WO2017117879A1

WO2017117879A1 - Personal identification processing method, apparatus and system

Info

Publication number: WO2017117879A1
Application number: PCT/CN2016/078901
Authority: WO
Inventors: 刘少麟
Original assignee: 中兴通讯股份有限公司
Priority date: 2016-01-08
Filing date: 2016-04-08
Publication date: 2017-07-13
Also published as: CN106960172A

Abstract

A personal identification processing method, apparatus and system. The method comprises: acquiring monitoring data within a pre-set area range (S102); matching pre-stored feature information about a person to be identified with the monitoring data (S104); and in the case of consistent matching, determining that the monitoring data comprises data of the person to be identified (S106). By means of the above-mentioned technical solution, the problems, that timeliness of tracking a person is poor and tracked people cannot be found in time, are effectively solved, and the searching efficiency is improved, and a search result is more accurate and timely.

Description

Person identification processing method, device and system

Technical field

The present invention relates to the field of communications, and in particular to a method, device and system for personnel identification processing.

Background technique

The existing methods for reporting missing persons or tracking criminals are limited, and they are basically performed by means of manual search and video recording, or by means of rewards and reports. The artificial method is characterized by high efficiency and low cost; the timeliness is poor. After the information is retrieved or the report is received, the problem has been passed for a period of time, and the problem is that the validity of the data is deteriorated.

In view of the related technology, the timeliness of the tracking person is poor, and the problem of tracking the person cannot be effectively found in time. There is no effective solution at present.

Summary of the invention

The invention provides a method, a device and a system for processing a person identification, so as to at least solve the problem that the tracking person has poor timeliness in the related art, and cannot effectively find the tracking person in time.

According to an embodiment of the present invention, a person identification processing method is provided, including:

Obtain monitoring data within a preset area;

Matching the pre-stored feature information of the person to be identified with the monitoring data;

In case the matching is consistent, it is determined that the monitoring data includes data of the person to be identified.

In this embodiment, the feature information includes at least one of the following: face feature information, voiceprint information, and identity feature information.

In this embodiment, the identity feature information includes: a phone number of the person to be identified, an identity card information of the person to be identified, a bank card information of the person to be identified, and a name information of the person to be identified .

In this embodiment, after determining that the monitoring data includes the data of the to-be-identified person, the method further includes at least one of the following:

Confirming current location information of the to-be-identified person according to location information in the monitoring data;

Sending alarm information, where the alarm information carries data of the to-be-identified person in the monitoring data;

Displaying data of the person to be identified in the monitoring data.

In this embodiment, the pre-stored feature information of the person to be identified further includes: Characteristic information of the joint personnel.

In this embodiment, the monitoring data includes at least one of the following: video monitoring data, voice monitoring data, card recording data of a bank card, communication data of a telephone network, and communication data of an internet network.

In this embodiment, the manner of acquiring the monitoring data in the preset area includes: acquiring the monitoring data by a message bus of a cluster mode or a message bus of a high-throughput distributed publishing and subscription message system kafka.

In this embodiment, matching the feature information of the to-be-identified person to the monitoring data in advance comprises: using different types of data in the feature information and the monitoring data in a distributed streaming processing framework. match.

According to another embodiment of the present invention, a person identification processing apparatus is provided, including:

Obtaining a module, configured to acquire monitoring data within a preset area;

a matching module, configured to match pre-stored feature information of the person to be identified with the monitoring data;

The determining module is configured to determine, in the case that the matching is consistent, the data of the to-be-identified person included in the monitoring data.

In this embodiment, the device further includes:

a positioning module, configured to confirm current location information of the to-be-identified person according to location information in the monitoring data;

The alarm module is configured to send alarm information, where the alarm information carries data of the to-be-identified person in the monitoring data;

a display module configured to display data of the person to be identified in the monitoring data.

In this embodiment, the feature information of the pre-stored person to be identified further includes: feature information of the associated person of the person to be identified.

According to another embodiment of the present invention, a person identification processing system is provided, including: tracking a human-computer interaction layer, Tracking acquisition layer, tracking processing layer;

The tracking human-machine interaction layer acquires feature information of the person to be identified;

The tracking acquisition layer acquires monitoring data within a preset area;

The tracking processing layer matches the feature information of the person to be identified with the monitoring data;

The tracking processing layer determines, in the case that the matching is consistent, the data of the to-be-identified person included in the monitoring data.

According to the present invention, the monitoring data in the preset area is obtained; the characteristic information of the pre-stored person to be identified is matched with the monitoring data; and if the matching is consistent, determining that the monitoring data includes the person to be identified The data solves the poor timeliness of the tracking person, can not effectively find the tracking person's problem in time, improves the search efficiency, and the search result is more accurate and timely.

DRAWINGS

The drawings described herein are intended to provide a further understanding of the invention, and are intended to be a part of the invention. In the drawing:

1 is a flowchart of a person identification processing method according to an embodiment of the present invention;

2 is a block diagram 1 of a structure of a person identification processing apparatus according to an embodiment of the present invention;

3 is a block diagram 2 of a structure of a person identification processing apparatus according to an embodiment of the present invention;

4 is a diagram showing an architecture of a person tracking a person in real time through big data according to a preferred embodiment of the present invention;

Figure 5 is a flow diagram of system processing in accordance with a preferred embodiment of the present invention.

detailed description

The invention will be described in detail below with reference to the drawings in conjunction with the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments may be combined with each other without conflict.

It is to be understood that the terms "first", "second" and the like in the specification and claims of the present invention are used to distinguish similar objects, and are not necessarily used to describe a particular order or order.

A person identification processing method is provided in this embodiment. FIG. 1 is a flowchart of a person identification processing method according to an embodiment of the present invention. As shown in FIG. 1, the flow includes the following steps:

Step S102, acquiring monitoring data within a preset area;

Step S104, matching pre-stored feature information of the person to be identified with the monitoring data;

In step S106, if the matching is consistent, it is determined that the monitoring data includes the data of the person to be identified.

Obtaining the monitoring data in the preset area by using the foregoing steps, and matching the pre-stored feature information of the to-be-identified person with the monitoring data; if the matching is consistent, determining that the monitoring data includes the to-be-identified person The data solves the poor timeliness of the tracking person, can not effectively find the tracking person's problem in time, improves the search efficiency, and the search result is more accurate and timely.

In this embodiment, the feature information includes at least one of the following: face feature information, voiceprint information, and identity feature information. The voiceprint information mainly collects data from the mobile phone voice call, the network voice call, and the network video call of the person to be identified.

In this embodiment, the identity feature information includes: a phone number of the person to be identified, identity card information of the person to be identified, bank card information of the person to be identified, and name information of the person to be identified. The phone number includes mobile phone contact, WeChat contact; ID card information includes passport, student ID, military card, etc., and even the bus card; the name information of the person to be identified is not only the name, the nickname of the relatives and friends on weekdays, the net name, etc.

Determining the current location information of the to-be-identified person according to the location information in the monitoring data, and determining the person to be identified according to the unique identifier of the monitoring location carried by the corresponding monitoring data after determining the person to be identified;

Sending the alarm information, where the alarm information carries the data of the person to be identified in the monitoring data, and sends an alarm message to prompt the attendant to pay attention to the person to be identified;

The data of the to-be-identified person in the monitoring data is displayed, and the display data includes a corresponding matching picture, voice phone content, and the like.

In this embodiment, the pre-stored feature information of the person to be identified further includes: feature information of the associated person of the person to be identified, and inputting information of a related person who is acquainted with the person to be identified is beneficial to find more The information of the person to be identified promptly identifies the person to be identified.

In this embodiment, the monitoring data includes at least one of the following: video monitoring data, voice monitoring data, card recording data of a bank card, communication data of a telephone network, communication data of an internet network, and fully utilizing big data in related technologies. The power to collect more types of data and quickly filter out useful information.

In this embodiment, the manner of acquiring the monitoring data in the preset area includes: acquiring the monitoring data by using a message bus of a cluster mode or a message bus of a high-throughput distributed publishing and subscription message system kafka.

In this embodiment, matching the pre-stored feature information of the to-be-identified person with the monitoring data includes: matching the feature information with different types of data in the monitoring data by using a distributed streaming processing framework. The distributed streaming framework can be Apache Storm, or Spark Streaming, but is not limited to the above two.

In this embodiment, a person identification processing device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and has not been described again. As used below, the term "module" may implement a combination of software and/or hardware of a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware, is also possible and contemplated.

2 is a block diagram of a structure of a person identification processing apparatus according to an embodiment of the present invention. As shown in FIG. 2, the apparatus includes:

The obtaining module 22 is configured to acquire monitoring data within a preset area;

The matching module 24 is connected to the obtaining module 22 and configured to match the pre-stored feature information of the person to be identified with the monitoring data.

The determining module 26 is connected to the matching module 24, and is configured to determine that the monitoring data includes the data of the person to be identified when the matching is consistent.

Through the above steps, the obtaining module 22 acquires the monitoring data in the preset area, and the matching module 24 matches the pre-stored feature information of the person to be identified with the monitoring data, and the determining module 26 determines the matching if the matching is consistent. The monitoring data includes the data of the person to be identified, solves the timeliness of the tracking person, can not effectively find the tracking person in time, improves the search efficiency, and the search result is more accurate and timely.

FIG. 3 is a block diagram of a structure of a person identification processing apparatus according to an embodiment of the present invention. As shown in FIG. 3, the apparatus includes, in addition to all the modules shown in FIG.

The positioning module 32 is connected to the determining module 26, and configured to confirm the current location information of the to-be-identified person according to the location information in the monitoring data;

The alarm module 34 is connected to the determining module 26 and configured to send the alarm information, where the alarm information carries the data of the person to be identified in the monitoring data;

The display module 36 is connected to the determining module 26 and configured to display data of the person to be identified in the monitoring data.

An embodiment of the present invention further provides a personnel identification processing system, including: a tracking human-computer interaction layer, a tracking acquisition layer, and a tracking processing layer;

The tracking human-computer interaction layer acquires feature information of the person to be identified;

The tracking acquisition layer acquires monitoring data within a preset area;

The tracking processing layer determines that the monitoring data includes data of the person to be identified when the matching is consistent.

It should be noted that each of the above modules may be implemented by software or hardware. For the latter, the foregoing may be implemented by, but not limited to, the foregoing modules are all located in the same processor; or, the modules are located in multiple In the processor.

The invention will now be described in detail in conjunction with the preferred embodiments and embodiments.

The preferred embodiment of the present invention relies on face recognition, speech recognition, and other data sources, combined with big data, to realize real-time automated processes such as tracking and warning of criminals and missing persons.

The system described in the preferred embodiment of the invention comprises the following functional modules:

Information entry module. The information of the person being followed is entered into the system, the face recognition is the face photo of the tracked person, the voice recognition is the name, nickname, nick number of the tracked person, and the voiceprint identifies the family and society of the tracked person. The fixed line of the person in charge, the mobile phone number, and the voiceprint information of the person being tracked.

Real-time face, voice matching module (corresponding to the matching module 24 of the above embodiment). From the information object input by the information input module, combined with the data source from the information collection module, real-time matching of face and voiceprint, or voice recognition, when there is matching data, submit corresponding original data and alarm Give the alarm and information display module module.

The information collection module (corresponding to the acquisition module 22 of the above embodiment) is mainly the source of data input. For face recognition, mainly from banks, supermarkets, communities, traffic intersections, surveillance video, from the video in a certain period of time, such as 5 seconds, the cycle is to obtain a relatively reasonable but effective amount of data, for Each monitoring needs to have a unique tag. When the data is reported, it also carries its own tag. For voice recognition, it is mainly a voice offload of a mobile phone or a fixed phone. When reporting information, it needs to carry the calling and called number. The relevant data is submitted together to the module real-time face and voice matching module.

The alarm and information display module (corresponding to the alarm module 34 and the display module 36 of the above embodiment). When the real-time face and voice matching module detects the qualified photo and the telephone voice report, the information is displayed on the interface in a prominent manner, and includes related pictures or telephone voices, and the video or voice associated with the alarm. Source data and other data source information.

The information input module, the alarm and the information display module are human-computer interaction modules, and the real-time face, voice matching module and information collection module are real-time internal modules.

FIG. 4 is a schematic diagram of a system for tracking people in real time through big data according to a preferred embodiment of the present invention. The relationship between modules in the architecture is as shown in FIG. 4 .

The system is generally divided into three layers: a tracking acquisition layer (corresponding to the acquisition module 22 in the above embodiment), a tracking processing layer (corresponding to the matching module 24 in the above embodiment), and a tracking human-computer interaction layer (including the alarm in the above embodiment). Module 34), the tracking acquisition layer is to determine the collection of information required by the tracking user, mainly to complete the monitoring image reporting, the listening voice reporting and other reports such as WeChat, card recording; tracking processing layer is mainly used for reporting data and The information of the tracked person matches, mainly including face recognition and voiceprint recognition. Because of the large amount of data to be processed, it needs to be a distributed stream processing framework, which can be Apache Storm or Spark Streaming, but is not limited to the above two. The tracking human-computer interaction layer mainly completes the entry of the tracked person information and the alarm and display of the information related to the successful determination.

Figure 5 is a flow chart showing the system processing of the preferred embodiment of the present invention, as shown in Figure 5.

Step S501: the tracked person picture, voiceprint or card information is entered, and the system input module of the human-machine interface provides an operation interface. After the user uploads the picture, voiceprint or card information, the system input module is persistent and transmitted to the system. Matching module, the matching module loads the information of each user into the system, and is used to match one by one in the subsequent matching process;

Step S502: monitoring needs to deploy software, and performing periodic image acquisition on the video, such as acquiring 5 seconds in the video. The image of the interval is sent to the matching module through a real-time message. The method can be a message bus in a cluster mode, which can be kafka, but is not limited to such a message bus, avoiding single point of failure and bottlenecks, and reporting the monitoring at the same time. The unique identifier is used to lock the specific location information after the matching; the voice interception needs to listen to the related family relationship of the tracked person, the fixed line of the social relationship person or the mobile phone, and the relevant voice performs the window period acquisition, such as acquiring 5 seconds. After the speech of the segment time is reported, the report is also sent in the form of a real-time message. The method can be a message bus in a cluster mode, which can be kafka but not limited to such a message bus, to avoid single point of failure and bottleneck, and to report the report at the same time. The calling number and the called number information of the secondary call; the card monitoring is responsible for recording the bank card information of the tracked person and the card swipe location, and reporting the information to the matching module.

Step S503: After receiving the reported information, the matching module determines that it is picture information, card information or voice information.

Step S504: After the image information is determined, the face in the list of the person to be tracked is compared with the face. If the condition is not met, the current time ends. If the condition is met, the person, the monitoring indicator, and the report are reported. The picture information is transmitted to the alarm display module.

Step S505: After the voice information is determined, the voiceprint is compared with the voiceprint in the list of persons to be tracked. If there is no condition, the current end is ended. If the condition is met, the person and the calling number are reported. The called number and the reported voice information are transmitted to the alarm display module.

Step S506: After determining that the card information is used, the card is compared with the identity feature information in the to-be-tracked person list. If the condition is not met, the current time ends. If the condition is met, the person is reported. Card information, the card address is transmitted to the alarm display module.

Step S507: After receiving the prompt information, the alarm module manually performs personnel check, and provides a matching contact manner for the surrounding police force for the monitoring mode, and further can directly send an alarm to the surrounding system, and the voice can be based on the mobile phone master. Call the number to perform the triangulation of the base station, obtain the relevant location information, and provide the matching contact information of the surrounding police force. Further, the alarm can be directly sent to the surrounding system to perform the police force transfer in time.

The data acquired in step S502 is not persisted in consideration of personal privacy issues, and is uploaded to the human-computer interaction module only when the conditions are matched, thereby avoiding legal risks.

Further, when the amount of data is too large, the system can be composed of a plurality of components, for example, according to the city, the provincial information is provided by all the cities, and each city only needs to report the qualified data and enter the information in various places. Track people and sync to the province's system. That is to say, the system is built up in an administrative-like manner and expanded.

The present invention will be described in detail below in conjunction with specific application scenarios.

Scene 1: Using face recognition to locate lost children and the elderly

Because of the negligence of guardians, children often find lost, which has a great impact on the family. After the 110 alarm is often used, due to the existing search method, it will take a long time to find, and the psychological pressure on the parents is great. After using the system, after receiving the 110 alarm, the photos of the related lost children are entered into the system for automatic tracking. Since the loss of children generally occurs in the community, shopping malls, supermarkets, the system needs to access the above monitoring system, and at the same time To access the monitoring of the transportation department, in order to obtain the process of change of whereabouts.

The loss of the elderly has become an increasingly serious social problem. It is understood that the elderly who are lost are mostly elderly patients with Alzheimer's disease. There have been media analysis that the frequent loss of the elderly is not only related to their own diseases, but also the constant changes in urban construction and lifestyle, which is why they are easily lost. Moreover, there are more and more "empty-nest elderly people". They have single members and no one to take care of in their daily lives. It is difficult to know when they are lost. Due to the existing search methods, it will take a long time to find; at the same time, family members often have to spend a lot of energy, and after the founding, they must prevent this from happening again, and at the same time bring a lot of work to the police. According to the analysis of psychologists, a lost experience will also bring great trauma to the elderly. After using the system, after receiving the 110 alarm, the photos of the relevant lost old people are entered into the system for automatic tracking.

The specific implementation steps are as follows:

Step 11. Access the monitoring equipment of the community, the shopping mall, the supermarket, and the transportation department, and number the monitoring equipment at the same time;

Step 12: deploy the system at the 110 alarm station, and when receiving the loss of the child or the elderly, obtain a photo of the lost child or the old person, and enter it into the system for automatic tracking;

Step 13. Match the monitoring match with the lost child or old man picture timing (5 seconds). If there is no match, proceed to the next one; the match proceeds to process 4;

Step 14: When the matching module matches the monitoring of the lost child or the elderly, the related monitoring identifier and related pictures are reported to the alarm display module;

Step 15. The alarm display module displays after receiving the relevant pictures and information. The operator manually checks whether the child is lost or the elderly, and can also send the photo to the parent of the relevant personnel for confirmation, and at the same time coordinate the surrounding police force, because this Timeliness is important;

Step 16. Confirm that the police force is not lost or the elderly; if yes, the police force continues the search process.

Scenario 2: Using card records to locate criminals

The specific implementation steps are as follows:

Step 21: accessing the card of the bank ATM machine, accessing the POS machine of the shopping mall or the like;

Step 22: Entering the ID card information of the criminal in the system, and the system sends the ID information to the matching module;

Step 23: The ATM and POS machines that are accessed are reported to the collection module identity card information, and the location of the ATM or POS machine when each transaction is generated;

Step 24: After matching the identity card information of the criminal, the matching module reports the alarm to the display module, and carries the location information of the ATM and the POS;

Step 25: The alarm display module displays after receiving the relevant information, and performs peripheral police force scheduling according to the location information of the ATM or the POS.

Scene 3: Using voiceprint recognition and speech recognition to locate criminals

The tracking of criminals is now lacking effective methods, and the search is often based on the importance of police input. The way of rewarding often reflects the importance of the offender and a manifestation of limited means. The investment is big but the effect is not obvious is the problem that needs to be solved at this stage.

After using the system, the voiceprints of the relevant criminals are entered into the system for automatic tracking.

The specific implementation steps are as follows:

Step 31: Analyze the family relationship and social relationship of the offender, find out the information of the fixed person and the mobile phone of the relevant personnel, and collect the information;

Step 32: The collected number is entered into the system, and the voiceprint of the criminal is also recorded into the system, and the system automatically tracks the voice detection. This is an efficient but not comprehensive approach, if you want to fully listen to all voices;

Step 33: Performing a report on the listening voice timing (5 seconds), first performing voiceprint matching. If there is no match, proceed to the next one; the match proceeds to process 5;

Step 34, and then perform speech recognition, mainly according to the name, nickname, and other name of the criminal, if it is found in the voice to identify the relevant information, then proceeds to process 5;

Step 35: When the matching module matches the voiceprint of the criminal or identifies the corresponding person name, nickname, and other name, the voice of the related call, and the related calling and called number and the information thereof are reported to the alarm display module;

Step 36: The alarm display module displays after receiving the sound and the information, and the operator manually checks whether the criminal is tracked, and the number is used to locate the number, and the surrounding police force is coordinated;

Step 37: After confirming that the offender is not being tracked, withdraw the police force; if yes, the police force continues the search process.

Scene 4: Using face recognition to locate criminals

After using the system, the relevant criminal's photos are entered into the system for automatic tracking.

The specific implementation steps are as follows:

Step 41: access the monitoring equipment of the community, the shopping mall, the supermarket, and the transportation department, and number the monitoring equipment at the same time;

Step 42: Obtain a photo of the criminal, and enter it into the system for automatic tracking;

Step 43, matching the monitoring match with the offender picture timing (5 seconds). If there is no match, proceed to the next one; the match proceeds to process 4;

Step 44: When the matching module matches the monitoring of the criminal, the related monitoring identifier and related pictures are reported to the alarm display module.

Step 45: The alarm display module displays after receiving the relevant pictures and information, and the operator manually checks whether the criminal is a criminal, and simultaneously coordinates the surrounding police force, because timeliness is important at this time;

Step 46: After confirming that it is not a criminal, withdraw the police force; if yes, the police force continues the search process.

The preferred embodiment of the present invention performs a system for missing persons and criminals tracking through big data real-time face and speech recognition. Compared with the existing criminal tracking and missing population query, the system is first automated, and the accuracy is greatly improved. Efficiency, followed by real-time characteristics, is more time-efficient in both scenarios, and combined with the current big data technology to improve timeliness, making related information more timely and effective.

Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware, but in many cases, the former is A better implementation. Based on such understanding, the technical solution of the present invention, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, The optical disc includes a number of instructions for causing a terminal device (which may be a cell phone, a computer, a server, or a network device, etc.) to perform the methods described in various embodiments of the present invention.

Embodiments of the present invention also provide a storage medium. Optionally, in this embodiment, the foregoing storage medium may be configured to store program code for performing the steps of the foregoing embodiments:

Optionally, the storage medium is further arranged to store program code for performing the steps of the above-described embodiments:

Optionally, in this embodiment, the foregoing storage medium may include, but not limited to, a USB flash drive, a Read-Only Memory (ROM), a Random Access Memory (RAM), a mobile hard disk, and a magnetic memory. A variety of media that can store program code, such as a disc or a disc.

Optionally, in this embodiment, the processor performs the method steps of the foregoing embodiments according to the stored program code in the storage medium.

For example, the specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the optional embodiments, and details are not described herein again.

It will be apparent to those skilled in the art that the various modules or steps of the present invention described above can be implemented by a general-purpose computing device that can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they may be implemented by program code executable by the computing device such that they may be stored in the storage device by the computing device and, in some cases, may be different from the order herein. The steps shown or described are performed, or they are separately fabricated into individual integrated circuit modules, or a plurality of modules or steps thereof are fabricated as a single integrated circuit module. Thus, the invention is not limited to any specific combination of hardware and software.

The above description is only the preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes can be made to the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and scope of the present invention are intended to be included within the scope of the present invention.

Industrial applicability

According to the foregoing technical solution provided by the embodiment of the present invention, the monitoring data in the preset area is acquired; the feature information of the to-be-identified person stored in advance is matched with the monitoring data; and if the matching is consistent, the monitoring data is determined. The data of the person to be identified is included, the timeliness of the tracking person is solved, the problem of tracking the person cannot be effectively found in time, the search efficiency is improved, and the search result is more accurate and timely.

Claims

A method for personnel identification processing, comprising:

Obtain monitoring data within a preset area;

Matching the pre-stored feature information of the person to be identified with the monitoring data;

In case the matching is consistent, it is determined that the monitoring data includes data of the person to be identified.
The method of claim 1, wherein the feature information comprises at least one of:

Face feature information, voiceprint information, identity feature information.
The method of claim 2 wherein said identity characteristic information comprises:

The telephone number of the person to be identified, the identity card information of the person to be identified, the bank card information of the person to be identified, and the name information of the person to be identified.
The method according to claim 1, wherein after determining that the monitoring data includes the data of the person to be identified, at least one of the following is further included:

Confirming current location information of the to-be-identified person according to location information in the monitoring data;

Sending alarm information, where the alarm information carries data of the to-be-identified person in the monitoring data;

Displaying data of the person to be identified in the monitoring data.
The method according to claim 1, wherein the pre-stored feature information of the person to be identified further includes:

Characteristic information of the associated person of the person to be identified.
The method of claim 1 wherein said monitoring data comprises at least one of:

Video surveillance data, voice monitoring data, card recording data for bank cards, communication data for telephone networks, and communication data for Internet networks.
The method according to claim 1, wherein the manner of acquiring monitoring data within a preset area includes:

The monitoring data is obtained by a message bus of a cluster mode or a message bus of a high-throughput distributed publish-subscribe messaging system kafka.
The method according to any one of claims 1 to 7, wherein matching pre-stored feature information of the person to be identified with the monitoring data comprises:

The feature information is matched with different types of data in the monitoring data by a distributed streaming processing framework.
A personnel identification processing device includes:

Obtaining a module, configured to acquire monitoring data within a preset area;

a matching module, configured to match pre-stored feature information of the person to be identified with the monitoring data;

The determining module is configured to determine, in the case that the matching is consistent, the data of the to-be-identified person included in the monitoring data.
The apparatus of claim 9, wherein the feature information comprises at least one of:

Face feature information, voiceprint information, identity feature information.
The apparatus of claim 10 wherein said identity characteristic information comprises:

The telephone number of the person to be identified, the identity card information of the person to be identified, the bank card information of the person to be identified, and the name information of the person to be identified.
The apparatus of claim 9 wherein said apparatus further comprises:

a positioning module, configured to confirm current location information of the to-be-identified person according to location information in the monitoring data;

The alarm module is configured to send alarm information, where the alarm information carries data of the to-be-identified person in the monitoring data;

a display module configured to display data of the person to be identified in the monitoring data.
The device according to claim 9, wherein the pre-stored feature information of the person to be identified further includes:

Characteristic information of the associated person of the person to be identified.
The apparatus of claim 9, wherein the monitoring data comprises at least one of:

Video surveillance data, voice monitoring data, card recording data for bank cards, communication data for telephone networks, and communication data for Internet networks.
The apparatus according to claim 9, wherein the manner of acquiring monitoring data within a preset area includes:

The monitoring data is obtained by a message bus of a cluster mode or a message bus of a high-throughput distributed publish-subscribe messaging system kafka.
The apparatus according to any one of claims 9 to 15, wherein matching the feature information of the person to be identified that is pre-stored with the monitoring data comprises:

The feature information is matched with different types of data in the monitoring data by a distributed streaming processing framework.
A system for personnel identification processing includes: tracking a human-computer interaction layer, tracking an acquisition layer, and tracking a processing layer;

The tracking human-machine interaction layer acquires feature information of the person to be identified;

The tracking acquisition layer acquires monitoring data within a preset area;

The tracking processing layer matches the feature information of the person to be identified with the monitoring data;

The tracking processing layer determines, in the case that the matching is consistent, the data of the to-be-identified person included in the monitoring data.