CN110909263B

CN110909263B - Method and device for determining companion relationship of identity characteristics

Info

Publication number: CN110909263B
Application number: CN201911202661.1A
Authority: CN
Inventors: 梁秀钦; 缪荣荣; 齐云飞
Original assignee: Beijing Mininglamp Software System Co ltd
Current assignee: Beijing Mininglamp Software System Co ltd
Priority date: 2019-11-29
Filing date: 2019-11-29
Publication date: 2022-10-25
Anticipated expiration: 2039-11-29
Also published as: CN110909263A

Abstract

The application provides an identification feature accompanying relation determining method and device, and space-time trajectory information of each identification feature is generated; for every two identity characteristics, determining the accompanying support degree of the two identity characteristics based on the space-time trajectory information; if the accompanying support degree is greater than a preset threshold value, determining the accompanying confidence degrees of the two identity characteristics; if the accompanying confidence degrees of the two identity characteristics are greater than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity characteristics; and sending the companion relationship between every two identity characteristics to the server so that the server determines the companion relationship between all the identity characteristics. Compared with the prior art, the method and the device have the advantages that various types of monitoring information can be collected on one monitoring terminal, the monitoring information is processed on the monitoring terminal, the preliminary accompanying relation is generated, and then the accompanying relation is sent to the server, so that the processing pressure of the server can be reduced, and the processing efficiency of accompanying analysis is improved.

Description

Method and device for determining companion relationship of identity characteristics

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for determining an accompanying relationship of identity features.

Background

With the continuous progress of science and technology, data mining has become a popular technology of the present day. In the technical field of data mining, the spatiotemporal trajectory can be used for analyzing the accompanying relationships of objects such as people, equipment and animals, so that the association among the objects can be learned, and the accompanying relationships can be applied to various scenes such as traffic management, tracking of people or objects, determination of related targets and the like.

At present, the conventional method for determining the accompanying relationship usually collects original monitoring data transmitted by different monitoring devices, and performs statistics and analysis on various monitoring data according to preset rules to complete determination of the accompanying relationship between different devices or people. However, in practical applications, the trace data is usually huge in volume, so that the calculation amount is far larger than the acceptable range, the server is difficult to bear a large amount of load in a short time, risks such as downtime are easily caused, and the calculation efficiency is low.

Disclosure of Invention

In view of the above, an object of the present invention is to provide a method and an apparatus for determining an association relationship of identity features, which can reduce processing pressure of a server and improve processing efficiency of association analysis.

The embodiment of the application provides an identity characteristic accompanying relation determining method, which is applied to a monitoring terminal and comprises the following steps:

generating time-space track information of each identity characteristic according to each identity characteristic monitored in a preset time period and position information and time information when the identity characteristic is monitored;

for every two identity features, determining the accompanying support degree of the two identity features based on the space-time trajectory information;

if the accompanying support degree is greater than or equal to a first preset threshold value, determining space-time trajectory feature vectors of the two identity features based on the space-time trajectory information;

determining the accompanying confidence degrees of the two identity features based on the space-time trajectory feature vector;

if the accompanying confidence degrees of the two identity features are larger than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity features;

and sending the adjoint relationship between every two identity characteristics to a server so that the server determines the adjoint relationship between all the identity characteristics according to the adjoint relationship sent by each monitoring terminal.

In some possible embodiments, after generating the spatiotemporal trajectory information for each identity feature, the method further comprises:

and generating spatiotemporal trajectory index information of each identity characteristic based on the spatiotemporal trajectory information.

In some possible embodiments, the determining the accompanying support of the two identity features based on the spatiotemporal trajectory information includes:

determining whether at least a preset number of moments exist in the preset time period or not based on the spatiotemporal trajectory index information, and simultaneously monitoring the two identity characteristics;

if so, determining the number of other monitoring devices which have at least a preset number of moments and simultaneously monitor the two identity characteristics from a plurality of other monitoring devices within the preset time period;

and taking the determined number of other monitoring devices as the accompanying support degree of the two identity characteristics.

In some possible embodiments, the determining the spatiotemporal trajectory feature vector of the two identity features based on the spatiotemporal trajectory information includes:

and determining space-time trajectory feature vectors of the two identity features based on the space-time trajectory index information.

In some possible embodiments, the determining the accompanying confidence degrees of the two identity features based on the spatiotemporal trajectory feature vector includes:

determining space-time trajectory feature vectors of the two identity features within a plurality of other preset time periods;

determining the similarity between the space track characteristic vectors of the two identity characteristics in each other time period and the space-time track characteristic vectors of the two identity characteristics in a preset time period;

and taking the number of the similarity degrees which are greater than or equal to a preset value in all the similarity degrees as the accompanying confidence degrees of the two identity characteristics.

The embodiment of the application provides another method for determining an accompanying relationship of identity characteristics, which is applied to a server and comprises the following steps:

receiving an accompanying relation between every two identity characteristics monitored by each monitoring terminal within a preset time period, wherein the accompanying relation is sent by each monitoring terminal;

and determining the accompanying relation among all the identity characteristics based on the accompanying relation between every two identity characteristics monitored by the monitoring terminal within a preset time period and sent by each monitoring terminal.

The embodiment of the present application further provides an apparatus for determining an accompanying relationship of identity characteristics, where the apparatus is used for monitoring a terminal, and the apparatus includes:

the first generation module is used for generating space-time track information of each identity characteristic according to each identity characteristic monitored in a preset time period and position information and time information when the identity characteristic is monitored;

a first determining module, configured to determine, for each two identity features, an accompanying support degree of the two identity features based on the spatio-temporal trajectory information;

a second determining module, configured to determine, based on the spatiotemporal trajectory information, spatiotemporal trajectory feature vectors of the two identity features if the accompanying support degree is greater than or equal to a first preset threshold;

a third determining module, configured to determine, based on the spatiotemporal trajectory feature vector, accompanying confidence levels of the two identity features;

a fourth determining module, configured to determine that an accompanying relationship exists between the two identity features if the accompanying confidence of the two identity features is greater than or equal to a second preset threshold;

and the sending module is used for sending the adjoint relationship between every two identity characteristics to the server so that the server determines the adjoint relationship between all the identity characteristics according to the adjoint relationship sent by each monitoring terminal.

In some possible embodiments, the apparatus further comprises:

and the second generation module is used for generating spatiotemporal trajectory index information of each identity characteristic based on the spatiotemporal trajectory information.

In some possible embodiments, the first determining module is specifically configured to:

In some possible embodiments, the second determining module is specifically configured to:

and determining space-time track characteristic vectors of the two identity characteristics based on the space-time track index information.

In a possible implementation manner, the third determining module is specifically configured to:

The embodiment of the present application further provides another apparatus for determining an accompanying relationship of identity features, where the apparatus is used for a server, and the apparatus includes:

the receiving module is used for receiving the adjoint relationship between every two identity characteristics monitored by each monitoring terminal within a preset time period, which is sent by each monitoring terminal;

and the accompanying relation determining module is used for determining the accompanying relation among all the identity characteristics based on the accompanying relation between every two identity characteristics which is sent by each monitoring terminal and monitored by the monitoring terminal in a preset time period.

An embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions being executable by the processor to perform the steps of the identity signature companion relationship determination method as described above.

Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the identity characteristic accompanying relationship determination method as described above.

According to the method and the device for determining the companion relationship of the identity characteristics, the time-space track information of each identity characteristic is generated according to each identity characteristic monitored in the preset time period and the position information and the time information when the identity characteristic is monitored; for every two identity features, determining the accompanying support degree of the two identity features based on the space-time trajectory information; if the accompanying support degree is greater than or equal to a first preset threshold value, determining space-time trajectory feature vectors of the two identity features based on the space-time trajectory information; determining the accompanying confidence degrees of the two identity features based on the space-time trajectory feature vector; if the accompanying confidence degrees of the two identity features are larger than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity features; and sending the adjoint relationship between every two identity characteristics to a server so that the server determines the adjoint relationship between all the identity characteristics according to the adjoint relationship sent by each monitoring terminal. Compared with the prior art, the method and the device have the advantages that various types of monitoring information can be collected on one monitoring terminal, the monitoring information is processed on the monitoring terminal, the preliminary accompanying relation is generated, and then the accompanying relation is sent to the server, so that the processing pressure of the server can be reduced, and the processing efficiency of accompanying analysis is improved.

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

Fig. 1 is a flowchart illustrating a method for determining an accompanying relationship of identity features according to an embodiment of the present application;

FIG. 2 is a flow chart of another method for determining an accompanying relationship of identity characteristics according to an embodiment of the present application;

FIG. 3 is a flowchart illustrating a method for determining an accompanying relationship of identity features according to an embodiment of the present application;

fig. 4 is a flowchart of an identity characteristic accompanying relationship determining method provided in an embodiment of the present application;

fig. 5 is a schematic structural diagram illustrating an apparatus for determining an accompanying relationship of identity features according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of another identity characteristic accompanying relationship determination apparatus provided in an embodiment of the present application;

fig. 7 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that one skilled in the art can obtain without inventive effort based on the embodiments of the present application falls within the scope of protection of the present application.

According to researches, the conventional accompanying relationship determining method generally collects original monitoring data transmitted by different monitoring devices, and performs statistics and analysis on various monitoring data according to preset rules to determine the accompanying relationship between different devices or people. However, in practical applications, the volume of the trace data is usually very large, so that the calculation amount of the trace data is much larger than the acceptable range, the server is difficult to bear a large amount of load in a short time, the risk of downtime and the like is easily caused, and the calculation efficiency is very low.

Based on this, the embodiment of the application provides a method for determining an association relationship of identity features, which can reduce the processing pressure of a server and improve the processing efficiency of association analysis.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for determining an association relationship of identity features according to an embodiment of the present disclosure. As shown in fig. 1, the method for determining an accompanying relationship of identity features provided in the embodiment of the present application is applied to a monitoring terminal, where the monitoring terminal is capable of monitoring multiple identity features, and the method includes:

s101, generating space-time track information of each identity characteristic according to each identity characteristic monitored in a preset time period and position information and time information when the identity characteristic is monitored.

In this step, the time-space trajectory information of each identity feature may be generated according to each identity feature monitored by the monitoring terminal, and the position information and the time information when the identity feature is monitored.

Here, each monitoring terminal may be fixed-position configured or movable, and when the monitoring terminal is fixed-position configured, in one monitoring terminal, the position information of each monitoring record is the same, but the monitored time information and the monitored identity characteristic may be different. For example, in a day, the identity characteristic a may be monitored several times, and the time monitored each time is different, and meanwhile, the identity characteristic B may be monitored several times, and the monitored time may be the same as part or all of the time of the identity characteristic a.

In the step, the identity characteristics monitored each time, and the corresponding time information and position information can be arranged into a piece of space-time trajectory information, and the space-time trajectory information of each identity characteristic can be composed of a plurality of pieces of space-time trajectory information at different times.

Here, the identity characteristic may be an entity ID, such as a human face characteristic, a MAC number of the mobile device, an international mobile subscriber identity, a license plate number of the vehicle, a virtual account number, and the like. Each monitoring terminal can monitor the identity characteristics, when the identity characteristics are monitored by the monitoring terminals, the monitoring terminals can record the monitored identity characteristics and record the time and the place for monitoring the identity characteristics, namely the time information and the position information are combined to form space-time track information.

Here, unified time service can be carried out on each monitoring terminal, the acquisition time can be set in a unified mode, and a plurality of monitoring terminals can carry out acquisition and processing synchronously.

S102, determining the accompanying support degree of each two identity features based on the space-time trajectory information.

In this step, it is noted that an accompanying relationship may exist between different identity features, and this accompanying relationship may be applied to a variety of scenarios, for example, a MAC number and an IMSI number of a mobile phone, and if the two numbers are simultaneously monitored by one monitoring terminal each time, the accompanying relationship may be considered to exist between the two numbers, and the two numbers may move together with a person belonging to one mobile phone or a person to which the two numbers belong, and may be a partner. By calculating the accompanying relationship, the relationship between the two can be determined, and the method can play a significant role in various application scenes, for example, in a public security scene, help can be provided for case clue analysis, and the account with the relationship can be found.

In this step, the total number of times of monitoring each identity feature may be determined from monitoring records in a preset time period of the monitoring terminal, and then the total number of times of monitoring other identity features is determined respectively, when the identity feature is monitored, the total number of times of monitoring other identity features is determined simultaneously, and the ratio of the total number of monitoring other identity features to the total number of monitoring the identity feature is the accompanying support degree between the two identity features.

S103, if the accompanying support degree is greater than or equal to a first preset threshold value, determining space-time trajectory feature vectors of the two identity features based on the space-time trajectory information.

Wherein the accompanying support degree represents the possibility that an accompanying relation exists between the two identity characteristics within a preset time period.

Specifically, if the accompanying support degree is low, although the two identity features are in a situation of being occurred at the same time, the probability is extremely low, and in this case, the possibility that the two identity features are in a simultaneous occurrence as an accidental situation is relatively high, it can be considered that the accompanying relationship does not exist between the two identity features.

And S104, determining the accompanying confidence degrees of the two identity characteristics based on the space-time trajectory characteristic vector.

In this step, the historical monitoring records stored in the monitoring terminal may be referred to, and the historical monitoring records belong to other preset time periods, for example, if the preset time period is within 6 months and 10 days, the historical monitoring records of a plurality of time periods before or after 6 months and 10 days may be obtained, the space-time trajectory characteristics of two identity characteristics in the plurality of other preset time periods may be determined, the space-time trajectory characteristics of the two identity characteristics in the other preset time periods may be compared, the similarity of the space-time trajectory characteristics may be determined, and the ratio of the number of similarity greater than the preset value to the total number of the other preset time periods may be used as the accompanying confidence.

S105, if the accompanying confidence degrees of the two identity characteristics are larger than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity characteristics.

The accompanying confidence degree represents the credibility of the accompanying relationship existing between the two identity features as a whole, and if the accompanying relationship also exists between the two identity features in a plurality of other preset time periods, the accompanying relationship also exists between the two identity features as a whole in a large probability.

S106, sending the adjoint relationship between every two identity characteristics to a server, so that the server determines the adjoint relationship between all the identity characteristics according to the adjoint relationship sent by each monitoring terminal.

In the step, after the companion relationship between every two identity features is determined, the companion relationship can be summarized and sent to the server, and the server determines the companion relationship between all the identity features according to the companion relationship sent by each monitoring terminal and determines the optimal companion relationship pairing result between different identity features.

In this way, the accompanying relationships among the identity features with high consumption calculation capacity are determined in advance on each monitoring terminal, and then the server performs summary analysis with low consumption, so that calculation tasks can be distributed to the monitoring terminals, the load of the server is further reduced, and the efficiency of determining the accompanying relationships is improved.

In one possible embodiment, after generating the spatiotemporal trajectory information for each identity feature, the method further comprises:

In the step, the space-time trajectory information can be standardized, then the fragmentation processing is carried out, and the index is established, so that the subsequent use is facilitated.

Specifically, the normalized data may be data in the form of the following tables 1 and 2. Please refer to tables 1 and 2, table 1 is standardized Wifi fence data, and table 2 is standardized electronic fence data.

Collection ID	Time	Monitoring terminal ID
			B5:C2:86:84:MX:66	2034-12-1709:31:28	05
DA:A5:11:19:AC:10	2034-12-1809:31:28	05

TABLE 1

Collection ID	Time	Monitoring terminal ID
			6546219847621494	2034-12-18 09:31:28	05
4652265161651655	2034-12-18 02:31:28	05

TABLE 2

Table 1 includes the monitored MAC address, the time for monitoring the MAC address, and the number of the monitoring terminal for monitoring the MAC address; table 2 includes the monitored IMSI address, the time when the IMSI address is monitored, and the number of the monitoring terminal that monitors the IMSI address.

In the step, after the spatiotemporal trajectory information of a plurality of identity characteristics is standardized, the spatiotemporal trajectory data can be stored in a partitioned manner according to the ID and time of the monitoring terminal, the ID of the monitoring terminal is used as a first-level directory, and then the spatiotemporal trajectory information is subjected to fragmentation processing according to a preset time period.

Specifically, the data can be equally divided by hours, and the repeated data is removed from the data in each hour, so that the number of the data can be greatly reduced.

It is noted that the hourly storage may be modified, such as being adjustable to minutes or seconds. By this transformation, a track can be made to pass through the path of such a fragment to find specific data. The effect of the specific slicing can be shown in table 3 and table 4 below.

Referring to tables 3 and 4, table 3 shows normalized data before slicing, and table 4 shows data after slicing.

Collection ID	Time of acquisition	Monitoring terminal ID	Collection ID type
				13	2034-12-17 06:31:28	05	MAC
13	2034-12-17 09:31:28	05	IMSI

TABLE 3

TABLE 4

As shown in table 3 and table 4, the collection ID is a number corresponding to the authentication identifier, the monitoring terminal ID is a number corresponding to the detection terminal that monitors the identity characteristic, and the monitoring terminal ID/time slice is a character string generated according to the monitoring terminal ID and the collection time.

In the step, after the fragmentation operation is carried out, a spatiotemporal index corresponding to spatiotemporal trajectory information can be established, spatiotemporal positions corresponding to each identity feature can be converted into spatiotemporal trajectory storage fragments, and then the information is compressed and stored in a file, so that spatiotemporal position points of specific identity features can be quickly retrieved. The form of the index may be as shown in table 5 below.

Referring to table 5, table 5 is an index list generated in this step, and as shown in table 5, each identity feature is sorted and sorted according to location fragmentation and time fragmentation.

TABLE 5

In one possible embodiment, the determining the accompanying support degree of the two identity features based on the spatiotemporal trajectory information comprises:

if so, determining that at least a preset number of moments exist in the preset time period from a plurality of other monitoring devices, and simultaneously monitoring the number of the other monitoring devices with the two identity characteristics;

In one possible embodiment, the determining the spatiotemporal trajectory feature vector of the two identity features based on the spatiotemporal trajectory information includes:

In the step, after the index is constructed, the specific space-time characteristics can be extracted through the space-time index.

For example, it may be defined that an identity is detected at a time to represent a 1 and not detected to represent a 0. Thereby representing the space-time trajectory data as a space-time feature vector for each identity feature, the feature vector representing a space-time feature space. Specifically, the extraction of the feature vector can be shown in table 6 below.

Referring to table 6, table 6 is a feature vector table obtained by extracting feature vectors. As shown in table 6, the number corresponding to the identity feature is included, and whether the identity feature appears under each place and time slice.

Collection ID	05/2034121706	05/2034121707	05/2034121714	05/2034121806
					13	1	0	1	1

TABLE 6

The feature vector in table 6 is expressed in the form of (001 10 1). Thus, the feature vectors can be calculated, and the similarity can be easily compared.

In one possible embodiment, the determining the accompanying confidence degrees of the two identity features based on the spatiotemporal trajectory feature vector includes:

According to the method for determining the companion relationship of the identity characteristics, the time-space track information of each identity characteristic is generated according to each identity characteristic monitored in a preset time period and the position information and the time information when the identity characteristic is monitored; for every two identity characteristics, determining the accompanying support degree of the two identity characteristics based on the space-time trajectory information; if the accompanying support degree is greater than or equal to a first preset threshold value, determining space-time trajectory feature vectors of the two identity features based on the space-time trajectory information; determining the accompanying confidence degrees of the two identity features based on the space-time trajectory feature vector; if the accompanying confidence degrees of the two identity features are larger than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity features; and sending the adjoint relationship between every two identity characteristics to a server so that the server determines the adjoint relationship between all the identity characteristics according to the adjoint relationship sent by each monitoring terminal. Compared with the prior art, the method and the device have the advantages that various types of monitoring information can be collected on one monitoring terminal, the monitoring information is processed on the monitoring terminal, the preliminary accompanying relation is generated, and then the accompanying relation is sent to the server, so that the processing pressure of the server can be reduced, and the processing efficiency of accompanying analysis is improved.

Referring to fig. 2, fig. 2 is a flowchart of a method for determining an association relationship of identity features according to another embodiment of the present application. As shown in fig. 2, the method for determining an accompanying relationship of identity features provided in the embodiment of the present application is applied to a server, and includes:

s201, receiving an accompanying relation between every two identity characteristics monitored by each monitoring terminal in a preset time period, wherein the accompanying relation is sent by each monitoring terminal.

For a detailed explanation, please refer to the above embodiments, which are not described in detail herein.

S202, determining the accompanying relation among all the identity characteristics based on the accompanying relation between every two identity characteristics monitored by the monitoring terminal in a preset time period and sent by each monitoring terminal.

In this step, after acquiring the association relationship sent by each detection terminal, the server may perform analysis and statistics on the association relationships, and associate the association relationships with the same identity feature to obtain the association relationships among all identity features.

Referring to fig. 3, fig. 3 is a flowchart of an association relationship determining method for identity characteristics according to another embodiment of the present application. As shown in fig. 3, the method for determining an accompanying relationship of identity features provided in the embodiment of the present application is applied to a monitoring terminal, and includes:

11 And reading the monitoring data of the monitoring terminal in one day.

12 Converting the detection data into a space-time format, and constructing a space-time data index of the acquired identity characteristics.

13 Etc.), determine the degree of accompanying support between each two identity features.

In this step, there may be a possibility that one identity feature corresponds to a plurality of identity features in a day.

14 And when the accompanying support degree satisfies the condition, performing spatio-temporal feature extraction on the identity features satisfying the condition.

15 Based on machine learning, determining the accompanying confidence of the two identity features in multiple days, and determining that the two identity features have an accompanying relationship when the accompanying confidence meets the requirement.

16 For other dates), repeating the process until the accuracy of the statistical result meets the preset probability.

17 The complete identity accompanying relationship is sent to the server.

Referring to fig. 4, fig. 4 is a flowchart of a method for determining an association relationship of identity features according to another embodiment of the present application. As shown in fig. 4, the method for determining an accompanying relationship of identity features provided in the embodiment of the present application is applied to a server, and includes:

21 And reading the data of the identity characteristic space-time accompanying relationship uploaded by each sensing device (monitoring terminal).

22 And the ratio of the association is counted.

23 And determining the optimal companion relationship pairing result among different identity characteristics according to the proportion of the companion relationship.

According to the method and the device for determining the companion relationship of the identity characteristics, the space-time trajectory information of each identity characteristic is generated according to each identity characteristic monitored in a preset time period and the position information and the time information when the identity characteristic is monitored; for every two identity features, determining the accompanying support degree of the two identity features based on the space-time trajectory information; if the accompanying support degree is greater than or equal to a first preset threshold value, determining space-time trajectory feature vectors of the two identity features based on the space-time trajectory information; determining the accompanying confidence degrees of the two identity features based on the space-time trajectory feature vector; if the accompanying confidence degrees of the two identity features are larger than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity features; and sending the adjoint relationship between every two identity characteristics to a server so that the server determines the adjoint relationship between all the identity characteristics according to the adjoint relationship sent by each monitoring terminal. Compared with the prior art, the method and the device have the advantages that various types of monitoring information can be collected on one monitoring terminal, the monitoring information is processed on the monitoring terminal, the preliminary accompanying relation is generated, and then the accompanying relation is sent to the server, so that the processing pressure of the server can be reduced, and the processing efficiency of accompanying analysis is improved.

Referring to fig. 5 and 6, fig. 5 is a schematic structural diagram of an association relationship determining apparatus for identity characteristics according to an embodiment of the present application, and fig. 6 is a schematic structural diagram of another association relationship determining apparatus for identity characteristics according to an embodiment of the present application. As shown in fig. 5, the identity feature accompanying relationship determination apparatus 500 includes:

a first generating module 510, configured to generate time-space trajectory information of each identity feature according to each identity feature monitored in a preset time period and position information and time information when the identity feature is monitored;

a first determining module 520, configured to determine, for each two identity features, an accompanying support degree of the two identity features based on the spatio-temporal trajectory information;

a second determining module 530, configured to determine a spatiotemporal trajectory feature vector of the two identity features based on the spatiotemporal trajectory information if the degree of accompanying support is greater than or equal to a first preset threshold;

a third determining module 540, configured to determine an accompanying confidence of the two identity features based on the spatiotemporal trajectory feature vector;

a fourth determining module 550, configured to determine that an accompanying relationship exists between the two identity features if the accompanying confidence of the two identity features is greater than or equal to a second preset threshold;

the sending module 560 is configured to send an accompanying relationship between every two identity features to the server, so that the server determines an accompanying relationship between all the identity features according to the accompanying relationship sent by each monitoring terminal.

In some possible embodiments, the identity feature accompanying relationship determination apparatus 500 further includes:

a second generating module 570, configured to generate spatiotemporal trajectory index information for each identity feature based on the spatiotemporal trajectory information.

In some possible embodiments, the first determining module 520 is specifically configured to:

determining whether at least a preset number of moments exist in the preset time period or not based on the time-space trajectory index information, and simultaneously monitoring the two identity characteristics;

In some possible embodiments, the second determining module 530 is specifically configured to:

In a possible implementation, the third determining module 540 is specifically configured to:

As shown in fig. 6, an embodiment of the present application further provides another apparatus for determining a companion relationship of an identity feature, where the apparatus 600 for determining a companion relationship of an identity feature includes:

a receiving module 610, configured to receive an accompanying relationship, sent by each monitoring terminal, between every two identity features monitored by the monitoring terminal within a preset time period;

an accompanying relationship determining module 620, configured to determine an accompanying relationship between all identity features based on an accompanying relationship between every two identity features that are sent by each monitoring terminal and monitored by the monitoring terminal within a preset time period.

The device for determining the accompanying relationship of the identity characteristics, provided by the embodiment of the application, generates the time-space trajectory information of each identity characteristic according to each identity characteristic monitored in a preset time period and the position information and the time information when the identity characteristic is monitored; for every two identity features, determining the accompanying support degree of the two identity features based on the space-time trajectory information; if the accompanying support degree is greater than or equal to a first preset threshold value, determining space-time trajectory feature vectors of the two identity features based on the space-time trajectory information; determining the accompanying confidence degrees of the two identity features based on the space-time trajectory feature vector; if the accompanying confidence degrees of the two identity characteristics are greater than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity characteristics; and sending the adjoint relationship between every two identity characteristics to a server so that the server determines the adjoint relationship between all the identity characteristics according to the adjoint relationship sent by each monitoring terminal. Compared with the prior art, the method and the device have the advantages that various types of monitoring information can be collected on one monitoring terminal, the monitoring information is processed on the monitoring terminal, the preliminary accompanying relation is generated, and then the accompanying relation is sent to the server, so that the processing pressure of the server can be reduced, and the processing efficiency of accompanying analysis is improved.

Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 7, the electronic device 700 includes a processor 710, a memory 720, and a bus 730.

The memory 720 stores machine-readable instructions executable by the processor 710, when the electronic device 700 runs, the processor 710 communicates with the memory 720 through the bus 730, and when the machine-readable instructions are executed by the processor 710, the steps of the method for determining an association relationship of identity features in the method embodiments shown in fig. 1, fig. 2, fig. 3, and fig. 4 may be executed.

An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program may perform the steps of the method for determining an association relationship between identity features in the method embodiments shown in fig. 1, fig. 2, fig. 3, and fig. 4.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in software functional units and sold or used as a stand-alone product, may be stored in a non-transitory computer-readable storage medium executable by a processor. Based on such understanding, the technical solutions of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present application and are intended to be covered by the appended claims. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for determining an adjoint relationship of identity characteristics is applied to a monitoring terminal, and the monitoring terminal can monitor a plurality of identity characteristics, and the method comprises the following steps:

generating space-time trajectory information of each identity characteristic according to each identity characteristic monitored in a preset time period and position information and time information when the identity characteristic is monitored;

generating spatiotemporal trajectory index information of each identity characteristic based on the spatiotemporal trajectory information;

determining whether at least a preset number of moments exist in the preset time period or not based on the time-space trajectory index information, and simultaneously monitoring two identity characteristics;

taking the determined number of other monitoring devices as the accompanying support degree of the two identity characteristics;

2. The method of claim 1, wherein determining the spatiotemporal trajectory feature vector of the two identity features based on the spatiotemporal trajectory information comprises:

3. The method of claim 1, wherein determining the concomitant confidence of the two identity features based on the spatiotemporal trajectory feature vector comprises:

4. An identity characteristic companion relationship determination method applied to a server, the method comprising:

determining an accompanying relation between all identity characteristics based on an accompanying relation between every two identity characteristics monitored by each monitoring terminal within a preset time period, which is sent by each monitoring terminal;

determining an accompanying relationship between each two identity characteristics according to the following steps:

and if the accompanying confidence degrees of the two identity characteristics are greater than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity characteristics.

5. An apparatus for determining an incidental relationship of identity characteristics, the apparatus being used for monitoring a terminal, comprising:

the first generation module is used for generating time-space track information of each identity characteristic according to each identity characteristic monitored in a preset time period and position information and time information when the identity characteristic is monitored;

the second generation module is used for generating spatiotemporal trajectory index information of each identity characteristic based on the spatiotemporal trajectory information;

the first determination module is used for determining the accompanying support degree of each two identity features based on the space-time trajectory information;

the first determining module is specifically configured to:

the fourth determining module is used for determining that an accompanying relation exists between the two identity features if the accompanying confidence degrees of the two identity features are larger than or equal to a second preset threshold;

6. An apparatus for determining an accompanying relationship of identity features, the apparatus being used for a server, the apparatus comprising:

the accompanying relation determining module is used for determining the accompanying relation among all the identity characteristics based on the accompanying relation between every two identity characteristics which is sent by each monitoring terminal and monitored by the monitoring terminal in a preset time period;

the receiving module is specifically configured to receive an accompanying relationship between every two identity features determined by each detection terminal according to the following steps:

for every two identity characteristics, determining the accompanying support degree of the two identity characteristics based on the space-time trajectory information;

and if the accompanying confidence degrees of the two identity features are larger than or equal to a second preset threshold value, determining that an accompanying relation exists between the two identity features.

7. An electronic device, comprising: processor, storage medium and bus, the storage medium storing machine readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is running, the processor executing the machine readable instructions to perform the steps of the method for determining the companion relationship of identity features according to any of claims 1 to 4.

8. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, performs the steps of the method for determining an associative relation of identity features according to any one of claims 1 to 4.