CN112738718B - Space-time big data track matching method based on LSA - Google Patents
Space-time big data track matching method based on LSA Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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Abstract
The invention provides a space-time big data track matching method based on LSA, comprising the following steps: pre-configuring a plurality of routers, wherein each router is provided with a routing list and positioning information corresponding to the router; the method comprises the steps that a distributed server collects routing lists of a plurality of routers at intervals of a preset collection time period, and labels the routing lists included in each router according to the collection time sequence, wherein each routing list comprises terminal IDs connected with the routers on the basis of an LSA; receiving space-time big data information input by an administrator, traversing each routing list of each router, and generating track information matched with the terminal ID based on the time sequence marked by the routing lists, wherein the track information comprises the time of the routing lists and positioning information corresponding to the time. The invention has the advantages of relatively accurate positioning effect and low data volume in the positioning process, namely, the purpose of generating corresponding track information is achieved, and the effect of saving the memory space is also achieved.
Description
Technical Field
The invention belongs to the technical field of big data, and particularly relates to a space-time big data track matching method based on an LSA.
Background
Due to the inherent characteristics of the space entity of the space where the space exists and the space phenomenon in three aspects of time, space and attribute, the space-time data presents the complexity of multi-dimensional, semantic and space-time dynamic association, so that the formal expression of the space-time big data multi-dimensional association description, the dynamic modeling of the association relation and the multi-scale association analysis method need to be researched, and the quick and accurate task-oriented association constraint is provided by the collaborative calculation and reconstruction of the space-time big data.
The space-time data comprises a plurality of types, wherein the track information belongs to one type of space-time big data, the most accurate track information acquisition mode is track information based on GPS positioning, but the track information has larger data volume and higher requirements on data storage space. And a positioning mode based on a base station is adopted, and although the time interval for acquiring the positioning data is small and the stored data amount is low, the positioning range is large, so that the obtained track information is not accurate enough.
Disclosure of Invention
The embodiment of the invention provides a space-time big data track matching method based on an LSA, which has the advantages of relatively accurate positioning effect and low data volume in the positioning process, namely, the purpose of generating corresponding track information is achieved, and the effect of saving memory space is achieved.
In a first aspect of the embodiments of the present invention, a space-time big data trajectory matching method based on an LSA is provided, including:
pre-configuring a plurality of routers, wherein each router is provided with a routing list and positioning information corresponding to the router;
the method comprises the steps that a distributed server collects routing lists of a plurality of routers at intervals of a preset collection time period, and labels the routing lists included in each router according to the collection time sequence, wherein each routing list comprises terminal IDs connected with the routers on the basis of an LSA;
receiving space-time big data information input by an administrator, wherein the space-time big data information comprises a terminal ID, traversing each routing list of each router, and generating track information matched with the terminal ID based on the time sequence marked by the routing lists, and the track information comprises the time of the routing lists and positioning information corresponding to the time.
Optionally, in one possible implementation manner of the first aspect, the spatiotemporal big data information includes a query time period;
traversing each routing list, and generating track information matched with the terminal ID based on the time sequence labeled by the routing list comprises the following steps:
acquiring the query time period, and selecting routing lists of different routers based on the query time period, wherein each router has a routing list at least one moment in the query time period;
if yes, judging that the number of the routing lists of one router at the current moment is more than or equal to two;
arranging the selected routing list according to the sequence of the acquisition time, and acquiring all terminal IDs in the routing list at the most front acquisition time;
sequentially acquiring terminal IDs in all the routing lists, and deleting the terminal ID in the next routing list when the terminal ID in the next routing list is the same as the terminal ID in the previous routing list to obtain a plurality of routing lists, wherein any two routing lists in the same router do not have repeated terminal IDs;
and processing the routing lists of the routers to obtain track information matched with the terminal ID.
Optionally, in a possible implementation manner of the first aspect, the pre-configuring the plurality of routers includes:
the method comprises the steps that a generation time period of a route list is configured in advance, and a router generates router lists at different moments based on the generation time period;
presetting a deletion time period, and acquiring all routing lists of any router in the deletion time period;
arranging the selected routing list according to the sequence of the acquisition time, and acquiring all terminal IDs in the routing list at the most front acquisition time;
and sequentially acquiring the terminal IDs in all the routing lists, and deleting the terminal ID in the next routing list when the terminal ID in the next routing list is the same as the terminal ID in the previous routing list to obtain a plurality of routing lists, wherein any two routing lists do not have repeated terminal IDs.
Optionally, in a possible implementation manner of the first aspect, the processing the route lists of the plurality of routers to obtain the trajectory information matching the terminal ID includes:
selecting one or more target terminal IDs;
acquiring all routing lists corresponding to the target terminal ID in all routing lists of all routers, and acquiring acquisition time and positioning information of each routing list with the target terminal ID;
and generating a time axis based on the acquisition time, and representing the positioning information corresponding to the time on the time axis to obtain the track information corresponding to the target terminal.
Optionally, in a possible implementation manner of the first aspect, a generation time period of the route list is configured in advance, and the router generating the router list at different times based on the generation time period includes:
acquiring all terminal IDs connected with the router in a generation time period;
deleting all terminal IDs of which the connection time with the router is less than or equal to a preset connection time period;
and extracting all terminal IDs of which the connection time with the router exceeds a preset connection time period to generate a router list.
Optionally, in a possible implementation manner of the first aspect, the extracting all terminal IDs whose connection time with the router exceeds a preset connection time period to generate the router list includes:
sending local area network inquiry information to terminals corresponding to all terminal IDs of which the connection time of the router exceeds a preset connection time period;
and receiving local area network confirmation information input by a terminal corresponding to the terminal ID, and adding the terminal ID to a router list corresponding to the current moment after receiving the local area network confirmation information.
Optionally, in a possible implementation manner of the first aspect, the processing the route lists of the plurality of routers to obtain the trajectory information matching the terminal ID includes:
acquiring a plurality of routing lists of a plurality of routers with the same positioning information;
and deleting terminal IDs which repeatedly appear in a plurality of routing lists of a plurality of routers with the same positioning information to generate a plurality of routing lists.
Optionally, in a possible implementation manner of the first aspect, the generating multiple routing lists after deleting terminal IDs that repeatedly appear in the multiple routing lists of multiple routers having the same positioning information includes:
selecting all routing lists with the same terminal ID in different routers;
and respectively acquiring the acquisition time of each routing list, storing the terminal ID by the routing list with the most front time, and deleting the terminal IDs in other routing lists.
Optionally, in a possible implementation manner of the first aspect, the method further includes acquiring different routing lists and obtaining corresponding track information, where the specific implementation steps are as follows:
acquiring parameter information of a plurality of routers according to the distributed server;
preprocessing the acquired parameter information to acquire a terminal ID set in each routing list;
wherein the content of the first and second substances,
is a natural constant and is a natural constant,
is a real-time tag, in seconds,
is as follows
The time acquisition connection router has a preset code value corresponding to the ID, and the value is
，
In order to preset the time period of the acquisition interval,
is as follows
Time and first
Within a period of timeThe number of times of terminal IDs connected to the router is collected,
respectively a first segment code, a second segment code, a third segment code and a fourth segment code of the ID of the acquisition terminal,
in order to perform the characteristic processing on each acquisition terminal ID and enable different terminal IDs to obtain unique characteristic values,
obtaining a terminal ID set in each routing list;
comparing the different route characteristic values, and screening according to the time labels to obtain the number of route lists with the repeated terminal ID values deleted;
wherein the content of the first and second substances,
for the number of ID addresses contained in each routing list,
is as follows
ID characteristic value P corresponding to the preset code value of each terminal ID,
for characterizing the value function, the evaluation formula is
，
and acquiring a track normalization value corresponding to each unrepeated entry ID according to the number of the routing lists.
Wherein the content of the first and second substances,
respectively are track normalization values of ASCII-converted first letters of provinces, cities and counties corresponding to each segment code of the ID address,
in order to obtain the track normalization value corresponding to each unrepeated entry ID, the track normalization value is obtainedAnd if the value is not 0, indicating that the current ID is in a non-fixed state, executing the operation of processing the routing list so as to obtain the track information matched with the terminal ID.
A second aspect of the embodiments of the present invention provides two readable storage media, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the method according to the first aspect of the present invention and various possible designs of the first aspect of the present invention.
According to the time-space big data track matching method based on the LSA, when the time-space big data is obtained, the positioning information is collected in a router-based mode, and the mode has the advantages of relatively accurate positioning effect and low data volume, namely the purpose of generating corresponding track information is achieved, and the effect of saving the memory space is achieved.
In addition, in the process of acquiring the terminal ID, the router end and the server end are respectively improved, so that the router end and the server end can delete the terminal ID which is repeated at different times, the data storage quantity of the router end and the server end is reduced, and the data transmission quantity of the router end and the server end is reduced.
In the process of searching for the repeated terminal ID, the invention provides an algorithm which can improve the searching efficiency of the repeated terminal ID and save the calculation power of the system and the CPU utilization rate. The algorithm can collect the ID address information of the connection router at the preset time interval, and carry out characterization processing on the terminal ID, so that a subsequent processor can rapidly judge the collected times of the same terminal ID at different time intervals through characteristic values, and intelligently eliminate historical records.
Drawings
FIG. 1 is a flow chart of a first embodiment of a space-time big data track matching method based on LSA;
FIG. 2 is a flowchart of an embodiment of step S130;
fig. 3 is a flowchart of an embodiment of step S110.
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 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 terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of A, B, C comprises, "comprises A, B and/or C" means that any 1 or any 2 or 3 of A, B, C comprises.
It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
The invention provides a space-time big data track matching method based on LSA, which is shown in a flow chart of a first implementation mode in figure 1 and comprises the following steps:
s110, a plurality of routers are configured in advance, wherein each router has a corresponding routing list and positioning information.
In the configuration process of the router, the router may be configured according to the needs, for example, a mall a, a mall B, an office building C, an office building D, an airport E, a train station F, a bus Q, and the like. Each router will have a respective routing list that can be used to store the terminal ID, which can be various types of character strings, to which it has been connected, as well as the connection time period, time of day, etc. After each router is installed, it configures the corresponding location information, for example, if the address of mall a is (longitude i, latitude p), the location information of the router placed in mall a at this time includes (longitude i, latitude p).
S120, the distributed server collects the routing lists of a plurality of routers at intervals of a preset collection time period, and labels the routing lists included in each router according to the collection time sequence, wherein each routing list includes terminal IDs connected with the routers on the basis of LSAs. Each terminal will have a unique terminal ID corresponding to it.
The distributed server collects the routing list of the router at intervals of a preset collection time period instead of real-time collection, so that the data interaction amount between the router and the distributed server can be reduced, and the burden of the router and the distributed server is reduced. The preset collection time period may be five minutes, ten minutes, and the like, that is, the distributed server collects the routing list every five minutes or ten minutes. And when the route list of the router is collected, the time of collecting the route list is labeled, and after the collecting time is labeled, a plurality of route lists included in each router are labeled according to the sequence of the collecting time.
An LSA (link state broadcast) is a packet used by the link state protocol that includes information about neighbor and channel costs. The LSA can be regarded as a communication link of a local area network, i.e. a terminal connects with a router through the communication link of the local area network, and during the connection, the router stores the terminal ID connected with the router in a routing list.
S130, receiving space-time big data information input by an administrator, wherein the space-time big data information comprises a terminal ID, traversing each routing list of each router, and generating track information matched with the terminal ID based on the time sequence marked by the routing lists, and the track information comprises the time of the routing lists and positioning information corresponding to the time.
The input of the spatiotemporal big data information may be regarded as screening certain latitude data, the spatiotemporal big data information includes a terminal ID to be queried, and the ID to be queried may be preset or randomly input, and the like. After the administrator inputs the ID to be inquired, the distributed server traverses the obtained route list of each router to obtain the track information matched with the terminal ID.
For example, the spatio-temporal big data information includes that the ID of the terminal to be queried is 110, the distributed server traverses all the routing lists with 110 at the background server, for example, the routing lists L1, L2, and L3 respectively have the terminal ID of 110, at this time, the terminal with ID 110 can be considered to go through the positions of the routers corresponding to L1, L2, and L3 respectively, and each routing list L1, L2, and L3 respectively has the acquisition time and \ or time sequence corresponding thereto, at this time, the acquisition times and \ or time sequences are sorted according to the acquisition time, for example, the acquisition time of the routing list L1 is 20 minutes at 9 10 days 12 and 9 days 10 at 2020, the acquisition time of the list L2 is 31 minutes at 8 days 12 and 9 days 8 at 2020 at 12, the acquisition time of the list L3 is 52 minutes at 12 days 9 at 12, the routing lists L1, L2, and L3 are sorted according to the time, and the information to which each routing list should be located, for example, the route list L1 corresponds to the router Q1, the location information of the router Q1 is the mall a (longitude i, latitude p), and the location information of the route list L1 is (longitude i, latitude p). And sequencing the positioning information according to the sequencing sequence of the routing list, thereby obtaining the track information.
In one embodiment, wherein the spatiotemporal big data information comprises a query time period. The space-time big data comprises the terminal ID and the query time period, and the expected effect which can be achieved is that track information of one or more terminals is queried in a specific time period. The query time period may be track information of the terminal having the ID of 110 from the time of query at 0 at 12 month and 30 days 0 at 2020 to 24 at 12 month and 30 days at 2020.
In step S130, as shown in fig. 2, the method further includes:
step S1301, obtaining the query time period, and selecting a routing list of different routers based on the query time period, where each router has a routing list at least at one time in the query time period. For example, when the query time period is from 0 at 12 months and 30 days 0 at 2020 to 3 at 30 days at 12 months and 30 days at 2020, and the preset acquisition time period in step S120 is 1 hour, 3 routing lists of one router can be obtained in the query time period, and then the distributed server obtains 3N routing lists in this time period, where N is the number of routers.
Step S1302, if yes, determining that the number of routing lists of one router at the current time is greater than or equal to two. Due to different query time periods, the number of routing lists obtained is also different, and may be 0, 1, or 2. And if the number of the route lists in the query time period is more than 2, proving that the distributed server collects the route lists of the router at least twice in the query time period.
Step S1303, arranging the selected routing lists according to the order of the acquisition time, and acquiring all the terminal IDs in the routing list at the earliest acquisition time. When the route list is collected for too many times, the same terminal ID may appear in different route lists, and at this time, all terminal IDs in the most previous route list are obtained first.
Step S1304, sequentially acquiring the terminal IDs in all the routing lists, and when the terminal ID in the next routing list is the same as the terminal ID in the previous routing list, deleting the terminal ID in the next routing list to obtain a plurality of routing lists, where any two routing lists in the same router do not have duplicate terminal IDs.
When a scene that one router generates a plurality of routing lists occurs in the query time period, the distributed server collects the routing lists for a plurality of times in the query time period, and the preset collection time period is smaller than the query time period.
When processing a plurality of routing lists of a router, the terminal IDs in the routing list at the first and/or the previous moment are collected, and the same terminal IDs in the following routing lists are deleted to obtain a plurality of final routing lists.
Steps S1301 to S1304 may be applicable to a data storage process, that is, in the data storage process, all routing lists of one router in a preset query time period are sorted, and repeated terminal IDs in each list are deleted, so that the data storage amount of the cloud server is reduced. For example, the usage scenario is a mall, the holder of the terminal a is a security guard, the holder of the terminal B is a shopper, the holder of the terminal C is a courier, the security guard may work continuously for 8 hours in the mall corresponding to the router, the shopper may visit the mall for 2 hours, the courier may take out the shopping mall for 20 minutes, at this time, if the preset collection time period is 1 hour, if the query time period is 24 hours, in all the routing lists acquired in the query time period, the terminal ID of the terminal a may appear for 8 times, the terminal ID of the terminal B may appear for 2 times, the terminal ID of the terminal C may appear for 1 time, at this time, a situation that a plurality of IDs repeatedly appear in different routing lists occurs may occur, in this process, a situation that the terminal a, the terminal B, and the terminal C are disconnected may also occur, and so no matter what happens, as long as the terminal ID appears in the preset collection time period, namely, the owner of the terminal is considered to pass through the market, namely, the terminal ID in the following routing list is deleted, so that the effect of reducing data information and data quantity in the process of storing the routing list is achieved.
Step S1305, the route lists of the plurality of routers are processed to obtain the trace information matching the terminal ID. Steps S1301 to S1304 may also be applied to the generation process of the trajectory information, that is, as long as the corresponding routing list appears, the time dimension of the trajectory information is considered to be at the collection time of the earliest appearing routing list, and the position dimension is the positioning information of the routing list. Therefore, the data processing amount is reduced in the process of generating the track information.
Steps S1301 to S1305 are applicable to the distributed server side.
In step S110, as shown in fig. 3, the method further includes:
step S1101, pre-configuring a generation time period of the route list, and the router generating the router list at different times based on the generation time period. The generation time period may be 5 minutes, 10 minutes, 1 hour, and so on. That is, the router list is generated every 5 minutes, 10 minutes, and 1 hour, for example, if the generation time period is 1 hour, a router list is generated 1 hour at the current time, and the router list includes the terminal ID connected and/or connected to the router within 1 hour.
Step S1102, a deletion time period is preset, and all routing lists of any router in the deletion time period are obtained. The deletion time period may be 5 minutes, 10 minutes, 1 hour, etc., for example, if the deletion time period is 1 hour, and the generation time period is 10 minutes, then when all the routing lists of one router in the deletion time period are obtained, 6 routing lists may be obtained.
Step S1103, arranging the selected routing list according to the order of the collection time, and acquiring all terminal IDs in the routing list at the earliest collection time. If 6 routing lists are collected in a deletion time period of one hour, namely the first collected routing list is the most front routing list, all terminal IDs in the routing list are obtained at the moment.
And step S1104, sequentially acquiring the terminal IDs in all the routing lists, and deleting the terminal ID in the next routing list when the terminal ID in the next routing list is the same as the terminal ID in the previous routing list, so as to obtain a plurality of routing lists, wherein any two routing lists do not have duplicate terminal IDs. Through the steps, all the route lists in one deletion time period are processed uniformly, so that all the route lists in one deletion time period do not have repeated IDs.
In steps 1101 to S1104, the routing lists collected by the routers may be processed, that is, terminal IDs in different routing lists are respectively extracted and compared within a deletion time period, and if a terminal ID identical to a terminal ID in a routing list at a previous time exists in a routing list at a later time, a terminal ID in a routing list at the later time is deleted. In the deleting process, the deleting is carried out based on a preset deleting time period, the deleting is not carried out randomly, the action track of each person is single, the person may come to a market A today, the person may come the next day, and if the deleting time period exceeds two days, the route list of the router of the market A only has a corresponding terminal ID once in two days, so that the deleting time can be adjusted according to the situation, and the requirement of track information is met while the data volume storage is reduced.
Steps S1101 to S1105 are applied to the router side.
In step S1305, the method further includes:
step S13051, one or more target terminal IDs are selected. The target terminal is the terminal which wants to generate the track information.
Step 13052, acquiring all routing lists corresponding to the target terminal ID in all routing lists of all routers, and obtaining the acquisition time and the positioning information of each routing list with the target terminal ID. The step is to compare the target terminal ID with all the routing lists to obtain all the routing lists with the target terminal ID.
Step 13053 is generating a time axis based on the acquisition time, and displaying the positioning information corresponding to the time on the time axis to obtain the track information corresponding to the target terminal. And displaying the positioning information according to the acquisition time to obtain track information.
In step S1101, the method further includes:
step S11011 acquires all the terminal IDs connected to the router during the generation period.
Step S11012, deleting all terminal IDs of which the connection time with the router is less than or equal to a preset connection time period;
and step S11013, extracting all terminal IDs of which the connection time with the router exceeds a preset connection time period to generate a router list.
Through the above steps, all the terminal IDs whose connection time with the router is less than or equal to the preset connection time period can be deleted, so as to avoid the situation of transient connection, for example, the owner of the terminal a drives a car to pass through the market a, and performs transient connection with the router in the market a when passing through a route near the market a, at this time, the router will have the terminal ID of the terminal a in the route list obtained after the generation time period, but the owner of the terminal a does not pass through the market a, and an error will occur at this time, so in order to avoid the occurrence of such an error, the certainty rate of the alignment track information is ensured, and all the terminal IDs whose connection time with the router is less than or equal to the preset connection time period are deleted.
In one embodiment, extracting all terminal IDs whose connection time with the router exceeds a preset connection time period to generate a router list includes:
and sending local area network inquiry information to the terminals corresponding to all the terminal IDs of which the router connection time exceeds a preset connection time period. The query information at this time may be "if you have connected to the local area network", "if you are willing to join the local area network XXX", "if you are willing to upload the location information to the XXX server", etc.
And receiving local area network confirmation information input by a terminal corresponding to the terminal ID, and adding the terminal ID to a router list corresponding to the current moment after receiving the local area network confirmation information. Through the steps, after the local area network confirmation information is received, the terminal ID is added into the routing list, namely the corresponding terminal is ensured to be a manually controlled terminal, privacy inquiry can also be carried out on the terminal, and a corresponding processing result is obtained, so that whether the terminal holder normally uses the terminal or not is judged.
In one embodiment, processing the routing lists of the plurality of routers to obtain the trace information matched with the terminal ID includes:
a plurality of routing lists of a plurality of routers having the same positioning information are obtained. In the actual use process, different routers may be used in a hospital, a square, a building and a school, but the same positioning information is obtained, and the routing lists of a plurality of routers with the same positioning information, namely all the routing lists of all the routers in a site, are obtained.
And deleting terminal IDs which repeatedly appear in a plurality of routing lists of a plurality of routers with the same positioning information to generate a plurality of routing lists. The terminal ID repeatedly appearing in the plurality of routers is deleted, and the purpose of this step is also to reduce the storage quantity of the terminal ID in the routing list of each router, for example, layer 1 of the market a is the router a, layer 2 of the market B is the router B, where layer 1 and layer 2 have different routers respectively. The customer is connected with the router at the layer 1 when visiting at the layer 1, the customer is connected with the router at the layer 2 when passing through the layer 1 to the layer 2, at the moment, the router A and the router B have the same positioning information, and the routing lists of the router A and the router B respectively store the terminal ID of the customer, namely the terminal ID in the routing list of the router B at the layer 2 is deleted, so that the memory of the distributed server and/or the router is reduced.
In one embodiment, the generating the multiple routing lists after deleting the terminal IDs that repeatedly appear in the multiple routing lists of the multiple routers having the same positioning information includes:
all routing lists with the same terminal ID in different routers are selected.
And respectively acquiring the acquisition time of each routing list, storing the terminal ID by the routing list with the most front time, and deleting the terminal IDs in other routing lists.
Through the steps, the priority of the routing list is determined according to the collected time, namely the terminal ID which is the same as the terminal ID at the previous time in the next time is deleted, so that the aim of reducing the data in the routing list is fulfilled. Further reducing the memory of the distributed server and/or router
In one embodiment, the method further includes acquiring different routing lists and acquiring corresponding track information, and the specific implementation steps are as follows:
step S210, collecting parameter information of a plurality of routers according to the distributed server;
step S220, preprocessing the collected parameter information to obtain a terminal ID set in each routing list;
wherein the content of the first and second substances,
is a natural constant and is a natural constant,
is a real-time tag, in seconds,
is as follows
The time acquisition connection router has a preset code value corresponding to the ID, and the value is
，
In order to preset the time period of the acquisition interval,
is as follows
Time and first
The number of times of terminal IDs connected to the router is collected within a time,
respectively a first segment code, a second segment code, a third segment code and a fourth segment code of the ID of the acquisition terminal,
performing characterization processing on each acquisition terminal ID to enable different terminal IDs to obtain unique characteristic values so as to obtain terminal ID sets in each routing list;
step S230, comparing the different routing characteristic values, and screening according to the time labels to obtain the number of routing lists with the duplicate terminal ID values deleted;
wherein the content of the first and second substances,
for the number of ID addresses contained in each routing list,
is as follows
ID characteristic values corresponding to the ID preset code values of the terminals,
for characterizing the value function, the evaluation formula is
，
and S240, acquiring a track normalization value corresponding to each unrepeated entry ID according to the number of the routing lists.
Wherein the content of the first and second substances,
respectively are track normalization values of ASCII-converted first letters of provinces, cities and counties corresponding to each segment code of the ID address,
and in order to obtain the track normalization value corresponding to each unretired ID, when the value is not 0, the current ID is in a non-fixed state, and the routing list is processed, so that the track information matched with the terminal ID is obtained.
Through steps S210 to S240, the efficiency of searching for duplicate terminal IDs can be improved, and the computational effort and CPU utilization of the system can be saved. The algorithm can collect the ID address information of the connection router at the preset time interval, and carry out characterization processing on the terminal ID, so that a subsequent processor can rapidly judge the collected times of the same terminal ID at different time intervals through characteristic values, and intelligently eliminate historical records.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
In the above embodiments of the terminal or the server, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A space-time big data track matching method based on LSA is characterized by comprising the following steps:
pre-configuring a plurality of routers, wherein each router is provided with a routing list and positioning information corresponding to the router;
the method comprises the steps that a distributed server collects routing lists of a plurality of routers at intervals of a preset collection time period, and labels the routing lists included in each router according to the collection time sequence, wherein each routing list comprises terminal IDs connected with the routers on the basis of an LSA;
receiving space-time big data information input by an administrator, wherein the space-time big data information comprises a terminal ID, traversing each routing list of each router, and generating track information matched with the terminal ID based on the time sequence marked by the routing lists, and the track information comprises the time of the routing lists and positioning information corresponding to the time;
the time-space big data information comprises a query time period;
traversing each routing list, and generating track information matched with the terminal ID based on the time sequence labeled by the routing list comprises the following steps:
acquiring the query time period, and selecting routing lists of different routers based on the query time period, wherein each router has a routing list at least one moment in the query time period;
if yes, judging that the number of the routing lists of one router at the current moment is more than or equal to two;
arranging the selected routing list according to the sequence of the acquisition time, and acquiring all terminal IDs in the routing list at the most front acquisition time;
sequentially acquiring terminal IDs in all the routing lists, and deleting the terminal ID in the next routing list when the terminal ID in the next routing list is the same as the terminal ID in the previous routing list to obtain a plurality of routing lists, wherein any two routing lists in the same router do not have repeated terminal IDs;
and processing the routing lists of the routers to obtain track information matched with the terminal ID.
2. The spatiotemporal big data trajectory matching method according to claim 1,
the pre-configuring the plurality of routers comprises:
the method comprises the steps that a generation time period of a route list is configured in advance, and a router generates router lists at different moments based on the generation time period;
presetting a deletion time period, and acquiring all routing lists of any router in the deletion time period;
arranging the selected routing list according to the sequence of the acquisition time, and acquiring all terminal IDs in the routing list at the most front acquisition time;
and sequentially acquiring the terminal IDs in all the routing lists, and deleting the terminal ID in the next routing list when the terminal ID in the next routing list is the same as the terminal ID in the previous routing list to obtain a plurality of routing lists, wherein any two routing lists do not have repeated terminal IDs.
3. The spatiotemporal big data trajectory matching method according to claim 1,
the processing the routing lists of the routers to obtain the track information matched with the terminal ID comprises the following steps:
selecting one or more target terminal IDs;
acquiring all routing lists corresponding to the target terminal ID in all routing lists of all routers, and acquiring acquisition time and positioning information of each routing list with the target terminal ID;
and generating a time axis based on the acquisition time, and representing the positioning information corresponding to the time on the time axis to obtain the track information corresponding to the target terminal.
4. The spatiotemporal big data trajectory matching method according to claim 2,
the method comprises the following steps of configuring a generation time period of a route list in advance, wherein the step of generating the router list at different moments by a router based on the generation time period comprises the following steps:
acquiring all terminal IDs connected with the router in a generation time period;
deleting all terminal IDs of which the connection time with the router is less than or equal to a preset connection time period;
and extracting all terminal IDs of which the connection time with the router exceeds a preset connection time period to generate a router list.
5. The spatio-temporal big data track matching method according to claim 4, wherein extracting all terminal IDs whose connection time with the router exceeds a preset connection time period to generate a router list comprises:
sending local area network inquiry information to terminals corresponding to all terminal IDs of which the connection time of the router exceeds a preset connection time period;
and receiving local area network confirmation information input by a terminal corresponding to the terminal ID, and adding the terminal ID to a router list corresponding to the current moment after receiving the local area network confirmation information.
6. The spatiotemporal big data trajectory matching method according to claim 1,
the processing the routing lists of the routers to obtain the track information matched with the terminal ID comprises the following steps:
acquiring a plurality of routing lists of a plurality of routers with the same positioning information;
and deleting terminal IDs which repeatedly appear in a plurality of routing lists of a plurality of routers with the same positioning information to generate a plurality of routing lists.
7. The spatiotemporal big data trajectory matching method according to claim 6,
the generating a plurality of routing lists after deleting terminal IDs which repeatedly appear in the plurality of routing lists of a plurality of routers having the same positioning information includes:
selecting all routing lists with the same terminal ID in different routers;
and respectively acquiring the acquisition time of each routing list, storing the terminal ID by the routing list with the most front time, and deleting the terminal IDs in other routing lists.
8. The spatiotemporal big data trajectory matching method according to claim 6,
the method also comprises the steps of collecting routing lists of different routers and acquiring track information corresponding to the terminal ID, and comprises the following steps:
acquiring parameter information of a plurality of routers according to the distributed server;
preprocessing the acquired parameter information to acquire a terminal ID set in each routing list;
wherein the content of the first and second substances,
is a natural constant and is a natural constant,
is a real-time tag, in seconds,
is as follows
Time-acquisition connection routingThe preset code value corresponding to the ID of the device is
，
In order to preset the time period of the acquisition interval,
is as follows
Time and first
The number of times of terminal IDs connected to the router is collected within a time,
respectively a first segment code, a second segment code, a third segment code and a fourth segment code of the ID of the acquisition terminal,
in order to perform the characteristic processing on each acquisition terminal ID and enable different terminal IDs to obtain unique characteristic values,
obtaining a terminal ID set in each routing list;
comparing the characteristic values of different routes, and screening according to the time labels to obtain the number of route lists after the repeated terminal IDs are deleted;
wherein the content of the first and second substances,
for the number of ID addresses contained in each routing list,
is as follows
ID characteristic values corresponding to the ID preset code values of the terminals,
for characterizing the value function, the evaluation formula is
acquiring a track normalization value corresponding to each unrepeated input ID according to the number of the routing lists;
wherein the content of the first and second substances,
respectively are track normalization values of ASCII-converted first letters of provinces, cities and counties corresponding to each segment code of the ID address,
and processing the routing list to acquire track information matched with the terminal ID when the track normalization value corresponding to each unrepeated input ID is not 0 and indicates that the current ID is in a non-fixed state.
9. A readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 8.
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| CN107333232A (en) * | 2016-04-29 | 2017-11-07 | 华为技术有限公司 | A kind of method of locating terminal and the network equipment |
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| CN107333232A (en) * | 2016-04-29 | 2017-11-07 | 华为技术有限公司 | A kind of method of locating terminal and the network equipment |
| CN106027960A (en) * | 2016-05-13 | 2016-10-12 | 深圳先进技术研究院 | Positioning system and method |
| CN106529815A (en) * | 2016-11-15 | 2017-03-22 | 同济大学 | Estimation method of passenger trip spatial-temporal trajectory of urban rail transit network and application thereof |
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