CN111723166A - Track data processing method and system - Google Patents
Track data processing method and system Download PDFInfo
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- CN111723166A CN111723166A CN201910211914.5A CN201910211914A CN111723166A CN 111723166 A CN111723166 A CN 111723166A CN 201910211914 A CN201910211914 A CN 201910211914A CN 111723166 A CN111723166 A CN 111723166A
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Abstract
The embodiment of the invention provides a track data processing method and a system, wherein the provided method comprises the following steps: acquiring a track processing parameter according to the privacy protection degree; according to the track processing parameters, carrying out offset processing on a starting point and an end point in original track data to obtain a starting point offset point and an end point offset point, and carrying out extension processing on the starting point offset point and the end point offset point to obtain a binding track section; selecting paths between all inner starting points and inner end points in the original trajectory data set, wherein PoI scores of the paths meet preset conditions, and using the paths as innermost trajectory sections; and connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a track after privacy protection. The method and the system provided by the invention improve the safety of the track data, simultaneously reserve and protect the information of the real start and stop points, and keep the usability of the published track data set in urban traffic planning.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a track data processing method and system.
Background
The position service based on the track data release is widely applied to daily life of people, valuable track data can be used for inquiring navigation information or traffic condition reports, the urban transfer quality can be effectively improved, and the travel efficiency is improved. But since the data directly submitted by the user may contain many personal sensitive information, such as identity information. If the real track information is directly published, the track information is easy to be attacked maliciously, and an attacker can analyze and mine the track data user or the published position, semantic content and other contents of the track data user, so that sensitive data such as family addresses, work places, health conditions and even social relations can be deduced.
In the existing track publishing privacy protection research work, a plurality of privacy protection methods have been proposed. Most of these methods protect trajectory data by employing techniques such as pseudonym substitution, trajectory clustering, etc., either independently or in combination. In the existing track publishing privacy protection method, the track clustering method is widely used. The track clustering method is to classify the tracks according to a certain rule and only release partial segments of the real tracks according to the rule.
However, in the prior art, compared with the real unprocessed trajectory data, the trajectory data processed by the trajectory clustering method is most likely to be composed of multiple segments of trajectories (which may not be connected), and the acquired trajectory data set cannot guide urban traffic planning and also cannot better protect the privacy data of the user.
Disclosure of Invention
The embodiment of the invention provides a track data processing method and system, which are used for solving the problems that in the prior art, the track data privacy protection degree for users is not high, and the processed track data cannot meet the usability in the aspect of urban traffic planning.
In a first aspect, an embodiment of the present invention provides a trajectory data processing method, including:
acquiring a track processing parameter according to the privacy protection degree;
according to the track processing parameters, carrying out offset processing on a starting point and an end point in original track data to obtain a starting point offset point and an end point offset point, and carrying out extension processing on the starting point offset point and the end point offset point to obtain a binding track section;
selecting paths between all inner starting points and all inner end points in the original track data set, wherein PoI scores meet preset conditions, and the semantic types of the passing points meet the preset conditions, and taking the paths as innermost track sections;
connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a track after privacy protection;
wherein, the internal starting point is the end point of the binding track section formed after the offset processing of the starting point in the original track data;
and the inner end point is the head end point of a binding track segment formed after the end point in the original track data is subjected to offset processing.
In a second aspect, an embodiment of the present invention provides a trajectory data processing system, including:
the parameter acquisition module is used for acquiring track processing parameters according to the privacy protection degree;
a binding track segment obtaining module, configured to perform offset processing on a starting point and an end point in original track data according to the track processing parameter to obtain a starting point offset point and an end point offset point, and perform extension processing on the starting point offset point and the end point offset point to obtain a binding track segment;
the innermost track section acquisition module is used for selecting a path of which the PoI score meets a preset condition and the semantic type of a passing point meets the preset condition from all paths between an inner starting point and an inner end point in the original track data set as an innermost track section;
the track generation module is used for connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a track after privacy protection;
wherein, the internal starting point is the end point of the binding track section formed after the offset processing of the starting point in the original track data;
and the inner end point is the head end point of a binding track segment formed after the end point in the original track data is subjected to offset processing.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the trajectory data processing method provided in the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the trajectory data processing method provided in the first aspect.
According to the method provided by the embodiment of the invention, the track information of the user is protected by randomly selecting the track segments with similar hot degree between the real start points and the real stop points, so that long-term observation attack can be resisted, meanwhile, the tracks before and after the real start points are increased, the information of the real start points and the real stop points is reserved and protected, and the usability of the released track data set in urban traffic planning is maintained.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a trajectory data processing method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a trajectory data processing system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Referring to fig. 1, fig. 1 is a schematic flow chart of a trajectory data processing method according to an embodiment of the present invention, where the method includes:
s1, acquiring track processing parameters according to the privacy protection degree;
s2, according to the track processing parameters, performing offset processing on a starting point and an end point in original track data to obtain a starting point offset point and an end point offset point, and performing extension processing on the starting point offset point and the end point offset point to obtain a binding track segment;
s3, selecting paths of which PoI scores meet preset conditions and pass point semantic types meet the preset conditions from all paths between an inner starting point and an inner end point in the original trajectory data set as innermost trajectory segments;
s4, connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a track after privacy protection;
wherein, the internal starting point is the end point of the binding track section formed after the offset processing of the starting point in the original track data;
the inner end point is a head end point of a binding track section formed after the end point in the original track data is subjected to offset processing;
the PoI score specifically adopts a quantitative scoring mode to evaluate PoI conditions in a certain distance range around all the passing points in one path.
Specifically, in the track data processing, in order to protect the privacy of the user, certain background knowledge needs to be established, so that a more effective and more reasonable privacy protection scheme is designed. The background knowledge in the embodiment of the invention mainly comprises three parts: the first part is the transition probability (entering the point or two directions from the point, namely the transition probability of the in-degree and the out-degree) of each point (namely the intersection) which is obtained by using a real and unprocessed track data set; the second part is that the PoI score of each point is obtained according to the peripheral PoI (Point of interest) condition of each point and is used for evaluating the enthusiasm degree of each point; and the third part is that the semantics of each point is determined by referring to the semantic division standard, and each point has one and only one semantics.
In this embodiment, the step of processing the trajectory data includes the input of parameters. And generating a real start-stop point offset and a binding track segment and generating a new track. Firstly, for inputting parameters, a track data owner determines parameters such as the range size of the deviation of a real start point and a real stop point, the length of a real start point and a real stop point binding track segment, the length of the outward extension of the real start point and the real stop point binding track segment when a new track is generated and the like according to subjective and objective conditions and the privacy protection degree required by the environment.
After acquiring the track processing parameters, generating a real start-stop point offset and binding track segment, wherein the step firstly selects offset points of which the start point and the end point in the original track data are within a certain range. And then extending forwards and backwards on the basis of the offset point to obtain a binding track section after the actual starting point and the actual end point are offset. And in the extension process, the transfer probability of each point is considered, the extension point is close to the real point on the PoI score as much as possible, and the extension point is not repeated with the real point or the selected extension point semantically as much as possible.
And in the step of generating a new track, firstly traversing paths between an inner starting point and an inner end point before and after the real and unprocessed track is concentrated, and then randomly selecting paths with PoI scores close to the PoI scores of the binding track segments and covering more different semantics to form the innermost track segment. And then connecting the innermost track section with the two binding track sections to form an inner track section. And then, extending the inner track section forwards and backwards, wherein the extending process is similar to the extending process of the second step, and finally forming the track which corresponds to the original track and is subjected to privacy protection.
Wherein, the forward or backward is compared with the direction of the real start and stop point vector, and the direction opposite to the direction of the start and stop point vector is called forward; otherwise, it is called backward. The point score represents the evaluation of all points of approach, point of interest, around a location or along a path in the form of a quantified score. The internal starting point refers to a binding track segment end point formed after the actual starting point is offset. The inner end point refers to a binding track segment head end point formed after the actual end point is deviated.
By the method, the track information of the user is protected by randomly selecting the track segments with similar hot degree between the real start points and the real stop points, long-term observation attack can be resisted, meanwhile, the tracks before and after the real start points are increased, the information of the real start points is reserved and protected, and the usability of the released track data set in urban traffic planning is maintained.
On the basis of the above embodiment, the step of obtaining the trajectory processing parameter according to the privacy protection degree specifically includes: determining a track processing parameter according to the privacy protection degree; the track processing parameters include, but are not limited to, areas of ranges where the start point and the end point offset points in the original track data are located, lengths of bound track segments, and outward extension lengths of bound track segments.
Specifically, the user determines appropriate parameters based on subjective and objective conditions and the degree of privacy protection required by the environment, which will affect the trajectory data privacy, required cost, and trajectory data availability of the solution.
The trajectory data privacy requires two dimensions: firstly, measuring the short-term privacy protection effect after release; and secondly, the long-term privacy protection effect after the data are released is measured. The short-term privacy protection effect after release is to consider only the adjustment condition of the currently released track data, the condition that the released track covers the real start and stop points and the whole background information; the long-term privacy protection effect after the data are released is that on the basis of the short-term protection effect, the track adjustment condition of the same real starting and stopping points in history is also considered.
The required cost mainly refers to calculation cost and historical data storage cost. The availability of the track data refers to that the track collects the track released by the user, and can still dig the information such as the travel habit, the travel demand and the like of the user.
The parameters include: the area r of the range of the selectable offset points of the real start and stop points, the length of the binding track segment of the real start and stop points, namely the total number of forward or backward transfer nodes (hops): k is a radical ofi∈[2，4](ii) a When a new track is generated, the length of the forward or backward extension of the real start-stop point binding track segment, namely the forward or backward transfer node (hop) number: k is a radical off∈[2，8]And kb∈[2，8]The acceptable PoI score deviation α for the middle trace segment selection (i.e., the PoI score L for the trace segment after the start point deviation)APoI score L of track segment after endpoint shiftBWhen the innermost track section is selected, the selectable track section PoI is divided into (L)A-α，LB+ α) or (LB-α，LA+α)。
By the method, various parameters are set, and the privacy, the required cost and the availability of the track data of the change are supported. Therefore, the trajectory publisher can change parameters at any time according to subjective and objective environments including the requirements of service objects (trajectory data users), so that dynamic balance of the privacy, the required cost and the availability of the trajectory data is realized.
On the basis of the above embodiment, the step of performing offset processing on the start point and the end point in the original trajectory data according to the estimation processing parameter to obtain a start point offset point and an end point offset point, and performing extension processing on the start point offset point and the end point offset point to obtain a bound trajectory segment specifically includes: shifting a starting point and an end point in original track data according to track processing parameters to obtain a starting point shift point and an end point shift point; and respectively taking the starting point offset point and the end point offset point as starting points, extending nodes of a preset number forwards and/or backwards, and generating a binding track section of the starting point offset point and a binding track section of the terminal offset point.
Specifically, the real start and stop points are shifted within a set small range according to parameter setting to obtain shift points of the real start and stop points, and a plurality of nodes are extended forwards/backwards by taking the shift points as starting points to meet the setting of the length of the binding track segment. Each node selected into a binding track segment should be semantically as different as possible from the node selected into the track segment and maintain a close proximity in the PoI score. The bound track segments are that under the condition of the same parameter setting, the bound track segments are not changed, and points on the bound track segments all have corresponding bound track segments.
On the basis of the above embodiment, the step of selecting, according to the paths between all the inner starting points and the inner end points in the original trajectory dataset, a path in which the PoI score meets the preset condition as the innermost trajectory segment specifically includes: traversing all track segments passing through the inner starting point and the inner end point in the original track data, and calculating PoI scores of all the track segments; and selecting a path with PoI score closest to the binding track segment and multiple semantic types of the passing points as an innermost track segment.
The passing point is a point where the track passes; the semantics represent the industrial function to which a place belongs, and convey the position information of the place, and each place has one and only one semantics.
The step of connecting the innermost track section with the binding track section to form an inner track section, extending the inner track section to obtain a track after privacy protection specifically includes: connecting the innermost track section with the binding track section to form an inner track section; according to preset conditions, extending the internal track section to form a track after privacy protection; wherein the preset conditions include, but are not limited to: a combination of one or more of an extension length, a transition probability, and a PoI score.
Specifically, in the new trajectory generation step after the extension, after the parameters and the trajectory segment after the actual start and stop point shift are obtained, the adjusted trajectory data is output according to the global background knowledge, the PoI score and the semantic condition.
And firstly, selecting the innermost track section, traversing all real track data to obtain all track sections sequentially passing through the inner starting point and the inner end point, and forming a candidate innermost track section set. And carrying out statistics of PoI scores and covering semantic quantities on each track segment in the candidate set. And randomly selecting one track section which has the track PoI score relatively close to the two binding track sections and has the passing point covering more semantic types from the track sections meeting the PoI score parameter requirement as the innermost track section. And connecting the innermost track section with the two binding track sections to form an internal track section. And generating tracks available for publishing on the basis of the internal track segments. And taking the starting point (end point) of the internal track segment as a starting point, and extending forwards (backwards) to meet the set parameters. And considering factors such as PoI score condition, coverage semantic condition, transition probability condition and the like of the selection point. And outputting tracks which are available for publishing and contain relevant information of the real start point and the real stop point.
By the method, all tracks of a real data set from a real starting point to a real end point are traversed, semantics which cover as much as possible are selected from the tracks, and meanwhile tracks with similar PoI scores at each point are accessed, so that the tracks are prevented from being reconstructed, meanwhile, various inference attacks can be resisted by fully considering the background knowledge of an attacker, wherein the background knowledge mainly comprises two aspects: road background knowledge, e.g., semantics and PoI score conditions for each point; and travel habit background knowledge, such as the outgoing transition probability and the incoming transition probability of each point.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a trajectory data processing system according to an embodiment of the present invention, where the trajectory data processing system includes: a parameter obtaining module 21, a binding track segment obtaining module 22, an innermost track segment obtaining module 23 and a track generating module 24.
The parameter obtaining module 21 is configured to obtain a track processing parameter according to the privacy protection degree.
The binding track segment obtaining module 22 is configured to perform offset processing on the starting point and the end point in the original track data according to the track processing parameter to obtain a starting point offset point and an end point offset point, and perform extension processing on the starting point offset point and the end point offset point to obtain a binding track segment.
The innermost track segment obtaining module 23 is configured to select, as an innermost track segment, a path in which the PoI score satisfies a preset condition and the semantic type of the passing point satisfies the preset condition, according to the paths between all the inner starting points and the inner end points in the original track data set.
The track generation module 24 is configured to connect the innermost track segment with the binding track segment to form an internal track segment, and extend the internal track segment to obtain a track after privacy protection.
Wherein, the internal starting point is the end point of the binding track section formed after the offset processing of the starting point in the original track data; the inner end point is a head end point of a binding track section formed after the end point in the original track data is subjected to offset processing; wherein, the PoI score specifically adopts a quantitative scoring form to evaluate all the path points in one path.
Specifically, for the input of parameters, the trajectory data owner determines parameters such as the range size of the deviation of the real start and stop points, the length of the binding trajectory segment of the real start and stop points, the length of the forward or backward extension of the binding trajectory segment of the real start and stop points when a new trajectory is generated, and the like according to subjective and objective conditions and the privacy protection degree required by the environment.
After acquiring the track processing parameters, generating a real start-stop point offset and binding track segment, wherein the step firstly selects offset points of which the start point and the end point in the original track data are within a certain range. And then extending forwards and backwards on the basis of the offset point to obtain a binding track section after the actual starting point and the actual end point are offset. And in the extension process, the transfer probability of each point is considered, the extension point is close to the real point on the PoI score as much as possible, and the extension point is not repeated with the real point or the selected extension point semantically as much as possible.
And in the step of generating a new track, firstly traversing paths between an inner starting point and an inner end point before and after the real and unprocessed track is concentrated, and then randomly selecting paths with PoI scores close to the PoI scores of the binding track segments and covering more different semantics to form the innermost track segment. And then connecting the innermost track section with the two binding track sections to form an inner track section. And then, extending the inner track section forwards and backwards, wherein the extending process is similar to the extending process of the second step, and finally forming the track which corresponds to the original track and is subjected to privacy protection.
Wherein, the forward or backward is compared with the direction of the real start and stop point vector, and the direction opposite to the direction of the start and stop point vector is called forward; otherwise, it is called backward. The point score represents that all the passing point (point of interest) conditions around a position or passing by a path are evaluated in a quantified scoring mode. The internal starting point refers to a binding track segment end point formed after the actual starting point is offset. The inner end point refers to a binding track segment head end point formed after the actual end point is deviated.
By the system, the track information of the user is protected by randomly selecting the track segments with similar hot degree between the real start points, long-term observation attack can be resisted, and meanwhile, the usability of the published track data set in urban traffic planning is maintained by increasing tracks before and after the real start points, reserving and protecting the information of the real start points.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the bus 304. Processor 301 may call logic instructions in memory 303 to perform methods including, for example: acquiring a track processing parameter according to the privacy protection degree; according to the track processing parameters, carrying out offset processing on a starting point and an end point in original track data to obtain a starting point offset point and an end point offset point, and carrying out extension processing on the starting point offset point and the end point offset point to obtain a binding track section; selecting a path of which the PoI score meets a preset condition and the semantic type of a passing point meets the preset condition as an innermost track section according to the paths between all inner starting points and all inner end points in the original track data set; and connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a track after privacy protection.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the method provided by the above method embodiments, for example, the method includes: acquiring a track processing parameter according to the privacy protection degree; according to the track processing parameters, carrying out offset processing on a starting point and an end point in original track data to obtain a starting point offset point and an end point offset point, and carrying out extension processing on the starting point offset point and the end point offset point to obtain a binding track section; selecting a path of which the PoI score meets a preset condition and the semantic type of a passing point meets the preset condition as an innermost track section according to the paths between all inner starting points and all inner end points in the original track data set; and connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a track after privacy protection.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above method embodiments, for example, including: acquiring a track processing parameter according to the privacy protection degree; according to the track processing parameters, carrying out offset processing on a starting point and an end point in original track data to obtain a starting point offset point and an end point offset point, and carrying out extension processing on the starting point offset point and the end point offset point to obtain a binding track section; selecting a path of which the PoI score meets a preset condition and the semantic type of a passing point meets the preset condition as an innermost track section according to the paths between all inner starting points and all inner end points in the original track data set; and connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a track after privacy protection.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A trajectory data processing method, comprising:
acquiring a track processing parameter according to the privacy protection degree;
according to the track processing parameters, carrying out offset processing on a starting point and an end point in original track data to obtain a starting point offset point and an end point offset point, and carrying out extension processing on the starting point offset point and the end point offset point to obtain a binding track section;
selecting paths between all inner starting points and all inner end points in the original track data set, wherein PoI scores meet preset conditions, and the semantic types of the passing points meet the preset conditions, and taking the paths as innermost track sections;
connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a complete track after privacy protection;
wherein, the internal starting point is the end point of the binding track section formed after the offset processing of the starting point in the original track data;
and the inner end point is the head end point of a binding track segment formed after the end point in the original track data is subjected to offset processing.
2. The method according to claim 1, wherein the step of obtaining the trajectory processing parameter according to the privacy protection degree specifically includes:
determining a track processing parameter according to the privacy protection degree;
wherein the track processing parameters include, but are not limited to, start and end deflectable range areas, bound track segment lengths, and bound track segment outward extension lengths in the original track data.
3. The method according to claim 1 or 2, wherein the step of performing offset processing on a start point and an end point in original trajectory data according to the estimation processing parameter to obtain a start point offset point and an end point offset point, and performing extension processing on the start point offset point and the end point offset point to obtain a bound trajectory segment specifically includes:
shifting a starting point and an end point in original track data according to track processing parameters to obtain a starting point shift point and an end point shift point;
and respectively taking the starting point offset point and the end point offset point as starting points, extending a preset number of nodes in the front and back directions, and generating a binding track section of the starting point offset point and a binding track section of the terminal offset point.
4. The method according to claim 1, wherein the step of selecting, as the innermost track segment, a path between all inner start points and inner end points in the original track dataset, where the poii score satisfies a preset condition and the semantic type of the point satisfies the preset condition, specifically comprises:
traversing all track segments passing through the inner starting point and the inner end point in the original track data, and calculating PoI scores of all the track segments;
and selecting a path with the PoI score closest to the binding track segment and the most semantic types of the route points as an innermost track segment.
5. The method according to claim 4, wherein the step of connecting the innermost track segment with the binding track segment to form an inner track segment, and extending the inner track segment outward to obtain a track after privacy protection specifically includes:
connecting the innermost track section with the binding track section to form an inner track section;
according to a preset rule, extending the internal track section to form a track after privacy protection;
wherein the preset rules include, but are not limited to: a combination of one or more of an extension length, a transition probability, and a PoI score.
6. A trajectory data processing system, comprising:
the parameter acquisition module is used for acquiring track processing parameters according to the privacy protection degree;
a binding track segment obtaining module, configured to perform offset processing on a starting point and an end point in original track data according to the track processing parameter to obtain a starting point offset point and an end point offset point, and perform extension processing on the starting point offset point and the end point offset point to obtain a binding track segment;
the innermost track section acquisition module is used for selecting a path of which the PoI score meets a preset condition and the semantic type of a passing point meets the preset condition from all paths between an inner starting point and an inner end point in the original track data set as an innermost track section;
the track generation module is used for connecting the innermost track section with the binding track section to form an internal track section, and extending the internal track section to obtain a track after privacy protection;
wherein, the internal starting point is the end point of the binding track section formed after the offset processing of the starting point in the original track data;
and the inner end point is the head end point of a binding track segment formed after the end point in the original track data is subjected to offset processing.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the trajectory data processing method according to any one of claims 1 to 5 are implemented when the program is executed by the processor.
8. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the trajectory data processing method according to any one of claims 1 to 5.
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