CN111198929B - Automatic processing method and system for fusing GPS track and activity log data - Google Patents
Automatic processing method and system for fusing GPS track and activity log data Download PDFInfo
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Abstract
The invention discloses an automatic processing method and system for fusing GPS track and activity log data, wherein the automatic processing method comprises the following steps: s1, acquiring GPS track data, and identifying a stop point and a moving point of the GPS track data; s2, acquiring activity log data and geocoding the activity log data to obtain activity log data containing a place list, wherein the place list comprises places, and longitude and latitude information and time information of corresponding places; s3, matching the activity log data of the S2 with the GPS track data of the S1, and correcting longitude and latitude information of the activity log data; and S4, matching the GPS track data with the activity log data corrected in the S3 to obtain the GPS track data with the log attribute. The automatic processing method and the system can simulate the process of understanding and judging data by an investigator, can replace manual work to arrange, match and supplement the GPS track data and the activity log data of a large sample, and have higher data processing efficiency.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an automatic processing method and system for fusing GPS track and activity log data.
Background
With the development of the technology, the space position can be rapidly positioned and recorded through equipment such as a smart phone and a portable GPS, so that the GPS tracks of a large number of travelers can be obtained. The research on the trajectory data has important significance and value, and is an important reference basis for city planning, traffic management and resident behavior research.
Due to the lack of corresponding attribute information for the GPS track as a reference. In the existing research methods, activity log data of a traveler is acquired through investigation, and a GPS track is manually fused with the activity log data to give activity log attributes corresponding to the GPS track.
However, in the existing research methods, a researcher understands the meaning of the GPS track based on activity log data, and matches the activity log data with the GPS track data item by means of subjective judgment of the researcher. The fusion of the GPS trace of the large sample with the activity log data is difficult to perform manually.
Disclosure of Invention
The invention aims to solve the technical problem that the fusion of a GPS track and activity log data of a large sample is difficult to perform by manual fusion, and provides an automatic processing method and system for the fusion of the GPS track and the activity log data.
In order to solve the problems, the invention is realized according to the following technical scheme:
the invention relates to an automatic processing method for fusing GPS track and activity log data, which comprises the following steps:
s1, acquiring GPS track data, and identifying a stop point and a moving point of the GPS track data;
s2, acquiring activity log data and carrying out geocoding on the activity log data to obtain activity log data containing a place list, wherein the place list comprises places, and longitude and latitude information and time information of the corresponding places;
s3, matching the activity log data of the S2 with the GPS track data of the S1, and correcting longitude and latitude information of the activity log data;
and S4, matching the GPS track data with the activity log data corrected in the S3 to obtain the GPS track data with the log attribute.
Further, S4 specifically includes the following steps:
s41, dividing the GPS track data according to the motion states of parking and moving to obtain a plurality of continuous sub-track data;
s42, identifying and obtaining vacant data from the plurality of sub-track data;
s421, combining a vacant data with the vacant data before and after the vacant data to obtain a plurality of vacant data sets with continuous time;
and S422, matching the blank data set with the activity log data corrected in the S3 according to the corresponding relation of time to obtain a blank data set with log attributes.
Further, S42 further includes:
s42, identifying and obtaining non-vacant data from the plurality of sub-track data;
s423, identifying the motion state of each piece of non-vacancy data, and merging one piece of non-vacancy data with the same front and back motion states to obtain a plurality of time-continuous non-vacancy data sets;
s424, matching the non-vacant data set with the activity log data corrected in the S3 according to the corresponding relation of time to obtain a non-vacant data set with log attributes;
and S425, combining the vacant data set with the log attribute in the S422 and the non-vacant data set with the log attribute in the S424 according to a time sequence to obtain the GPS track data with the log attribute.
Further, S3 specifically includes the following steps:
s31, dividing the activity log data according to the motion state to obtain a plurality of sections of first log data sets; the first log data set comprises a start time, an end time, and an active ground warp latitude;
s32, respectively matching the first log data sets of a plurality of sections with GPS track data according to the corresponding relation between time and motion state; if the time and the motion state are consistent, the matching is successful, calculating the longitude and latitude average value of the corresponding GPS track data, and replacing the longitude and latitude average value with the active ground latitude of the first log data set; if the matching is not successful, executing S33;
s33, judging whether the time length of the part of the log data which are consistent with the matching condition and are continuous in the front and back in the first log data set is greater than a preset parameter or not;
if yes, calculating the longitude and latitude mean value of the GPS track data corresponding to the partial log data meeting the matching condition, and replacing the longitude and latitude mean value with the movable ground warp latitude of the first log data set;
if not, the active ground warp latitude of the first log data set is not replaced.
Preferably, after S4, the method further comprises:
and S5, inputting the GPS track data with the log attributes and the activity log data corrected in the S3 into a matching model, and correcting the time information of the activity log data.
Further, S5 specifically includes the following steps:
s51, dividing the activity log data corrected in the S3 according to the motion state to obtain a plurality of sections of second log data sets; the second log data set comprises a start time and an end time;
s52, inputting the plurality of second log data sets and the GPS track data with the log attributes of the S51 into a matching model, wherein the matching model is matched based on the following algorithm:
s521, according to the corresponding relation between time and motion state, respectively matching a plurality of second log data sets with the GPS track data with day attribute:
if the time and the motion state are consistent, the matching is successful, a second log data set which is not successfully matched is obtained, and S522 is executed;
s522, identifying the continuous partial log data in the second log data set which is unsuccessfully matched and which accords with the matching condition;
s523, acquiring the starting time DSn/the ending time DEn of the partial log data in the second log data set in the S522, and finding out a corresponding time point GSn/time point GEn in the GPS track data;
s524, judging whether the front/back i time points GSn-i/GEn + i of the time point GSn/GEn in the GPS track data are in a parking state or not;
if yes, continuing to search forwards/backwards;
if not, stopping searching, and updating the time point: DSn = GSn-i, DEn = GEn + i;
and S53, updating the corresponding start time and the corresponding end time in the second log data set of the activity log data according to the matching in the S52.
Preferably, before S1, the method further includes a preprocessing step of replacing the GPS track data with an integer minute time value, specifically:
sequentially integrating all track numerical values of each minute of GPS track data by taking the minute as a unit to obtain a minute track numerical value set;
calculating the median or mean of all the trace values of the minute trace value set to obtain minute trace points;
and integrating all the minute track points according to the time sequence to obtain the preprocessed GPS track data.
Specifically, the step S1 of judging and obtaining a stopping point of the GPS track data specifically includes the following steps:
s11, obtaining candidate points from the GPS track data based on a window distance matrix algorithm;
s12, integrating the candidate points with continuous time to obtain a candidate point set; judging the time length of each candidate point set, and keeping the current candidate point set when the time length is greater than a preset time length threshold;
s13, calculating the maximum value of the window distance matrix of the candidate points one by one from the reserved candidate point set;
if the maximum value of the window distance matrix of the previous candidate point is smaller than the preset distance parameter, adding the maximum value of the window distance matrix of the next candidate point until the maximum value of the window distance matrix is larger than the preset distance parameter, and stopping calculation to obtain a stopping point;
and S14, updating the track data of the stopping point in the S13 to the GPS track data.
Further, the activity log data with the place list of S2 is subjected to the processing of replacing the time value of the hour of the whole hour.
The invention also provides an automatic processing system for fusing the GPS track and the activity log data, which comprises the following steps:
the track data module is used for acquiring GPS track data and identifying stop points and moving points of the GPS track data;
the log data module is used for acquiring activity log data and geocoding the activity log data to obtain the activity log data containing a place list, wherein the place list comprises places, and longitude and latitude information and time information of corresponding places;
the longitude and latitude correction module is used for matching the activity log data of the log data module with the GPS track data of the track data module and correcting the longitude and latitude information of the activity log data;
the data matching module is used for matching the GPS track data with the activity log data of the log data processing module to obtain GPS track data with log attributes;
and the time correction module is provided with a matching model and corrects the time information of the activity log data by inputting the GPS track data with the log attribute and the activity log data of the data matching module into the matching model.
Compared with the prior art, the invention has the beneficial effects that:
the automatic processing method and the system can simulate the process of understanding and judging data by an investigator, can replace manual work to arrange, match and supplement the GPS track data and the activity log data of a large sample, and have higher data processing efficiency.
Drawings
Embodiments of the invention are described in further detail below with reference to the attached drawing figures, wherein:
FIG. 1 is a flow diagram of the present invention of an automated process for GPS trace and activity log data fusion;
FIG. 2 is a flowchart of the technique of the present invention for matching GPS trace data with activity log data with latitude and longitude;
FIG. 3 is a flow chart of a technique for determining stopping points in accordance with the present invention;
FIG. 4 is a flow diagram of a technique of correcting activity log data of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
In the prior art, a GPS track is a series of active location points acquired by a GPS track recorder, and each location point at least includes information of date, time, longitude, latitude, altitude, and speed representing a motion state.
The GPS track data of the application is acquired by enabling the examinee to carry a portable GPS track recorder or other intelligent mobile equipment. The activity log data is filled by the corresponding respondents, and the content comprises all research-related activities, trips, corresponding spatio-temporal information and the like of the surveyor in corresponding time.
Reference is made to the prior art with respect to GPS traces and activity logs, and not to the extensive description herein.
Processing GPS track data and activity log data:
because there are many problems of non-standard formats in the GPS track data and the activity log data, it is not favorable for the fusion of the two data. For this reason, the present invention preferably performs the following processing on the GPS track data and the activity log data before executing the present data fusion method.
(1) Unified data format
Firstly, setting an irregular data format in two kinds of data into a uniform format, wherein the specific mode is as follows:
the date format is unified: there are different naming schemes in the date format, e.g. 01 and 1, different separators, e.g. -and/. In this embodiment, the date formats of both data are unified.
The time format is unified: a number of naming schemes, such as 01 seconds and 01.0 seconds, are formatted as well as present in the time format. In this embodiment, the time formats of the two data are unified.
(2) Unified timescale
Both data sources are relegated to a uniform temporal resolution scale. Wherein, the GPS track data is processed by replacing the integral point minute numerical value. Similarly, activity log data is similarly processed to a minute accuracy after being geocoded. The treatment of replacing the integral minute numerical value specifically comprises the following steps:
firstly, track values of each minute of GPS track data are integrated in sequence by taking the minute as a unit to obtain a minute track value set.
And secondly, calculating the median or the mean of all the track values of the minute track value set to obtain minute track points.
And finally, integrating all minute track points according to the time sequence to obtain the preprocessed GPS track data.
In the present embodiment, the track value of each minute is the track value of all the location points in one minute, for example, the track value includes longitude, latitude, speed, and the like. The GPS track data is replaced by the integral point minute numerical value, and the minute track points are used for replacing all position points in one minute. The processing amount of data is reduced, and the mutual fusion of the two data is facilitated by unifying the time scale.
(3) Incomplete data supplementation
There may be a problem of discontinuity due to the raw GPS trajectory data, which may cause matching problems. Therefore, after the unified time scale processing is carried out, the time completion operation can be carried out before the data of the unified time scale processing and the data of the unified time scale processing are matched. The concrete completion method comprises the following steps:
the earliest and latest time existing in the GPS track data is taken as the starting and stopping time, discontinuous data positions are filled with data items calculated by minutes, the filled data values have dates and times, and the change date is increased along with the time.
In this embodiment, the populated data entry also contains other nulled data values that are used to write matching data when the GPS track data and the activity log data are merged with one another.
As one example, incomplete data is supplemented to GPS track data, discontinuous data positions are filled with data items calculated by minutes, a new minute track point comprising date and time is formed, and other track values of the filled minute track point are set to be null.
Example 1
The invention provides an automatic processing method for fusing GPS track and activity log data, which is concretely shown in the following example.
Referring to fig. 1, a flow of an embodiment of an automatic processing method for fusing a GPS track and activity log data is shown, which specifically includes the following steps:
s100, GPS track data are obtained, and stopping points and moving points of the GPS track data are identified and obtained.
In the face of a large amount of GPS track data, the stagnation point is represented as space staying, time consumption and certain requirement meeting. In terms of travel, emphasis is placed on an individual moving from one stopping point to the next by some means of transportation. The identification of the stopping point is an important basis for using the GPS track data for estimating the travel mode and the travel purpose, and is information contained in the GPS track data output by the fusion method.
Referring to fig. 3, the present fusion method further provides an identification technology for a parking point, which specifically includes the following steps:
setting preset parameters:
eps: a threshold value of the number of points to be searched;
searchLen: the length of the search time;
disThre1: search distance length (the larger the parameter, the more candidate points become);
time thread: time slice duration threshold (the smaller this parameter, the more the parking point will become);
disThre2,: distance parameters in the stopping point judging process;
wherein, the total segment number of Stay and Move states is controlled by disThre1 and timeThre.
And S110, obtaining candidate points from the GPS track data based on a window distance matrix algorithm.
In this embodiment, for the minute trace point of each minute of the GPS trace data, searchlen minutes are searched upward and searchlen minutes are searched downward, and whether there is a missing value is determined for the searched minute trace point.
If the missing value of the searched minute trace point is found (for example, the data item filled subsequently has a vacant trace numerical value), when the trace numerical values of the longitude and the latitude in (2 × searchlen + 1) minute are completely the same, the minute trace point is determined as a candidate point.
If no missing value of the minute trace point is found, if the distance between the middle minute trace point and the minute trace points of the other 2 × search chlen minutes is less than or equal to eps minute trace points of disThre1, the minute trace point is judged as a candidate point.
S120, integrating the candidate points with continuous time to obtain a candidate point set; and judging the time length of each candidate point set, and keeping the current candidate point set when the time length is greater than a preset time length threshold value.
In this embodiment, the candidate points in S110 are sliced, and candidate points with continuous time are sliced into one piece to obtain a candidate point set. And judging the time length of each candidate point set, skipping if the time length is less than timeThre, not entering the parking point judgment process of the candidate point set, and entering parking point judgment if the time length of the candidate point set is long enough (namely, is more than timeThre).
S130, calculating the maximum value of the window distance matrix of the candidate points one by one from the reserved candidate point set;
and if the maximum value of the window distance matrix of the previous candidate point is smaller than the preset distance parameter, adding the maximum value of the window distance matrix of the next candidate point until the maximum value of the window distance matrix is larger than the preset distance parameter, and stopping calculation to obtain the stopping point.
In this embodiment, for each incoming segment of the candidate point set, the first candidate point in the set is determined one by one. And if the maximum value of the distance matrix of the previous candidate point is less than disThre2, adding the next candidate point together to calculate the distance matrix, and circulating until the maximum value of the distance matrix is greater than disThre2. If the loop stops, a stopping point is calculated on behalf of the candidate point set. The longitude and latitude of the stopping point are the average value of the longitude and latitude of each candidate point participating in the distance matrix calculation.
And S140, updating the track data of the stopping point in the S130 to the GPS track data.
In this embodiment, the candidate points that satisfy the determination criteria of the parking point are updated, and the original longitude and latitude coordinates are replaced with the longitude and latitude average value calculated in the previous step.
S200, obtaining the activity log data and carrying out geocoding on the activity log data to obtain the activity log data containing a place list, wherein the place list comprises places, and longitude and latitude information and time information of the corresponding places.
In the application, the location information, the longitude and latitude information of the location and the time information of the corresponding location are obtained on the one hand by geocoding the activity log data. And processing the place list on a unified time scale.
It should be noted that, in the conventional investigation method, when the activity log is filled in by the examinee, the examinee can remember the location and time of the activity of the examinee, the time, location, and manner of traveling, and the like. For this reason, the activity log data usually includes a location, activity items corresponding to the location, longitude and latitude information, and time information, a travel mode, a travel location, a travel time period, and the like. Based on the travel mode, the activity items and the like, the motion state of the person to be investigated, such as the state of parking or moving, can be judged accordingly.
In one embodiment, the activity log data records a driving trip, and the log data in the time period can be marked as a moving state; such as activity in a room, log data for that period of time may be marked as docked.
Geocoding of activity log data is prior art in the art and is not overly elaborated herein.
And S300, matching the activity log data of the S200 with the GPS track data of the S100, and correcting longitude and latitude information of the activity log data.
In the step, the GPS track data is used for interpolating the activity log data, and track data such as longitude and latitude of the activity log data is corrected, so that the time resolution of the activity log data is improved. In one embodiment, the method specifically comprises the following steps:
s310, dividing the activity log data according to the motion state to obtain a plurality of sections of first log data sets; the first log data set includes a start time, an end time, and an active ground warp latitude.
In one embodiment, the activity log data is divided according to the motion state, and data items with continuous time are integrated into a set and are respectively cut out to obtain a plurality of first log data sets, wherein the first log data sets comprise a starting time, an ending time and an activity ground latitude. And partial first log data sets have data items with missing data values, so the first log data sets also comprise vacant data values, and the GPS track data is used for interpolating the activity log data, and the filled data items are inserted into track data such as longitude and latitude.
In this embodiment, the first log data set may be only a log data set in a parking state, may be only a log data set in a moving state, or may be a log data set including both a parking state and a moving state. Can be adjusted by those skilled in the art according to actual needs.
Preferably, the first log data set in this step is a log data set in a parking state.
S320, respectively matching the first log data sets of a plurality of sections with the GPS track data according to the corresponding relation between time and motion state; if the time and the motion state are consistent, the matching is successful, calculating the longitude and latitude average value of the GPS track data corresponding to the first log data set, and replacing the longitude and latitude average value with the active ground latitude of the first log data set; if the matching is not successful, S330 is executed.
In this embodiment, a matching data segment may be searched for in the GPS track data specifically according to the start time and the end time of the first log data set, and if data in the same time segment is searched, it is determined whether the motion states of the first log data set and the data segment are the same, for example, both the first log data set and the data segment are in a parking state. And when the time and the motion state of the first log data set are consistent with those of the corresponding GPS track data, the matching is regarded as successful.
The matching of both time and motion states of the first log data set and the GPS trajectory data is achievable by those skilled in the art and will not be overviewed herein.
S330, judging whether the time length of the front and back continuous partial log data which meet the matching condition (the time and the motion state are consistent) in the first log data set is greater than a preset parameter or not;
if yes, calculating the longitude and latitude average value of the GPS track data corresponding to partial log data meeting the matching conditions (the time and the motion state are consistent), and replacing the longitude and latitude average value with the active ground warp latitude of the first log data set;
if not, the active ground latitude of the first log data set is not replaced.
In this embodiment, the partial log data that meets the matching condition, specifically, the partial log data exists in the first log data set, and the time information thereof can correspondingly search the data with the same time period in the GPS track data, and the motion states of the two are the same. The GPS track data corresponding to the partial log data that meets the matching condition may be based on a correspondence of time. A start-stop time of the partial log data, the time period corresponding to a parameter in the GPS trace data.
Based on the above S310-S330, the longitude and latitude information of the activity log data is corrected.
And S400, matching the GPS track data with the activity log data corrected in the S300 to obtain the GPS track data with the log attribute.
Due to the problems of self error of a machine, unstable field signals and the like, GPS data has data vacancy in different degrees, for example, the data is abnormal due to abnormal positioning of a GPS instrument in a short time, GPS positioning information deficiency, insufficient electric quantity of the instrument and the like. In combination with the consideration, the method debugs the threshold parameters of the stopping point, the moving point and the log matching, and respectively processes abnormal data and vacant data in the GPS track data.
Referring to fig. 2, the method specifically includes the following steps:
and S410, dividing the GPS track data according to the motion states of parking and moving to obtain a plurality of continuous sub-track data.
The specific division process is as follows:
firstly, according to the motion states of parking and moving, the track numerical value of each minute track point is identified, and the motion state and the vacant track numerical value of each minute track point are judged. And secondly, integrating continuous minute track points with consistent motion states and consistent vacant track numerical values into sub-track data. The inconsistent motion state or the inconsistent state of the vacant data is another new sub-track data. Therefore, the GPS track data is divided to obtain a plurality of continuous sub-track data.
And S420, identifying and obtaining vacant data and non-vacant data from the plurality of sub-track data.
And S421, merging the vacancy data with the preceding and following vacancy data to obtain a plurality of time-continuous vacancy data sets.
And S422, matching the blank data set with the activity log data corrected in the S300 according to the corresponding relation of time to obtain a blank data set with log attributes.
In this embodiment, according to the correspondence relationship between the time and the time information of the blank data set, the activity log data of the corresponding time period is supplemented to the information of the blank data set of the GPS track data, and the blank data set correspondingly retrieves the matching motion state and the like in the activity log data.
And S423, identifying the motion state of each non-vacancy data, and combining one non-vacancy data with the same front and back motion states to obtain a plurality of time-continuous non-vacancy data sets.
S424, matching the non-vacant data set with the activity log data corrected in the S300 according to the corresponding relation of time to obtain a non-vacant data set with log attributes;
reference may be made to S422 for a specific matching manner.
And S425, combining the vacant data set with the log attribute in the S422 and the non-vacant data set with the log attribute in the S424 according to a time sequence to obtain the GPS track data with the log attribute.
Example 2
Because the activity log data covers the functional parking information with the specific activity purpose, and the parking state of the track data is mainly considered in the GPS track data and activity log data matching process, the track time period of the activity log data is integrated, the refined matching of the activity log data is realized by using the matching model, and then the functional parking time period of the activity log data is restored.
For this reason, this embodiment 2 further corrects the time information in the parked state of the activity log data based on the GPS track data with log attributes output in embodiment 1.
Referring to fig. 4, after S400, the method further includes the steps of:
and S500, inputting the GPS track data with the log attribute and the activity log data corrected in the S300 into a matching model, and correcting the time information of the activity log data.
S510, dividing the activity log data corrected in the S300 according to the motion state to obtain a plurality of sections of second log data sets; the second log data set comprises a start time and an end time;
in the present embodiment, the activity log data is activity log data with a uniform time scale, that is, the time unit of the activity log data is consistent with the time unit of the GPS track data. The log data of unit time is activity log data in one minute, and comprises date, time, latitude and longitude interpolation, information representing motion state and the like. The second log data set is formed by a plurality of unit time log data sets with the same motion state and continuous time. Specifically, reference may be made to the relevant content of the first log data set in S300.
In an embodiment, the second log data set is a time-continuous unit time log data set in a parked state.
S520, inputting the plurality of second log data sets and the GPS track data with the log attributes of the S510 into a matching model, wherein the matching model is matched based on the following algorithm:
s521, according to the corresponding relation between time and motion state, respectively matching a plurality of second log data sets with the GPS track data with day attribute:
and when the time and the motion state are consistent, the matching is successful, and when at least one data of the time and the motion state is inconsistent, the matching is not successful. Acquiring a second log data set with unsuccessful matching, and executing S522;
s522, identifying continuous partial log data before and after meeting matching conditions (time and motion states are consistent) in a second log data set with unsuccessful matching;
s523, obtaining the start time DS of the partial log data in the second log data set in S522 n End time DE n And finding out corresponding time point GS in GPS track data n Time point GE n ；
S524, judging the time point GS in the GPS track data n Time point GE n Before/after i time points GS n-i Time point GE n+i Whether the parking state is adopted;
if yes, continuing to search forwards/backwards;
if not, stopping searching, and updating the time point: DS (direct sequence) system n ＝GS n-i ，DE n ＝GE n+i ；
And S530, updating the corresponding start time and the corresponding end time in the second log data set of the activity log data according to the matching in the S520.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method according to the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but the former is a better implementation mode in many cases.
Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The embodiment further provides an automatic processing system for fusing the GPS track and the activity log data, which is used for implementing the above embodiments and preferred embodiments, and the description of the system is omitted.
As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
The invention relates to an automatic processing system for fusing GPS track and activity log data, which comprises:
the track data module is used for acquiring GPS track data and identifying stop points and moving points of the GPS track data;
the log data module is used for acquiring activity log data and geocoding the activity log data to obtain the activity log data containing a place list, wherein the place list comprises places, longitude and latitude information and time information of the corresponding places;
the longitude and latitude correction module is used for matching the activity log data of the log data module with the GPS track data of the track data module and correcting the longitude and latitude information of the activity log data;
the data matching module is used for matching the GPS track data with the activity log data of the log data processing module to obtain GPS track data with log attributes;
and the time correction module is provided with a matching model and corrects the time information of the activity log data by inputting the GPS track data with the log attribute and the activity log data of the data matching module into the matching model.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, so that any modification, equivalent change and modification made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (7)
1. An automatic processing method for fusing GPS track and activity log data is characterized by comprising the following steps:
s1, acquiring GPS track data, and identifying a stop point and a moving point of the GPS track data;
s2, acquiring activity log data and carrying out geocoding on the activity log data to obtain activity log data containing a place list, wherein the place list comprises places, and longitude and latitude information and time information of the corresponding places;
s3, matching the activity log data of the S2 with the GPS track data of the S1, and correcting longitude and latitude information of the activity log data;
s4, matching the GPS track data with the activity log data corrected in the S3 to obtain GPS track data with log attributes;
wherein, S3 specifically comprises the following steps:
s31, dividing the activity log data according to the motion state to obtain a plurality of sections of first log data sets; the first log data set comprises a start time, an end time, and an active ground warp latitude;
s32, respectively matching the first log data sets of a plurality of sections with the GPS track data according to the corresponding relation between time and motion state; if the time and the motion state are consistent, the matching is successful, calculating the longitude and latitude mean value of the corresponding GPS track data, and replacing the longitude and latitude mean value with the active ground warp latitude of the first log data set; if the matching is not successful, executing S33;
s33, judging whether the time length of the part of the log data which accords with the matching condition and is continuous in the front and the back in the first log data set is greater than a preset parameter or not;
if yes, calculating the longitude and latitude mean value of the GPS track data corresponding to the partial log data meeting the matching condition, and replacing the longitude and latitude mean value with the movable ground warp latitude of the first log data set;
if not, not replacing the active ground warp latitude of the first log data set;
wherein, S4 specifically comprises the following steps:
s41, dividing the GPS track data according to the motion states of parking and moving to obtain a plurality of continuous sub-track data;
s42, identifying and obtaining vacant data from the plurality of sub-track data, wherein S42 specifically comprises the following steps:
s421, combining a vacant data with the vacant data before and after the vacant data to obtain a plurality of vacant data sets with continuous time;
s422, matching the blank data set with the activity log data corrected in the S3 according to the corresponding relation of time to obtain a blank data set with log attributes;
s423, identifying the motion state of each non-vacancy data, and combining one non-vacancy data with the same front and back motion states to obtain a plurality of time-continuous non-vacancy data sets;
s424, matching the non-vacant data set with the activity log data corrected in the S3 according to the corresponding relation of time to obtain a non-vacant data set with log attributes;
and S425, combining the vacant data set with the log attribute in the S422 and the non-vacant data set with the log attribute in the S424 according to a time sequence to obtain the GPS track data with the log attribute.
2. The method of claim 1, wherein after S4, the method further comprises:
and S5, inputting the GPS track data with the log attributes and the activity log data corrected in the S3 into a matching model, and correcting the time information of the activity log data.
3. The automatic processing method for fusing the GPS track and the activity log data according to claim 2, wherein S5 specifically includes the steps of:
s51, dividing the activity log data corrected in the S3 according to the motion state to obtain a plurality of sections of second log data sets; the second log data set comprises a start time and an end time;
s52, inputting the plurality of second log data sets and the GPS track data with the log attributes of the S51 into a matching model, wherein the matching model is matched based on the following algorithm:
s521, according to the corresponding relation between time and motion state, respectively matching a plurality of second log data sets with the GPS track data with day attribute:
if the time and the motion state are consistent, the matching is successful, a second log data set with unsuccessful matching is obtained, and S522 is executed;
s522, identifying the continuous partial log data in the second log data set which is unsuccessfully matched and which accords with the matching condition;
s523, acquiring the start time DS of the partial log data in the second log data set in the S522 n End time DE n And finding out corresponding time point GS in GPS track data n Time point GE n ；
S524, judging the time point GS in the GPS track data n Time point GE n Before/after i time points GS n-i Time point GE n+i Whether the parking state is adopted;
if yes, continuing to search forwards/backwards;
if not, stopping searching, and updating the time point: DS (direct sequence) n ＝GS n-i ，DE n ＝GE n+i ；
And S53, updating the corresponding start time and the corresponding end time in the second log data set of the activity log data according to the matching in the S52.
4. The method for automatically processing fusion of the GPS track and the activity log data according to any one of claims 1 to 3, characterized in that before S1, a preprocessing of replacing the GPS track data by an integer minute time value is further included, specifically:
sequentially integrating all track numerical values of each minute of GPS track data by taking the minute as a unit to obtain a minute track numerical value set;
calculating the median or mean of all the trace values of the minute trace value set to obtain minute trace points;
and integrating all the minute track points according to the time sequence to obtain the preprocessed GPS track data.
5. The automatic processing method for fusing the GPS track and the activity log data according to claim 4, wherein the step S1 of judging and obtaining the parking point of the GPS track data specifically comprises the following steps:
s11, obtaining candidate points from the GPS track data based on a window distance matrix algorithm;
s12, integrating the candidate points with continuous time to obtain a candidate point set; judging the time length of each candidate point set, and keeping the current candidate point set when the time length is greater than a preset time length threshold value;
s13, calculating the maximum value of the window distance matrix of the candidate points one by one from the reserved candidate point set;
if the maximum value of the window distance matrix of the previous candidate point is smaller than the preset distance parameter, adding the maximum value of the window distance matrix of the next candidate point until the maximum value of the window distance matrix is larger than the preset distance parameter, and stopping calculation to obtain a stopping point;
and S14, updating the track data of the stop point in the S13 to the GPS track data.
6. The method of claim 4, wherein the GPS trace and activity log data fusion is performed by:
the activity log data with the place list in S2 is subjected to a process of replacing the hour value of the whole minute.
7. An automated processing system for fusing GPS trajectory and activity log data, comprising:
the track data module is used for acquiring GPS track data and identifying a stop point and a moving point of the GPS track data;
the log data module is used for acquiring activity log data and geocoding the activity log data to obtain the activity log data containing a place list, wherein the place list comprises places, and longitude and latitude information and time information of corresponding places;
the longitude and latitude correction module is used for matching the activity log data of the log data module with the GPS track data of the track data module and correcting the longitude and latitude information of the activity log data;
the data matching module is used for matching the GPS track data with the activity log data of the log data processing module to obtain GPS track data with log attributes;
the time correction module is provided with a matching model and corrects the time information of the activity log data by inputting the GPS track data with the log attribute and the activity log data of the data matching module into the matching model;
the data matching module matches the activity log data of the log data module with the GPS track data of the track data module and corrects the longitude and latitude information of the activity log data, and the method specifically comprises the following steps:
s31, dividing activity log data according to the motion state to obtain a plurality of sections of first log data sets; the first log data set comprises a start time, an end time, and an active ground warp latitude;
s32, respectively matching the first log data sets of a plurality of sections with the GPS track data according to the corresponding relation between time and motion state; if the time and the motion state are consistent, the matching is successful, calculating the longitude and latitude average value of the corresponding GPS track data, and replacing the longitude and latitude average value with the active ground latitude of the first log data set; if the matching is not successful, executing S33;
s33, judging whether the time length of the part of the log data which accords with the matching condition and is continuous in the front and the back in the first log data set is greater than a preset parameter or not;
if yes, calculating the longitude and latitude mean value of the GPS track data corresponding to the partial log data meeting the matching condition, and replacing the longitude and latitude mean value with the movable ground warp latitude of the first log data set;
if not, not replacing the active ground track latitude of the first log data set;
the method comprises the following steps that a time correction module matches GPS track data with activity log data of a log data processing module to obtain GPS track data with log attributes, and specifically comprises the following steps:
s41, dividing the GPS track data according to the motion states of parking and moving to obtain a plurality of continuous sub-track data;
s42, identifying and obtaining vacant data from the plurality of sub-track data, wherein S42 specifically comprises the following steps:
s421, combining a vacant data with the vacant data before and after the vacant data to obtain a plurality of vacant data sets with continuous time;
s422, matching the blank data set with the corrected activity log data according to the corresponding relation of time to obtain a blank data set with log attributes;
s423, identifying the motion state of each piece of non-vacancy data, and merging one piece of non-vacancy data with the same front and back motion states to obtain a plurality of time-continuous non-vacancy data sets;
s424, matching the non-vacant data set with the activity log data corrected by the data matching module according to the corresponding relation of time to obtain a non-vacant data set with log attributes;
and S425, combining the vacant data set with the log attribute in the S422 and the non-vacant data set with the log attribute in the S424 according to a time sequence to obtain the GPS track data with the log attribute.
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