CN114205733B

CN114205733B - Abnormal perception event positioning method for expressway user

Info

Publication number: CN114205733B
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: 2023-12-26
Anticipated expiration: 2040-08-31
Also published as: CN114205733A

Abstract

The embodiment of the invention relates to the field of computer data processing, and discloses an anomaly-aware event positioning method for expressway users, which comprises the following steps: acquiring a plurality of pieces of MR data of a user to be positioned, and determining reporting time points of the MR data and occupying 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, and acquiring an abnormal event sensing 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 closest to the abnormal event sensing time point as a reference time point; determining a reference relative displacement according to the reference time point; determining longitude and latitude of a reference point according to MR data taking the reporting time point as the reference time point; and taking the expressway grid identifier matched with the longitude and latitude of the reference point as a reference grid identifier, and determining a target grid according to the reference grid identifier. The invention improves the accuracy of the abnormal sensing and positioning of the high-speed user.

Description

Abnormal perception event positioning method for 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 locating 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 quality of user signals and improve the perceived experience of users, a private network is usually arranged along a high-speed line to isolate private network cells from common cells, and users traveling at high speed basically use the private network, so that the improvement of the quality of the private network cells is particularly important.

At present, the quality of private network cells is evaluated by adopting the perception information of road mobile users, the basic practice is to calculate the user speed according to the design requirements such as road running speed and the like and the user signaling data including road passing time, displacement information and the like, and when the user meets the road design basic speed interval, the user is considered as the road user. And (3) for the identified road user, utilizing MR information which is regularly reported by the mobile user, estimating the position of the user according to specific rule matching through a road fingerprint database, displaying the position information, positioning the perception abnormal event of the user and the like.

However, in the prior art, the location fingerprint characteristic index in the location fingerprint database for user location generally adopts the signal intensity of the cell and the neighboring cell extracted from the MR data reported by the user, but the factors influencing the signal intensity are not only related to the location of the user terminal, but also influenced by a plurality of other factors, so that the situation of user terminal location deviation can occur, the final location result is influenced, and the problems of low location proportion, low precision, error in index statistics and the like are further caused, thereby influencing the situation of the final location result

Disclosure of Invention

In view of the above problems, an embodiment of the present invention provides a method for locating an abnormal event of an expressway user, which is used for solving the problem in the prior art that the accuracy of locating the abnormal event of the expressway user is low.

According to an aspect of the embodiment of the present invention, there is provided an abnormal event locating method for 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 and an occupied main cell corresponding to each piece of MR data;

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 or not;

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

acquiring a reporting time point nearest to the abnormal event sensing time point contained in the MR data 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 sensing 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 the reference point;

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

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

In an alternative manner, the expressway fingerprint library also comprises a main cell identifier and a neighbor cell identifier which are 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 user to be positioned is determined to be the high-speed user, the method further comprises the following steps:

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 identifier 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 library with the minimum difference value as an alternative main cell grid identifier;

matching a target neighbor cell identifier of the target neighbor cell with neighbor cell identifiers corresponding to the candidate main cell grid identifiers in the expressway fingerprint library;

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

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

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

In an alternative embodiment, after determining the abnormal event sensing time point according to the abnormal event sensing information, the method further comprises:

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

the sensing abnormal main cell is matched with a cell switching pair contained in a preset switching fingerprint library, and a cell occupied before abnormal sensing and a cell occupied after abnormal sensing are determined according to the matched cell switching pair, wherein the switching fingerprint library comprises at least one pair of cell switching pairs consisting of cells where switching occurs and cells switched to, which are included in each target driving road section, and expressway grid identifications corresponding to the switching position longitude and latitude of the cell switching pair;

Determining MR data occupying a main cell in the MR data as the pre-abnormal-perception occupied cell as pre-abnormal-perception reporting data, and determining MR data occupying the main cell in the MR data as the post-abnormal-perception occupied cell as the post-abnormal-perception reporting data;

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

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

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

determining a ratio of taking the interval duration between the abnormal event sensing time point and the occupation starting time point as a numerator and taking the 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 covered grids and the grid coverage of the sensing abnormal cell.

In an optional embodiment, the cell handover fingerprint library includes a plurality of cell handover pairs, expressway identifiers corresponding to the cell handover pairs, cell handover pair identifiers, and handover position longitudes and latitudes, and the determining process of the cell handover fingerprint library further includes:

acquiring multiple network pulling data, and determining switching information corresponding to each expressway identifier according to the multiple network pulling data;

determining a cell where switching occurs and a cell of a target cell to which switching is performed 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;

matching the longitude and latitude of the switching position with the longitude and latitude of the grids in the expressway fingerprint library, obtaining expressway grid identifications corresponding to the matched longitude and latitude of the grids, and determining the expressway grid identifications, the cell switching pair identifications 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, according to the MR data, the average running speed of the user to be located, and determining whether the user to be located is a high-speed user, further includes:

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

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-section;

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

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

and determining whether the driving road characteristic information is matched with the expressway reference information, and determining the user to be positioned as an expressway user when the driving road characteristic information is matched with the expressway reference information.

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

obtaining the unit raster road length corresponding to each expressway raster, and determining the displacement passing raster number according to the reference relative displacement and the unit raster road length;

and determining a target grid corresponding to the abnormal event sensing information according to the displacement passing grid number and the reference cell grid identification.

According to another aspect of the embodiment 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 in a preset time period, and determining a reporting time point and an occupied main cell corresponding to each piece of MR data;

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 or not;

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

The reference time point determining module is used for acquiring a reporting time point nearest to the abnormal event sensing 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 when the user to be positioned is a high-speed user;

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 sensing time point and the average running speed;

the reference point longitude and latitude determining module is used for acquiring MR data with reporting time points being the reference time points 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 points as the longitude and latitude of the reference point according to the reference point MR data;

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 expressway fingerprint library, and determining an expressway grid identifier corresponding to the longitude and latitude of the reference point as a reference grid identifier, wherein the expressway fingerprint library comprises a plurality of expressway grid identifiers and the longitude and latitude of the grid corresponding to the expressway grid identifier;

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

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

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

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

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

According to another aspect of the embodiment of the present invention, there is provided an expressway user abnormality event positioning apparatus including:

According to yet another aspect of an embodiment of the present invention, there is provided a computer-readable storage medium having stored therein at least one executable instruction for causing an expressway user exception event localization apparatus/device to:

the executable instructions are further for:

In an alternative embodiment, the executable instructions are further for:

According to the embodiment of the invention, a plurality of pieces of MR data of a user to be positioned are acquired, and a reporting time point and an occupied main cell corresponding to each piece of MR data are determined;

and finally, determining a target grid corresponding to the abnormal event perception information according to the reference relative displacement and the reference grid identification. In the invention, feature matching of MR data reported in the running process of the expressway user is carried out by introducing an expressway fingerprint library and a cell switching fingerprint library which combine expressway features with cell features, so that the positioning accuracy of abnormal event perception of the expressway user is improved.

The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and may be implemented according to the content of the specification, so that the technical means of the embodiments of the present invention can be more clearly understood, and the following specific embodiments of the present invention are given for clarity and understanding.

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 designate like parts throughout the figures. In the drawings:

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

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

FIG. 3 illustrates a flow diagram for determining a target grid in which a user is to be located, in one embodiment;

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

FIG. 5 is a flow chart illustrating a method for determining a target grid corresponding to abnormal event awareness information in another embodiment;

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

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

FIG. 8 is a schematic diagram showing the construction of an embodiment of the expressway user abnormality location apparatus of the invention;

fig. 9 is a schematic diagram showing the construction of an embodiment of the expressway user abnormality event positioning apparatus of the 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 present invention are shown in the drawings, it should be understood that the present invention may 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 abnormal 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: acquiring a plurality of pieces of MR data of a user to be positioned in a preset duration, and determining a reporting time point and an occupied main cell corresponding to each piece of MR data.

The user to be located may be the device to be located first, and the user reports MR data at regular intervals, for example, 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 or not.

Specific step 120 may include steps 1201-1206, which may include those illustrated in fig. 2. FIG. 2 illustrates a flow chart of determining whether a user to be located is a high-speed user in one embodiment.

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

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 expressway connection points contained in the coverage area of the occupied main cell as expressway connection points corresponding to the reporting time points.

First, the expressway connection point mainly comprises preset entrances and exits on each expressway and intercommunication, wherein the expressway intercommunication is a junction of one expressway and other roads, and is generally in an overpass form. For example, for a target expressway G318, expressway connection points included in G318 may be an entrance F1, an entrance F2, an interconnection F3, an entrance F4, an interconnection F5, an interconnection F6, an entrance F7, and an entrance F8. Sub-sections of each expressway can be defined through each gateway and intercommunication. The user may enter or leave a section of expressway through various expressway connection points. The road section travelled by the user can be determined by determining the expressway connection point travelled by the user in the adjacent time period.

In addition, it should be noted that the coverage area of a cell is limited, generally in 200 meters to 500 meters, and the distance between the high-speed intercommunication ports and the gateway is generally far beyond the coverage area of the cell, so that there is no cell covering a plurality of high-speed intercommunications 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 by the current MR data of the user with the longitude and latitude of the expressway intersection.

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

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

And the expressway connection point corresponding to the cell a is S1, and the expressway connection point corresponding to the cell B is S2. Therefore, the user K can be seen as moving from the expressway junction S1 to S2 at 16:30:00-16:50:00, and the user K can be considered to traverse the sub-section between the junction S1 and the junction S2, so that the sub-sections S1-S2 are the target driving sections for the user K to drive.

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

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

Correspondingly, since the reporting time of the MR data corresponding to the cell a is 16:30:00 on the month of 2020, and the reporting time of the MR data corresponding to the cell B is 16:50:00 on the month of 2020, the driving time interval on the road segments S1-S2 is 16:30:00-16:50:00 on the month of 2020, and the reporting time of the MR data corresponding to the cell B is 16:50:00 on the month of 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 length of the travel time interval is determined, and the ratio of the length to the length is taken as the average travel speed.

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

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

Firstly, the industrial parameter cell and the non-industrial parameter cell are preset in an industrial parameter table, the number of industrial parameter/non-industrial parameter cells corresponding to one expressway is also fixed, and whether the currently occupied main cell is an industrial parameter or a non-industrial parameter can be determined.

Therefore, the number of tunnels included in all the main cells corresponding to the MR data reported by the user on the whole road section, the number of industrial parameter cells in all occupied cells, and the number of non-industrial parameter cells in occupied cells are analyzed to be matched with the road characteristics (the number of crossing various types of cells and the number of tunnels) of the target driving road section, and under the condition that the matching is successful, the user K can be considered to be actually driving on the corresponding expressway.

It should be noted that, in counting the number of the passing industrial parameter cells of the user K in the driving time interval, the preset expressway connection point is eliminated, so that in order to count the cells with obvious marks of high speed, which are also high-speed cells (i.e. cells distributed on the expressway), when the user's speed does not meet the high-speed, whether the user is a high-speed user can be judged additionally according to whether the cell occupied by the user is a high-speed cell.

Alternatively, the number of bridges covered by each main cell occupied during driving can be counted to compare with the number of bridges in the road section.

In consideration of the fact that in the actual road driving process, there may be a situation that the speed of the high-speed user cannot reach the speed threshold of the current driving road section due to the fact that the user has a short rest in the service area of the high-speed road or is blocked on the high-speed road and is forced to run at a reduced speed.

Therefore, in order to determine whether a user occupying a cell corresponding to a highway section actually travels on the highway section, in addition to comparing the traveling speed with the road speed threshold value according to the prior art, information of a parameter table corresponding to the highway section, such as a parameter cell covered by the highway section, a non-parameter cell, a bridge, a tunnel, and the like, through which each cell (called a high-speed cell) passes on the highway section, and corresponding information of MR data of the user may be compared.

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

Step 1206: and determining whether the driving road characteristic information is matched with the expressway reference information, and determining the user to be positioned as an expressway user when the driving road characteristic information is matched with the expressway reference information.

It should be noted that, in an alternative embodiment, in order to avoid inaccuracy in determining the speed of the high-speed user, when the average speed meets the speed threshold, the reference information of the highway on the target driving road section and the cell industrial parameter feature of the main cell occupied by the driving time interval may be further compared, so as to improve accuracy of determining the high-speed user.

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

First, an exception event is a perceived exception event, including but not limited to: VOLTE single pass, VOLTE swallow word, VOLTE intermittent, 4G off-line, A request failure, attachment failure, low download rate, etc.

The time point when the user senses the abnormal event is determined as the abnormal event sensing time point, and may be, for example, 7 months and 15 days in 2020:49:10.

Step 140: and acquiring a reporting time point nearest to the abnormal event sensing time point in the reporting time points contained in the running time interval corresponding to the user to be positioned as a reference time point.

That is, the user travels on the road section a-B in the travel time interval of 16:30:00-16:50:00 on 15 th 7 th 2020, and the MR data may be reported by the user K every 5 minutes in the travel time interval of 16:30:00-16:50:00, so the reference time point herein is determined as 16:50:00 on 15 th 7 th 2020.

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

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

The specific reference relative displacement refers to the distance traveled by the high-speed user between the reference point in time (there is a report of MR data) and the abnormal event sensing time (there is not necessarily a report 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 sensing time point and the average running speed.

Step 160: and acquiring MR data with reporting time points being the reference time points 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 points according to the reference point MR data as the longitude and latitude of the reference point.

That is, the longitude and latitude positions of the high-speed user at the reference time point are determined by acquiring 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: matching the longitude and latitude of the reference point with the longitude and latitude of grids contained in a preset expressway fingerprint library, and determining an expressway grid identifier corresponding to the longitude and latitude of the reference point as a reference grid identifier, wherein the expressway fingerprint library comprises a plurality of expressway grid identifiers and the longitude and latitude of the grids corresponding to the expressway grid identifiers.

First, the expressway fingerprint library further includes a main cell identifier and a neighboring cell identifier associated with each expressway grid identifier, a main cell RSRP value corresponding to the main cell identifier, and a neighboring cell RSRP value corresponding to the neighboring cell identifier, where the RSRP value is a level value.

That is, an expressway (length is generally greater than several thousand meters) may be divided into a plurality of grids of a predetermined length, such as a grid every 50 meters, and considering that the coverage area of a serving cell is generally 500 meters or more, a plurality of different grids may be covered in one cell, and the measured signal level values from different cells in different grids may be different due to the distance, so that the level values of the main cells in grids of different longitude and latitude positions may be different even if they are covered by the same main cell.

And a main cell corresponds to a plurality of neighbor cells, and also, even if the same neighbor cell is covered, the measured level values of the neighbor cells in grids at different positions are different, thereby forming a specific expressway grid fingerprint.

In particular, grid locating for high speed users may include steps 1701-1706 shown in FIG. 3, where FIG. 3 shows a flow chart of determining a target grid in which a user to be located is located, in one embodiment.

Step 1701: and taking the 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.

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 identifier 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 be corresponding to one grid, and the level values of the different primary cells in the grid may also be different due to the distances from the current grid, so that there may be a plurality of primary cell identifiers of different grids that match the target primary cell identifier, for example, the target primary cell is D1, and the primary cell identifier corresponding to the grid M1 may be D1, D2, D3, and D4.

It is therefore necessary to acquire the grid corresponding to the primary cell in the expressway fingerprint library 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 an alternative main cell grid identifier.

For example, in the case where the target primary cell is D1 and its level value is Y1, there may be 10 grids in the fingerprint library corresponding to the primary cell including D1, and the RSRP values of the primary cells D1 in the grids M1, M2, and 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 neighbor cell identifier of the target neighbor cell with the neighbor cell identifier corresponding to the candidate main cell grid identifier in the expressway fingerprint library.

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

Step 1705: and calculating a difference value between the RSRP value of the neighbor cell in the matched expressway fingerprint library and the RSRP value of the target neighbor cell, and acquiring an expressway grid identifier corresponding to the neighbor cell in the expressway fingerprint library with the minimum difference value as an alternative neighbor cell grid identifier.

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

Also, it is considered that MR data generally includes a main cell and a plurality of neighboring cells corresponding to the main cell, and a fingerprint library includes a plurality of main cells and a plurality of neighboring cells corresponding to each grid, and the identities and level values of the main cell and the neighboring cells corresponding to different grids may be different. There may be two or more grids with the same corresponding primary cell level value and both closest to the target primary cell level value in the MR data, so that the level values of neighboring cells in the grids are further matched, respectively.

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

And further, according to a preset exponential function, calculating the selection weight of the difference value of the level values of each matched neighbor cell to determine the grid identification of the candidate neighbor cell, wherein if the grid corresponding to the matched neighbor cell with smaller level value difference is used as the selection weight of the grid of the candidate neighbor cell, the selection weight of the grid of the candidate neighbor cell is larger.

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

And judging whether the candidate main cell grid identification and the candidate neighbor cell grid identification correspond to the same grid, 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 can 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 library), calculating the weight coefficient of the difference value matched to the neighbor cell in each candidate neighbor cell grid, and determining the grid with the highest weight score as the target grid according to the weight coefficient.

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

Specifically, step 180 may also include steps 1801-1802 shown in FIG. 4. FIG. 4 illustrates a flow diagram for determining a target grid corresponding to abnormal event awareness information in one embodiment.

Step 1801: and obtaining the unit raster road length corresponding to each expressway raster, and determining the number of the raster passing by the displacement according to the reference relative displacement and the unit raster road length.

Namely, how many road grids the displacement of the high-speed user passes between the reference time point and the abnormal event sensing time point is calculated.

Step 1802: and determining a target grid corresponding to the abnormal event sensing information according to the displacement passing grid number and the reference cell grid identification.

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

It should be noted that, in the present invention, it is assumed that the user travels at a constant speed while traversing each cell/grid, so that a certain deviation may exist between the final grid positioning result and the coverage area of the cell 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 corresponding to the current grid position comprises the current longitude and latitude or not, if yes, whether the cell coverage is within 200 meters or not.

In addition, since the concept of positioning an abnormal event is to determine a grid position and a space-time point with a known elapsed (occupied) time as reference points, the abnormal event is positioned by calculating the relative distance between the time or the position and the reference points. Therefore, in an alternative embodiment, besides positioning according to the number of passing grids calculated by the relative displacement, positioning can also be performed according to the passing grid condition in the cell occupied by the high-speed user in the abnormal event sensing, and the reference space-time point in the embodiment is the time of starting occupying the cell where the abnormal event occurs and ending occupying.

Thus, in an alternative embodiment, grid locating of anomaly-aware events for high-speed users may also include steps 2101-2105 shown in FIG. 5. FIG. 5 illustrates a flow chart of determining a target grid corresponding to abnormal event awareness information in another embodiment.

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

Step 2102: and matching the sensing abnormal main cell with a cell switching pair contained in a preset switching fingerprint library, and determining an occupied cell before abnormal sensing and an occupied cell 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 cells where switching occurs and cells switched to, which are included in each target driving road section, the switching position longitude and latitude of the cell switching pair and expressway grid identifiers corresponding to the switching position longitude and latitude.

For example, the perceived 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, based on the matching in the cell handover fingerprint library.

Meanwhile, according to MR data reported by a user on a target driving road section, the occupied cells in the MR report data closest to the abnormal event sensing time point and occupied by the main cell in the target cell switching pair are D1 and D4, so that the cells D1 and D4 are respectively used as occupied cells before abnormal sensing and occupied cells after abnormal sensing.

It should be noted that, the data in the fingerprint database for handover 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 ensured that the handover occurs adjacently.

In general, the handover fingerprint library mainly includes three main information of a handover source cell, a handover target cell and a handover location, and in the use process, when two cells in the MR data and two cells in the handover fingerprint library are identical, the two cells are associated with one location information, and if the location information meets the reference location condition of sensing an abnormal event (i.e. the reporting time of the MR data is very close to the abnormal sensing time), the location information can be used as a reference location for sensing the abnormal event.

Firstly, a cell switching fingerprint library comprises a plurality of cell switching pairs, expressway identifications corresponding to the cell switching pairs, cell switching pair identifications and switching position longitudes and latitudes.

A cell handover pair includes the cell in which the handover occurred and the target cell to which the handover was to occur. The cell handover pair identifier includes an ECI identifier of a handover occurrence cell and an ECI identifier of a target cell to which the handover is to be performed.

In particular, the determination of the cell handover fingerprint library may comprise steps 21021-21024 shown in fig. 6. Figure 6 illustrates a flow diagram for determining a cell switch fingerprint library in one embodiment.

Step 21021: and acquiring multiple network pulling data, and determining switching information corresponding to each expressway identifier according to the multiple network pulling data.

The pull data is used for determining the switching point of one cell and another cell, so that the switching positions of different cells (which can be regarded as boundaries between different cells) are determined according to a plurality of switching points, and therefore, when a high-speed user occupies different cells in the driving process, when the switching occurs in a main cell, a unique grid is determined in the cells before and after abnormal event sensing.

The specific processing for the pull net data can be as follows: and allowing a certain switching point not to appear in the single-time network drawing data, taking the mean value of the longitude and latitude of the position of the switching point if the switching point appears multiple times in the same time network drawing data, and taking the mean value of the longitude and latitude of the switching point if the certain switching point is in multiple times of network drawing data.

According to the line information in the expressway fingerprint, the longitude and latitude of the network drawing data are matched to each line, and the distance between the longitude and latitude after deviation correction and the endpoint and the expressway grid are calculated.

Step 21022: and determining a cell where the switching occurs and a cell of a target cell to which the switching is performed 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, forming a handover band. 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: matching the longitude and latitude of the switching position with the longitude and latitude of the grids in the expressway fingerprint library, obtaining expressway grid identifications corresponding to the matched longitude and latitude of the grids, and determining the expressway grid identifications, the cell switching pair identifications 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 expressway into a plurality of grids, the signal characteristics of each grid are determined, and the associated information in each grid of each cell, that is, the information of the cell handover position included in each grid, can be further increased in consideration of the division fineness (50 meters) of the grids than the division fineness (200 meters) of the cells.

Step 2103: and determining that the MR data occupying the main cell in the MR data is the MR data occupying the cell before abnormal perception as the reporting data before abnormal perception, and determining that the MR data occupying the main cell in the MR data is the MR data occupying the cell after abnormal perception as the reporting data after abnormal perception.

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

It is easy to understand that the time when the user K switches from the cell D1 to the perceived abnormal cell D3 is taken as the occupation start time point, and the time when the user K switches from the perceived abnormal cell D3 to the cell D4 is taken as the occupation end time point.

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

Similar to the foregoing process of calculating the target grid from the number of grids passed by the relative displacement, the target grid is calculated here from the number of grids and time passed by the user in the entire process from the start of occupation to the end of occupying a certain cell, and the time at which an abnormality occurs. Specific step 2105 may include steps 21051-21052 shown in FIG. 7, with FIG. 7 showing a flowchart for determining a target grid corresponding to the exception event in one embodiment.

Step 21051: and determining a ratio taking the interval duration between the abnormal event sensing time point and the occupation starting time point as a numerator and taking 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 covered grids and the grid coverage of the sensing abnormal cell.

Specifically, the number of covered grids is multiplied by the target movement multiple, so that the high-speed user can obtain how many grids pass after entering the sensing abnormal cell to sense the abnormality, then the grid identification interval of the whole sensing abnormal cell is determined according to the grid coverage, and the target grids can be determined according to the number of passing grids and the grid identification interval.

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

In an alternative embodiment, after determining the grid information perceived by the abnormal event, the abnormal grid position information may be marked on the map and displayed by the display device.

The method can further count the sensing quantity of abnormal events of the corresponding grids in each cell in a certain time period, judge whether the sensing quantity is larger than a preset abnormal quantity threshold value, determine the cell larger than the abnormal quantity threshold value as a target cell to be improved with poor network signal quality, and send the position of the target cell to be improved to related personnel for processing so as to improve the signals of the specific cell on the expressway.

Fig. 8 is a schematic diagram showing the construction of an embodiment of the expressway user abnormality positioning apparatus of the invention. As shown in fig. 8, the apparatus 300 includes: a data acquisition module 310, a high-speed user determination module 320, an abnormal event 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.

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

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

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

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

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 running speed;

a reference point longitude and latitude determining module 360, configured to obtain MR data with a reporting time point being the reference time point from the plurality of pieces of MR data as reference point MR data, and determine, according to the reference point MR data, longitude and latitude of the user to be located 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 the longitude and latitude of a grid included in a preset expressway fingerprint library, and determine an expressway grid identifier corresponding to the longitude and latitude of the reference point as a reference grid identifier, where the expressway fingerprint library includes a plurality of expressway grid identifiers and the longitude and latitude of the grid corresponding to the expressway grid identifier;

the grid positioning module 380 is configured to determine a target grid corresponding to the abnormal event sensing 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 alternative manner, the abnormal event aware point in time determination 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 shows a schematic structural diagram of an embodiment of the expressway user abnormal event positioning apparatus of the present invention, and the embodiment of the present invention is not limited to the specific implementation of the expressway user abnormal event positioning apparatus.

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

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

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

The processor 402 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the expressway user anomaly event positioning device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.

Memory 406 for storing programs 410. The memory 406 may correspond to a high-speed RAM memory or may further include a non-volatile memory (non-volatile memory), such as at least one disk memory.

Program 410 may be specifically invoked by processor 402 to cause the highway user exception event localization apparatus to:

In an alternative manner, the program 410 is also invoked by the processor 402 to cause the highway user exception event localization apparatus to:

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

The embodiment of the invention provides a computer readable storage medium, which stores at least one executable instruction, and when the executable instruction runs on an expressway user abnormal event positioning device/apparatus, the expressway user abnormal event positioning device/apparatus executes the expressway user abnormal event positioning method in any of the above method embodiments.

The executable instructions may be specifically for causing the highway user abnormal event localization apparatus/device to:

In an alternative embodiment, the executable instructions may be specifically configured to cause the highway user anomaly event locating device/apparatus to:

The embodiment of the invention provides a computer program which can be called by a processor to enable an expressway user abnormal event positioning device to execute the expressway user abnormal event positioning method in any of the above method embodiments.

An embodiment of the present invention provides a computer program product, including a computer program stored on a computer readable storage medium, the computer program including program instructions which, when run on a computer, cause the computer to perform the expressway user anomaly event localization method in any of the method embodiments described above.

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 a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.

In the description provided herein, numerous specific details are set forth. However, it is understood 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 above 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 disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim.

Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component, and they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units 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 use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.

Claims

1. A method for locating an abnormal event of a highway user, the method comprising:

2. The method of claim 1, wherein the expressway fingerprint library further includes a main cell identifier and a neighbor cell identifier associated with each expressway raster 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 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 method further comprises:

3. The method of claim 1, further comprising, after said determining an abnormal event awareness time point from said abnormal event awareness information:

determining an occupation starting time point of the user to be positioned for the sensing abnormal main cell according to the reporting data before abnormal sensing, and determining an occupation ending time point of the user to be positioned for the sensing abnormal main cell according to the reporting data after abnormal sensing;

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

4. The method of claim 3, wherein the determining the coverage grid range of the perceived abnormal primary cell determines the target grid corresponding to the abnormal event according to the abnormal event perceived time point, the occupied start time point, the occupied end time point, and the coverage grid range, and further comprising:

And determining a target grid corresponding to the abnormal event according to the target movement multiple, the number of the covered grids and the grid coverage of the perceived abnormal main cell.

5. The method according to claim 3, wherein the preset handover fingerprint library includes a plurality of cell handover pairs, expressway identifiers corresponding to the cell handover pairs, cell handover pair identifiers, and handover position longitudes and latitudes, and before the step of matching the perceived abnormal primary cell with the cell handover pairs included in the preset handover fingerprint library, determining an occupied cell before abnormal perception and an occupied cell after abnormal perception according to the matched cell handover pairs, further includes:

matching the longitude and latitude of the switching position with the longitude and latitude of the grids in the expressway fingerprint library, obtaining expressway grid identifiers corresponding to the matched longitude and latitude of the grids, and determining the expressway grid identifier, the cell switching pair identifier and the switching position longitude and latitude corresponding to each expressway identifier as one fingerprint in the preset switching fingerprint library.

6. The method of claim 3, wherein the determining the average travel speed of the user to be located based on the MR data, determining whether the user to be located is a high speed user, further comprises:

7. The method of claim 3, wherein the determining the coverage grid range of the perceived abnormal primary cell determines the target grid corresponding to the abnormal event according to the abnormal event perceived time point, the occupied start time point, the occupied end time point, and the coverage grid range, and further comprising:

and determining a target grid corresponding to the abnormal event sensing information according to the displacement passing grid number and the reference grid identification.

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

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

the memory is configured to store at least one executable instruction that causes the processor to perform the operations of the method for locating an abnormal event for an expressway user as set forth in any one of claims 1-7.

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