CN109376178A - Space-time big data trajectory analysis platform, method, server and storage medium - Google Patents
Space-time big data trajectory analysis platform, method, server and storage medium Download PDFInfo
- Publication number
- CN109376178A CN109376178A CN201810939977.8A CN201810939977A CN109376178A CN 109376178 A CN109376178 A CN 109376178A CN 201810939977 A CN201810939977 A CN 201810939977A CN 109376178 A CN109376178 A CN 109376178A
- Authority
- CN
- China
- Prior art keywords
- space
- big data
- time big
- time
- measurand
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention discloses a kind of space-time big data trajectory analysis platform, method, server and storage medium, by searching for space-time big data, display site position distribution and flow information, and site traffic prediction is carried out based on neural network algorithm, according to space-time big data, the relative trajectory information for searching for and showing measurand in preset time period, searches for according to space-time big data and shows in preset time period, and the access frequency of measurand is more than the site information of predeterminated frequency threshold value；Other objects relevant to measurand are searched according to preset rules according to the incidence relation between space-time big data and each object；It is searched to measurand according to space-time big data with the object and related location position information occurred；According to preset track similitude rule, object similar with measurand track is searched in space-time big data.The present invention can effectively improve the reliability to space-time big data analysis, and can effectively promote social safety risk analysis predictive ability.
Description
Technical field
The present invention relates to field of computer technology, more particularly to a kind of space-time big data trajectory analysis platform, method, clothes
Business device and computer readable storage medium.
Background technique
Space-time big data refers to while having the data of time and Spatial Dimension, data in the real world be more than 80% with
Geographical location is related.Space-time big data includes time, space, attribute three-dimensional information, has multi-source, magnanimity, updates quickly spy
Point.Due to the inherent characteristics of space-time big data, the associated complexity of multidimensional, semanteme, space-time dynamic is showed.Especially with
The fast development of smart city and Internet of Things, immanent sensor network will generate extremely large number data, so that the world
Into real big data era, wherein largely space-time big data related with space-time position needs to be stored, analysis and place
Reason.Increasingly extensive due to application range, space-time big data becomes more and more important one of the branch of big data field.
It not only include timing since structure is complicated and source multiplicity for space-time big data itself compared with traditional data mining
Data model, there is also graph model, and it is existing not yet mature to space-time big data mining analysis technology and system, therefore, how
Realize that carrying out more reliable, more effective and more practical management to space-time big data becomes now urgently problem to be solved.
Summary of the invention
The present invention provides a kind of space-time big data trajectory analysis platform, method, server and computer-readable storage mediums
Matter is low to the Reliability comparotive of space-time big data analysis in the prior art to solve the problems, such as.
On the one hand, the present invention provides a kind of space-time big data trajectory analysis platforms, this method comprises:
Memory module is used for recording space-time big data for transferring；
Website distribution and object flow prediction module, for searching for the space-time big data, display site position distribution with
Flow information, and site traffic prediction is carried out based on preset algorithm；
Object trajectory enquiry module, for searching for and showing in preset time period according to the space-time big data, tested pair
The relative trajectory information of elephant；
Object accesses hot spot module, for searching for and showing in preset time period according to the space-time big data, tested pair
The access frequency of elephant is more than the site information of predeterminated frequency threshold value；
Object relating module is used for according to the incidence relation between the space-time big data and each object, according to pre-
If regular, other objects relevant to measurand are searched, and shown；
Object is with module, for the object according to the space-time big data, lookup and measurand with appearance, and
Related location position information；
Track similarity analysis module, for being looked into the space-time big data according to preset track similitude rule
Object similar with measurand track is looked for, and is shown.
Preferably, the website distribution is specifically used for object flow prediction module, searches for the space-time big data, is counting
The objects traffic conditions such as distribution situation and people's vehicle of visual inspection website or bayonet on map in calculation machine screen, and according to pre-
Imputation method is trained the data of people's vehicle flowrate in history, with trained model to people's vehicle flowrate in following certain time
It is predicted, obtains people's vehicle flowrate prediction result.
Preferably, the object relating module is specifically used for, and searches quilt in the space-time big data according to user's instruction
Survey other relevant objects in the associated preset time period of object.
Preferably, this method further include: object warning module searches nearest two for searching in the space-time big data
Secondary movable time span exceeds the object of preset time span threshold value, and carries out visualization push.
Second aspect of the present invention provides a kind of space-time big data trajectory analysis method, and this method applies any of the above-described kind of institute
The space-time big data trajectory analysis platform stated, this method comprises:
The space-time big data, display site position distribution and flow information are searched for, and website is carried out based on preset algorithm
Volume forecasting；
According to the space-time big data, searches for and show in preset time period, the relative trajectory information of measurand；
It according to the space-time big data, searches for and shows in preset time period, the access frequency of measurand is more than default
The site information of frequency threshold；
According to the incidence relation between the space-time big data and each object, according to preset rules, search and tested
Other relevant objects of object, and shown；
According to the space-time big data, search with measurand with the object occurred, and related location position information；
According to preset track similitude rule, it is similar with measurand track right to search in the space-time big data
As, and shown.
Preferably, search for the space-time big data, display site position distribution and flow information, and based on preset algorithm into
The prediction of row site traffic, comprising:
The space-time big data is searched for, the distribution feelings of visual inspection website or bayonet on the map in computer screen
Objects traffic conditions such as condition and people's vehicle, and being trained according to data of the preset algorithm to people's vehicle flowrate in history, with training
Model people's vehicle flowrate in following certain time is predicted, obtain people's vehicle flowrate prediction result.
Preferably, it is looked into according to the incidence relation between the space-time big data and each object according to preset rules
Look for other objects relevant to measurand, comprising:
According to the incidence relation between user's instruction and each object, measurand is searched in the space-time big data
Other relevant objects in associated preset time period.
Preferably, this method further include: search in the space-time big data, it is super to search recently movable time span twice
The object of preset time span threshold value out, and carry out visualization push.
Third aspect present invention additionally provides a kind of computer readable storage medium, and the computer readable storage medium is deposited
The computer program of signal mapping is contained, it is above-mentioned any to realize when the computer program is executed by least one processor
A kind of space-time big data trajectory analysis method.
Fourth aspect present invention provides a kind of server, which includes space-time big data described in any of the above-described kind
Track analyzing device.
The present invention has the beneficial effect that:
The present invention is since the advanced technologies such as comprehensive utilization machine learning are in social safety risk management and control field, it is possible to have
Effect improves the reliability to space-time big data analysis, and can effectively promote social safety risk analysis predictive ability, reduces the people
Masses' life and property loss.
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention,
And it can be implemented in accordance with the contents of the specification, and in order to allow above and other objects of the present invention, feature and advantage can
It is clearer and more comprehensible, the followings are specific embodiments of the present invention.
Detailed description of the invention
By reading the following detailed description of the preferred embodiment, various other advantages and benefits are common for this field
Technical staff will become clear.The drawings are only for the purpose of illustrating a preferred embodiment, and is not considered as to the present invention
Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings:
Fig. 1 is a kind of structural schematic diagram of space-time big data trajectory analysis platform of the embodiment of the present invention；
Fig. 2 is the structural schematic diagram of another space-time big data trajectory analysis platform of the embodiment of the present invention；
Fig. 3 is the flow diagram of the website distribution and object volume forecasting of the embodiment of the present invention；
Fig. 4 is the flow diagram of the object trajectory enquiry module of the embodiment of the present invention；
Fig. 5 is the flow diagram of the object accesses hot spot module of the embodiment of the present invention；
Fig. 6 is the flow diagram of the object relating module of the embodiment of the present invention；
Fig. 7 is the object of the embodiment of the present invention with the flow diagram of module；
Fig. 8 is the flow diagram of the object early warning of the embodiment of the present invention；
Fig. 9 is the flow diagram of the track similarity analysis module of the embodiment of the present invention.
Specific embodiment
Exemplary embodiments of the present disclosure are described in more detail below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
It is fully disclosed to those skilled in the art.
First embodiment of the invention provides a kind of space-time big data trajectory analysis platform, referring to Fig. 1, comprising:
Memory module is used for recording space-time big data for transferring；
Website distribution and object flow prediction module, for searching for the space-time big data, display site position distribution with
Flow information, and site traffic prediction is carried out based on preset algorithm；
Object trajectory enquiry module, for searching for and showing in preset time period according to the space-time big data, tested pair
The relative trajectory information of elephant；
Object accesses hot spot module, for searching for and showing in preset time period according to the space-time big data, tested pair
The access frequency of elephant is more than the site information of predeterminated frequency threshold value；
Object relating module is used for according to the incidence relation between the space-time big data and each object, according to pre-
If regular, other objects relevant to measurand are searched, and shown；
Object is with module, for the object according to the space-time big data, lookup and measurand with appearance, and
Related location position information；
Track similarity analysis module, for being looked into the space-time big data according to preset track similitude rule
Object similar with measurand track is looked for, and is shown.
Since the advanced technologies such as present invention comprehensive utilization machine learning are in social safety risk management and control field, it is possible to have
Effect improves the reliability to space-time big data analysis, and can effectively promote social safety risk analysis predictive ability, reduces the people
Masses' life and property loss.
It should be noted that preset algorithm described in the embodiment of the present invention includes the various algorithms such as neural network algorithm, tool
When body is implemented, those skilled in the art can be configured according to actual needs, and the present invention is not especially limited this.
When it is implemented, the website distribution is specifically used for object flow prediction module in present invention implementation, institute is searched for
Space-time big data is stated, the objects such as distribution situation and people's vehicle of visual inspection website or bayonet on the map in computer screen
Traffic conditions, and be trained according to data of the neural network algorithm to people's vehicle flowrate in history, with trained model to not
The flow of the people come in certain time is predicted, people's vehicle flowrate prediction result is obtained.
When it is implemented, the object relating module is specifically used in present invention implementation, according to user's instruction when described
Other relevant objects in the associated preset time period of measurand are searched in empty big data.
When it is implemented, in present invention implementation, the platform further include: object warning module, for searching the space-time
In big data, searches recently movable time span twice and exceed the object of preset time span threshold value, and visualized
Push.
Corresponding with Fig. 1, the embodiment of the invention also provides a kind of space-time big data trajectory analysis method, feature exists
In, this method comprises:
The space-time big data, display site position distribution and flow information are searched for, and website is carried out based on preset algorithm
Volume forecasting；
According to the space-time big data, searches for and show in preset time period, the relative trajectory information of measurand；
It according to the space-time big data, searches for and shows in preset time period, the access frequency of measurand is more than default
The site information of frequency threshold；
According to the incidence relation between the space-time big data and each object, according to preset rules, search and tested
Other relevant objects of object, and shown；
According to the space-time big data, search with measurand with the object occurred, and related location position information；
According to preset track similitude rule, it is similar with measurand track right to search in the space-time big data
As, and shown.
Wherein, in the embodiment of the present invention, the space-time big data, display site position distribution and flow information are searched for, and
Site traffic prediction is carried out based on neural network algorithm, comprising:
The space-time big data is searched for, the distribution feelings of visual inspection website or bayonet on the map in computer screen
The objects traffic conditions such as condition and people's vehicle, and be trained according to data of the neural network algorithm to people's vehicle flowrate in history, with instruction
The model perfected predicts people's vehicle flowrate in following certain time, obtains people's vehicle flowrate prediction result.
When it is implemented, being closed in the embodiment of the present invention according to the association between the space-time big data and each object
Other objects relevant to measurand are searched according to preset rules by system, comprising:
According to the incidence relation between user's instruction and each object, measurand is searched in the space-time big data
Other relevant objects in associated preset time period.
When it is implemented, the embodiment of the present invention the method also includes: search in the space-time big data, search nearest two
Secondary movable time span exceeds the object of preset time span threshold value, and carries out visualization push.
In order to preferably be explained the present invention, below will using people and Che as specific object for the present invention into
Row explains in detail and illustrates:
The object of the present invention is to provide a kind of space-time big data trajectory analysis platforms of security fields towards the society --- it grinds
Rail realizes that the object trajectories such as macroscopical early warning and micro-analysis, including objects volume forecasting, people's vehicle such as website distribution and people's vehicle are looked into
The association of the objects such as object accesses hot spot, people's vehicles such as inquiry, people's vehicle, everybody etc. objects early warning, the track such as adjoint, the corpse vehicle of objects it is similar
Property analysis etc. functions.
The present invention provides a kind of space-time big data trajectory analysis platforms of security fields towards the society --- rail is ground, including
The objects such as object accesses hot spot, people's vehicles such as object trajectories inquiry, people's vehicles such as objects volume forecasting, people's vehicle such as website distribution and people's vehicle
Association, everybody etc. objects early warning, the track similarity analysis such as adjoint, the corpse vehicle of objects etc., function structure is as shown in Fig. 2.
As shown in figure 3, the website distribution of the embodiment of the present invention includes: with objects method for predicting such as people's vehicles
Website distribution shows the distribution of the site locations such as bayonet and flow information with method for predicting, and is based on neural network
Algorithm carries out site traffic prediction, provides aid decision for the deployment of personnel's distributed in demand.This method process is as shown in Fig. 3, first
First, this method can on the map in computer screen the distribution situation of the websites such as visual inspection station, bayonet and people's vehicle etc.
Object traffic conditions, secondly, being trained using data of the neural network algorithm to people's vehicle flowrate in history, with trained mould
Type predicts following certain time (such as flow of the people daily in one week future), obtains people's vehicle flowrate prediction result.
As shown in figure 4, the present invention includes: to the object trajectories querying method such as people's vehicle
It may specify search time section, the relative trajectory letter of the objects such as inquirer's vehicle in the object trajectories querying method such as people's vehicle
Breath, and visualized on map.This method process is as shown in Fig. 4, firstly, the period to be inquired of input, such as
Input on August 1,10:00 10:00 to 2017 years on the 1st January in 2017；Secondly, input need inquire personnel identity card number or
The object retrievals information such as license plate number, to meeting the period and identification card number or license plate number in the databases such as personnel and vehicle
Information is inquired, and the location position information that the objects such as personnel or vehicle pass through is obtained；Finally, the objects such as personnel or vehicle are existed
The location position information occurred in certain period shows and shows on the computer screen on map.
As shown in figure 5, the embodiment of the present invention includes: to the object accesses analysis of central issue method such as people's vehicle
People's vehicle accesses in analysis of central issue method, and personnel and vehicle etc. in certain period of time can be visualized on map
The higher site information of object access frequency.This method process is as shown in Fig. 5, firstly, the period to be inquired of input, than
Such as input on August 1,10:00 10:00 to 2017 years on the 1st January in 2017；Secondly, the object accesses frequency threshold value such as setting people's vehicle, such as
It is 200 times that threshold value, which is arranged,；Finally, retrieval access thresholds are higher than 200 place positions and visual on map in the database
Change and shows.
As shown in fig. 6, the objects association analysis method such as people's vehicle of the embodiment of the present invention includes:
The objects association analysis method such as people's vehicle, which is realized, to be looked for vehicle with people or looks for people with vehicle, and by objects such as related personnel's vehicles
Trace information is shown in map visualization.This method process is as shown in Fig. 6, is being looked in vehicle method with people, firstly, entry personnel
Identification card number or other information；Possess under one's name or the once vehicle number of drive the cross secondly, retrieving the identification card number in the database
Code；Finally, showing the location information that the personnel and vehicle occurred in the recent period on map.It is being looked in people's method with vehicle, firstly, defeated
Enter license plate number；Secondly, retrieving the identification card number of the corresponding car owner of license plate number or history driver in the database；Finally,
The location information that the vehicle and counterpart personnel occurred in the recent period is shown on map.
As shown in fig. 7, the embodiment of the present invention includes: to everybody equal objects adjoint analysis method
Everybody equal objects adjoint analysis method can find out the relative companion personal information of personnel in certain period of time and by phase
Tracing point information is answered to show in map visualization.This method process is as shown in Fig. 7, firstly, the body of the wanted personnel query of input
Part card number；Secondly, the period to be inquired of input, for example input August 1 day 10 10:00 to 2017 years on the 1st January in 2017:
00；Third is inquired and the neighbouring personal information for being engaged in certain behavior by certain places or simultaneously of the personnel in the database；
Finally, by being shown on map within this time with the place position occurred with personnel.
As shown in figure 8, the objects method for early warning such as corpse vehicle of the embodiment of the present invention includes:
The objects such as corpse vehicle method for early warning can find out the corpse vehicle in the places such as cell, market, can by its location information map
Depending on changing and carrying out early warning push.This method process is as shown in Fig. 8, firstly, when the objects such as setting vehicle are movable twice recently
Between length threshold, such as select 30 days as threshold value；Secondly, retrieval meets the object informations such as the vehicle of the threshold value in the database；
Finally, by the information of vehicles and location information map visualization and carrying out early warning push.
As shown in figure 9, track of embodiment of the present invention similarity analysis method includes:
Track similarity analysis method is based on the tracks such as Euclidean distance similitude rule, finds out and personnel or vehicle etc. pair
As the similar object in track and carry out map visualization displaying.This method process is as shown in figure 9, firstly, what input to be inquired
The object informations such as personnel identity card number or license plate number；Secondly, similar with the personnel or track of vehicle right in calculating database
Image information；Finally, the objects such as similar personnel or vehicle are arranged from high to low according to similarity degree.
The present invention proposes a kind of big number of space-time of security fields towards the society according to the space-time big data in social safety field
According to trajectory analysis platform --- rail is ground, realizes macroscopical early warning and micro-analysis to space-time big data, this method includes website point
The objects such as object accesses hot spot, people's vehicles such as object trajectories inquiry, people's vehicles such as objects volume forecasting, people's vehicles such as cloth and people's vehicle are associated with,
Objects early warning, the track similarity analysis such as everybody equal adjoint, the corpse vehicle of objects etc..Wherein, website distribution and method for predicting
The main prediction for realizing the flows such as bayonet website distribution visualization and people's vehicle；In the inquiry of the object trajectories such as people's vehicle, identity card is inputted
Or the retrieval such as license plate number information can realize the inquiry to object trajectories such as personnel or vehicles；The object accesses such as people's vehicle analysis of central issue side
Method realizes the visualization of the hot zones more to object accesses such as people's vehicles by input time section.The association point of the objects such as people's vehicle
Analysis method can realize with people look for vehicle perhaps with vehicle look for people input identification card number can inquire personnel information of vehicles under one's name or
Input license plate number can inquire car owner and history personnel of driving information, and by related personnel's track of vehicle information in map visualization exhibition
Show；In everybody equal objects adjoint analysis method, input needs the period inquired and personnel identity card number that can find out a suspect
Relative companion personal information and by corresponding trace information map visualization show；The objects such as corpse vehicle method for early warning can be found out
The corpse vehicle in the places such as cell, market by its location information map visualization and carries out early warning push；Track similarity analysis side
Method is based on track similitude rule, exports personnel similar with personnel or track of vehicle and vehicle and carries out map visualization exhibition
Show.
Since the advanced technologies such as this method comprehensive utilization machine learning are in social safety risk management and control field, have important
Science and technology value, can effectively promote social safety risk analysis predictive ability, reduce people's life's property loss, we
Method is by the prediction and warning ability of General Promotion criminal offence, the science of commanding and decision-making when promoting country's reply social safety risk
And validity.
The embodiment of the invention also provides a kind of computer readable storage medium, the computer-readable recording medium storage
The computer program for thering is signal to map, when the computer program is executed by least one processor, to realize any of the above-described kind
The space-time big data trajectory analysis method.
It should be noted that computer readable storage medium described in the embodiment of the present invention and storage medium are same Jie
Matter.
The embodiment of the invention also provides a kind of server, which includes space-time big data described in any of the above-described kind
Track analyzing device.
The relevant portion of the embodiment of the present invention can be found in embodiment of the method and Installation practice is understood, not do herein in detail
Carefully repeat.
Algorithm and display are not inherently related to any particular computer, virtual system, or other device provided herein.
Various general-purpose systems can also be used together with teachings based herein.As described above, it constructs required by this kind of system
Structure be obvious.In addition, the present invention is also not directed to any particular programming language.It should be understood that can use various
Programming language realizes summary of the invention described herein, and the description done above to language-specific is to disclose this hair
Bright preferred forms.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that implementation of the invention
Example can be practiced without these specific details.In some instances, well known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this specification.
Similarly, it should be understood that in order to simplify the disclosure and help to understand one or more of the various inventive aspects,
Above in the description of exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the disclosed method should not be interpreted as reflecting the following intention: i.e. required to protect
Shield the present invention claims features more more than feature expressly recited in each claim.More precisely, as following
Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following specific embodiment are expressly incorporated in the specific embodiment, wherein each claim itself
All as a separate embodiment of the present invention.
Those skilled in the art will understand that can be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more devices different from this embodiment.It can be the module or list in embodiment
Member or component are combined into a module or unit or component, and furthermore they can be divided into multiple submodule or subelement or
Sub-component.Other than such feature and/or at least some of process or unit exclude each other, it can use any
Combination is to all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so disclosed
All process or units of what method or apparatus are combined.Unless expressly stated otherwise, this specification is (including adjoint power
Benefit require, abstract and attached drawing) disclosed in each feature can carry out generation with an alternative feature that provides the same, equivalent, or similar purpose
It replaces.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included certain features rather than other feature, but the combination of the feature of different embodiments mean it is of the invention
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
Meaning one of can in any combination mode come using.
Various component embodiments of the invention can be implemented in hardware, or to run on one or more processors
Software module realize, or be implemented in a combination thereof.It will be understood by those of skill in the art that can be used in practice
Microprocessor or digital signal processor (DSP) realize that Distributed File System Data according to an embodiment of the present invention imports
The some or all functions of some or all components in device.The present invention is also implemented as being retouched here for executing
The some or all device or device programs (for example, computer program and computer program product) for the method stated.
It is such to realize that program of the invention can store on a computer-readable medium, or can have one or more signal
Form.Such signal can be downloaded from an internet website to obtain, be perhaps provided on the carrier signal or with it is any its
He provides form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and ability
Field technique personnel can be designed alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between parentheses should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" located in front of the element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real
It is existing.In the unit claims listing several devices, several in these devices can be through the same hardware branch
To embody.The use of word first, second, and third does not indicate any sequence.These words can be explained and be run after fame
Claim.
Claims (10)
1. a kind of space-time big data trajectory analysis platform characterized by comprising
Memory module is used for recording space-time big data for transferring；
Website distribution and object flow prediction module, for searching for the space-time big data, display site position distribution and flow
Information, and site traffic prediction is carried out based on preset algorithm；
Object trajectory enquiry module, for searching for and showing in preset time period according to the space-time big data, measurand
Relative trajectory information；
Object accesses hot spot module, for searching for and showing in preset time period according to the space-time big data, measurand
Access frequency is more than the site information of predeterminated frequency threshold value；
Object relating module is used for according to the incidence relation between the space-time big data and each object, according to default rule
Then, other objects relevant to measurand are searched, and are shown；
Object is with module, for searching the object with measurand with appearance, and related according to the space-time big data
Location position information；
Track similarity analysis module, for according to preset track similitude rule, searched in the space-time big data with
The similar object in measurand track, and shown.
2. platform according to claim 1, which is characterized in that
The website distribution is specifically used for object flow prediction module, the space-time big data is searched for, in computer screen
Map on visual inspection website or bayonet the objects traffic conditions such as distribution situation and people's vehicle, and according to preset algorithm to going through
The data of people's vehicle flowrate are trained in history, are predicted with trained model people's vehicle flowrate in following certain time,
Obtain people's vehicle flowrate prediction result.
3. platform according to claim 1, which is characterized in that
The object relating module is specifically used for, and it is associated that measurand is searched in the space-time big data according to user's instruction
Preset time period in other relevant objects.
4. platform described in any one of -3 according to claim 1, which is characterized in that further include:
Object warning module, for searching in the space-time big data, search recently twice movable time span beyond default
Time span threshold value object, and carry out visualization push.
5. a kind of space-time big data trajectory analysis method, which is characterized in that apply space-time of any of claims 1-4
Big data trajectory analysis platform, comprising:
The space-time big data, display site position distribution and flow information are searched for, and website is carried out based on neural network algorithm
Volume forecasting；
According to the space-time big data, searches for and show in preset time period, the relative trajectory information of measurand；
It according to the space-time big data, searches for and shows in preset time period, the access frequency of measurand is more than predeterminated frequency
The site information of threshold value；
According to the incidence relation between the space-time big data and each object, according to preset rules, lookup and measurand
Other relevant objects, and shown；
According to the space-time big data, search with measurand with the object occurred, and related location position information；
According to preset track similitude rule, object similar with measurand track is searched in the space-time big data,
And it is shown.
6. according to the method described in claim 5, it is characterized in that, searching for the space-time big data, display site position distribution
With flow information, and based on neural network algorithm carry out site traffic prediction, comprising:
Search for the space-time big data, on the map in computer screen the distribution situation of visual inspection website or bayonet and
The objects traffic conditions such as people's vehicle, and be trained according to data of the preset algorithm to people's vehicle flowrate in history, with trained mould
Type predicts people's vehicle flowrate in following certain time, obtains people's vehicle flowrate prediction result.
7. according to the method described in claim 5, it is characterized in that, according between the space-time big data and each object
Incidence relation search other objects relevant to measurand according to preset rules, comprising:
According to the incidence relation between user's instruction and each object, it is related that measurand is searched in the space-time big data
Other relevant objects in the preset time period of connection.
8. the method according to any one of claim 5-7, which is characterized in that further include:
It searches in the space-time big data, searches recently pair that movable time span twice exceeds preset time span threshold value
As, and carry out visualization push.
9. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has signal mapping
Computer program, the computer program by least one processor execute when, to realize any one of claim 5-8
The space-time big data trajectory analysis method.
10. a kind of server, which is characterized in that the server includes the big number of space-time described in any one of claim 1-4
According to track analyzing device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810939977.8A CN109376178A (en) | 2018-08-17 | 2018-08-17 | Space-time big data trajectory analysis platform, method, server and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810939977.8A CN109376178A (en) | 2018-08-17 | 2018-08-17 | Space-time big data trajectory analysis platform, method, server and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109376178A true CN109376178A (en) | 2019-02-22 |
Family
ID=65403708
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810939977.8A Pending CN109376178A (en) | 2018-08-17 | 2018-08-17 | Space-time big data trajectory analysis platform, method, server and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109376178A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110517503A (en) * | 2019-08-28 | 2019-11-29 | 武汉烽火众智数字技术有限责任公司 | Corpse vehicle analysis and early warning method and device based on big data |
| CN111199644A (en) * | 2020-01-06 | 2020-05-26 | 山东合天智汇信息技术有限公司 | Method and system for automatically fitting people and vehicles |
| CN112712392A (en) * | 2020-12-31 | 2021-04-27 | 京东数字科技控股股份有限公司 | Message pushing method and device, electronic equipment and storage medium |
| CN112860821A (en) * | 2019-11-27 | 2021-05-28 | 深圳云天励飞技术有限公司 | Human-vehicle trajectory analysis method and related product |
| CN112948531A (en) * | 2021-04-02 | 2021-06-11 | 方正国际软件(北京)有限公司 | Massive track query method, retrieval server and system |
| CN113327079A (en) * | 2021-05-28 | 2021-08-31 | 东北师范大学 | Path selection potential factor visual analysis method based on network car booking track |
| CN113505312A (en) * | 2021-07-21 | 2021-10-15 | 车主邦（北京）科技有限公司 | Display method, server, medium and computer equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447119A (en) * | 2016-10-11 | 2017-02-22 | 济南观澜数据技术有限公司 | Short-term traffic flow prediction method and system based on convolutional neural network |
| CN107169260A (en) * | 2017-03-23 | 2017-09-15 | 四川省公安厅 | Based on space-time track isomerous multi-source resonance data system and method |
| CN107248317A (en) * | 2017-07-27 | 2017-10-13 | 武汉市艾派智能科技有限公司 | A kind of empty vehicle parking position of parking lot number Forecasting Methodology based on Vehicle License Plate Recognition System |
| CN107820214A (en) * | 2017-09-28 | 2018-03-20 | 长安大学 | A kind of user trajectory analysis system based on time suboptimal control |
| CN108257378A (en) * | 2016-12-29 | 2018-07-06 | 杭州海康威视数字技术股份有限公司 | traffic flow forecasting method and device |
-
2018
- 2018-08-17 CN CN201810939977.8A patent/CN109376178A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447119A (en) * | 2016-10-11 | 2017-02-22 | 济南观澜数据技术有限公司 | Short-term traffic flow prediction method and system based on convolutional neural network |
| CN108257378A (en) * | 2016-12-29 | 2018-07-06 | 杭州海康威视数字技术股份有限公司 | traffic flow forecasting method and device |
| CN107169260A (en) * | 2017-03-23 | 2017-09-15 | 四川省公安厅 | Based on space-time track isomerous multi-source resonance data system and method |
| CN107248317A (en) * | 2017-07-27 | 2017-10-13 | 武汉市艾派智能科技有限公司 | A kind of empty vehicle parking position of parking lot number Forecasting Methodology based on Vehicle License Plate Recognition System |
| CN107820214A (en) * | 2017-09-28 | 2018-03-20 | 长安大学 | A kind of user trajectory analysis system based on time suboptimal control |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110517503A (en) * | 2019-08-28 | 2019-11-29 | 武汉烽火众智数字技术有限责任公司 | Corpse vehicle analysis and early warning method and device based on big data |
| CN112860821A (en) * | 2019-11-27 | 2021-05-28 | 深圳云天励飞技术有限公司 | Human-vehicle trajectory analysis method and related product |
| CN111199644A (en) * | 2020-01-06 | 2020-05-26 | 山东合天智汇信息技术有限公司 | Method and system for automatically fitting people and vehicles |
| CN112712392A (en) * | 2020-12-31 | 2021-04-27 | 京东数字科技控股股份有限公司 | Message pushing method and device, electronic equipment and storage medium |
| CN112948531A (en) * | 2021-04-02 | 2021-06-11 | 方正国际软件(北京)有限公司 | Massive track query method, retrieval server and system |
| CN112948531B (en) * | 2021-04-02 | 2023-12-15 | 方正国际软件(北京)有限公司 | Massive track query method, retrieval server and system |
| CN113327079A (en) * | 2021-05-28 | 2021-08-31 | 东北师范大学 | Path selection potential factor visual analysis method based on network car booking track |
| CN113505312A (en) * | 2021-07-21 | 2021-10-15 | 车主邦（北京）科技有限公司 | Display method, server, medium and computer equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109376178A (en) | Space-time big data trajectory analysis platform, method, server and storage medium | |
| Karagoz et al. | A novel intuitionistic fuzzy MCDM-based CODAS approach for locating an authorized dismantling center: a case study of Istanbul | |
| Tomaszewska et al. | Urban smart mobility in the scientific literature—bibliometric analysis | |
| CN104715612A (en) | Suspected vehicle tracking method and device achieved based on driving track matching | |
| US20180268305A1 (en) | Retrospective event verification using cognitive reasoning and analysis | |
| CN112131325A (en) | Track determination method, device and equipment and storage medium | |
| CN105468671A (en) | Method for realizing personnel relationship modeling | |
| Peng et al. | A driving cycle construction methodology combining k-means clustering and Markov model for urban mixed roads | |
| Krogh et al. | Efficient in-memory indexing of network-constrained trajectories | |
| Khasawneh et al. | E-Mobility Advisor for Connected and Autonomous Vehicles Environments. | |
| Biggi et al. | Artificial intelligence in self-driving cars research and innovation: A scientometric and bibliometric analysis | |
| Kong et al. | TBI2Flow: Travel behavioral inertia based long-term taxi passenger flow prediction | |
| Liu et al. | Top-k competitive location selection over moving objects | |
| Zhu et al. | Navigation application programming interface route fuel saving opportunity assessment on large-scale real-world travel data for conventional vehicles and hybrid electric vehicles | |
| CN109145225A (en) | A kind of data processing method and device | |
| Brantingham et al. | Modeling criminal activity in urban landscapes | |
| Berger et al. | ZEBRA: Z-order Curve-based Event Retrieval Approach to Efficiently Explore Automotive Data | |
| Nguyen-Ho et al. | Text query based traffic video event retrieval with global-local fusion embedding | |
| Hu et al. | Test scenario generation method for autonomous vehicles based on combinatorial testing and Bayesian network | |
| CN109492821A (en) | A kind of stability maintenance method for early warning and system, electronic equipment | |
| Chen et al. | Uncertainty-aware visual analytics for exploring human behaviors from heterogeneous spatial temporal data | |
| Shein et al. | Discovery of evolving companion from trajectory data streams | |
| CN111062555B (en) | City portrait construction method, device, medium and electronic equipment | |
| Li et al. | Driving cycle prediction based on Markov chain combined with driving information mining | |
| Xu et al. | Understanding human mobility: A multi-modal and intelligent moving objects database |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |