CN105095991A - Method and device for crowd risk early warning - Google Patents
Method and device for crowd risk early warning Download PDFInfo
- Publication number
- CN105095991A CN105095991A CN201510427845.3A CN201510427845A CN105095991A CN 105095991 A CN105095991 A CN 105095991A CN 201510427845 A CN201510427845 A CN 201510427845A CN 105095991 A CN105095991 A CN 105095991A
- Authority
- CN
- China
- Prior art keywords
- presumptive area
- crowd
- user terminal
- location data
- risk parameter
- 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
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Landscapes
- Alarm Systems (AREA)
Abstract
The invention discloses a method and device for crowd risk early warning. The embodiment of the method includes obtaining effective positioning data of a user terminal in a predetermined area, determining the risk parameters of the predetermined area according to the effective positioning data, determining whether the risk parameters satisfy the predetermined early-warning conditions, and if yes, giving early-warning for the predetermined area. The problem of low accuracy for crowd risk early warning in the prior art is solved, and the efficiency of crowd risk early warning is improved.
Description
Technical field
The application relates to field of computer technology, particularly relates to the method for crowd's Risk-warning and device.
Background technology
Large-scale public place or tourist attraction, when festivals or holidays or large-scale activity, can assemble a large amount of crowds usually.How effectively can measure, to predict density, the flow direction of crowd, find crowd's risk in advance and carry out early warning, be an important problem of management of public safety all the time.
Existing technology mainly relies on video sensor to estimate that people's current density and crowd flow to by artificial or methods of video analyses.General implementation is mainly by the individual volume tracing in video or the flow direction analyzing crowd based on the optical flow computation of continuous videos.But block because density is too high, between individuality, the reason such as illumination variation, these methods are difficult to effectively follow the tracks of individual and carry out early warning.The defect of above-mentioned technology is: the accuracy of early warning is low, and efficiency is low.
Summary of the invention
This application provides a kind of method for crowd's Risk-warning and device.Solve the accuracy of early warning in prior art low, inefficient technical matters.
First aspect, this application provides a kind of method for crowd's Risk-warning, and described method comprises: the effective location data obtaining user terminal in presumptive area; The risk parameter of described presumptive area is determined based on described effective location data; Judge whether described risk parameter meets predetermined early-warning conditions; If so, early warning is carried out for described presumptive area.
In some embodiments, the risk parameter of described presumptive area comprises following at least one item: the total number of persons of described presumptive area; The crowd density of effective coverage in described presumptive area; And crowd's entropy of described presumptive area; Wherein, described crowd's entropy characterizes the confusion degree of crowd's moving direction.
In some embodiments, determine the total number of persons of described presumptive area based on described effective location data, comprising: the number determining user terminal in described presumptive area based on described effective location data; Obtain the correction coefficient that described presumptive area is corresponding; The product of correction parameter corresponding with described presumptive area for the number of user terminal in described presumptive area is defined as the total number of persons of described presumptive area.
In some embodiments, determine crowd's entropy of described presumptive area based on described effective location data, comprising: determine each user terminal sense of displacement vector within a predetermined period of time based on described effective location data; Corresponding sense of displacement angle is determined based on described sense of displacement vector; Add up the probability distribution of described sense of displacement angle within the scope of different angles; Crowd's entropy of described presumptive area is determined based on described probability distribution.
In some embodiments, the described risk parameter determining described presumptive area, comprising: the practical risk parameter determining presumptive area described in current time; And/or presumptive area described in prediction predetermined instant estimate risk parameter.
In some embodiments, what adopt presumptive area described in the forecast model of training in advance prediction predetermined instant estimates risk parameter.
In some embodiments, the effective location data of user terminal in described acquisition presumptive area, comprising: the locator data gathering user terminal in presumptive area; Find out the misdata in described locator data and repeating data; Delete described misdata and repeating data, to obtain effective location data.
In some embodiments, describedly meet predetermined early-warning conditions, comprising: the risk parameter of predetermined number/ratio is more than or equal to corresponding predetermined threshold; Or the weighted sum of all risk parameters is more than or equal to predetermined threshold.
Second aspect, this application provides a kind of device for crowd's Risk-warning, and described device comprises: acquiring unit, for obtaining the effective location data of user terminal in presumptive area; Determining unit, for determining the risk parameter of described presumptive area based on described effective location data; Judging unit, for judging whether described risk parameter meets predetermined early-warning conditions; Prewarning unit, for when described risk parameter meets predetermined early-warning conditions, carries out early warning for described presumptive area.
In some embodiments, the risk parameter of described presumptive area comprises following at least one item: the total number of persons of described presumptive area; The crowd density of effective coverage in described presumptive area; And crowd's entropy of described presumptive area; Wherein, described crowd's entropy characterizes the confusion degree of crowd's moving direction.
In some embodiments, determining unit is configured for: the number determining user terminal in described presumptive area based on described effective location data; Obtain the correction coefficient that described presumptive area is corresponding; The product of correction parameter corresponding with described presumptive area for the number of user terminal in described presumptive area is defined as the total number of persons of described presumptive area.
In some embodiments, determining unit is configured for: determine each user terminal sense of displacement vector within a predetermined period of time based on described effective location data; Corresponding sense of displacement angle is determined based on described sense of displacement vector; Add up the probability distribution of described sense of displacement angle within the scope of different angles; Crowd's entropy of described presumptive area is determined based on described probability distribution.
In some embodiments, described determining unit is configured for: the practical risk parameter determining presumptive area described in current time; And/or presumptive area described in prediction predetermined instant estimate risk parameter.
In some embodiments, described acquiring unit comprises: gather subelement, for gathering the locator data of user terminal in presumptive area; Search subelement, for finding out misdata in described locator data and repeating data; Delete subelement, for deleting described misdata and repeating data, to obtain effective location data.
In some embodiments, describedly meet predetermined early-warning conditions, comprising: the risk parameter of predetermined number/ratio is more than or equal to corresponding predetermined threshold; Or the weighted sum of all risk parameters is more than or equal to predetermined threshold.
The method for crowd's Risk-warning that the application provides and device, by determining corresponding risk parameter based on the effective location data of user terminal in presumptive area, and when this risk parameter meets predetermined early-warning conditions, carry out early warning for this presumptive area.Solve the problem that the accuracy of crowd's Risk-warning in prior art is low, improve the efficiency of crowd's Risk-warning.
Accompanying drawing explanation
By reading the detailed description done non-limiting example done with reference to the following drawings, the other features, objects and advantages of the application will become more obvious:
Fig. 1 is the process flow diagram of an embodiment of the method for crowd's Risk-warning that the embodiment of the present application provides;
Fig. 2 is the process flow diagram of an embodiment of the method for the total number of persons based on effective location data determination presumptive area that the embodiment of the present application provides;
Fig. 3 is the process flow diagram of an embodiment of the method based on effective location data determination presumptive area crowd entropy that the embodiment of the present application provides;
Fig. 4 is the process flow diagram of an embodiment of the method for the effective location data of user terminal in the acquisition presumptive area that provides of the embodiment of the present application;
Fig. 5 is the structural representation that the embodiment of the present application is provided for an embodiment of the device of crowd's Risk-warning;
Fig. 6 is the exemplary system architecture figure that can apply the embodiment of the present application;
Fig. 7 is the structural representation of the computer system be suitable for for the terminal device or server realizing the embodiment of the present application.
Embodiment
Below in conjunction with drawings and Examples, the application is described in further detail.Be understandable that, specific embodiment described herein is only for explaining related invention, but not the restriction to this invention.It also should be noted that, for convenience of description, in accompanying drawing, illustrate only the part relevant to Invention.
It should be noted that, when not conflicting, the embodiment in the application and the feature in embodiment can combine mutually.Below with reference to the accompanying drawings and describe the application in detail in conjunction with the embodiments.
Terminal involved by the application has and opens positioning function.For example, object and is for simplicity described, in ensuing discussion, in conjunction with have and the terminal opening positioning function to describe the exemplary embodiment of the application.Terminal can include but not limited to smart mobile phone, panel computer, personal digital assistant, pocket computer on knee and desktop computer etc.
Please refer to Fig. 1, it illustrates the flow process 100 of an embodiment of the method for crowd's Risk-warning according to the application.
As shown in Figure 1, in a step 101, the effective location data of user terminal in presumptive area are obtained.
In the present embodiment, presumptive area can be the region at place, a city, also can be the region at a regional place, can also be the region at place, tourist attractions.Be appreciated that the concrete division of the application to presumptive area does not limit.User terminal is having of carrying with of user and opens the terminal device of positioning function, can be smart mobile phone, wearable eyes, Intelligent bracelet, intelligent watch etc.The effective location data of user terminal are the locator data of the holder position of embodiment terminal that can be unique, do not comprise the locator data of mistake and repetition in the effective location data of user terminal.
Then, in a step 102, based on the risk parameter of above-mentioned effective location data determination presumptive area.
In the present embodiment, risk parameter is a parameter after the hazard level of the crowd of presumptive area being quantized, and its size can embody the hazard level of the crowd of presumptive area, and in general, normally risk parameter is larger, and the hazard level of crowd is higher.The risk parameter of presumptive area can comprise following at least one item: the total number of persons of this presumptive area; The crowd density of effective coverage in this presumptive area; And crowd's entropy of this presumptive area; Wherein, crowd's entropy characterizes the confusion degree of crowd's moving direction.
It should be noted that, in presumptive area, effective coverage is the region that can hold crowd.Such as, in certain park, there is the lake that very large, also have a lot of greenbelt, also comprise a lot of road, view and admire platform etc.Wherein, the region of lake and greenbelt cannot hold crowd, is therefore non-active area, and road and the region viewing and admiring platform can hold crowd, are therefore effective coverage.In presumptive area, the crowd density of effective coverage can be obtained divided by the area of effective coverage in presumptive area by the total number of persons of presumptive area.
In general, except crowd's total number of persons and crowd density, the flowing of crowd is also a key factor relevant with crowd's risk.Suppose using the complication system of crowd as a flowing, everyone in crowd, as the particle of motion, can characterize the degree of stability of crowd with crowd's entropy.Specifically, crowd's entropy can be determined according to the directional spreding of crowd's movement, then adopt crowd's entropy to characterize the confusion degree of crowd.Normally, the numerical value of crowd's entropy is larger, and represent that the direction of crowd's flowing is more chaotic, the risk of crowd is also larger.
In the present embodiment, based on the effective location data of user terminal in presumptive area in the certain hour section comprising current time, the practical risk parameter of current time presumptive area can be determined.Also risk parameter can be estimated according to the following presumptive area sometime of the practical risk parameter prediction of current time presumptive area.Be appreciated that the application is to not limiting in this respect.
In the present embodiment, what can adopt the following presumptive area sometime of training in advance good forecast model prediction estimates risk parameter.Such as, according to the historical record of the practical risk parameter of certain section of time period presumptive area, machine learning can be carried out, obtain a forecast model, based on the practical risk parameter of current time presumptive area, that can predict following presumptive area sometime by this forecast model estimates risk parameter.
Then, in step 103, judge whether risk parameter meets predetermined early-warning conditions.
In the one of the present embodiment realizes, meeting predetermined early-warning conditions can be that the risk parameter of predetermined number/ratio is more than or equal to corresponding predetermined threshold.Such as, suppose that the risk parameter of presumptive area comprises: the total number of persons of this presumptive area; The crowd density of effective coverage in this presumptive area; And crowd's entropy of this presumptive area.In advance a threshold value is set to each risk parameter, meeting predetermined early-warning conditions can be that at least one risk parameter is more than or equal to corresponding predetermined threshold, also can be that at least two risk parameters are more than or equal to corresponding predetermined threshold, can also be that all risk parameter is more than or equal to corresponding predetermined threshold.
Again such as, suppose that the risk parameter of presumptive area comprises 6, in advance a threshold value is set to each risk parameter, meet predetermined early-warning conditions can be at least 50% risk parameter be more than or equal to corresponding predetermined threshold, also can be that at least 30% risk parameter is more than or equal to corresponding predetermined threshold.
In the another kind of the present embodiment realizes, meeting predetermined early-warning conditions can be that the weighted sum of all risk parameters is more than or equal to predetermined threshold.Such as, suppose that the risk parameter of presumptive area comprises: the total number of persons of this presumptive area; The crowd density of effective coverage in this presumptive area; And crowd's entropy of this presumptive area.Set a weight coefficient to each risk parameter in advance, the parameter larger to crowd's venture influence, it is larger that its weight coefficient can set.
Be appreciated that predetermined early-warning conditions can also be other condition, the particular content of the application to predetermined early-warning conditions does not limit.
Finally, at step 104, if risk parameter meets predetermined early-warning conditions, early warning is carried out for presumptive area.
In the present embodiment, early warning can have various ways, can be push early warning information to staff, also can be the alarm of the form of sounding, can also be identify above-mentioned presumptive area (e.g., above-mentioned presumptive area of highlighted display etc. on map) with predetermined form, be appreciated that, can also have the form of other early warning, the concrete form of the application to early warning does not limit.
The method for crowd's Risk-warning that above-described embodiment of the application provides, by determining corresponding risk parameter based on the effective location data of user terminal in presumptive area, and when this risk parameter meets predetermined early-warning conditions, carry out early warning for this presumptive area.Solve the problem that the accuracy of crowd's Risk-warning in prior art is low, improve the efficiency of crowd's Risk-warning.
With further reference to Fig. 2, it illustrates the flow process 200 of an embodiment of the method for the total number of persons based on effective location data determination presumptive area.
As shown in Figure 2, in step 201, based on the number of user terminal in effective location data determination presumptive area.
In the present embodiment, because the effective location data of user terminal are the locator data of the holder position of embodiment terminal that can be unique, therefore, can according to the number of user terminal in effective location data determination presumptive area.
Then, in step 202., correction coefficient corresponding to above-mentioned presumptive area is obtained.
In general, be not that everyone carries with and has and open the terminal of positioning function, but in the certain area of regular period, the quantity of carrying the people of above-mentioned terminal accounts for the ratio of crowd's total number of persons should close to constant.
In the present embodiment, by the locator data to the user terminal in the True Data (statistics, the admission ticket data at scenic spot, the ticketing data etc. of subway public transport as official) of crowd's total number of persons in the certain area of regular period and the period of correspondence and region, the ratio that the quantity of carrying the people of above-mentioned terminal accounts for crowd's total number of persons can be investigated.Using the inverse of this ratio value as correction coefficient corresponding to above-mentioned presumptive area.
Finally, in step 203, the product of correction parameter corresponding with presumptive area for the number of user terminal in presumptive area is defined as the total number of persons of presumptive area.
With further reference to Fig. 3, it illustrates the flow process 300 of an embodiment of the method based on effective location data determination presumptive area crowd entropy according to the application.
As shown in Figure 3, in step 301, each user terminal sense of displacement vector is within a predetermined period of time determined based on effective location data.
In the present embodiment, using the complication system of crowd as a flowing, everyone in crowd, as the particle of motion, can characterize the degree of stability of crowd with crowd's entropy.Specifically, crowd's entropy can be determined according to the directional spreding of crowd's movement, then adopt crowd's entropy to characterize the confusion degree of crowd.Normally, the numerical value of crowd's entropy is larger, and represent that the direction of crowd's flowing is more chaotic, the risk of crowd is also larger.
Specifically, first to determine the positioning track of each user terminal in certain hour section according to effective location data, then determine this user terminal sense of displacement vector within a predetermined period of time according to the positioning track of each user terminal
Then, in step 302, corresponding sense of displacement angle is determined based on above-mentioned sense of displacement vector.
In the present embodiment, corresponding sense of displacement angle is determined, as following formula based on above-mentioned sense of displacement vector:
wherein, α represents sense of displacement angle,
represent sense of displacement vector.
Then, in step 303, statistically rheme moves the probability distribution of deflection within the scope of different angles.
In the present embodiment, 2 π are divided into several angular ranges, statistically rheme moves the probability distribution situation of deflection within the scope of different angles.Use p
irepresent the probability of above-mentioned sense of displacement angle in i-th angular range.
Finally, in step 304, crowd's entropy of this presumptive area is determined based on above-mentioned probability distribution.
In the present embodiment, crowd's entropy of this presumptive area is calculated, as following formula:
crowdentropy＝-Σp
ilogp
i
Wherein, crowdentropy represents crowd's entropy of this presumptive area, p
irepresent the probability of above-mentioned sense of displacement angle in i-th angular range.
With further reference to Fig. 4, it illustrates the flow process 400 of an embodiment of the method for the effective location data obtaining user terminal in presumptive area.
As shown in Figure 4, in step 401, the locator data of user terminal in presumptive area is gathered.
In the one of the present embodiment realizes, can send the request of uploading locating information to user terminal, user terminal upon receiving a request, uploads the locating information that it obtains from location-server in real time.Meanwhile, the device identification of upload user terminal, or user related information etc.
In the another kind of the present embodiment realizes, can directly from the locating information of user in real terminal the location daily record of location-server.Meanwhile, obtain the device identification of user terminal, or user related information etc.
Be appreciated that the locator data that can also gather user terminal in presumptive area by another way, the concrete mode of the application to the locator data gathering user terminal in presumptive area does not limit.
Then, in step 402, the misdata in above-mentioned locator data and repeating data is found out.
In general, in the process that user terminal is positioned, the situation of Wrong localization may be there is, also may user carried with two or more can locating terminal.At this moment, the misdata in above-mentioned locator data and repeating data should be found out.
In the present embodiment, by the translational speed of user and moving displacement etc., Search and Orientation mistake can be carried out.Such as, determine the translational speed of user in each moment according to the motion track of user terminal, if translational speed is sometime excessive, illustrate that corresponding locator data is wrong.Again such as, certain region may be a lake, but the motion track of user terminal appears at this region, then illustrate that corresponding locator data is wrong.
In the present embodiment, repeating data can be searched by the user related information of user.Such as, the plural terminal that same user carries with may have identical user related information.Wherein, user related information can include but not limited to unique identification marking of user, the account that user registers on Mobile solution, the hardware device number of user, the WIFI information of user's url history, track characteristic of user (e.g., the positioning track of two terminals just the same etc.) etc. identifies repeating data.
Finally, in step 403, above-mentioned misdata and repeating data is deleted, to obtain effective location data.
Although it should be noted that the operation describing the inventive method in the accompanying drawings with particular order, this is not that requirement or hint must perform these operations according to this particular order, or must perform the result that all shown operation could realize expectation.On the contrary, the step described in process flow diagram can change execution sequence.Such as, in the flow process 200 of Fig. 2, first can perform step 202, obtain the correction coefficient that presumptive area is corresponding, and then perform step 201, based on the number of user terminal in effective location data determination presumptive area.Additionally or alternatively, some step can be omitted, multiple step be merged into a step and perform, and/or a step is decomposed into multiple step and perform.
With further reference to Fig. 5, it illustrates the structural representation of an embodiment of the device for crowd's Risk-warning according to the application.
As shown in Figure 5, the device 500 of the present embodiment comprises: acquiring unit 501, determining unit 502, judging unit 503 and prewarning unit 504.Wherein, acquiring unit 501 is for obtaining the effective location data of user terminal in presumptive area.Determining unit 502 is for determining the risk parameter of this presumptive area based on above-mentioned effective location data.Judging unit 503 is for judging whether above-mentioned risk parameter meets predetermined early-warning conditions.Prewarning unit 504, for when above-mentioned risk parameter meets predetermined early-warning conditions, carries out early warning for this presumptive area.
In some Alternate embodiments, the risk parameter of presumptive area comprises following at least one item: the total number of persons of this presumptive area, the crowd density of effective coverage in this presumptive area, and crowd's entropy of this presumptive area.Wherein, crowd's entropy characterizes the confusion degree of crowd's moving direction.
In some Alternate embodiments, determining unit is configured for: the number determining user terminal in this presumptive area based on above-mentioned effective location data.Obtain the correction coefficient that above-mentioned presumptive area is corresponding.The product of correction parameter corresponding with presumptive area for the number of user terminal in presumptive area is defined as the total number of persons of presumptive area.
In some Alternate embodiments, determining unit is configured for: determine each user terminal sense of displacement vector within a predetermined period of time based on above-mentioned effective location data.Corresponding sense of displacement angle is determined based on above-mentioned sense of displacement vector.The probability distribution of statistics sense of displacement angle within the scope of different angles.Based on crowd's entropy of above-mentioned probability distribution determination presumptive area.
In some Alternate embodiments, determining unit is configured for: the practical risk parameter determining current time presumptive area.And/or prediction predetermined instant presumptive area estimate risk parameter.
In some Alternate embodiments, acquiring unit 501 comprises collection subelement, searches subelement, deletes subelement (not shown).Wherein, subelement is gathered for gathering the locator data of user terminal in presumptive area.Search subelement for finding out misdata in above-mentioned locator data and repeating data.Delete subelement for deleting above-mentioned misdata and repeating data, to obtain effective location data.
In some Alternate embodiments, meet predetermined early-warning conditions, comprising: the risk parameter of predetermined number/ratio is more than or equal to corresponding predetermined threshold.Or the weighted sum of all risk parameters is more than or equal to predetermined threshold.
Should be appreciated that all unit or the module of record in device 500 are corresponding with each step in the method described with reference to figure 1-4.Thus, above for the unit that operation and the feature of method description are equally applicable to device 500 and wherein comprise, do not repeat them here.Device 500 can pre-set in the server, also can be loaded in server by modes such as downloads.Corresponding units in device 500 can cooperatively interact with the unit in server to realize the scheme for crowd's Risk-warning.
Fig. 6 shows the exemplary system architecture 600 can applying the embodiment of the present application.
As shown in Figure 6, system architecture 600 can comprise terminal device 601,602, network 603 and server 604.Network 603 in order to provide the medium of communication link between terminal device 601,602 and server 604.Network 603 can comprise various connection type, such as wired, wireless communication link or fiber optic cables etc.
User 610 can use terminal device 601,602 mutual by network 603 and server 604, to receive or to send message etc.Terminal device 601,602 can be provided with the application of various telecommunication customer end.
Terminal device 601,602 can be various electronic equipment, includes but not limited to smart mobile phone, panel computer, personal digital assistant, pocket computer on knee and Intelligent wearable equipment etc.
Server 604 can be to provide the server of various service.The process such as server can store the data received, analysis, and result is fed back to terminal device.
Should be appreciated that, the number of the terminal device in Fig. 6, network and server is only schematic.According to realizing needs, the terminal device of arbitrary number, network and server can be had.
Below with reference to Fig. 7, it illustrates the structural representation of the computer system 700 of terminal device or the server be suitable for for realizing the embodiment of the present application.
As shown in Figure 7, computer system 700 comprises CPU (central processing unit) (CPU) 701, and it or can be loaded into the program random access storage device (RAM) 703 from storage area 708 and perform various suitable action and process according to the program be stored in ROM (read-only memory) (ROM) 702.In RAM703, also store system 700 and operate required various program and data.CPU701, ROM702 and RAM703 are connected with each other by bus 704.I/O (I/O) interface 705 is also connected to bus 704.
I/O interface 705 is connected to: the importation 706 comprising keyboard, mouse etc. with lower component; Comprise the output 707 of such as cathode-ray tube (CRT) (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.; Comprise the storage area 708 of hard disk etc.; And comprise the communications portion 709 of network interface unit of such as LAN card, modulator-demodular unit etc.Communications portion 709 is via the network executive communication process of such as the Internet.Driver 710 is also connected to I/O interface 705 as required.Detachable media 711, such as disk, CD, magneto-optic disk, semiconductor memory etc., be arranged on driver 710 as required, so that the computer program read from it is mounted into storage area 708 as required.
Especially, according to embodiment of the present disclosure, the process that reference flow sheet describes above may be implemented as computer software programs.Such as, embodiment of the present disclosure comprises a kind of computer program, and it comprises the computer program visibly comprised on a machine-readable medium, and described computer program comprises the program code for the method shown in flowchart.In such embodiments, this computer program can be downloaded and installed from network by communications portion 709, and/or is mounted from detachable media 711.
Process flow diagram in accompanying drawing and block diagram, illustrate according to the architectural framework in the cards of the system of various embodiments of the invention, method and computer program product, function and operation.In this, each square frame in process flow diagram or block diagram can represent a part for module, program segment or a code, and a part for described module, program segment or code comprises one or more executable instruction for realizing the logic function specified.Also it should be noted that at some as in the realization of replacing, the function marked in square frame also can be different from occurring in sequence of marking in accompanying drawing.Such as, in fact the square frame that two adjoining lands represent can perform substantially concurrently, and they also can perform by contrary order sometimes, and this determines according to involved function.Also it should be noted that, the combination of the square frame in each square frame in block diagram and/or process flow diagram and block diagram and/or process flow diagram, can realize by the special hardware based system of the function put rules into practice or operation, or can realize with the combination of specialized hardware and computer instruction.
Be described in unit module involved in the embodiment of the present application to be realized by the mode of software, also can be realized by the mode of hardware.Described unit module also can be arranged within a processor, such as, can be described as: a kind of processor comprises acquiring unit, determining unit, judging unit, prewarning unit.Wherein, the title of these unit modules does not form the restriction to this unit module itself under certain conditions, and such as, acquiring unit can also be described to " for obtaining the unit of the effective location data of user terminal in presumptive area ".
As another aspect, present invention also provides a kind of computer-readable recording medium, this computer-readable recording medium can be the computer-readable recording medium comprised in device described in above-described embodiment; Also can be individualism, be unkitted the computer-readable recording medium allocated in terminal.Described computer-readable recording medium stores more than one or one program, and described program is used for performance description in the method for crowd's Risk-warning of the application by one or more than one processor.
More than describe and be only the preferred embodiment of the application and the explanation to institute's application technology principle.Those skilled in the art are to be understood that, invention scope involved in the application, be not limited to the technical scheme of the particular combination of above-mentioned technical characteristic, also should be encompassed in when not departing from described inventive concept, other technical scheme of being carried out combination in any by above-mentioned technical characteristic or its equivalent feature and being formed simultaneously.The technical characteristic that such as, disclosed in above-mentioned feature and the application (but being not limited to) has similar functions is replaced mutually and the technical scheme formed.
Claims (15)
1. for a method for crowd's Risk-warning, it is characterized in that, described method comprises:
Obtain the effective location data of user terminal in presumptive area;
The risk parameter of described presumptive area is determined based on described effective location data;
Judge whether described risk parameter meets predetermined early-warning conditions;
If so, early warning is carried out for described presumptive area.
2. method according to claim 1, is characterized in that, the risk parameter of described presumptive area comprises following at least one item:
The total number of persons of described presumptive area;
The crowd density of effective coverage in described presumptive area; And
Crowd's entropy of described presumptive area;
Wherein, described crowd's entropy characterizes the confusion degree of crowd's moving direction.
3. method according to claim 2, is characterized in that, determines the total number of persons of described presumptive area, comprising based on described effective location data:
The number of user terminal in described presumptive area is determined based on described effective location data;
Obtain the correction coefficient that described presumptive area is corresponding;
The product of correction parameter corresponding with described presumptive area for the number of user terminal in described presumptive area is defined as the total number of persons of described presumptive area.
4. method according to claim 2, is characterized in that, determines crowd's entropy of described presumptive area, comprising based on described effective location data:
Each user terminal sense of displacement vector is within a predetermined period of time determined based on described effective location data;
Corresponding sense of displacement angle is determined based on described sense of displacement vector;
Add up the probability distribution of described sense of displacement angle within the scope of different angles;
Crowd's entropy of described presumptive area is determined based on described probability distribution.
5. method according to claim 1, is characterized in that, the described risk parameter determining described presumptive area, comprising:
Determine the practical risk parameter of presumptive area described in current time; And/or
Described in prediction predetermined instant, presumptive area estimates risk parameter.
6. method according to claim 5, is characterized in that, what adopt presumptive area described in the forecast model of training in advance prediction predetermined instant estimates risk parameter.
7. method according to claim 1, is characterized in that, the effective location data of user terminal in described acquisition presumptive area, comprising:
Gather the locator data of user terminal in presumptive area;
Find out the misdata in described locator data and repeating data;
Delete described misdata and repeating data, to obtain effective location data.
8. method according to claim 1, is characterized in that, describedly meets predetermined early-warning conditions, comprising:
The risk parameter of predetermined number/ratio is more than or equal to corresponding predetermined threshold; Or
The weighted sum of all risk parameters is more than or equal to predetermined threshold.
9. for a device for crowd's Risk-warning, it is characterized in that, described device comprises:
Acquiring unit, for obtaining the effective location data of user terminal in presumptive area;
Determining unit, for determining the risk parameter of described presumptive area based on described effective location data;
Judging unit, for judging whether described risk parameter meets predetermined early-warning conditions;
Prewarning unit, for when described risk parameter meets predetermined early-warning conditions, carries out early warning for described presumptive area.
10. device according to claim 9, is characterized in that, the risk parameter of described presumptive area comprises following at least one item:
The total number of persons of described presumptive area;
The crowd density of effective coverage in described presumptive area; And
Crowd's entropy of described presumptive area;
Wherein, described crowd's entropy characterizes the confusion degree of crowd's moving direction.
11. devices according to claim 10, it is characterized in that, determining unit is configured for:
The number of user terminal in described presumptive area is determined based on described effective location data;
Obtain the correction coefficient that described presumptive area is corresponding;
The product of correction parameter corresponding with described presumptive area for the number of user terminal in described presumptive area is defined as the total number of persons of described presumptive area.
12. devices according to claim 10, it is characterized in that, determining unit is configured for:
Each user terminal sense of displacement vector is within a predetermined period of time determined based on described effective location data;
Corresponding sense of displacement angle is determined based on described sense of displacement vector;
Add up the probability distribution of described sense of displacement angle within the scope of different angles;
Crowd's entropy of described presumptive area is determined based on described probability distribution.
13. devices according to claim 9, is characterized in that, described determining unit is configured for:
Determine the practical risk parameter of presumptive area described in current time; And/or
Described in prediction predetermined instant, presumptive area estimates risk parameter.
14. devices according to claim 9, is characterized in that, described acquiring unit comprises:
Gather subelement, for gathering the locator data of user terminal in presumptive area;
Search subelement, for finding out misdata in described locator data and repeating data;
Delete subelement, for deleting described misdata and repeating data, to obtain effective location data.
15. devices according to claim 9, is characterized in that, describedly meet predetermined early-warning conditions, comprising:
The risk parameter of predetermined number/ratio is more than or equal to corresponding predetermined threshold; Or
The weighted sum of all risk parameters is more than or equal to predetermined threshold.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510427845.3A CN105095991A (en) | 2015-07-20 | 2015-07-20 | Method and device for crowd risk early warning |
| PCT/CN2015/096130 WO2017012236A1 (en) | 2015-07-20 | 2015-12-01 | Method and apparatus for crowd risk early warning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510427845.3A CN105095991A (en) | 2015-07-20 | 2015-07-20 | Method and device for crowd risk early warning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105095991A true CN105095991A (en) | 2015-11-25 |
Family
ID=54576363
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510427845.3A Pending CN105095991A (en) | 2015-07-20 | 2015-07-20 | Method and device for crowd risk early warning |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN105095991A (en) |
| WO (1) | WO2017012236A1 (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105426553A (en) * | 2016-01-15 | 2016-03-23 | 四川农业大学 | Target real-time tracking and early warning method and system based on intelligent equipment |
| CN105719428A (en) * | 2016-03-21 | 2016-06-29 | 上海斐讯数据通信技术有限公司 | Security prewarning system and method for scenic spot |
| CN105844572A (en) * | 2016-03-25 | 2016-08-10 | 腾讯科技（深圳）有限公司 | Congestion risk monitoring method and congestion risk monitoring device |
| WO2017012236A1 (en) * | 2015-07-20 | 2017-01-26 | 百度在线网络技术（北京）有限公司 | Method and apparatus for crowd risk early warning |
| CN106856026A (en) * | 2015-12-08 | 2017-06-16 | ***通信集团安徽有限公司 | A kind of multi-user's area monitoring and the method and system of early warning |
| CN107832912A (en) * | 2017-10-09 | 2018-03-23 | 中国电子科技集团公司第二十八研究所 | A kind of dense population risk analysis method and system |
| CN107889054A (en) * | 2017-12-08 | 2018-04-06 | 沈阳首视科技有限责任公司 | Crowd's station acquisition device, in real time monitoring guidance system and interaction monitoring method |
| CN109451432A (en) * | 2018-11-14 | 2019-03-08 | 浙江每日互动网络科技股份有限公司 | A method of early warning is carried out based on mobile terminal locations data |
| CN112015834A (en) * | 2019-05-31 | 2020-12-01 | 口碑(上海)信息技术有限公司 | Target offline position identification method and device, storage medium and computer equipment |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110147923B (en) * | 2019-04-04 | 2023-07-11 | 创新先进技术有限公司 | Method and device for identifying risk users |
| CN113498012A (en) * | 2020-04-08 | 2021-10-12 | 上海黄浦数字商圈运营有限公司 | Passenger flow early warning method, electronic device and readable storage medium |
| CN111652161A (en) * | 2020-06-08 | 2020-09-11 | 上海商汤智能科技有限公司 | Crowd excess density prediction method and device, electronic equipment and storage medium |
| CN113923598A (en) * | 2021-07-28 | 2022-01-11 | 上海易同科技股份有限公司 | Personnel management method and equipment based on Bluetooth positioning |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102215468A (en) * | 2010-04-02 | 2011-10-12 | 广东宜通世纪科技股份有限公司 | Crowd distribution monitoring method and system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ATE400037T1 (en) * | 2004-03-17 | 2008-07-15 | Link Norbert Prof Dr | DEVICE AND METHOD FOR DETECTING AND TRACKING PERSONS IN AN INVESTIGATION SECTION |
| CN104408066A (en) * | 2014-10-29 | 2015-03-11 | 小米科技有限责任公司 | Alarm prompting method, device and equipment based on position information |
| CN104540168B (en) * | 2014-12-30 | 2018-05-11 | 杭州华为数字技术有限公司 | A kind of method, apparatus and system for measuring crowded degree |
| CN104635706B (en) * | 2015-02-05 | 2017-09-19 | 上海市城市建设设计研究总院（集团）有限公司 | The cluster monitored by personnel detected based on information source and the application method of pre-warning system |
| CN105095991A (en) * | 2015-07-20 | 2015-11-25 | 百度在线网络技术（北京）有限公司 | Method and device for crowd risk early warning |
-
2015
- 2015-07-20 CN CN201510427845.3A patent/CN105095991A/en active Pending
- 2015-12-01 WO PCT/CN2015/096130 patent/WO2017012236A1/en active Application Filing
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102215468A (en) * | 2010-04-02 | 2011-10-12 | 广东宜通世纪科技股份有限公司 | Crowd distribution monitoring method and system |
Non-Patent Citations (1)
| Title |
|---|
| 林沁 等: "基于灰度共生矩阵和光流法的人群异动事件检测", 《计算机与现代化》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017012236A1 (en) * | 2015-07-20 | 2017-01-26 | 百度在线网络技术（北京）有限公司 | Method and apparatus for crowd risk early warning |
| CN106856026A (en) * | 2015-12-08 | 2017-06-16 | ***通信集团安徽有限公司 | A kind of multi-user's area monitoring and the method and system of early warning |
| CN105426553A (en) * | 2016-01-15 | 2016-03-23 | 四川农业大学 | Target real-time tracking and early warning method and system based on intelligent equipment |
| CN105426553B (en) * | 2016-01-15 | 2018-09-11 | 四川农业大学 | A kind of object real-time tracking method for early warning and system based on smart machine |
| CN105719428A (en) * | 2016-03-21 | 2016-06-29 | 上海斐讯数据通信技术有限公司 | Security prewarning system and method for scenic spot |
| CN105844572A (en) * | 2016-03-25 | 2016-08-10 | 腾讯科技（深圳）有限公司 | Congestion risk monitoring method and congestion risk monitoring device |
| CN107832912A (en) * | 2017-10-09 | 2018-03-23 | 中国电子科技集团公司第二十八研究所 | A kind of dense population risk analysis method and system |
| CN107889054A (en) * | 2017-12-08 | 2018-04-06 | 沈阳首视科技有限责任公司 | Crowd's station acquisition device, in real time monitoring guidance system and interaction monitoring method |
| CN109451432A (en) * | 2018-11-14 | 2019-03-08 | 浙江每日互动网络科技股份有限公司 | A method of early warning is carried out based on mobile terminal locations data |
| CN112015834A (en) * | 2019-05-31 | 2020-12-01 | 口碑(上海)信息技术有限公司 | Target offline position identification method and device, storage medium and computer equipment |
| CN112015834B (en) * | 2019-05-31 | 2024-04-16 | 口碑(上海)信息技术有限公司 | Target line lower position identification method, device, storage medium and computer equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2017012236A1 (en) | 2017-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105095991A (en) | Method and device for crowd risk early warning | |
| Kong et al. | Time-location-relationship combined service recommendation based on taxi trajectory data | |
| Zhao et al. | A trajectory clustering approach based on decision graph and data field for detecting hotspots | |
| Fan et al. | Citymomentum: an online approach for crowd behavior prediction at a citywide level | |
| Ratti et al. | Mobile landscapes: using location data from cell phones for urban analysis | |
| Huang et al. | Predicting human mobility with activity changes | |
| Tang et al. | A network Kernel Density Estimation for linear features in space–time analysis of big trace data | |
| CN111212383B (en) | Method, device, server and medium for determining number of regional permanent population | |
| Chen et al. | Constructing and comparing user mobility profiles | |
| CN106767835B (en) | Positioning method and device | |
| CN112200067A (en) | Intelligent video event detection method, system, electronic equipment and storage medium | |
| CN109949063A (en) | A kind of address determines method, apparatus, electronic equipment and readable storage medium storing program for executing | |
| US9167389B1 (en) | Clustering location data to determine locations of interest | |
| CN108197619A (en) | A kind of localization method based on signboard image, device, equipment and storage medium | |
| Zhao et al. | A network distance and graph-partitioning-based clustering method for improving the accuracy of urban hotspot detection | |
| Shi et al. | Detecting spatiotemporal extents of traffic congestion: A density-based moving object clustering approach | |
| KR20180134553A (en) | System and method for providing the degree of congestion | |
| Ma et al. | Public transportation big data mining and analysis | |
| CN110276387B (en) | Model generation method and device | |
| CN110674208A (en) | Method and device for determining position information of user | |
| Feng et al. | A survey of visual analytics in urban area | |
| CN110895543A (en) | Population migration tracking display method and device and storage medium | |
| Markou et al. | Use of taxi-trip data in analysis of demand patterns for detection and explanation of anomalies | |
| CN104121917A (en) | Method and device for automatically discovering new bridge | |
| CN115169466A (en) | Method and device for drawing image of land, electronic equipment and computer readable medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20151125 |