CN117591757A - Track data processing method and device - Google Patents
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Abstract
The application provides a track data processing method and device, wherein the method comprises the following steps: receiving natural language query information aiming at a non-relational track storage database, and converting the natural language query information into a target query request formed by database query sentences; and extracting corresponding target track data aiming at a single object or a plurality of objects from a non-relational track storage database based on a preset multi-level index according to the query keywords which are extracted from the target query request and contain the time range and/or the space range. The method and the device can realize track data query for single or multiple objects on the basis of improving track data query efficiency, can meet efficient execution of multiple query conditions based on time ranges and/or space ranges, and can effectively improve convenience and reliability of track data query.
Description
Technical Field
The present disclosure relates to the field of track data processing technologies, and in particular, to a track data processing method and apparatus.
Background
The track data is data with time and space double attributes generated by related sensor equipment (such as a mobile phone) in the moving process of people, vehicles and objects in the city. The track data can reveal the movement rule of people, vehicles and objects, and is widely applied to various fields of intelligent cities such as intelligent transportation, intelligent tourism, social security and the like in recent years. The trace data has remarkable characteristics of mass, and how to store mass traces and realize efficient trace retrieval has important value for analysis and application of the trace data.
Currently, in the existing track data processing mode, in order to improve query efficiency, a learner uses HBase as a track storage database, and a row key (RowKey) uses area index and time as an index of the database, so as to solve the problem of fusion of ship tracks and meteorological data.
However, the above track data processing method can meet the requirement of quick query of a single track point, but cannot solve the query problem of track segments and whole tracks. Meanwhile, the existing track data processing mode cannot meet efficient execution of various query conditions based on a time range and/or a space range. In addition, in the prior art, operators are usually required to know the query language of the corresponding database, and the track has wide application in various fields of smart cities, so that the conventional track data processing mode also has the problem of poor query convenience.
Disclosure of Invention
In view of this, embodiments of the present application provide a trajectory data processing method and apparatus to obviate or mitigate one or more disadvantages in the prior art.
One aspect of the present application provides a trajectory data processing method, including:
receiving natural language query information aiming at a non-relational track storage database, and converting the natural language query information into a target query request formed by database query sentences;
And extracting corresponding target track data aiming at a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords which are extracted from the target query request and contain a time range and/or a space range.
In some embodiments of the present application, the non-relational track storage database comprises: an HBase database;
the multi-level index includes: a row key and a secondary index table corresponding to the HBase track table in the HBase database;
wherein the row key comprises: the corresponding relation among track generation date, track section start time index, track section end time index and space range index;
the HBase track table further comprises: identifying a column cluster and a detail column cluster; wherein the identification column cluster comprises: a unique number of the track and an object unique identification generating the track; the detail column cluster includes: the space range index and the track section respectively correspond to an original track position sequence and an original track time sequence;
the secondary index table is used for storing the unique identification of the object, the spatial range index and the corresponding relation between the row keys.
In some embodiments of the present application, the original track position sequence is used to store a geographic number corresponding to the latitude and longitude data;
the original track time sequence is used for storing the difference between each time stamp and the time represented by the track segment start time index.
In some embodiments of the present application, before the receiving the natural language query information for the non-relational track storage database, the method further comprises:
collecting each initial track sample, wherein the initial track sample comprises a unique object identifier, spatial longitude and latitude data and a corresponding relation between sampling time;
grouping the initial track samples according to different object unique identifiers, and sorting the initial track samples in each group after grouping according to the sampling time to obtain track data corresponding to each object unique identifier;
calculating the stay time of each position point in the track data, respectively deleting, retaining or segmenting each position point according to the stay time based on a preset preprocessing rule to obtain a plurality of track segments corresponding to the track data, and performing noise filtering processing on each track segment;
And storing each track segment corresponding to each track data into the HBase database to construct or update the HBase track table and the secondary index table.
In some embodiments of the present application, the receiving natural language query information for a non-relational track storage database and converting the natural language query information into a target query request formed by database query statements includes:
receiving natural language query information aiming at a non-relational track storage database;
inputting the natural language query information into a preset database query statement conversion model so that the database query statement conversion model outputs a database query statement corresponding to the natural language query information as a current target query request;
the database query statement conversion model is formed by performing fine tuning training on a pre-trained large language model in advance based on a plurality of preset natural language query information and preset database query statements corresponding to each preset natural language query information.
In some embodiments of the present application, if the target query request includes: key object track inquiry requests taking object unique identifiers and time ranges as keywords;
Correspondingly, the extracting the corresponding target track data for a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords including the time range and/or the space range extracted from the target query request comprises the following steps:
determining a corresponding first row key range according to the time range in the target query request, and searching a first track data set corresponding to the first row key range from the HBase track table;
acquiring a corresponding second row key range from the secondary index table according to the unique object identifier in the target query request, and searching a second track data set corresponding to the second row key range from the HBase track table;
and taking the intersection of the first track data set and the second track data set, and taking the track data in the intersection as target track data aiming at the target query request.
In some embodiments of the present application, if the target query request includes: a first key region track query request taking a space range as a keyword;
correspondingly, the extracting the corresponding target track data for a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords including the time range and/or the space range extracted from the target query request comprises the following steps:
Generating a corresponding target spatial range index according to the spatial range in the target query request;
searching a corresponding third row of key range from the secondary index table according to the target space range index;
and searching a corresponding third track data set from the HBase table based on the third row key range to serve as target track data aiming at the target query request.
In some embodiments of the present application, if the target query request includes: a second heavy point region track query request taking the space range and the time range as keywords;
correspondingly, the extracting the corresponding target track data for a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords including the time range and/or the space range extracted from the target query request comprises the following steps:
determining a corresponding fourth row key range according to the time range in the target query request, and searching a fourth track data set corresponding to the fourth row key range from the HBase track table;
generating a corresponding target space range index according to the space range in the target query request, searching a corresponding fifth row key range from the secondary index table based on the target space range index, and searching a fifth track data set corresponding to the fifth row key range from the HBase track table;
And taking an intersection of the fourth track data set and the fifth track data set, and taking track data in the intersection as target track data aiming at the target query request.
In some embodiments of the present application, if the target query request includes: and inquiring the request by using the track similarity deviation information, the time range and the space range as keywords, wherein the track similarity deviation information comprises the following components: unique identification of the track, a time deviation threshold and a distance deviation threshold;
correspondingly, the extracting the corresponding target track data for a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords including the time range and/or the space range extracted from the target query request comprises the following steps:
determining the query starting time, the query ending time and the space range corresponding to the target query request according to the unique identifier, the time deviation threshold and the distance deviation threshold of the track in the target query request;
acquiring corresponding track data from the HBase track table according to the query starting time, the query ending time and the space range corresponding to the target query request to form a track similarity calculation candidate set;
Based on a preset track similarity algorithm and original track data corresponding to the unique identification of the track, calculating a similarity result of each track data in the track similarity calculation candidate set compared with the original track data;
and selecting one of the track similarity calculation candidate sets as target track data aiming at the target query request according to the similarity result.
Another aspect of the present application provides a trajectory data processing device, including:
the request conversion module is used for receiving the natural language query information of the non-relational track storage database and converting the natural language query information into a target query request formed by database query sentences;
and the space-time index query module is used for extracting corresponding target track data aiming at a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords which are extracted from the target query request and contain a time range and/or a space range.
A third aspect of the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the trajectory data processing method when executing the computer program.
A fourth aspect of the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the trajectory data processing method.
The track data processing method provided by the application receives natural language query information of a non-relational track storage database, and converts the natural language query information into a target query request formed by database query sentences; according to the query keywords which are extracted from the target query request and contain the time range and/or the space range, the corresponding target track data aiming at a single object or a plurality of objects is extracted from the non-relational track storage database based on the preset multi-level index, so that the track data query aiming at the single object or the plurality of objects can be realized on the basis of improving the track data query efficiency, the efficient realization of various common track queries can be realized on the basis of one data storage, and further, the efficient execution of various query conditions based on the time range and/or the space range can be met.
Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present application are not limited to the above-detailed description, and that the above and other objects that can be achieved with the present application will be more clearly understood from the following detailed description.
Drawings
The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this application. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. Corresponding parts in the drawings may be exaggerated, i.e. made larger relative to other parts in an exemplary device actually manufactured according to the present application, for convenience in showing and describing some parts of the present application. In the drawings:
fig. 1 is a schematic flow chart of a track data processing method according to an embodiment of the present application.
Fig. 2 is a second flowchart of a track data processing method according to an embodiment of the present application.
FIG. 3 is a schematic diagram illustrating the execution logic of the trace data processing procedure in one example of the present application.
Fig. 4 is a schematic diagram of a first configuration of a track data processing apparatus according to an embodiment of the present application.
Fig. 5 is a second schematic structural diagram of the track data processing apparatus in an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the embodiments and the accompanying drawings. The exemplary embodiments of the present application and their descriptions are used herein to explain the present application, but are not intended to be limiting of the present application.
It should be noted here that, in order to avoid obscuring the present application due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present application are shown in the drawings, while other details not greatly related to the present application are omitted.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
It is also noted herein that the term "coupled" may refer to not only a direct connection, but also an indirect connection in which an intermediate is present, unless otherwise specified.
Hereinafter, embodiments of the present application will be described with reference to the drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.
Traditional relational databases, such as MySQL Spatial and PostGIS, create indexes for Spatial data based on R tree, K-D tree and other technologies, support storage and operation of Spatial data types, but solve the problem of expansibility of massive track data storage. Therefore, some researchers solve the problem of expansibility of mass data, such as SpatialHadoop, by storing and analyzing track data based on a distributed framework Hadoop. However, such schemes require frequent access to the disk, and the read-write efficiency of data is low. In recent years, storing trajectory data based on a NoSQL database has become a mainstream scheme. For example, geomeasa converts multidimensional track data into one-dimensional key values for efficient query by way of space filling county. However, when the time range of the data to be queried is large and the space range is small, the existing scheme still needs to scan a large number of key values, and the query efficiency is low.
Therefore, in order to improve the query efficiency, a learner stores a database by using HBase as a track, and a row key (RowKey) uses a region index and time as an index of the database to solve the problem of fusion of ship tracks and meteorological data. However, the track data processing method can meet the requirement of quick query of a single track point, but cannot solve the query problem of track segments and whole tracks. Meanwhile, the existing track data processing mode cannot meet efficient execution of various query conditions based on a time range and/or a space range. In addition, in the prior art, operators are usually required to know the query language of the corresponding database, and the track has wide application in various fields of smart cities, so that the conventional track data processing mode also has the problem of poor query convenience.
Based on the above, in order to solve the problems that efficient query of track data cannot be guaranteed at the same time, query of a track section and a whole track can be realized, and the existing track data processing method cannot simultaneously meet efficient execution of various query conditions based on a time range and/or a space range, poor query convenience and the like, the embodiments of the present application respectively provide a track data processing method, a track data processing device, an electronic device and a computer readable storage medium for executing the track data processing method, which can realize track data query for a single or multiple objects on the basis of improving track data query efficiency, and can meet efficient execution of various query conditions based on a time range and/or a space range, and can effectively improve convenience and reliability of track data query.
The following examples are provided to illustrate the invention in more detail.
Based on this, the embodiment of the present application provides a track data processing method that may be implemented by a track data processing device, referring to fig. 1, where the track data processing method specifically includes the following contents:
step 100: and receiving natural language query information aiming at the non-relational track storage database, and converting the natural language query information into a target query request formed by database query sentences.
In particular, the target query requests trajectory data for querying corresponding single or multiple objects from a temporal and/or spatial dimension.
In one or more embodiments of the present application, the target query request may include at least the following four types:
(1) Key object track inquiry requests taking object unique identifiers and time ranges as keywords;
the key object track query request is suitable for track query of key equipment/people: inquiring track data based on the device/user ID and the time interval;
(2) A first key region track query request taking a space range as a keyword;
the first key region track query request is applicable to key region track query: inquiring track data based on the space range;
(3) A second heavy point region track query request taking the space range and the time range as keywords;
the second heavy point area track query request is also applicable to the heavy point area track query: inquiring the track data based on the space and the time range;
(4) And inquiring the request by using the track similarity deviation information, the time range and the space range as keywords.
Wherein the companion track query request is adapted for a companion track query: and inquiring the track data based on the track similarity.
However, the existing track data processing method cannot simultaneously satisfy the efficient execution of the above four query conditions. Accordingly, the present embodiment solves this problem by setting the following step 200.
Step 200: and extracting corresponding target track data aiming at a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords which are extracted from the target query request and contain a time range and/or a space range.
In one or more embodiments of the present application, the object refers to an object that has generated or is still generating track data during the moving process, where the object may refer to a person, a car, an object, etc., and may be set according to actual requirements.
As can be seen from the above description, the track data processing method provided by the embodiment of the application can realize track data query for a single or multiple objects on the basis of improving track data query efficiency, can meet the efficient implementation of multiple common track queries based on one data storage, and further can meet the efficient execution of multiple query conditions based on a time range and/or a space range.
In order to further improve the efficiency, effectiveness and reliability of track data query and storage, in the track data processing method provided by the embodiment of the present application, the non-relational track storage database in the track data processing method may specifically be an HBase database; the multi-level index includes: and row keys corresponding to the HBase track table in the HBase database and a secondary index table. In a specific implementation, the NoSQL database HBase may be selected for track storage, and the elastic search may be selected for secondary index storage.
First, the row key includes: track generation date, track segment start time index, track segment end time index, and spatial range index.
Specifically, HBase row key (RowKey) design: in order to support the flexible and efficient track inquiry requirement, the invention designs a row key (RowKey) of a track storage table as follows:
“Date::StartTimeBin::EndTimeBin::XZ2(lat_min,lng_min,lat_max,lng_max)”
"indicates a splicing operation," Date "indicates a Date of track generation, for example 20230919," XZ2 "is a spatial two-dimensional index in the open source engine geomeasa, XZ2 (lat_min, lng_min, lat_max, lng_max) can also be written as a spatial range index," StartTimeBin "and EndTimeBin" respectively indicate a track segment start time index and a track segment end time index; the lat refers to the space latitude of the track, and the lat_min and the lat_max are respectively the space latitude minimum value and the space latitude maximum value of the track; "lng" refers to the spatial longitude of the track, and "lng_min" and "lng_max" are the spatial longitude minimum and the spatial longitude maximum, respectively, of the track.
In one example, taking 1 minute as a time Bin, there are:
"starttimebin=time.localname (StartTimestamp). Tm_min
“EndTimeBin＝time.localtime(EndTimestamp).tm_min”；
Where "time.localname" is a time processing function in python, "StartTimestamp" represents the start timestamp of the track segment and "EndTimestamp" represents the end timestamp of the track segment.
Secondly, the HBase track table further comprises: identifying a column cluster and a detail column cluster; wherein the identification column cluster comprises: a unique number of the track and an object unique identification generating the track; the detail column cluster includes: the space range index and the track section respectively correspond to an original track position sequence and an original track time sequence.
Specifically, the identification column cluster may be written as an id column cluster, and the detail column cluster may be written as a detail column cluster.
The id column cluster contains: track number "id: tid" as a unique number of a track, moving object number "id: oid" as a unique identification of an object generating the track; the detail column cluster contains: spatial range index of track "detail: XZ2 (lat_min, lng_min, lat_max, lng_max) ", original track position sequence" detail: traj_s ", and original track time sequence" detail: traj_t ".
And thirdly, the secondary index table is used for storing the unique identification of the object, the spatial range index and the corresponding relation among the row keys.
Specifically, to support more flexible track queries, the present application further contemplates a secondary index table of tracks that contains three fields:
the object unique identifier "oid", the spatial range index "XZ2 (lat_min, lng_min, lat_max, lng_max)" and the row key (RowKey).
In a specific example, the secondary index table may be stored in the elastic search, and the row key (RowKey) of the corresponding track data may be quickly acquired according to "oid", "XZ2 (lat_min, lng_min, lat_max, lng_max)", and then other track data may be quickly acquired from the HBase track table based on the row key (RowKey).
In order to further improve the efficiency of track data query and storage, in the track data processing method provided by the embodiment of the present application, the original track position sequence in the track data processing method is used for storing geographic numbers corresponding to longitude and latitude data; the original track time sequence is used for storing the difference between each time stamp and the time represented by the track segment start time index.
Specifically, in order to reduce the track storage space requirement, the original track position sequence "track: traj_s" does not store the original longitude and latitude data, but stores the corresponding geocodes thereof, such as Geohash codes and Google S2 codes, and in this embodiment, the Geohash codes with 10 bits are selected, so that compared with the original 2 double-precision data storage, the space storage overhead of the track can be greatly reduced.
The original track time sequence "track_t" stores the difference value of each time stamp relative to the time indicated by "StartTimeBin", for example, "StartTimeBin" is 100, which indicates that the 1 point on the day is 40 minutes, and the first time stamp in the track sequence indicates that the 1 point on the day is 40 minutes and 30 seconds, then only the difference value of 30 seconds is stored, so that the time storage overhead is greatly reduced.
In order to further improve efficiency, reliability and effectiveness of track data query and storage, in the track data processing method provided in the embodiment of the present application, referring to fig. 2 and fig. 3, before track query in step 100 in the track data processing method, track preprocessing, track storage, track index construction and the like are further specifically included, and specifically includes the following contents:
step 010: and collecting each initial track sample, wherein the initial track sample comprises a unique object identifier, spatial longitude and latitude data and a corresponding relation between sampling time.
Specifically, trace data from sources such as Kafka, hive, FTP, clickhouse can be accessed based on an open source Seatunel data synchronization engine, and each piece of data includes: the object uniquely identifies "oid", and the correspondence between the spatial longitude and latitude data "lat" and "lng" and the sampling time "t" of the track point.
Step 020: grouping the initial track samples according to different object unique identifiers, and sorting the initial track samples in each group after grouping according to the sampling time to obtain track data corresponding to each object unique identifier.
Step 030: calculating the stay time of each position point in the track data, respectively deleting, retaining or segmenting each position point according to the stay time based on a preset preprocessing rule so as to obtain a plurality of track segments corresponding to the track data, and carrying out noise filtering processing on each track segment.
Step 040: and storing each track segment corresponding to each track data into the HBase database to construct or update the HBase track table and the secondary index table.
Specifically, grouping the accessed track data according to oid, and sequencing the track data in each group according to the track point sampling time T to obtain a complete track T;
calculating the dwell time s of the trajectory T at each position, where s=t next -t prev ；
t prev Representing the time of the trajectory T at the current location point; t is t next The time of track T at the latter point in position;
The starting and ending position points of the trajectory T are kept, and each intermediate position point is detected accordingly, if the dwell time s of one position point<Δ t And the position distance between the position point and the front and back neighbor track points is not more than delta d The trace point is considered to contain less valuable information and can be deleted, thereby reducing the storage overhead of the trace and obtaining a simplified trace T', in one example, a threshold delta t And delta d Respectively taking 60 seconds and 3 kilometers;
if the target stays at a certain position point for a time s>Δ′ t Then the track is segmented by taking the position point as a demarcation point to obtain a plurality of track segments with stronger continuity, in one example, a threshold delta' t Take the value for 30 minutes;
for each track segment, track noise is filtered based on a track noise reduction algorithm. In this embodiment, track noise filtering is performed based on a speed anomaly method, and first, the moving speed v of the target at each track point is calculated:
v＝Dist(lat next ,lng next ,lat prev ,lng prev )/(t next -t prev ) Dist represents a general spherical distance calculation formula; lat prev Representing the spatial latitude of the current position point; lat next Representing the spatial latitude of the latter position point; lng prev Space longitude representing the current location point; lng next Representing the spatial longitude of the latter location point.
If v >Δ v The second trace point is considered to be a noise point and needs to be filtered out.
In order to further improve the efficiency and convenience of track data query, in the track data processing method provided in the embodiment of the present application, referring to fig. 2, step 100 in the track data processing method specifically includes the following:
step 110: natural language query information is received for a non-relational track storage database.
Step 120: inputting the natural language query information into a preset database query statement conversion model so that the database query statement conversion model outputs a database query statement corresponding to the natural language query information as a current target query request; the database query statement conversion model is formed by performing fine tuning training on a pre-trained large language model in advance based on a plurality of preset natural language query information and preset database query statements corresponding to each preset natural language query information.
Specifically, the query natural language and the corresponding SQL statement data set are pre-built, and fine tuning training is performed based on the open source pre-training large language model to form a fine-tuned pre-training large language model, where the fine-tuned pre-training large language model may be referred to as a database query statement conversion model in the embodiment of the present application, and is used to implement conversion from the natural language to the database query language, in one example, the open source pre-training model Falcon-7b may be selected and further fine-tuned in combination with the lorea method, and dropout of the LoRA may be set to 0.05.
In applications facing social security, the following trajectory query needs generally need to be addressed: (1) focused device/person trajectory query: inquiring track data based on the device/user ID and the time interval; (2) key region track inquiry: inquiring the track data based on the space range or the space and time range; (3) accompanying track queries: and inquiring the track data based on the track similarity. However, the existing methods cannot simultaneously satisfy the efficient execution of the above four query conditions.
Based on this, in order to realize efficient track query for key devices/people, in the track data processing method provided in the embodiment of the present application, referring to fig. 2, step 200 in the track data processing method specifically includes the following:
step 210: if the target query request includes: determining a corresponding first row key range according to a time range in the target query request by using an object unique identifier and the time range as key object track query requests, and searching a first track data set corresponding to the first row key range from the HBase track table; acquiring a corresponding second row key range from the secondary index table according to the unique object identifier in the target query request, and searching a second track data set corresponding to the second row key range from the HBase track table; and taking the intersection of the first track data set and the second track data set, and taking the track data in the intersection as target track data aiming at the target query request.
Wherein the first track data set may be written as S1 and the first track data set may be written as S2.
Specifically, a first row key range may be determined according to a time range, then a scan query interface of the HBase is called, a first track data set S1 to be queried is determined in the HBase track table according to the first row key range, then a second row key (RowKey) range to be queried is obtained from a secondary index table in the elastic search according to "oid", a get query interface of the HBase is called, and the second track data set S2 corresponding to the second row key range is searched from the HBase track table, and an intersection of S1 and S2 is taken as a track data result to be queried finally.
Step 220: if the target query request includes: a first key region track query request taking a space range as a keyword generates a corresponding target space range index according to the space range in the target query request; searching a corresponding third row of key range from the secondary index table according to the target space range index; and searching a corresponding third track data set from the HBase table based on the third row key range to serve as target track data aiming at the target query request.
Wherein the third set of track data is writable as S3.
Specifically, a target spatial region index may be constructed based on the spatial range of the input:
“XZ2(lat_min,lng_min,lat_max,lng_max)”，
and acquiring a third row key (RowKey) range to be queried from a secondary index table in the elastic search, and then calling a get query interface of the HBase and the like to search a third track data set S3 corresponding to the third row key range from the HBase track table.
Step 230: if the target query request includes: a second heavy point region track inquiry request taking the space range and the time range as keywords determines a corresponding fourth row key range according to the time range in the target inquiry request, and searches a fourth track data set corresponding to the fourth row key range from the HBase track table; generating a corresponding target space range index according to the space range in the target query request, searching a corresponding fifth row key range from the secondary index table based on the target space range index, and searching a fifth track data set corresponding to the fifth row key range from the HBase track table; and taking an intersection of the fourth track data set and the fifth track data set, and taking track data in the intersection as target track data aiming at the target query request.
Wherein the fourth track data set may be written as S4 and the fifth track data set may be written as S5.
Specifically, a corresponding fourth row key range may be determined according to a time range, a scan query interface of the HBase is called, a fourth track data set S4 to be primarily queried is determined from the HBase track table according to the fourth row key range, and then a target spatial range index is constructed based on the input spatial range:
“XZ2(lat_min,lng_min,lat_max,lng_max)”，
inquiring a fifth line key (RowKey) range to be inquired is obtained from a two-index table in the elastic search, a get inquiring interface of the HBase is called to search a fifth track data set S5 corresponding to the fifth line key range from the HBase track table, and an intersection of the S4 and the S5 is taken as a track data result to be inquired finally.
In addition, in the prior art, a learner also provides a track similarity query method based on equipment numbers and space grid indexes, and the method needs to calculate the similarity upper bounds of all tracks and tracks to be queried and then prune, when the track data scale is large, the method still has high calculation complexity, and the query requirement of the tracks in a certain period of time of certain equipment cannot be met efficiently. Based on this, in order to solve the technical problem, step 200 of the present application further specifically includes the following:
Step 240: if the target query request includes: and inquiring the request by using the track similarity deviation information, the time range and the space range as keywords, wherein the track similarity deviation information comprises the following components: determining the query starting time, the query ending time and the space range corresponding to the target query request according to the unique identifier, the time deviation threshold and the distance deviation threshold of the track in the target query request; acquiring corresponding track data from the HBase track table according to the query starting time, the query ending time and the space range corresponding to the target query request to form a track similarity calculation candidate set; based on a preset track similarity algorithm and original track data corresponding to the unique identification of the track, calculating a similarity result of each track data in the track similarity calculation candidate set compared with the original track data; and selecting one of the track similarity calculation candidate sets as target track data aiming at the target query request according to the similarity result.
Specifically, the start and end times of the query, as well as the spatial extent, are first determined from the input trajectory, the time deviation threshold e, and the distance deviation threshold δ.
Time range of query: [ StartTimeBin-E, endTimeBin+ [ E ];
spatial index range of query: XZ2 (lat) min -δ,lng min -δ,lat max +δ,lng max +δ)；
And setting a scanning range of a row key (RowKey) according to the method, acquiring corresponding track data as a track similarity calculation candidate set, calculating track similarity based on original track data according to a track similarity calculation method, and returning the most similar track as a query result.
The optional track similarity calculation method comprises traditional DTW, similarity calculation based on depth track representation learning and the like, and a DTW algorithm is selected in the embodiment.
That is, the track data processing method provided by the embodiment of the application realizes the track query based on natural language based on the large language model, and can effectively reduce the technical requirements of the track query application on operators; a novel space-time index method is created, and the multi-dimensional track retrieval efficiency is effectively improved.
Specifically, natural language-based trajectory queries are supported based on a large language model. The method creates a brand new space-time index scheme based on the NoSQL database, and can meet the high-efficiency realization of the four types of common track queries based on one data storage. In addition, the existing method can only provide services for personnel with a certain database operation skill, and the method can support natural language-based track inquiry by introducing a large language model related technology, so that the dependence of the method on the professional skill of the personnel is greatly reduced.
From the software aspect, the present application further provides a track data processing apparatus for executing all or part of the track data processing method, referring to fig. 4, where the track data processing apparatus specifically includes the following contents:
a request conversion module 10, configured to receive natural language query information of a database for non-relational track storage, and convert the natural language query information into a target query request formed by database query sentences;
the space-time index query module 20 is configured to extract, from the non-relational track storage database, target track data corresponding to a single object or multiple objects based on a preset multi-level index according to a query keyword including a time range and/or a spatial range extracted from the target query request.
The embodiment of the track data processing apparatus provided in the present application may be specifically used to execute the processing flow of the embodiment of the track data processing method in the above embodiment, and the functions thereof are not described herein again, and reference may be made to the detailed description of the embodiment of the track data processing method.
The part of the track data processing device for track data processing can be executed in a server or can be completed in a client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The present application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor for specific processing of the trajectory data processing.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Any suitable network protocol may be used for communication between the server and the client device, including those not yet developed at the filing date of this application. The network protocols may include, for example, TCP/IP protocol, UDP/IP protocol, HTTP protocol, HTTPS protocol, etc. Of course, the network protocol may also include, for example, RPC protocol (Remote Procedure Call Protocol ), REST protocol (Representational State Transfer, representational state transfer protocol), etc. used above the above-described protocol.
As can be seen from the above description, the track data processing device provided by the embodiment of the application can realize track data query for a single or multiple objects on the basis of improving track data query efficiency, can meet the efficient implementation of multiple common track queries based on one data storage, and further can meet the efficient execution of multiple query conditions based on a time range and/or a space range.
In a specific application example of the track data processing apparatus, referring to fig. 5, the request conversion module 10 and the space-time index query module 20 may together form a track query module 4, and on this basis, the track data processing apparatus may further include a track data access module 1, a track preprocessing module 2, and a track storage and index module 3;
the track data access module 1 is used for executing step 010, constructing a data connector based on a distributed and massive data integration framework, and accessing real-time or offline massive track data.
The track preprocessing module 2 is used for executing steps 020 and 030, preprocessing data of massive tracks based on a real-time and offline data processing engine, filtering track noise, identifying track residence points and segmenting the tracks.
The track storage and indexing module 3 is configured to execute step 040, and store the original track data and the characteristic data thereof based on the NoSQL database. And creating a brand new space-time index scheme to realize efficient track retrieval. Based on the large language model, the track query based on natural language is realized.
The track inquiry module 4 is used for executing the steps 100 and 200 to realize track inquiry based on user ID and time, track inquiry based on space range, track inquiry based on space-time range and track inquiry based on similar track.
The embodiment of the application further provides an electronic device, which may include a processor, a memory, a receiver, and a transmitter, where the processor is configured to perform the track data processing method mentioned in the foregoing embodiment, and the processor and the memory may be connected by a bus or other manners, for example, through a bus connection. The receiver may be connected to the processor, memory, by wire or wirelessly.
The processor may be a central processing unit (Central Processing Unit, CPU). The processor may also be any other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the trajectory data processing method in the embodiments of the present application. The processor executes the various functional applications of the processor and data processing by running the non-transitory software programs, instructions and modules stored in the memory, i.e., implementing the trace data processing method in the method embodiments described above.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created by the processor, etc. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory that, when executed by the processor, perform the trace data processing method of the embodiments.
In some embodiments of the present application, the user equipment may include a processor, a memory, and a transceiver unit, where the transceiver unit may include a receiver and a transmitter, and the processor, the memory, the receiver, and the transmitter may be connected by a bus system, the memory storing computer instructions, and the processor executing the computer instructions stored in the memory to control the transceiver unit to transmit and receive signals.
As an implementation manner, the functions of the receiver and the transmitter in the present application may be considered to be implemented by a transceiver circuit or a dedicated chip for transceiver, and the processor may be considered to be implemented by a dedicated processing chip, a processing circuit or a general-purpose chip.
As another implementation manner, a manner of using a general-purpose computer may be considered to implement the server provided in the embodiments of the present application. I.e. program code for implementing the functions of the processor, the receiver and the transmitter are stored in the memory, and the general purpose processor implements the functions of the processor, the receiver and the transmitter by executing the code in the memory.
The present embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the aforementioned trajectory data processing method. The computer readable storage medium may be a tangible storage medium such as Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disk, a removable memory disk, a CD-ROM, or any other form of storage medium known in the art.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or a combination of both. The particular implementation is hardware or software dependent on the specific application of the solution and the design constraints. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The features described and/or illustrated in this application for one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The foregoing description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the embodiment of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A track data processing method, comprising:
receiving natural language query information aiming at a non-relational track storage database, and converting the natural language query information into a target query request formed by database query sentences;
and extracting corresponding target track data aiming at a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords which are extracted from the target query request and contain a time range and/or a space range.
2. The trajectory data processing method of claim 1, wherein the non-relational trajectory storage database comprises: an HBase database;
the multi-level index includes: a row key and a secondary index table corresponding to the HBase track table in the HBase database;
wherein the row key comprises: the corresponding relation among track generation date, track section start time index, track section end time index and space range index;
the HBase track table further comprises: identifying a column cluster and a detail column cluster; wherein the identification column cluster comprises: a unique number of the track and an object unique identification generating the track; the detail column cluster includes: the space range index and the track section respectively correspond to an original track position sequence and an original track time sequence;
The secondary index table is used for storing the unique identification of the object, the spatial range index and the corresponding relation between the row keys.
3. The track data processing method according to claim 2, wherein the original track position sequence is used for storing geographic numbers corresponding to longitude and latitude data;
the original track time sequence is used for storing the difference between each time stamp and the time represented by the track segment start time index.
4. The trajectory data processing method of claim 2, further comprising, prior to said receiving natural language query information for a non-relational trajectory storage database:
collecting each initial track sample, wherein the initial track sample comprises a unique object identifier, spatial longitude and latitude data and a corresponding relation between sampling time;
grouping the initial track samples according to different object unique identifiers, and sorting the initial track samples in each group after grouping according to the sampling time to obtain track data corresponding to each object unique identifier;
calculating the stay time of each position point in the track data, respectively deleting, retaining or segmenting each position point according to the stay time based on a preset preprocessing rule to obtain a plurality of track segments corresponding to the track data, and performing noise filtering processing on each track segment;
And storing each track segment corresponding to each track data into the HBase database to construct or update the HBase track table and the secondary index table.
5. The track data processing method according to claim 1, wherein the receiving natural language query information for the non-relational track storage database and converting the natural language query information into the target query request formed by the database query statement includes:
receiving natural language query information aiming at a non-relational track storage database;
inputting the natural language query information into a preset database query statement conversion model so that the database query statement conversion model outputs a database query statement corresponding to the natural language query information as a current target query request;
the database query statement conversion model is formed by performing fine tuning training on a pre-trained large language model in advance based on a plurality of preset natural language query information and preset database query statements corresponding to each preset natural language query information.
6. The trajectory data processing method of claim 2, wherein if the target query request includes: key object track inquiry requests taking object unique identifiers and time ranges as keywords;
Correspondingly, the extracting the corresponding target track data for a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords including the time range and/or the space range extracted from the target query request comprises the following steps:
determining a corresponding first row key range according to the time range in the target query request, and searching a first track data set corresponding to the first row key range from the HBase track table;
acquiring a corresponding second row key range from the secondary index table according to the unique object identifier in the target query request, and searching a second track data set corresponding to the second row key range from the HBase track table;
and taking the intersection of the first track data set and the second track data set, and taking the track data in the intersection as target track data aiming at the target query request.
7. The trajectory data processing method of claim 2, wherein if the target query request includes: a first key region track query request taking a space range as a keyword;
correspondingly, the extracting the corresponding target track data for a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords including the time range and/or the space range extracted from the target query request comprises the following steps:
Generating a corresponding target spatial range index according to the spatial range in the target query request;
searching a corresponding third row of key range from the secondary index table according to the target space range index;
and searching a corresponding third track data set from the HBase table based on the third row key range to serve as target track data aiming at the target query request.
8. The trajectory data processing method of claim 2, wherein if the target query request includes: a second heavy point region track query request taking the space range and the time range as keywords;
correspondingly, the extracting the corresponding target track data for a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords including the time range and/or the space range extracted from the target query request comprises the following steps:
determining a corresponding fourth row key range according to the time range in the target query request, and searching a fourth track data set corresponding to the fourth row key range from the HBase track table;
generating a corresponding target space range index according to the space range in the target query request, searching a corresponding fifth row key range from the secondary index table based on the target space range index, and searching a fifth track data set corresponding to the fifth row key range from the HBase track table;
And taking an intersection of the fourth track data set and the fifth track data set, and taking track data in the intersection as target track data aiming at the target query request.
9. The trajectory data processing method of claim 2, wherein if the target query request includes: and inquiring the request by using the track similarity deviation information, the time range and the space range as keywords, wherein the track similarity deviation information comprises the following components: unique identification of the track, a time deviation threshold and a distance deviation threshold;
correspondingly, the extracting the corresponding target track data for a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords including the time range and/or the space range extracted from the target query request comprises the following steps:
determining the query starting time, the query ending time and the space range corresponding to the target query request according to the unique identifier, the time deviation threshold and the distance deviation threshold of the track in the target query request;
acquiring corresponding track data from the HBase track table according to the query starting time, the query ending time and the space range corresponding to the target query request to form a track similarity calculation candidate set;
Based on a preset track similarity algorithm and original track data corresponding to the unique identification of the track, calculating a similarity result of each track data in the track similarity calculation candidate set compared with the original track data;
and selecting one of the track similarity calculation candidate sets as target track data aiming at the target query request according to the similarity result.
10. A track data processing apparatus, comprising:
the request conversion module is used for receiving the natural language query information of the non-relational track storage database and converting the natural language query information into a target query request formed by database query sentences;
and the space-time index query module is used for extracting corresponding target track data aiming at a single object or a plurality of objects from the non-relational track storage database based on a preset multi-level index according to the query keywords which are extracted from the target query request and contain a time range and/or a space range.
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