CN113483729A - Longitude and latitude distance measurement method based on privacy protection and machine load balance - Google Patents
Longitude and latitude distance measurement method based on privacy protection and machine load balance Download PDFInfo
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- CN113483729A CN113483729A CN202110768484.4A CN202110768484A CN113483729A CN 113483729 A CN113483729 A CN 113483729A CN 202110768484 A CN202110768484 A CN 202110768484A CN 113483729 A CN113483729 A CN 113483729A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C1/00—Measuring angles
- G01C1/02—Theodolites
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3476—Special cost functions, i.e. other than distance or default speed limit of road segments using point of interest [POI] information, e.g. a route passing visible POIs
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Abstract
The method provides a longitude and latitude distance measurement method based on privacy protection and machine load balance. In some cases, indexes cannot be simultaneously applied to two columns, and by using the geohash, only the indexes are applied to one column, so that the resource and cost consumption are saved. Second, the geohash represents not a point but a rectangular area. The method can indicate that the user is located near a certain area, and the accurate coordinates of the user are not exposed, so that privacy protection is facilitated. Third, the encoded prefix may represent a larger region. This feature can be used for nearby location searching. Firstly, computing the geohash according to the current coordinates of the user, and then taking the prefix of the geohash to query, namely querying all nearby places. The geohash is much more efficient than the calculation directly using latitude and longitude (hemiversine formula). The method effectively solves the problems of privacy protection and machine load balancing.
Description
The technical field is as follows:
the invention relates to a longitude and latitude distance measuring method based on privacy protection and machine load balance, and belongs to the technical field of mobile computing, the geographic field and the preprocessing field.
Background art:
with the development of big data technology and the popularization of mobile terminal equipment, a large amount of track data can be collected and stored on the internet. The trajectory data that is available more easily nowadays is more and more concerned by researchers, and since these raw trajectory data are affected by sampling frequency, sampling precision and laws and regulations and cannot be directly applied to various kinds of mining algorithms, preprocessing is usually required. These trajectory data include, but are not limited to: the system comprises social network card-punching information, mobile phone signaling data, car-punching information, traffic card passing information, taxi positioning information, bus real-time data, animal migration data and the like. The application fields of these trajectory data are quite wide, including and not limited to: smart cities, traffic network optimization, social recommendation, life mode mining, public security solution, fake plate detection and the like. The analysis and mining of trajectory data has become a popular research direction.
Meanwhile, the cost of research and test and the security work of related information such as users and data are also an irrevocable part, and the importance degree is not inferior to the research in the technical field. While the simplicity of the algorithm is improved, privacy protection and load reduction should be achieved.
The invention content is as follows:
the invention aims to provide a longitude and latitude distance measuring method based on privacy protection and machine load balance, which can effectively improve the confidentiality, solve the problems of large track distance or environmental factor error and very resource consumption calculated by using a semi-positive vector formula of longitude and latitude and reduce the load.
1. Therefore, the longitude and latitude distance measuring method based on privacy protection and machine load balance comprises the following steps:
step 1: binary processing is carried out on the acquired longitude and latitude coordinates of each point to obtain a new binary longitude and latitude coordinate of the point;
step 2: according to the obtained binary longitude and latitude coordinates of each point, carrying out merging operation, and placing latitude at odd positions and placing longitude at even positions to obtain a long binary code of the point;
and step 3: dividing the obtained combined binary codes once every five bits, performing decimal conversion, and corresponding to base32 codes one by one to obtain the geohash codes of the point;
and 4, step 4: aiming at the obtained geohash codes of two points, judging the distance between the two points by comparing the similarity of the code prefixes;
and 5: solving the defect caused by the geohash algorithm, namely the boundary problem;
step 6: the problems of sampling frequency, sampling longitude, noise point filtering and the like are solved, and data processing is finished.
2. The longitude and latitude distance measuring method based on privacy protection and machine load balance as claimed in claim 1, wherein in step 1, only the position information determined by using longitude and latitude is targeted, and the position information passing through the position number is not considered.
3. The transit ranging method based on privacy protection and machine load balancing as claimed in claim 1, wherein in the step 3, base32 coding is adopted. The geohash is actually to divide the whole map or a certain divided area once, and a base32 coding mode is adopted, that is, each letter or number (for example, w in wx4g0 e) in the geohash is composed of 5bits (2^5 ^ 32, base32), and the 5bits can have different combinations (0-31) in 32, so that the whole map area can be divided into 32 areas, and the 32 areas are identified by 00000-11111. Through experimental operation between the areas, the position privacy is well protected.
4. The longitude and latitude distance measuring method based on privacy protection and machine load balancing as claimed in claim 1, wherein in the step 4, prefix matching is performed. In the geohash coding, the similar representation distances of the character strings are close, so that the prefix matching of the character strings can be utilized to inquire the nearby POI information. For example, in two-piece areas of urban and suburban areas, the geohash character strings in urban areas are similar to each other, and the character strings in suburban areas are also similar to each other, but the similarity between the geohash character strings in urban and suburban areas is lower. In addition, different encoding lengths represent different range intervals, and the longer the character string, the more precise the range represented. The original hemiversine formula is replaced, and the load cost and the resource consumption are reduced.
The geohash algorithm comprises the following specific steps:
and the latitude and longitude values are as follows: (116.389550, 39.928167) to perform algorithm description, approximate encoding is performed on latitude 39.928167 (the latitude interval of the earth is [ -90,90 ]).
1) The interval [ -90,90] is divided into [ -90,0) and [0,90], called left and right interval, and it can be determined 39.928167 that belongs to the right interval [0,90], given the label 1.
2) The interval [0,90] is then subdivided into [0,45) and [45,90], and it can be determined 39.928167 that belongs to the left interval [0,45), given a label of 0.
3) Recursion the above procedure 39.928167 always belongs to a certain interval a, b. The interval a, b is always shrinking with each iteration and is approaching 39.928167 more and more. Which can be understood as a dichotomy.
4) If a given latitude x (39.928167) belongs to the left interval, a 0 is recorded, if it belongs to the right interval, a 1 is recorded, and the length of the sequence is related to the given number of interval divisions.
5) Similarly, the Earth longitude interval is [ -180,180], and longitude 116.389550 may be encoded. By the above calculation, the latitude-generated code is 110100101100010, and the longitude-generated code is 101110001100011.
6) Merging: longitude is set at even number position, latitude is set at odd number position, and 2 series codes are combined to generate new series. Obtaining: 11100111010010001111000001101.
7) 11100111010010001111000001101 is firstly converted into decimal, and the base32 code corresponding to 28, 29, 4, 15, 0 and 13 decimal is wx4g0 e.
To this end, the transformation of the geohash code is completed.
5. The longitude and latitude distance measuring method based on privacy protection and machine load balance as claimed in claim 1, wherein in the step 5, the defect consideration of boundary problem is taken into account. This is done by regressing the original to the haversian equation (before optimization) and taking into account the specificity. The formula is as follows:
hav(d/R)＝hav(|φ1-φ2|)+cos(φ1)cos(φ2)hav(|λ1–λ2|)
d is the distance between two points, R is the radius of the earth, hav represents the basic algorithm of the hemiversine formula, and is related to two factors, namely the distance and the radius of the earth. φ 1 and φ 2 represent the latitudes of two points, and λ 1 and λ 2 represent the longitudes of two points.
When only the boundary problem is present, the optimization problem is not considered for the purpose of elaboration.
When the base32 rectangular area is used, the distance between two points can be approximated by the distance between rectangles due to the extremely convenient calculation of the distance between rectangles, so that similar tracks are found, and a relatively accurate distance measuring method is achieved.
The advantages are that:
first, the geohash represents two coordinates of longitude and latitude by one character string. In some cases, indexes cannot be applied to two columns at the same time, and only one column needs to be applied with indexes by using the geohash.
Second, the geohash represents not a point but a rectangular area. Such as code wx4g0ec19, which represents a rectangular area. The user can issue the address code, which can indicate that the user is near a certain area and can not expose the accurate coordinate of the user, thereby being beneficial to privacy protection.
Third, the encoded prefix may represent a larger region. For example wx4g0ec1, whose prefix wx4g0e indicates a larger range that contains the code wx4g0ec 1. This feature can be used for nearby location searching. Firstly, computing the geohash according to the current coordinates of the user, and then taking the prefix of the geohash to query, namely querying all nearby places.
The geohash is much more efficient than the calculation directly using latitude and longitude (hemiversine formula).
The mode of calculating the distance by the hemiversine formula consumes resources very much when calculating the distance between the trace points in a large batch, at least 10 milliseconds are needed when calculating the distance between 10 ten thousand points, more than 150 milliseconds are needed when the magnitude reaches 100 ten thousand, and the performance is unacceptable especially in the scene that similar trace points need to be found from massive trace data.
In conclusion, the method effectively solves the problems of privacy protection and machine load balancing.
Description of the drawings:
FIG. 1 is a flow chart of a longitude and latitude distance measurement method based on privacy protection and machine load balancing according to the present invention;
FIG. 2 is a coding diagram of base 32;
FIG. 3 is a similar trace simulated using a geohash;
the specific implementation mode is as follows:
in order to make the objects, technical solutions and advantages of the present method more clearly understood, the present method is described in detail below with reference to the accompanying drawings and specific steps.
Firstly, privacy protection and load balance optimization are mainly performed on coordinate points with longitude and latitude, and machine learning is required for position information without coordinates. As shown in the flow chart of fig. 1, in step 1, only the point with the coordinates needs to be considered, so that the longitude and latitude coordinates of each collected point are subjected to binary processing to obtain a new binary longitude and latitude coordinate of the point.
And continuously dividing the interval into two parts by using a dichotomy, and in the process, adopting a recursive algorithm to obtain the binary longitude and latitude coordinates.
And 2, performing combination operation according to the obtained binary longitude and latitude coordinates of each point, and obtaining a long binary code of the point by latitude scaling at odd bits and longitude scaling at even bits.
Combining the steps 3 and 4, carrying out separation once for every five bits of the obtained merged binary codes, carrying out decimal conversion, corresponding to base32 codes one by one, as shown in fig. 2, obtaining the geohash codes of the point, and judging the distance between the two points by comparing the similarity of the code prefixes according to the obtained geohash codes of the two points.
The obtained character string has a lot of advantages, the problems that the load for calculating the track interval (distance between the distance measuring points) by using a hemiversive formula is overlarge and the resource cost is consumed are solved, the distance measuring range can be judged by matching the prefixes, and the space resource and the time resource are effectively saved. Second, what is represented by a string is not a dot but a rectangular area. The user can issue the address code, which can indicate that the user is near a certain area and can not expose the accurate coordinate of the user, thereby being beneficial to privacy protection.
When the base32 rectangular area is used, the distance between two points can be approximated by the distance between rectangles due to the extremely convenient calculation of the distance between rectangles, so that similar tracks are found, and a relatively accurate distance measuring method is achieved. As shown in fig. 3, which is a simulated similar trajectory.
And 5, solving the defect caused by the geohash algorithm, namely the boundary problem. Because the following problems cannot be avoided: if the two locations are close together, but they are located in different rectangular areas, we will have errors in our decision. This has to be returned to the original method, which, although it adds a little load, is improved with respect to accuracy. In contrast, accuracy is more important where the cost is not particularly high.
And in the final step 6, the problems of sampling frequency, sampling longitude, noise point filtering and the like are solved, and data processing is finished.
Compared with the original method, the distance measuring method greatly improves the practicability and the calculating speed, lightens the cost and the load, and ensures the privacy and the data integrity.
In summary, the preferred embodiments of the present invention are described above, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the scope of the present invention, and equivalents and modifications of the technical solutions and concepts of the present invention should be included in the scope of the present invention.
Claims (5)
1. A longitude and latitude distance measurement method based on privacy protection and machine load balance comprises the following steps:
step 1: binary processing is carried out on the acquired longitude and latitude coordinates of each point to obtain a new binary longitude and latitude coordinate of the point;
step 2: according to the obtained binary longitude and latitude coordinates of each point, carrying out merging operation, and placing latitude at odd positions and placing longitude at even positions to obtain a long binary code of the point;
and step 3: dividing the obtained combined binary codes once every five bits, performing decimal conversion, and corresponding to base32 codes one by one to obtain the geohash codes of the point;
and 4, step 4: aiming at the obtained geohash codes of two points, judging the distance between the two points by comparing the similarity of the code prefixes;
and 5: solving the defect caused by the geohash algorithm, namely the boundary problem;
step 6: the problems of sampling frequency, sampling longitude, noise point filtering and the like are solved, and data processing is finished.
2. The longitude and latitude distance measuring method based on privacy protection and machine load balance as claimed in claim 1, wherein in step 1, only the position information determined by using longitude and latitude is targeted, and the position information passing through the position number is not considered.
3. The transit ranging method based on privacy protection and machine load balancing as claimed in claim 1, wherein in the step 3, base32 coding is adopted. The geohash is actually to divide the whole map or a certain divided area once, and a base32 coding mode is adopted, that is, each letter or number (for example, w in wx4g0 e) in the geohash is composed of 5bits (2^5 ^ 32, base32), and the 5bits can have different combinations (0-31) in 32, so that the whole map area can be divided into 32 areas, and the 32 areas are identified by 00000-11111. Through experimental operation between the areas, the position privacy is well protected.
4. The longitude and latitude distance measuring method based on privacy protection and machine load balancing as claimed in claim 1, wherein in the step 4, prefix matching is performed. In the geohash coding, the similar representation distances of the character strings are close, so that the prefix matching of the character strings can be utilized to inquire the nearby POI information. For example, in two-piece areas of urban and suburban areas, the geohash character strings in urban areas are similar to each other, and the character strings in suburban areas are also similar to each other, but the similarity between the geohash character strings in urban and suburban areas is lower. In addition, different encoding lengths represent different range intervals, and the longer the character string, the more precise the range represented. The original hemiversine formula is replaced, and the load cost and the resource consumption are reduced.
The geohash algorithm comprises the following specific steps:
and the latitude and longitude values are as follows: (116.389550, 39.928167) to perform algorithm description, approximate encoding is performed on latitude 39.928167 (the latitude interval of the earth is [ -90,90 ]).
1) The interval [ -90,90] is divided into [ -90,0) and [0,90], called left and right interval, and it can be determined 39.928167 that belongs to the right interval [0,90], given the label 1.
2) The interval [0,90] is then subdivided into [0,45) and [45,90], and it can be determined 39.928167 that belongs to the left interval [0,45), given a label of 0.
3) Recursion the above procedure 39.928167 always belongs to a certain interval a, b. The interval a, b is always shrinking with each iteration and is approaching 39.928167 more and more. Which can be understood as a dichotomy.
4) If a given latitude x (39.928167) belongs to the left interval, a 0 is recorded, if it belongs to the right interval, a 1 is recorded, and the length of the sequence is related to the given number of interval divisions.
5) Similarly, the Earth longitude interval is [ -180,180], and longitude 116.389550 may be encoded. By the above calculation, the latitude-generated code is 110100101100010, and the longitude-generated code is 101110001100011.
6) Merging: longitude is set at even number position, latitude is set at odd number position, and 2 series codes are combined to generate new series. Obtaining: 11100111010010001111000001101.
7) 11100111010010001111000001101 is firstly converted into decimal, and the base32 code corresponding to 28, 29, 4, 15, 0 and 13 decimal is wx4g0 e.
To this end, the transformation of the geohash code is completed.
5. The longitude and latitude distance measuring method based on privacy protection and machine load balance as claimed in claim 1, wherein in the step 5, the defect consideration of boundary problem is taken into account. This is done by regressing the original to the haversian equation (before optimization) and taking into account the specificity. The formula is as follows:
d is the distance between two points, R is the radius of the earth, hav represents the basic algorithm of the hemiversine formula, and is related to two factors, namely the distance and the radius of the earth.
And
denotes the latitude of two points, and λ 1 and λ 2 denote the longitude of two points.
When only the boundary problem is present, the optimization problem is not considered for the purpose of elaboration.
When the base32 rectangular area is used, the distance between two points can be approximated by the distance between rectangles due to the extremely convenient calculation of the distance between rectangles, so that similar tracks are found, and a relatively accurate distance measuring method is achieved.
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| CN117249843A (en) * | 2023-11-14 | 2023-12-19 | 广州斯沃德科技有限公司 | Path planning method, path planning device, electronic equipment and computer readable storage medium |
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