CN111666358A - Track collision method and system - Google Patents
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Abstract
The invention provides a track collision method and a track collision system, wherein the method comprises the following steps: time slicing is carried out on the trajectory flow data to be matched; judging whether the track flow data to be matched exists in a specified time slice; if yes, calculating the average coordinate of the trajectory flow data to be matched in the appointed time slice, and solving the corresponding geohash value; and finding out the trajectory stream data matched with the geohash value in the trajectory stream data to be searched through the geohash value to be matched. Hash mapping of data is realized through the concept of geohash, judgment of track similarity is respectively carried out in time and space dimensions by combining a Key-Value storage mode, and a track collision method is realized through quick search of position sensitive Hash in the data reading process; the scheme is simple to implement, the timeliness of collision results is high, large-scale data volume processing can be performed, and the method can be used for online real-time collision and offline collision.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of track collision, in particular to a track collision method.
[ background of the invention ]
And the track collision means that another track which is relatively similar to a certain specified track is searched, and the two tracks are determined to belong to the same attribute through the collision of the track similarity. The method can be applied to two different systems, such as the trajectory collision of face recognition and wifi positioning; the method can also be applied to the same system, such as wifi positioning and the analysis of the same row of wifi positioning.
Taking the most well-known public security system as an example, the method generally comprises face recognition based on video and Wifi positioning based on Wifi probe equipment, when a certain pedestrian appears in a coverage area of the public security system, the video can recognize the face of the pedestrian to obtain video coordinate information comprising the face and the positioned face, and meanwhile, the Wifi probe equipment can position a mobile phone carried with the pedestrian to obtain Wifi coordinate information comprising the mobile phone MAC and the positioned face. In a public security system, millions of user information are stored, and other information with the same attribute as a certain user is quickly found in the data, for example, mobile phone information carried by the user is found through certain fixed face information, or the face of a user behind the user is found through the mobile phone information carried by the user, or information which may be the same as the user or another mobile phone of the user is found through the mobile phone information carried by the user.
A common trajectory collision method is to store the longitude and latitude of the target location, and calculate the longitude and latitude according to the longitude and latitude provided by the user through a spherical distance formula, where the formula is:
wherein lat1 is the longitude of the coordinate to be collided, lat2 is the longitude of a certain position coordinate in the trajectory stream, long 1 is the latitude of the coordinate to be collided, long 2 is the latitude of a certain position coordinate in the trajectory stream, R is the spherical radius, and S is the spherical distance. And calculating the spherical distance of all the coordinate information in the time range, and sequencing according to the spherical distance to find out the coordinate information with the minimum distance, namely the coordinate information which is most similar to the coordinate information to be matched. The scheme has a simple principle, but in the implementation process, all data need to be traversed, the longitude and latitude distances of the coordinates to be collided and all stored data need to be calculated, and then sorting and screening are carried out according to the distance, so that the problems of huge calculation amount and low efficiency are solved. Due to the fact that the calculated amount is large, the timeliness of a collision result is poor, efficiency is low, and the significance of collision is lost.
[ summary of the invention ]
The invention provides a track collision method, which is characterized in that Hash mapping of data is realized through the concept of geohash, track similarity judgment is respectively carried out in time and space dimensions by combining a Key-Value storage mode, and the track collision method is realized through quick search of position sensitive Hash in the data reading process; the scheme is simple to implement, the timeliness of collision results is high, large-scale data volume processing can be performed, and the method can be used for online real-time collision and offline collision.
To solve the foregoing technical problem, in one aspect, an embodiment of the present invention provides a trajectory collision method, including: time slicing is carried out on the trajectory flow data to be matched; judging whether the track flow data to be matched exists in a specified time slice; if yes, calculating the average coordinate of the trajectory flow data to be matched in the appointed time slice, and solving the corresponding geohash value; and finding out the trajectory stream data matched with the geohash value in the trajectory stream data to be searched through the geohash value to be matched.
Preferably, the trajectory stream data to be matched includes: the positioning equipment calculates first coordinate data of the equipment to be positioned; and storing the first coordinate data as second coordinate data in a Key-main content Key-Value mode by using the geohash and the time as Key words and using the positioning mark information as main content Value.
Preferably, time slicing the trajectory stream data to be matched comprises: and inputting the trajectory flow data to be matched.
Preferably, the first coordinate data includes: coordinate information, time information and positioning mark information positioned by the positioning equipment.
Preferably, the finding, from the to-be-searched trajectory stream data, trajectory stream data matched with the to-be-searched geo hash value by using the to-be-matched geo hash value includes: and taking out a positioning mark information set which meets the similarity of the track data to be matched in the specified time slice and the track data in the time and space dimensions.
Preferably, the extracting of the set of locator information satisfying the similarity of the trajectory in the time and space dimensions with the trajectory flow data to be matched within the specified time slice includes:
acquiring a geohash value of the trajectory flow data to be matched according to the specified time slice and the trajectory flow data to be matched;
traversing the specified time slice, and generating keywords at different moments in the specified time slice by combining the geohash value of the trajectory data to be matched;
and taking out a corresponding positioning mark information set from a Key-Value system according to the keywords at different moments in the specified time slice.
Preferably, after finding the trajectory stream data matched with the geohash value in the trajectory stream data to be searched through the geohash value to be matched, the method further includes: and updating the times of successful matching of the trajectory stream data to be matched and the trajectory stream data to be searched in the specified time slice.
Preferably, the positioning device is a wifi positioning device or a video recognition device or a bluetooth positioning device.
Preferably, after the updating of the number of times of successful matching between the trajectory stream data to be matched and the trajectory stream data to be searched within the specified time slice, the method further includes: and storing and outputting the track collision result.
Preferably, the updating the number of times of successful matching between the trajectory stream data to be matched and the trajectory stream data to be searched within the specified time slice includes: and if the track flow data to be matched in the appointed time slice is successfully matched with the track flow data to be searched, adding one to the matching times of the track flow data to be matched and the track flow data to be searched in the original appointed time slice.
In another aspect, an embodiment of the present invention provides a trajectory collision system, where the system includes a positioning device and a server, and the system executes the trajectory collision method.
Compared with the prior art, the technical scheme has the following advantages: hash mapping of data is realized through the concept of geohash, judgment of track similarity is respectively carried out in time and space dimensions by combining a Key-Value storage mode, and a track collision method is realized through quick search of position sensitive Hash in the data reading process; the scheme is simple to realize, the timeliness of the collision result is high, large-scale data volume processing can be carried out, and the method can be used for online real-time collision and offline collision; the method is not limited to the track collision among different electronic devices, and can also be used for the analysis of the same line among various electronic devices, such as Bluetooth and Bluetooth, Bluetooth and wifi, wifi and video, video and Bluetooth and the like; the application scenes comprise various indoor and outdoor wireless scenes, and the application field can be expanded to speech recognition, face recognition, big data analysis and the like.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a diagram of a matching principle of hash in the prior art.
FIG. 2 is a flow chart of a trajectory collision method of the present invention.
FIG. 3 is a schematic diagram of a Key-Value storage structure in the trajectory collision method of the present invention.
Fig. 4 is a flowchart of the trajectory collision in fig. 2.
FIG. 5 is a schematic diagram of a trajectory collision system of the present invention.
Fig. 6 is a schematic diagram of a storage structure of the database server in fig. 5.
Fig. 7 is a schematic diagram of a storage structure of the positioning server in fig. 5.
[ detailed description ] embodiments
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The basic idea of hashing (Hash, Hash function) is: after two data points in the original data space are subjected to the same mapping or projection transformation, if the two data points are adjacent in the original data space, the probability that the two data points are still adjacent in a new data space is very high, and the probability that two data points which are not adjacent in the original data space are mapped to the same bucket is very low, that is, after the original data are subjected to some hash mapping, the two original adjacent data are expected to be hashed to the same bucket and have the same bucket number, and a hash matching idea schematic diagram is shown in fig. 1. As shown in fig. 1, fig. 1 is a matching schematic diagram of hash in the prior art.
The GeoHash converts the latitude and longitude of two dimensions into character strings, and each character represents a certain rectangular area. That is, all points (longitude and latitude coordinates) in the rectangular area share the same GeoHash character string, which can protect privacy (only indicate the approximate area position rather than specific points) and is easier to cache. The longer the string, the more precise the range, e.g., 5-bit encoding can represent a rectangular area of 10 square kilometers, while 6-bit encoding can represent a finer area (about 0.34 square kilometers), with similar distances of string representation. The geohash represents not a point but a rectangular area. The user can issue the address code, can indicate that the user is near a certain address, and can not expose the accurate coordinate of the user, so that the privacy protection is facilitated, and the geohash is more efficient than the direct use of longitude and latitude. In this case, the concept of geohash is used to implement hash mapping of data.
The geohash code defaults to 12 bits, and the precision range corresponding to the first 9 bits is as follows.
| geohash length | lat bits | lng bits | lat error | lng | km error | |
| 1 | 2 | 3 | ±23 | ±23 | ±2500 | |
| 2 | 5 | 5 | ±2.8 | ±5.6 | ±630 | |
| 3 | 7 | 8 | ±0.70 | ±0.70 | ±78 | |
| 4 | 10 | 10 | ±0.087 | ±0.18 | ±20 | |
| 5 | 12 | 13 | ±0.022 | ±0.022 | ±2.4 | |
| 6 | 15 | 15 | ±0.0027 | ±0.0055 | ±0.61 | |
| 7 | 17 | 18 | ±0.00068 | ±0.00068 | ±0.076 | |
| 8 | 20 | 20 | ±0.000085 | ±0.00017 | ±0.019 | |
| 9 | 22 | 23 | ±0.000021 | ±0.000021 | ±0.00478 |
Organizations of various sizes are now beginning to have a need to handle large data, and relational databases are now almost reaching their limits in terms of scalability. One solution is to use a Key-Value storage database, which is a NoSQL (non-relational database) model, whose data is organized, indexed, and stored in the form of Key-Value pairs. The Key-Value storage is very suitable for the service data which does not relate to excessive data relation service relation, can effectively reduce the times of reading and writing the disk, and has better reading and writing performance compared with the SQL database storage.
Common Key-Value storage systems include Redis, Hbase, Leveldb, Scalaris, HyperDex, and the like.
Example one
FIG. 2 is a flow chart of a trajectory collision method of the present invention. A trajectory collision method comprising the steps of: inputting trajectory stream data to be matched; time slicing is carried out on the trajectory flow data to be matched; judging whether the track flow data to be matched in the appointed time slice is available; calculating the average coordinate of the trajectory flow data to be matched in the current time slice (the geohash value can be calculated for all the points, but the calculated amount is larger, the average value is to simplify a plurality of points into one point, and finally only one geohash value is obtained, so that the calculation is simplified, and the corresponding geohash value is calculated; and finding out the trajectory stream data matched with the geohash value in the trajectory stream data to be searched through the geohash value to be matched. The information such as the geohash value, the time and the like is stored in the trajectory stream data to be searched, and the stored content to be searched is correspondingly stored through the geohash value to be matched and the time, so that the trajectory stream data matched with the stored content can be found. The trajectory data to be matched includes: the positioning equipment calculates first coordinate data of the equipment to be positioned; and storing the first coordinate data as second coordinate data in a Key-main content Key-Value mode by using the geohash and the time as keywords and the positioning mark information as main content Value. The first coordinate data includes: coordinate information, time information and positioning mark information positioned by the positioning equipment. Through the geohash value to be matched, finding the trajectory stream data matched with the geohash value from the trajectory stream data to be searched comprises the following steps: and extracting a set of positioning mark information which is similar to the track flow data to be matched in the specified time slice in the time and space dimensions. The step of extracting the set of the positioning mark information which satisfies the similarity of the track flow data to be matched in the time and space dimensions in the appointed time slice comprises the following steps: acquiring a geohash value of the trajectory flow data to be matched according to the specified time slice and the trajectory flow data to be matched; traversing the specified time slice, and generating keywords at different moments in the specified time slice by combining the geohash value of the trajectory stream data to be matched; and taking out a corresponding positioning mark information set from the Key-Value system according to keywords at different moments in the appointed time slice. Through the geohash value to be matched, in the trace stream data to be searched, after the trace stream data matched with the geohash value is found, the method further comprises the following steps: and updating the times of successful matching of the trajectory stream data to be matched and the trajectory stream data to be searched in the specified time slice.
Many positioning devices can be wifi positioning devices, video identification devices, Bluetooth positioning devices and other devices with positioning functions. The following embodiments are illustrated with wifi positioning devices as examples.
The wifi algorithm positioning process is as follows: the wifi device carries out positioning firstly, and then the position coordinates of the device to be positioned are calculated. The step of calculating the position coordinates of the equipment to be positioned comprises the following steps: collecting fingerprint information, wherein the fingerprint information comprises fingerprint coordinates and RSSI (received signal strength indicator), and generating a distance d between the fingerprint coordinates and wifi equipment coordinates and a loss parameter and a constant K of the RSSI; generating a fingerprint database; and matching the signal data received by the wifi equipment in real time with a fingerprint database, and calculating the coordinate data of the equipment to be positioned according to the fingerprint coordinate data.
The result located by the wifi algorithm mainly includes coordinate and time information, and the location result is (mac, lat, ling, time) taking longitude and latitude coordinates as an example. And when the positioning result is stored, adding a geohash code, using the geohash + time as a Key word Key and the mac as a content Value, and storing the result in a Key-Value mode. The memory structure is shown in fig. 3. FIG. 3 is a schematic diagram of a Key-Value storage structure in the trajectory collision method of the present invention. As shown in fig. 3, Key-Value is stored in several blocks of data according to time slices, and each Key corresponds to different storage contents.
It should be noted that, in order to meet the requirements of different collision precisions, when the Key-Value mode is adopted for storage, the geohash does not store all 12 bits, but obtains the first few bits according to the precision requirement. For example, the precision is 76m range when the first 7 bits of the geohash are stored; taking the first 8 bits, the precision is 19m range.
In this case, the trajectory collision between the video and wifi is taken as an example to explain the trajectory collision method, and a schematic diagram is shown in fig. 4, where fig. 4 is a flowchart of the trajectory collision in fig. 2.
The video identification system stores video stream data of different users, the video stream data is composed of position information at different moments, the position information comprises (id, lat, lng, time), the longitude and latitude coordinates of the id at the moment of time are lat and lng, the id represents a face identification code of the user, and the ids of the different users are different.
Track flow data of different mobile phone MACs are stored in the wifi positioning system, the track flow data of each MAC is composed of position information at different moments, and the position information is geohash + time: the mac set means that a series of mac sets are included at a certain time within the position specification accuracy range.
The trajectory collision method is to find two trajectories with similar distances in time and space. Taking the example that a face searches for corresponding positioning mark information such as a mobile phone MAC address, the specific implementation method comprises the following processes:
(1) inputting trajectory stream data to be matched
In this case, the corresponding mobile phone MAC is searched for by a human face, so that video stream data of a certain user is input. In the specific implementation process, the track collision can be carried out on line in real time, and only the current data stream in the latest period of time is input each time to carry out track collision processing. And off-line track collision can be carried out, and track flow data to be matched in historical time is input.
(2) Time slicing input trace stream data
In general, for two independent systems, the time processing is difficult to completely synchronize; even in the same system, it is difficult to ensure that the detection time of two different users is synchronized at the same time, so that the trajectory collision can tolerate a certain time deviation of the collided trajectory stream data. Time slicing means that track stream data is segmented in a time dimension, the compared track positions are ensured to be within a tolerable time range, and if the compared track positions exceed the tolerable time range, the time dimension cannot be matched, and the significance of collision is avoided.
Assuming that the time span of the input trajectory data to be matched is 1min, and the length of the time slice is 10s, i.e. the tolerable time range is 10s, the data of 1min can be divided into 6 time slices.
It should be noted that:
a. in practical systems, the detected trace stream data is not completely continuous, so that the input trace stream data is not contained in all of the 6 time slices.
b. The time tolerance ranges under different project requirements are different, and the principle is that if the time dimension tolerance range is loose, the space dimension tolerance range is strict; on the contrary, if the tolerance range in the time dimension is strict, the tolerance range in the space dimension is loose.
(3) Judging whether the time slice has the input track flow data
If yes, performing the step (4); if not, continuing the processing of the next time slice and simultaneously performing the step (3).
(4) Calculating the average coordinate of the input trace stream in the current time slice, and solving the corresponding geohash value
(5) Matching spatial dimensions with trajectory data to be searched
And (2) obtaining the range t 0-t 1 of the current time slice, and (4) obtaining the geohash Value of the data to be matched, traversing the time range t 0-t 1, generating keywords at different moments by combining the geohash, and taking out a corresponding mac set in a Key-Value system according to the keywords. Namely, the MAC address set which is similar to the track flow data to be matched in the time slice and satisfies the track in the time and space dimensions.
It should be noted that:
a. the precision of the geohash Value to be matched needs to be consistent with the geohash precision stored by the Key-Value.
b. Since the geohash represents a rectangular area, not a circular area, the corresponding accuracy cannot be fully guaranteed at a certain position. Therefore, neighbor geohash can be introduced to expand the area range.
(6) Updating the number of matches for each match
And (5) updating the matching times of the information matched in time and space in the step (5).
Taking the matched mac set as an example, if the matched mac already exists, adding 1 to the original matching times; if not, the matching times are 1.
(7) Judging whether all time slices are traversed
And (4) if the traversal is not finished, processing the next time slice, and performing the step (3), otherwise, performing the step (8).
(8) And outputting a collision result and storing the collision result.
It should be noted that:
a. the scheme can realize the simultaneous collision of a plurality of trajectory stream data in a parallel mode.
b. The collision result may include a plurality of collision results, and the track collision times with the same attribute will slowly become prominent over time through an online business mode.
In specific implementation, the locator information may be various, such as time information, spatial information, etc., and key value information, such as MAC address information. Here, the description is given only by taking the MAC address as an example, and is not limited to the MAC address locator information.
Example two
FIG. 5 is a schematic diagram of a trajectory collision system of the present invention. The system comprises: the system executes the positioning method. Here, the positioning device is exemplified by a wifi device, wherein the wifi device is exemplified by a wifi probe device. Wifi probe device, generally including the following functions:
(1) the built-in induction module transmits a high connection frequency SSID to induce the equipment to be positioned to be connected, and the probability of capturing the MAC address of the equipment to be positioned is increased.
(2) And scanning all channels, and capturing the MAC address of the equipment to be positioned without missing packets.
(3) And encrypting and returning information such as the strength of the marked MAC signal, the connection time difference and the like to a position calculation server to perform accurate calculation of the position coordinate of the equipment to be positioned.
And the POE power supply module is used for transmitting the signal data received by the wifi equipment in real time back to the database server while supplying power to the wifi probe equipment.
The server comprises a database server and a positioning database. The database server is used as a database for storing the MAC address of the equipment to be positioned, which is captured by the Wifi probe equipment, is quickly compared, the successfully compared data is transmitted to the positioning server, information such as connection duration, connection time and position of the equipment marked with the MAC is updated into the fingerprint database, and the storage schematic diagram of the data server is shown in the attached drawing 6. Fig. 6 is a schematic diagram of the data server in fig. 5. The data server stores the format that each row of data represents the serial number ID of the equipment to be positioned, the MAC address of the equipment to be positioned, the name of the equipment to be positioned, the discovery time of the equipment to be positioned and the RSSI (received signal strength indicator) detected by the first wifi probe equipment1RSSI of the signal detected by the second wifi probe device2… …, signal strength RSSI detected by the n-1 th wifi probe devicen-1RSSI (received Signal Strength indicator) detected by nth wifi probe devicen。
And the positioning server runs a positioning algorithm, matches the signals received in real time through calculation with the data of the fingerprint database, calculates the coordinates to be positioned according to the fingerprint coordinates, and a schematic diagram for storing the positioning result is shown in the attached figure 7. Fig. 7 is a schematic diagram of the location server storage in fig. 5. The storage format of the positioning server is that each row of data represents the number ID of the equipment to be positioned, the MAC address of the equipment to be positioned, the name of the equipment to be positioned, the X coordinate of the equipment to be positioned, the Y coordinate of the equipment to be positioned and the report time.
The trajectory collision system executes the trajectory collision method described above. The track collision method has been described in detail above, and is not described herein again.
As can be seen from the above description, with the track collision method and system according to the present invention, hash mapping of data is implemented through the concept of geohash, judgment of track similarity is performed in time and space dimensions respectively in combination with a Key-Value storage manner, and a track collision method is implemented through fast search of position sensitive hash in a data reading process; the scheme is simple to implement, the timeliness of collision results is high, large-scale data volume processing can be performed, and the method can be used for online real-time collision and offline collision.
The above embodiments of the present invention are described in detail, and the principle and the implementation of the present invention are explained by applying specific embodiments, and the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. A trajectory collision method, comprising:
time slicing is carried out on the trajectory flow data to be matched;
judging whether the track flow data to be matched exists in a specified time slice;
if yes, calculating the average coordinate of the trajectory flow data to be matched in the appointed time slice, and solving the corresponding geohash value;
and finding out the trajectory stream data matched with the geohash value in the trajectory stream data to be searched through the geohash value to be matched.
2. The trajectory collision method according to claim 1, wherein the trajectory stream data to be matched comprises:
the positioning equipment calculates first coordinate data of the equipment to be positioned;
and storing the first coordinate data as second coordinate data in a Key-main content Key-Value mode by using the geohash and the time as keywords and the positioning mark information as main content Value.
3. The trajectory collision method according to claim 1, characterized in that before time-slicing trajectory stream data to be matched, it comprises: and inputting the trajectory flow data to be matched.
4. The trajectory collision method according to claim 2, characterized in that the first coordinate data comprises: coordinate information, time information and positioning mark information positioned by the positioning equipment.
5. The trajectory collision method according to claim 1, wherein the finding of trajectory stream data matching the geohash value in the trajectory stream data to be found comprises: and taking out a positioning mark information set which meets the similarity of the track data to be matched in the specified time slice and the track data in the time and space dimensions.
6. The trajectory collision method according to claim 5, wherein the extracting of the set of locator information satisfying trajectory similarity in temporal and spatial dimensions with the trajectory stream data to be matched within the specified time slice comprises:
acquiring a geohash value of the trajectory flow data to be matched according to the specified time slice and the trajectory flow data to be matched;
traversing the specified time slice, and generating keywords at different moments in the specified time slice by combining the geohash value of the trajectory data to be matched;
and taking out a corresponding positioning mark information set from a Key-Value system according to the keywords at different moments in the specified time slice.
7. The trajectory collision method according to claim 1, wherein the step of finding the trajectory stream data matching the geohash value in the trajectory stream data to be found further comprises, after finding the trajectory stream data matching the geohash value, the step of: and updating the times of successful matching of the trajectory stream data to be matched and the trajectory stream data to be searched in the specified time slice.
8. The trajectory collision method of claim 2, characterized in that the positioning device is a wifi positioning device or a video recognition device or a bluetooth positioning device.
9. The trajectory collision method according to claim 7, wherein after the updating of the number of times of successful matching between the trajectory stream data to be matched and the trajectory stream data to be searched within the specified time slice, the method further comprises: and storing and outputting the track collision result.
10. The trajectory collision method of claim 7, wherein the updating the number of times that the trajectory stream data to be matched and the trajectory stream data to be searched within the specified time slice are successfully matched comprises: and if the track flow data to be matched in the time slice and the track flow data to be searched are successfully matched, adding one to the matching times of the track flow data to be matched and the track flow data to be searched in the original specified time slice.
11. A trajectory collision system, characterized in that the system comprises a positioning device and a server, and the system executes the method of any one of claims 1 to 10.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910165930.5A CN111666358A (en) | 2019-03-05 | 2019-03-05 | Track collision method and system |
| PCT/CN2019/112554 WO2020177335A1 (en) | 2019-03-05 | 2019-10-22 | Path collision method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910165930.5A CN111666358A (en) | 2019-03-05 | 2019-03-05 | Track collision method and system |
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