CN110765128A - Optimized storage method based on large-scale GPS data - Google Patents
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Abstract
The invention belongs to the field of large-scale GPS data storage, and particularly relates to an optimized storage method based on large-scale GPS data, which can realize storage optimization in a database for storing the GPS data so as to improve the efficiency of inquiring and retrieving the data. The method divides GPS data by using grids, determines the size of a minimum storage unit used for storing the data into a database according to the division condition, and comprises the following specific processing steps: 1) preprocessing data; 1-1) cleaning data, and removing null data from the original data collected by the sensor; 1-2) determining data fields needing to be stored; 1-3) saving in a data format to a database; storing the files in a file in a plurality of mpoint formats, and importing the files into a database; 2) grid division; 3) calculating the average track number of each grid in the step 2).
Description
Technical Field
The invention belongs to the field of large-scale GPS data storage, and particularly relates to an optimized storage method based on large-scale GPS data, which can realize storage optimization in a database for storing the GPS data so as to improve the efficiency of inquiring and retrieving the data.
Background
With the rapid development of sensor technology, more and more sensors are embedded in various new applications, and these sensor devices record many useful data, such as GPS data for recording the position, and the GPS technology is not only used for the geographical position location of mobile phone users for civil applications, but also used in military applications: tracking fleets, navigating vessels, tracking wildlife, etc. Over the past few years we have seen that the deployment of location sensing devices and applications that make use of this information has increased rapidly, and this trend is likely to accelerate in the near future, and so we are quickly confronted with the task of managing large amounts of trajectory data. For example, if GPS sensor readings are to be collected from a fleet of hundred thousand trucks, transmitting a position once per minute, the data set is increased by 144M per day, and these trajectory data sets can be used in a variety of ways, including analyzing factors that cause an accident, and so forth.
Currently, there are two main methods for storing large-scale GPS data:
the first storage method is fixed-length storage, which requires that before storing data, the data storage format is preset to be fixed-length fields, for example, four storage fields are set, 2 storage spaces will remain for data with only 2 fields, and for data with 6 fields, the data intercepted from the last field is changed into data with 2 fields for storage, so that the method is not only suitable for data with fixed fields, but also space waste can be generated for data with less than the predetermined field, data with length exceeding the predetermined field will be intercepted to cause data information loss, but because the data length is fixed, when searching data, the data to be searched can be quickly positioned across the fixed-length data, so that the fixed-length storage can quickly respond to the query and search requirements of users. The GPS data has a large amount of data with unfixed fields and variable lengths, so if the method is used for storing large-scale GPS data, the waste of the generated storage space is very considerable;
the second storage method is variable-length storage, which dynamically allocates appropriate storage space according to the size of data, and compact data storage makes space utilization very high, but because the data is different in size and the storage space is irregularly distributed, each time data is searched, traversal is started from a file header of the stored data, which wastes time and labor, and thus the query efficiency is very low. The large-scale GPS data has large scale and high requirements for quick query and retrieval of the data, and the response query speed of the variable-length storage cannot meet the requirement of the GPS speed.
Therefore, how to store efficiently while improving the efficiency of retrieval and query is a basic challenge of current large-scale GPS data storage.
Disclosure of Invention
The invention aims to provide an optimized storage method based on large-scale GPS data aiming at the defects, changes the common method of mutually separating fixed length storage and variable length storage and combines the fixed length storage and the variable length storage.
The invention is realized by adopting the following technical scheme:
the optimized storage method based on the large-scale GPS data divides the GPS data by using grids, and determines the size of a minimum storage unit used for storing the data into a database according to the division condition, wherein the specific processing steps are as follows:
1) preprocessing data;
the data recorded by the sensor can not be completely and directly stored in the database except for the null data or the default data, and the data is required to be preprocessed because the storage space of the database is limited and only required fields can be stored in the database;
1-1) cleaning data, and removing null data from the original data collected by the sensor;
when the sensor loaded in the application collects information, the sensor is possibly interfered by signals or generates null data or data with default values due to hardware problems, and the data have no significance to analysis and research work and are required to be cleaned; the specific cleaning method comprises the following steps: introducing data into a database management tool like mysql, screening the number of pieces of null data by using conditional judgment statements such as select and where, positioning the pieces of null data, and deleting the pieces of null data by using delete;
1-2) determining data fields needing to be stored;
the sensors with different applications collect different information, and the GPS data fields mainly comprise ID, longitude coordinates, latitude coordinates and recording time, so that the data fields to be stored can be additionally added according to needs except for determining the four data fields;
1-3) saving in a data format to a database;
after removing the null data in the step 1-1) and determining the stored data field in the step 1-2), obtaining clean data, namely no null data; storing the clean data in a file in a plurality of mpoint formats, and importing the file into a database, namely finishing the storage of the data in the database; the clean data comprises column data required by ID, longitude coordinate, latitude coordinate, recording time and the like; the mpoint is a moving object track over the whole time period;
2) grid division;
dividing a two-dimensional plane where a moving object to which the whole GPS data belongs is located into n multiplied by n grids, obtaining an area S according to the space occupied by the data, using a formula S/(n multiplied by n), namely obtaining the size of n identical grids, organizing moving object track data close to each other together, and reducing the time for searching and retrieving data; dividing the grids, and decomposing all moving objects into a grid containing a section of track;
3) calculating the average track number of each grid in the step 2);
3-1) according to the grid division method in the step 2), distributing each moving object track to one or more grids, and counting the number of uppoints distributed to each grid by each moving object, namely the number of moving tracks of the moving object in each minimum time interval;
3-2) adding the upoint numbers of all the moving objects together, and dividing the upoint numbers by the grid numbers occupied by all the moving objects to obtain the upoint number contained in each grid on average; the average upoint number of each grid reflects the average distribution condition of each grid under the current data scale, and the upoint number is used as the size of a basic storage unit in the fixed-length record, so that the compact storage of data can be ensured, the retrieval time can be reduced, and the data processing efficiency can be improved.
The upoint represents the moving track of the moving object in each minimum time interval, and the upoint is composed of the starting time, the ending time, the starting position and the ending position of the object.
The mpoint is composed of several upoids, representing the moving object trajectory over the whole time period.
In the step 2), when n =1, the state is the original state of the data, and no division is represented; when n is greater than 1, the two-dimensional plane is divided into n meshes; for example, when n =2, the two-dimensional plane is divided into 4 grids, when n =4, the two-dimensional plane is divided into 16 grids, …, and so on. When n is large, the number of grids is very large, and each track is divided into a plurality of grid cells.
The method of the invention combines the fixed length record storage and the variable length record storage, takes account of the distribution rule of the fixed length mode data storage space, is convenient for searching data and saves the retrieval time; and the method also has the advantages of fully utilizing the storage space in a variable length mode, reducing the space waste, saving the cost and minimizing the unnecessary overhead of large-scale GPS data storage.
Drawings
The invention will be further explained with reference to the drawings, in which:
FIG. 1 is a schematic diagram of a current method for storing large-scale GPS data using fixed-length storage;
FIG. 2 is a schematic diagram of a current method for storing large-scale GPS data using variable length storage;
FIG. 3 is a schematic illustration of data types involved in the method of the present invention;
FIG. 4 is a schematic diagram of the meshing of two-dimensional planes in the method of the present invention;
FIG. 5 is a schematic diagram illustrating the calculation of the average trajectory in the method of the present invention;
FIG. 6 is a diagram of an embodiment of the relationship of upoint and mpooint in the method of the present invention.
Detailed Description
The method of the present invention will be described in detail below with reference to the accompanying drawings and specific examples.
Currently, there are two main methods for storing large-scale GPS data:
the first storage method is fixed-length storage (as shown in fig. 1), which requires that before storing data, the data storage format is preset to be fixed-length fields, for example, four storage fields are set, 2 storage spaces will remain for data with only 2 fields, and for data with 6 fields, the data intercepted from the last field is changed into data with 2 fields for storage, so that the method is not only suitable for data with fixed fields, but also space waste can be generated for data with less than the predetermined field, data with length exceeding the predetermined field can be intercepted to cause data information loss, but because the data length is fixed, when searching data, the data to be searched can be quickly positioned across the fixed-length data, so that the fixed-length storage can quickly respond to the query and search requirements of a user. There is a large amount of data with unfixed and variable length fields in the GPS data, so if this method is used to store large-scale GPS data, the waste of storage space is significant.
The second storage method is variable-length storage (as shown in fig. 2), and this method dynamically allocates an appropriate storage space according to the size of the data itself, so that the space utilization rate is very high due to compact data storage, but because the data size is different and the storage space distribution is irregular, each time data is searched, traversal is started from the file header of the stored data, which is time-consuming and labor-consuming, and thus the query efficiency is very low. The large-scale GPS data has large scale and high requirements for quick query and retrieval of the data, and the response query speed of the variable-length storage cannot meet the requirement of the GPS speed.
The invention is based on the optimized storage method of large-scale GPS data, the GPS data is divided by using grids, and the size of the minimum storage unit used for storing the data into a database is determined according to the division condition.
The moving object in the present invention refers to a space object whose position changes continuously with time, for example, in 2019, 8, 21, morning, 10: 00: 00 to 10: 03: 00 a trolley running on a certain road section is a moving object, and 1 second is taken as the minimum time interval of the trolley, so that the position information data of the moving object within the three minutes can be collected in a mode of recording the position once per second. Upoint is introduced to represent the moving track of the moving object in each minimum time interval, wherein upoint is composed of the starting time, the ending time, the starting position and the ending position of the object, and specifically, the data structure of upoint is shown in FIG. 3 and is composed of a class object _ interval defined by a composite type and data x0, y0, x1 and y1 defined by four basic data types; the composite type class object _ interval represents a time interval, which comprises two basic data types Alpha and boul, and the start, end, lc and rc variables defined by the two basic data types respectively represent the start time, the end time, whether the left range is closed or not and whether the right range is closed or not of the interval; the data x0, y0, x1, y1 defined by four basic data types respectively represent two coordinate points (x 1, y 0) and (x 1, y 1).
To better understand the concept of upoint and the data storage mechanism, a specific example is given here for illustration, for an upoint: ("2003-10-10-06: 06", "2003-10-10-06: 07" TRUETTRUE) (0.00.01.01.0) which represents a straight line trajectory segment moving from the (0.0, 0, 0) position to (1.0, 1, 0) from 06 am 06 minutes on 10 th of 2003 to 06 am 06:07 on 10 th of 2003. In the database storing the data of the moving object, the fixed-length record means that the data type is a fixed-length value, for example: each storage unit can be selected to contain one upoint, which is the most basic storage unit, or the most basic storage unit can be set to be 2 uppoints long, 4 uppoints long, …, and so on, and all the contents of one unit can be returned by once access, so that the speed of querying and retrieving the fixed-length record storage data is high. But fixed-length record storage has drawbacks: if the basic unit is set to be 4 upoint, when only 1 upoint is stored to enter the basic unit, 3 upoint storage space of the basic unit is wasted. In practical application, the collected data are different in size and length, so that the situation of space waste can occur during data storage, and particularly, the waste is considerable for large-scale GPS data storage.
The present invention introduces the concept of mpoint, which is composed of multiple upoids, and represents not the moving object track in the minimum time period, but the moving object track in the whole time period, and is exemplified herein as an illustration, as shown in fig. 6, where mpoint is a track composed of four upoids (upoid 1, upoid 2, upoid 3, upoid 4), and represents a track route of a moving object, such as an automobile, moving in the time period from 2003-11-20-06:06 to 2003-11-20-06:08, and the specific track value is shown in fig. 6. The database stores large-scale GPS data, and a variable-length record storage mode can be used, wherein the variable-length record means that the data type is a value with an unfixed size, and the storage space is properly distributed according to the space occupied by the object. For example: each storage unit may be selected to contain an mpoint, that is, to contain an object, so that when an object consists of 10 upoids, the storage space occupies 10 upoids, and when an object consists of 100 upoids, the storage space occupies 100 upoids, …, and so on, the whole data content of a mobile object may be accessed at a time. Because the storage space of each mpoint is not necessarily as large and the distribution of the storage space is irregular, the storage with a longer length is stored with a longer length, the storage space is saved, the principle of how much the storage space is used is followed, and unnecessary space waste is avoided, but the data searching time is longer, so the data searching and retrieving speed is slower.
The invention provides that the size of the minimum data unit for storing GPS data can be judged according to the result of dividing data and space by a user, and specifically: the two-dimensional plane of the moving object to which the whole GPS data belongs can be divided into n × n grids (the size of n can be estimated to obtain a better value), and the purpose of the division is to organize the moving object trajectory data close to each other, so as to reduce the time for searching and retrieving the data.
Referring to fig. 4, when n =1 is a state of data origin, indicating no division; n =2, the two-dimensional plane is divided into 4 grids, n =4, the two-dimensional plane is divided into 16 grids, …, and so on. As shown in fig. 4, (a) is the division of the mesh and the distribution of the moving objects when n =2, (b) is the division of the mesh and the distribution of the moving objects when n =4, and (c) is the division of the mesh and the distribution of the moving objects when n =8, when n is large, the number of meshes is very large, and each trajectory is divided into a plurality of mesh units. Through the division of the grids, all moving objects are decomposed into a grid containing a section of track.
For the variable length record storage, the average track number of all moving objects is the sum of the upoint number corresponding to each moving object divided by the total number of the moving objects, and the average track number reflects the number of tracks each moving object averagely contains in the variable length record storage. In this method, the track segment of each moving object is allocated to a plurality of grids, the sum of the upoint numbers in each grid replaces the sum of the upoint numbers corresponding to each moving object in the variable length storage, the total number of the allocated grids replaces the total number of the moving objects, and the number of upoint numbers contained in each grid on average can be obtained by dividing the sum of the upoint numbers in each grid by the total number of the allocated grids, as shown in the effect diagram 5. The average number reflects the average number of tracks of the moving object on each grid, and the average value calculated by the method can be used as the size of the basic storage unit in the fixed-length record.
For example: in the variable length storage, assuming that there are three moving objects, O1, O2, O3, which contain 30, 24, 9 uppoints, respectively, the average number of tracks is (30 +24+ 9)/3 =21 for the three moving objects. In this method, assume that O1 is allocated to three grids, and the upoint data contained in each grid is: 10 upoint, 15 upoint, 5 upoint, O2 are allocated to three grids, each grid containing upoint data: 8 upoint, 7 upoint, 9 upoint, O3 are distributed into two grids, and the upoint data contained in each grid is respectively: 3 upoids, 6 upoids, then for these three moving objects, the average number of tracks is (10 +15+5+8+7+9+3+ 6)/9 =7, then the size of the total basic storage unit of the fixed-length records may be taken as 7 upoids, and when the average value is 6, the size of 7 upoids may also be taken, because the partition n of the grid is a square number, the value is as close to the square number as possible, such as 2, 4, 8, and so on.
Based on the above description, the method of the present invention uses a combination of fixed length record storage and variable length record storage, takes into account the distribution rule of the fixed length data storage space, is convenient for searching data, saves the retrieval time, takes into account the variable length data storage space, reduces the space waste, saves the cost, and minimizes the unnecessary overhead of large-scale GPS data storage. Effectively utilize memory space, reduce data retrieval time, improve the efficiency of processing data.
The method of the invention innovatively develops advantages of long-term and short-term storage, develops the advantages of high data retrieval speed of long-term storage and space saving of long-term storage; the short places of fixed length storage space waste and slow variable length storage retrieval speed are avoided, and the problems existing in the large-scale GPS data at the present stage are solved.
Claims (4)
1. An optimized storage method based on large-scale GPS data is characterized in that a grid is used for dividing the GPS data, the size of a minimum storage unit used for storing the data into a database is determined according to the dividing condition, and the specific processing steps are as follows:
1) preprocessing data;
1-1) cleaning data, and removing null data from the original data collected by the sensor;
when the sensor loaded in the application collects information, the sensor is interfered by signals or generates null data or data with default values due to hardware problems, and the data have no significance to analysis and research work and are required to be cleaned;
1-2) determining data fields needing to be stored;
the sensors with different applications collect different information, and the GPS data fields mainly comprise ID, longitude coordinates, latitude coordinates and recording time, so that the data fields to be stored can be additionally added according to needs except for determining the four data fields;
1-3) saving in a data format to a database;
after removing the null data in the step 1-1) and determining the stored data field in the step 1-2), obtaining clean data, namely no null data; storing the clean data in a file in a plurality of mpoint formats, and importing the file into a database, namely finishing the storage of the data in the database; the clean data comprises column data required by ID, longitude coordinate, latitude coordinate, recording time and the like; the mpoint is a moving object track over the whole time period;
2) grid division;
dividing a two-dimensional plane where a moving object to which the whole GPS data belongs is located into n multiplied by n grids, obtaining an area S according to the space occupied by the data, using a formula S/(n multiplied by n), namely obtaining the size of n identical grids, organizing moving object track data close to each other together, and reducing the time for searching and retrieving data; dividing the grids, and decomposing all moving objects into a grid containing a section of track;
3) calculating the average track number of each grid in the step 2);
3-1) according to the grid division method in the step 2), distributing each moving object track to one or more grids, and counting the number of uppoints distributed to each grid by each moving object, namely the number of moving tracks of the moving object in each minimum time interval;
3-2) adding the upoint numbers of all the moving objects together, and dividing the upoint numbers by the grid numbers occupied by all the moving objects to obtain the upoint number contained in each grid on average; the average upoint number of each grid reflects the average distribution condition of each grid under the current data scale, and the upoint number is used as the size of a basic storage unit in the fixed-length record, so that the compact storage of data can be ensured, the retrieval time can be reduced, and the data processing efficiency can be improved.
2. The optimized storage method based on large-scale GPS data according to claim 1, wherein the upoint represents the moving track of the moving object in each minimum time interval, and the upoint is composed of the starting time, the ending time, the starting position and the ending position of the object; the mpoint is composed of several upoids, representing the moving object trajectory over the whole time period.
3. The optimized storage method based on large-scale GPS data according to claim 1 or 2, wherein the specific cleaning method in the step 1-2) is as follows: and (3) introducing data into the database management tool like mysql, screening the number of pieces of null data by using conditional judgment statements such as select and where, positioning the pieces of null data, and deleting the pieces of null data by using delete.
4. The optimized large-scale GPS data-based storage method according to claim 1, wherein in the step 2), when n =1, the data is in an original state, which indicates that there is no division; when n is greater than 1, the two-dimensional plane is divided into n meshes; each track is divided into a plurality of grid cells.
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