternative main cell grid identifier and the alternative neighbor cell grid identifier.

And judging whether the grid identifier of the alternative main cell and the grid identifier of the alternative adjacent cell correspond to the same grid or not, and determining the grid as a target grid under the condition of corresponding to the same grid. In the case of not corresponding to the same grid, an exponential function may be used

(X0 is the level value of the target neighbor cell, X1 is the level value of the cell in each grid in the fingerprint database) to calculate each cellAnd determining the grid with the highest weight score as the target grid according to the weight coefficient of the difference value of each matched adjacent cell in the grids of the alternative adjacent cells.

Step 180: and determining a target grid corresponding to the abnormal event perception information according to the reference relative displacement and the reference grid identification.

Specifically, the step 180 may further include the

step

1801 and 1802 shown in fig. 4. FIG. 4 illustrates a flow diagram for determining a target grid corresponding to anomalous event awareness information in one embodiment.

Step 1801: and acquiring the length of a unit grid road corresponding to each expressway grid, and determining the number of grids passed by the displacement according to the reference relative displacement and the length of the unit grid road.

I.e. how many road grids the displacement of the high speed user between the reference point in time and the point in time of the anomalous perception has passed.

Step 1802: and determining a target grid corresponding to the abnormal event perception information according to the number of the displaced passing grids and the grid identifier of the reference cell.

That is, if the reference cell grid at the reference time point is identified as M5, the calculated displacement passing grid number is 10, and thus the target grid is M15.

It should be particularly noted that, in the present invention, it is assumed that the user is driving at a constant speed when traversing each cell/grid, and therefore, a final grid positioning result may have a certain deviation from a cell coverage area occupied by the user, and optionally, a screening may be performed on the grid positioning result.

And calculating whether the distance between the target grid position and the cell occupied by the reference time point meets a preset distance threshold value or not so as to judge whether the cell coverage range corresponding to the current grid position includes the current longitude and latitude or not, and if so, judging whether the cell coverage range is within 200 meters or not.

In addition, since the idea of locating an abnormal event is to determine a space-time point where both the grid position and the elapsed (occupied) time are known as a reference point, the abnormal event is located by calculating the relative distance from the reference point in time or position. Therefore, in an optional embodiment, in addition to performing positioning by calculating the number of passed grids according to the relative displacement, positioning may be performed according to the condition of passed grids in a cell occupied by a high-speed user when sensing an abnormal event, where the reference time-space point in this embodiment is the time for beginning to occupy the cell where the abnormality occurs and ending to occupy.

Thus, in an alternative embodiment, the grid location of anomalous perception events for high speed users may also include

step

2101 and 2105 shown in FIG. 5. FIG. 5 shows a flow diagram for determining a target grid corresponding to anomalous event awareness information in another embodiment.

Step 2101: and determining a primary cell occupied by the user to be positioned at the reference time point as an abnormal perception primary cell.

Step 2102: and matching the sensing abnormal main cell with a cell switching pair contained in a preset switching fingerprint library, and determining the cell occupied before abnormal sensing and the cell occupied after abnormal sensing according to the matched cell switching pair, wherein the switching fingerprint library comprises at least one pair of cell switching pairs consisting of the cell where switching occurs and the cell switched to, the switching position longitude and latitude of the cell switching pairs, and the expressway grid identification corresponding to the switching position longitude and latitude.

For example, the perceptually abnormal primary cell is D3, and the cell handover pair containing D3 may be D2-D3, D1-D3, D3-D4, D3-D5 as the matched target cell handover pair according to the matching in the cell handover fingerprint database.

Meanwhile, the cells which are reported by the user in the MR data closest to the abnormal sensing time point and occupied by the user in the MR reported data are determined to be D1 and D4 according to analysis of the MR data reported by the user on the target driving road section, so that the cells D1 and D4 are respectively used as the occupied cells before abnormal sensing and the occupied cells after abnormal sensing.

It should be noted that the data of whether handover occurs in the handover fingerprint database is from the S1-MME data, and the S1-MME data has explicit information of handover from the source cell to the target cell, including time, so that it can be guaranteed that the handover occurs adjacently.

In general, the handover fingerprint database mainly includes three main information, namely a handover source cell, a handover target cell and a handover position, and in the using process, when two cells in the MR data are the same as the two cells in the handover fingerprint database, the handover fingerprint database is associated with one piece of position information, and if the position information meets the reference position condition of sensing the abnormal event (that is, the reporting time of the MR data is very close to the abnormal sensing time), the position information can be used as a reference position for positioning the sensing abnormal event.

First, the cell switching fingerprint database includes a plurality of cell switching pairs, an expressway identifier corresponding to each cell switching pair, a cell switching pair identifier, and a switching position longitude and latitude.

A cell handover pair includes the cell where the handover occurred and the target cell to which the handover occurred. The cell switching pair identification comprises an ECI identification of the cell where switching occurs and an ECI identification of the target cell to which switching occurs.

Specifically, the determination process of the cell switch fingerprint database may include steps 21021-21024 shown in fig. 6. Figure 6 illustrates a flow diagram for determining a cell switch fingerprint repository in one embodiment.

Step 21021: and acquiring multi-time network pulling data, and determining switching information corresponding to each height road mark according to the multi-time network pulling data.

The network pulling data is used for determining switching points of one cell and another cell, so that switching positions of different cells (which can be regarded as boundaries between different cells) are determined according to a plurality of switching points, different cells are occupied by a high-speed user in the driving process, and when a main cell is switched, a unique grid is determined in the cells before and after the main cell is sensed through abnormal events.

The specific processing for the pull data may be as follows: and allowing a certain switching point not to appear in the single-time network pulling data, if the switching point appears in the same network pulling data for multiple times, averaging the longitude and latitude of the position of the switching point, and if the certain switching point appears in the multiple-time network pulling data, taking a median value aiming at the longitude and latitude of the switching point.

And matching the longitude and latitude of the pull-up data to each line according to the line information in the express way fingerprint, and calculating the distance between the corrected longitude and latitude and the end point and the express way grid.

Step 21022: and determining the cell where the switching occurs and the cell of the target cell to be switched as a pair of cell switching pairs according to the switching information.

It is readily understood that there may be multiple pairs of handover locations corresponding between different cells, thereby forming a handover zone. And one handover information corresponds to one handover location point and a pair of cell handover pairs.

Step 21023: and determining the cell switching pair identification and the switching position longitude and latitude corresponding to the cell switching pair.

Step 21024: and matching the longitude and latitude of the switching position with the longitude and latitude of the grid in the altitude road fingerprint library to obtain the corresponding expressway grid identification of the matched grid longitude and latitude, and determining the expressway grid identification, the cell switching pair identification and the switching position longitude and latitude corresponding to each expressway identification as one fingerprint in the cell switching fingerprint library.

That is, after dividing each of the highways into a plurality of grids, the signal characteristics of each grid are determined, and the association information in each grid of each cell, that is, the information of the cell switching position included in each grid, may be further increased in consideration of the fineness of division (50 m) of the grid as compared with that (200 m) of the cell.

Step 2103: and determining MR data occupying the main cell in the MR data as the occupation cell before abnormal sensing as reported data before abnormal sensing, and determining MR data occupying the main cell in the MR data as the occupation cell after abnormal sensing as reported data after abnormal sensing.

Step 2104: and determining the occupation starting time point of the user to be positioned for the abnormal perception cell according to the reported data before abnormal perception, and determining the occupation ending time point of the user to be positioned for the abnormal perception cell according to the reported data after abnormal perception.

It is easily understood that the time when the user K is handed over from the cell D1 to the perceptually abnormal cell D3 is taken as an occupation start time point, and the time when the user K is handed over from the perceptually abnormal cell D3 to the cell D4 is taken as an occupation end time point.

Step 2105: and determining a coverage grid range of the abnormal cell to be positioned, and determining a target grid corresponding to the abnormal event according to the abnormal sensing time point, the occupation starting time point, the occupation ending time point and the coverage grid range.

Similar to the aforementioned process of calculating the target grid from the number of grids passed by the relative displacement, here the target grid is calculated from the number of grids passed and the time in the whole process from the start of occupation to the end of occupation of a certain cell by the user and the time when an abnormality occurs. The specific step 2105 may include the

step

21051 and 21052 shown in fig. 7, and fig. 7 shows a flowchart for determining a target grid corresponding to the abnormal event in one embodiment.

Step 21051: and determining a ratio of the interval duration between the abnormal sensing time point and the occupation starting time point as a numerator and the interval duration between the occupation ending time point and the occupation starting time point as a denominator as a target movement multiple.

Step 21052: and determining a target grid corresponding to the abnormal event according to the target movement multiple, the number of the covering grids and the grid coverage range of the abnormal cell to be positioned.

Specifically, the target movement multiple is multiplied by the number of the covered grids to obtain how many grids the high-speed user perceives the abnormality after entering the cell with the abnormal perception, and then the grid identification interval of the whole cell with the abnormal perception is determined according to the grid coverage range, namely the target grid is determined according to the number of the passed grids and the grid identification interval.

Specifically, the grid identification interval of the perceptually abnormal cell D3 is M1-M30, and the number of passing grids is 15 according to the target movement multiple and the number of coverage grids, so that the target grid is M15.

In an alternative embodiment, after the grid information sensed by the abnormal event is determined, the abnormal grid position information can be marked on a map and displayed through a display device.

The abnormal event perception quantity of the corresponding grids in each cell in a certain time period can be further counted, whether the abnormal event perception quantity is larger than a preset abnormal quantity threshold value or not is judged, the cell larger than the abnormal quantity threshold value is determined to serve as a target cell to be improved with poor network signal quality, and the position of the target cell to be improved is sent to relevant personnel to be processed, so that signals of a specific cell on the expressway are improved.

Fig. 8 is a schematic structural diagram illustrating an embodiment of the device for locating an abnormal event of an expressway user according to the present invention. As shown in fig. 8, the apparatus 300 includes: the system comprises a data acquisition module 310, a high-speed user determination module 320, an abnormal perception time point determination module 330, a reference time point determination module 340, a relative displacement determination module 350, a reference point longitude and latitude determination module 360, a grid matching module 370 and a grid positioning module 380.

The data acquisition module 310 is configured to acquire multiple pieces of MR data of a user to be positioned within a preset time duration, and determine a reporting time point corresponding to each piece of MR data and an occupied main cell;

the high-speed user determining module 320 is configured to determine an average traveling speed of the user to be positioned according to the MR data, and determine whether the user to be positioned is a high-speed user;

an abnormal sensing time point determining module 330, configured to obtain abnormal event sensing information of the user to be positioned, and determine an abnormal event sensing time point according to the abnormal event sensing information;

a reference time point determining module 340, configured to, when the user to be located is a high-speed user, obtain, as a reference time point, a reporting time point that is closest to the abnormal event sensing time point among reporting time points included in a travel time interval corresponding to the user to be located;

a relative displacement determining module 350, configured to determine a reference relative displacement of the user to be positioned according to the reference time point, the abnormal event sensing time point, and the average traveling speed;

a reference point longitude and latitude determining module 360, configured to acquire, from the multiple pieces of MR data, MR data whose reporting time point is the reference time point as reference point MR data, and determine, according to the reference point MR data, longitude and latitude of the user to be positioned at the reference time point as reference point longitude and latitude;

the grid matching module 370 is configured to match the longitude and latitude of the reference point with a grid longitude and latitude included in a preset highway fingerprint library, and determine a highway grid identifier corresponding to the longitude and latitude of the reference point as a reference grid identifier, where the highway fingerprint library includes a plurality of highway grid identifiers and a grid longitude and latitude corresponding to the highway grid identifier;

and the grid positioning module 380 is configured to determine a target grid corresponding to the abnormal event perception information according to the reference relative displacement and the reference grid identifier.

In an alternative manner, the high speed user determination module 320 is further configured to:

In an optional manner, the above-mentioned anomaly-aware time point determining module 330 is further configured to:

In an alternative embodiment, the grid positioning module 380 is further configured to:

In an alternative embodiment, the grid positioning 380 module is further configured to:

In an alternative embodiment, the high speed user determination module 320 is further configured to:

Fig. 9 is a schematic structural diagram illustrating an embodiment of an expressway user abnormal event positioning device according to the present invention, and the specific embodiment of the present invention is not limited to the specific implementation of the expressway user abnormal event positioning device.

As shown in fig. 9, the expressway user abnormal event locating apparatus may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.

Wherein: the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408. A communication interface 404 for communicating with network elements of other devices, such as clients or other servers. The processor 402, configured to execute the program 410, may specifically perform the relevant steps in the above embodiments of the method for locating an exceptional event of an expressway user.

In particular, program 410 may include program code comprising computer-executable instructions.

The processor 402 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The expressway user abnormal event positioning device comprises one or more processors, which can be the same type of processors, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.

And a memory 406 for storing a program 410. The memory 406 may correspond to a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

The routine 410 may be specifically invoked by the processor 402 to cause the expressway user exception event locating apparatus to perform the following operations:

In an alternative, the program 410 is further invoked by the processor 402 to cause the expressway user exception event locating apparatus to:

In an alternative, the program 410 is invoked by the processor 402 to cause the expressway user exception event locating apparatus to:

An embodiment of the present invention provides a computer-readable storage medium, where the storage medium stores at least one executable instruction, and when the executable instruction runs on an expressway user abnormal event positioning apparatus/device, the expressway user abnormal event positioning apparatus/device is caused to execute the expressway user abnormal event positioning method in any method embodiment described above.

The executable instructions may be specifically configured to cause the expressway user exception event locating apparatus/device to:

In an alternative embodiment, the executable instructions may be specifically configured to cause the expressway user exception event locating apparatus/device to perform the following operations:

Embodiments of the present invention provide a computer program that can be invoked by a processor to enable an expressway user abnormal event positioning apparatus to execute an expressway user abnormal event positioning method in any of the above method embodiments.

Embodiments of the present invention provide a computer program product comprising a computer program stored on a computer-readable storage medium, the computer program comprising program instructions that, when run on a computer, cause the computer to perform a method for locating an exceptional event of an expressway user in any of the above-mentioned method embodiments.

The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "corresponding" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.

Claims

1. A method for locating an abnormal event of an expressway user, comprising:

2. The method of claim 1, wherein the highway fingerprint library further comprises a primary cell identifier and a neighbor cell identifier associated with each of the highway grid identifiers, a primary cell RSRP value corresponding to the primary cell identifier, a neighbor cell RSRP value corresponding to the neighbor cell identifier,

3. The method of claim 1, wherein after said determining an exceptional perception time point based on said exceptional perception information, further comprising:

4. The method of claim 3, wherein the determining a target grid corresponding to the abnormal event according to the abnormal sensing time point, the occupation starting time point, the occupation ending time point and the coverage grid range further comprises:

5. The method according to claim 3, wherein the cell switching fingerprint database includes a plurality of cell switching pairs, an expressway identifier corresponding to each of the cell switching pairs, a cell switching pair identifier, and a switching location latitude and longitude, and the determining process of the cell switching fingerprint database further includes:

6. The method of claim 1, wherein said determining an average travel speed of said user to be located from said MR data, determining whether said user to be located is a high speed user, further comprises:

7. The method according to claim 3, wherein the determining a target grid corresponding to the abnormal event awareness information according to the reference relative displacement and the reference cell grid identifier further comprises:

8. An abnormal event locating device for an expressway user, comprising:

9. An abnormal event locating apparatus for an expressway user, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;

the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation of the abnormal event positioning method of the expressway user as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the storage medium has stored therein at least one executable instruction, which when run on an exceptional positioning device/apparatus of an expressway user, causes the exceptional positioning device/apparatus of the expressway user to perform the operation of the exceptional positioning method of the expressway user as recited in any one of claims 1 to 7.