CN113537130B - Data processing method, device, equipment and storage medium - Google Patents
Data processing method, device, equipment and storage medium Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
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Abstract
本申请实施例涉及一种数据处理方法、装置、设备和存储介质。该方法包括：获取目标区域在检测时段内的活跃前科人员的人脸信息；其中，活跃前科人员为有盗窃车辆历史行为的人员；根据活跃前科人员的人脸信息以及检测时段内出入特定场所的人员的抓拍数据，获取检测时段内出入特定场所的目标活跃前科人员的行为数据；当根据行为数据确定目标活跃前科人员为涉及盗窃车辆的可疑人员时，向用户终端发送目标活跃前科人员的信息。该方法对有盗窃车辆历史行为的活跃前科人员进行重点分析，预警车辆盗窃可疑人员，实现了案件侦查思路从“由案到人”到“由人到案”的转变，从而大大提升了车辆盗窃案件的侦查效率，同时降低车辆盗窃案件的发生率。
The embodiments of the present application relate to a data processing method, device, equipment and storage medium. The method includes: obtaining facial information of active criminals in the target area within the detection period; wherein, active criminals are people with a history of stealing vehicles; obtaining behavioral data of target active criminals entering and leaving a specific place during the detection period based on the facial information of active criminals and the captured data of people entering and leaving a specific place during the detection period; when the target active criminal is determined to be a suspicious person involved in vehicle theft based on the behavioral data, the information of the target active criminal is sent to the user terminal. The method focuses on analyzing active criminals with a history of vehicle theft, warns suspicious persons of vehicle theft, and realizes the transformation of the case investigation idea from "from case to person" to "from person to case", thereby greatly improving the efficiency of vehicle theft case investigation and reducing the incidence of vehicle theft cases.
Description
Technical Field
The present application relates to the field of internet applications, and in particular, to a data processing method, apparatus, device, and storage medium.
Background
In the traditional technology, after the theft case occurs, a case handling person analyzes the video data in the time period frame by taking the video data around the place where the theft case occurs and combining the occurrence time of the theft case reported by a misowner so as to lock suspicious personnel for performing the theft. However, under the condition that the basic level police strength is limited, police strength resources often become bottlenecks for detecting such cases, and the detection efficiency of the stolen cases is seriously affected.
Disclosure of Invention
Based on the above, it is necessary to provide a data processing method, device, equipment and storage medium for solving the technical problem that the detection efficiency of the theft case is low in the conventional manner.
In a first aspect, an embodiment of the present application provides a data processing method, including:
Acquiring face information of active forensics personnel in a detection period of a target area, wherein the active forensics personnel are personnel with vehicle theft history behaviors and activity tracks in the target area in the detection period;
Acquiring behavior data of target active forensic personnel entering and exiting a specific place in the detection period according to the face information of the active forensic personnel and snapshot data of the personnel entering and exiting the specific place in the detection period, wherein the specific place is located in the target area;
And when the target active front personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data, sending information of the target active front personnel to a user terminal.
In a second aspect, an embodiment of the present application provides a data processing apparatus, including:
The system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring face information of active forensic personnel in a target area in a detection period, the active forensic personnel are personnel with vehicle theft history behaviors and activity tracks in the target area in the detection period;
The second acquisition module is used for acquiring behavior data of a target active front department personnel entering and exiting a specific place in the detection period according to the face information of the active front department personnel and the snapshot data of the personnel entering and exiting the specific place in the detection period, wherein the specific place is located in the target area;
And the sending module is used for sending the information of the target active forensic personnel to the user terminal when the target active forensic personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data.
In a third aspect, an embodiment of the present application provides a data processing apparatus, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the data processing method provided in the first aspect of the embodiment of the present application when the processor executes the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the data processing method provided in the first aspect of the embodiments of the present application.
According to the technical scheme provided by the embodiment of the application, according to the face information of the active forensic personnel in the detection period of the target area and the snapshot data of the personnel entering and exiting the specific place in the detection period of the target area, the behavior data of the target active forensic personnel entering and exiting the specific place in the detection period of the target area are obtained, and when the target active forensic personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data, the information of the target active forensic personnel is sent to the user terminal so as to remind a case handling personnel to conduct key investigation on the target active forensic personnel. According to the technical scheme, key analysis is carried out on active forerunner staff with vehicle theft history behaviors, vehicle theft suspicious staff is early warned, and the case investigation thought is changed from case to person to case, so that the investigation efficiency of vehicle theft cases is greatly improved, and meanwhile, the occurrence rate of the vehicle theft cases is reduced.
Drawings
FIG. 1 is a schematic flow chart of a data processing method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of another data processing method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of determining active front personnel according to an embodiment of the present application;
fig. 4 is a schematic flow chart of sending information of a target active front office staff according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;
Fig. 6 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be further described in detail by the following embodiments with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that, the execution body of the method embodiments described below may be a data processing apparatus, and the apparatus may be implemented as part or all of a data processing device by software, hardware, or a combination of software and hardware. Alternatively, the data processing device may be a mobile terminal or a server. The mobile terminal may be any one of a PDA (personal digital assistant), a PAD (tablet personal computer), a PMP (portable multimedia player), a vehicle-mounted terminal (for example, a vehicle-mounted navigation terminal), a mobile phone, and the like, and the server may be an independent server or a server cluster formed by a plurality of servers. The following method embodiments are described taking the execution subject as a data processing apparatus as an example.
Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present application. As shown in fig. 1, the method may include:
s101, acquiring face information of active front personnel in a detection period of a target area.
The target area may be understood as a geographical area oriented to vehicle theft analysis, and may be divided by administrative areas, such as nationwide, province, city, district/county, village and town, etc. Of course, the target area may be divided by communities. In practical application, the target area can be set according to practical requirements. The detection period can be understood as a time domain range facing the vehicle theft analysis, the granularity of the division can be month, week, day, hour and the like, and the detection period can be set correspondingly based on actual requirements, such as setting the detection period to be one week and the like. Alternatively, the vehicle may be an electric vehicle, a motorcycle or a bicycle.
The active foreman is a person with a history of vehicle theft and a moving track in a target area in a detection period. In general, a vehicle theft case is mostly treated with scofflaw or recidivism, so that the track of a front department person having an activity track in a detection period in a target area can be focused, and suspicious persons related to the vehicle theft case can be locked by performing a research analysis on the track. In practical application, the database of the public security system stores the certificate information of the front personnel of the vehicle theft case, so that the data processing equipment can acquire the certificate information of the active front personnel of the target area in the detection period from the historical past cases stored in the database, and extract the face information of the active front personnel from the certificate information.
S102, acquiring behavior data of a target active front department personnel entering and exiting a specific place in the detection period according to face information of the active front department personnel and snapshot data of the personnel entering and exiting the specific place in the detection period.
Wherein the specific location is located in the target area, and the specific location can be a vehicle sales point, such as a second-hand vehicle trading market, and the like. The target active forensic person refers to an active forensic person who enters and exits a specific place in a detection period. In general, monitoring devices (such as cameras) are installed at the entrances and exits of specific places and inside, and the monitoring devices can shoot people and vehicles entering and exiting the specific places to form snapshot data.
After acquiring the face information of the active forensic person, the data processing apparatus may match the face information of the active forensic person with snapshot data of the person who enters and exits the specific place in the detection period to determine whether the active forensic person appears in the specific place in the detection period. If the detected time period is matched with the detected time period, the active forensic person is determined to appear in the specific place, and the active forensic person appearing in the specific place is determined to be the target active forensic person. Meanwhile, behavior data of the active front personnel of the target are acquired based on the matched target snapshot data. Alternatively, the behavior data may include information such as facial expressions and behavior.
The specific process of the matching may be that a first face feature of the active front department personnel is extracted from face information of the active front department personnel, a second face feature (wherein the face feature may be a facial feature and a facial feature) of the personnel in the snapshot data is extracted, feature similarity of the first face feature and the second face feature is calculated, and if the feature similarity exceeds a preset threshold, face information of the active front department personnel is determined to be matched in the snapshot data.
And S103, when the target active forensic personnel are suspicious personnel related to vehicle theft according to the behavior data, sending information of the target active forensic personnel to a user terminal.
Since the target active forensic person enters and exits the specific place within the detection period, it is necessary to perform a focused analysis on the whereabouts of the target active forensic person. After obtaining the behavioral data of the target active front office personnel, it may be determined from the behavioral data whether the target active front office personnel is a suspicious person involved in theft of the vehicle. If yes, information of the target active forensics is sent to the user terminal. For example, information of the target active forensic person is sent to a user terminal held by a case transacting person in the target area.
Taking information such as facial expression and behavior as an example, when the target active forensic person's facial expression is tension and behavior is sneak, the target active forensic person can be determined to be a suspicious person involved in vehicle theft.
According to the data processing method provided by the embodiment of the application, according to the face information of the active forensic personnel in the detection period of the target area and the snapshot data of the personnel entering and exiting the specific place in the detection period, the behavior data of the target active forensic personnel entering and exiting the specific place in the detection period of the target area are obtained, and when the target active forensic personnel are suspicious personnel related to vehicle theft according to the behavior data, the information of the target active forensic personnel is sent to the user terminal so as to remind the case handling personnel to conduct key investigation on the target active forensic personnel. According to the technical scheme, key analysis is carried out on active forerunner staff with vehicle theft history behaviors, vehicle theft suspicious staff is early warned in advance, and the transition of case investigation thought from case to person to case is realized, so that the investigation efficiency of vehicle theft cases is greatly improved, and the occurrence rate of vehicle theft cases is reduced.
In one embodiment, the behavior data of the target active front personnel may optionally include the number of times the target active front personnel is present at a specific location, the riding status when the target active front personnel is coming in and going out of the specific location, and the attribute characteristics of the riding vehicle in the case of the riding vehicle. The attribute features of the vehicle may include a vehicle type, a vehicle color, a vehicle lamp shape, and the like. Based on this, the following embodiments also provide another data processing method. It should be noted that, S202 to S203 described below are an alternative embodiment of S102 described above, and S204 to S206 described below are an alternative embodiment of S103 described above. As shown in fig. 2, the method may include:
S201, face information of active front personnel in the target area in the detection period is acquired.
S202, matching face information of the active forensic personnel with snapshot data of personnel entering and exiting a specific place in the detection period to obtain the occurrence times of target active forensic personnel in the specific place in the detection period.
The data processing device may match face information of the active front personnel with snapshot data of personnel entering and exiting a specific place in a detection period, if the face information of the active front personnel is matched with the snapshot data, it may be determined that the active front personnel appears in the specific place in the detection period, the active front personnel appearing in the specific place is determined to be a target active front personnel, and the matched target snapshot data is archived in an activity track record of the target active front personnel. After all the snapshot data in the detection period are processed, counting the number of the target snapshot data stored in the activity track records of the target active forensics, and determining the number as the occurrence number of the target active forensics in a specific place.
S203, performing scene snapshot picture structural analysis on the matched target snapshot data to acquire the riding state of the target active front personnel when the target active front personnel enter and exit the specific place each time and the attribute characteristics of the riding vehicle of the target active front personnel under the condition of the riding vehicle.
In order to acquire the key information of a deeper level, scene snapshot picture structural analysis can be performed on the matched target snapshot data, namely, structural analysis is performed on a target object in the target snapshot data, and semantic description of texts is performed, wherein the semantic description comprises pedestrian structuring, vehicle structuring, human riding structuring and the like. The cyclist riding structuring is to perform structuring processing and recognition on cyclists in the target snapshot data, and the structuring processing comprises the structuring processing of the cyclist's clothing, vehicle characteristics and the like. Through carrying out scene snapshot picture structural analysis on the target snapshot data, whether riding vehicles exist or not when the target active front department personnel come in and go out of a specific place each time, and vehicle attribute characteristics such as vehicle types, vehicle colors, vehicle lamp shapes and the like of the riding vehicles of the target active front department personnel under the condition that the riding vehicles exist can be obtained.
For example, assuming that the number of occurrences of the target active front personnel in the specific place is 5 in the detection period, after the scene snapshot picture structural analysis is performed on the target snapshot data, it can be obtained that the target active front personnel has no riding vehicle when entering the specific place twice before, has no riding vehicle when entering the specific place three times later, has no riding vehicle when leaving the specific place, and has different vehicle attribute characteristics such as vehicle type, vehicle color, vehicle lamp shape and the like when entering the specific place three times later.
And S204, when the occurrence number is greater than or equal to the preset occurrence number, determining the riding number of the target active forensic personnel under the preset condition according to the riding state of the target active forensic personnel when the target active forensic personnel enter and exit the specific place each time.
The preset condition is that the riding vehicle is when entering a specific place, and the riding vehicle is not when leaving the specific place. In practical application, the preset occurrence times can be correspondingly set based on the practical service requirements.
After the behavior data of the target active forepart staff are obtained, judging whether the occurrence number of the target active forepart staff in the specific place in the detection period is more than or equal to the preset occurrence number, if so, determining the riding number of the target active forepart staff under the preset condition according to the riding state of the target active forepart staff when the target active forepart staff enters and exits the specific place each time. If not, analyzing the track of the next target active forerunner in the specific place. The riding state is used for indicating whether the target active front personnel have a riding vehicle or not.
Continuing with the example in S203 described above, it is assumed that the number of occurrences of the target active forensic person in the specific place in the detection period is 5 times, and at the same time, the preset number of occurrences is 2 times. Because the occurrence frequency of the target active front department personnel in the specific place is larger than the preset occurrence frequency, the riding frequency of the target active front department personnel under the condition that the preset condition is met can be further determined based on the riding state of the target active front department personnel when the target active front department personnel enter and exit the specific place each time. Since no riding vehicle is used when the user enters the specific place for the first two times, no riding vehicle is used when the user enters the specific place for the last three times, and no riding vehicle is used when the user leaves the specific place. Therefore, it can be determined that the number of rides of the target active front personnel under the preset condition is 3.
And S205, if the riding times are greater than or equal to the preset riding times, judging whether the attribute characteristics of the riding vehicles of the target active front personnel are the same each time.
Wherein the preset riding times are smaller than or equal to preset occurrence times. Similarly, in practical application, the preset riding times can be set correspondingly based on the actual service requirement.
After the riding times of the target active front department personnel under the preset condition are obtained, judging whether the riding times are larger than or equal to the preset riding times, if so, further judging whether the vehicle attribute characteristics such as the vehicle type, the vehicle color, the vehicle lamp shape and the like of the vehicle ridden by the target active front department personnel each time are the same. If the track is the same, the track of the next target active forerunner in the specific place is analyzed, and if the track is not the same, the following step S206 is executed.
S206, determining the target active forensic personnel as suspicious personnel involved in vehicle theft, and sending information of the target active forensic personnel to a user terminal.
In this embodiment, scene snapshot picture structural analysis is performed on the matched target snapshot data to extract deeper key information, such as the occurrence times of the target active front personnel in a specific place, the riding state when the target active front personnel enter and exit the specific place each time, and vehicle attribute features such as the vehicle type, the vehicle color, the vehicle lamp shape and the like of the riding vehicle under the condition of having the riding vehicle. Meanwhile, by combining the occurrence times of the target active front personnel in the specific place, the riding state when the target active front personnel come in and go out of the specific place each time and the vehicle attribute characteristics such as the vehicle type, the vehicle color, the vehicle lamp shape and the like of the riding vehicle under the condition of the riding vehicle, whether the target active front personnel are suspicious personnel related to vehicle theft or not is determined, and the accuracy of analysis results is improved by judging and analyzing deeper behavior data, so that the early warning accuracy is improved.
In one embodiment, a specific process of determining the activity of the subject area for the detection period is also provided. On the basis of the above embodiment, optionally, as shown in fig. 3, before the step S101, the method may further include:
S301, acquiring information of all forensics related to theft of the vehicle case.
In general, vehicle theft cases are mostly owned by scofflaw, recidivism, and thus, attention may be paid to all forensics involved in theft of the vehicle case. In practical application, the database of the public security system stores information of the front personnel of the vehicle theft case, so that the data processing equipment can acquire information of all the front personnel involved in the vehicle theft case from historical past cases stored in the database.
S302, acquiring an active personnel information set.
The information of people with activity tracks in the detection period of the target area is stored in the active person information set. And (3) comprehensively gathering the activity track data information of the personnel in the target area in the detection period, including traffic ticket purchasing information, travel accommodation information, internet bar registration information, medical clinic registration information and the like, drawing the activity track of the personnel in the target area according to the time sequence, and storing the activity track in an activity personnel information set.
And S303, respectively matching the information of the front personnel with the active personnel information set to determine the active front personnel of the target area in the detection period.
The active foreman refers to a foreman with an activity track in a target area in a detection period. After information of all forecourts involved in theft of the vehicle case and the information set of the active staff are obtained, the information of each forecourt is respectively matched with the information set of the active staff, and the forecourts with the activity track in the target area in the detection period are screened out, namely the active forecourts are determined. Subsequently, the track of the active forerunner is subjected to a key analysis to check suspicious persons related to vehicle theft.
After determining the suspicious person involved in the vehicle theft, in order to improve the capturing efficiency of the suspicious person, the process of sending the information of the target active front office staff to the user terminal may optionally include, as shown in fig. 4:
s401, acquiring the activity track of the target active front personnel.
After determining that the target active front office person is a suspicious person involved in vehicle theft, the activity trajectory of the target active front office person is obtained from the active person information set.
S402, determining the target address where the target active forensic personnel frequently appear according to the activity track.
After the activity track of the target active front office staff is obtained, the target address where the target active front office staff frequently appears can be determined by analyzing the activity track. For example, based on the time in the activity trajectory, the place where the night occurs may be determined to be the constant place of the targeted active front personnel. Therefore, when the data processing equipment sends the personal data of the target active forensic personnel to the user terminal, the target address frequently appearing by the target active forensic personnel can be sent to the user terminal held by the case handling personnel, so that accurate capture of the target active forensic personnel is realized.
S403, the personal data of the target active forerunner and the target address are sent to a user terminal held by a case handling person.
In the embodiment, the information of all the forecourts involved in the vehicle theft case is matched with the active staff information set to determine the active forecourt staff of the target area in the detection period, and the active forecourt staff is focused on the activity track of the active forecourt staff, so that redundant data are reduced, the analysis range of the vehicle theft case is shortened, and the detection efficiency of the theft case is further improved. Meanwhile, after the target active forepart personnel are determined to be suspicious personnel related to vehicle theft, personal data of the target active forepart personnel and frequently-occurring target addresses are sent to a user terminal held by a case handling personnel, so that accurate capture of the target active forepart personnel is realized, and occurrence of vehicle theft cases can be prevented in advance.
Fig. 5 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application. As shown in fig. 5, the apparatus may include a first acquisition module 501, a second acquisition module 502, and a transmission module 503.
Specifically, the first obtaining module 501 is configured to obtain face information of an active front office worker in a detection period, where the active front office worker is a person having a vehicle history behavior of theft and having an activity track in the target area in the detection period;
The second obtaining module 502 is configured to obtain, according to face information of the active front department personnel and snapshot data of personnel entering and exiting a specific location in the detection period, behavioral data of a target active front department personnel entering and exiting the specific location in the detection period, where the specific location is located in the target area;
The sending module 503 is configured to send information of the target active front office personnel to a user terminal when it is determined that the target active front office personnel is a suspicious person related to vehicle theft according to the behavior data.
According to the data processing device provided by the embodiment of the application, according to the face information of the active forensic personnel in the detection period of the target area and the snapshot data of the personnel entering and exiting the specific place in the detection period, the behavior data of the target active forensic personnel entering and exiting the specific place in the detection period of the target area are obtained, and when the target active forensic personnel are suspicious personnel related to vehicle theft according to the behavior data, the information of the target active forensic personnel is sent to the user terminal so as to remind a case handling personnel to conduct key investigation on the target active forensic personnel. According to the technical scheme, key analysis is carried out on active forerunner staff with vehicle theft history behaviors, vehicle theft suspicious staff is early warned, and the case investigation thought is changed from case to person to case, so that the investigation efficiency of vehicle theft cases is greatly improved, and meanwhile, the occurrence rate of the vehicle theft cases is reduced.
Optionally, the behavior data includes a number of times that the behavior data is presented at the particular location, a riding status when the behavior data is presented at the particular location, and an attribute characteristic of the riding vehicle if the riding vehicle is present.
On the basis of the above embodiment, optionally, the second obtaining module 502 is specifically configured to match face information of the active front personnel with snapshot data of personnel entering and exiting a specific location in the detection period to obtain the number of occurrences of the target active front personnel in the specific location in the detection period, and perform scene snapshot picture structural analysis on the matched target snapshot data to obtain a riding state of the target active front personnel each time the target active front personnel enters and exits the specific location and attribute characteristics of a vehicle on which the target active front personnel rides in the presence of the riding vehicle.
Optionally, the device further comprises an analysis module based on the embodiment.
The analysis module is used for determining the riding times of the target active forensic personnel under the condition that the target active forensic personnel meet the preset conditions according to the riding state of the target active forensic personnel when the occurrence times are larger than or equal to the preset occurrence times, judging whether the attribute characteristics of the target active forensic personnel on which the target active forensic personnel ride each time are the same or not if the riding times are larger than or equal to the preset riding times, and determining that the target active forensic personnel are suspicious personnel involved in vehicle theft if the target active forensic personnel are not the same, wherein the preset conditions are that the target active forensic personnel have riding vehicles when entering the specific place and have no riding vehicles when leaving the specific place, and the preset riding times are smaller than or equal to the preset occurrence times.
On the basis of the embodiment, the device optionally further comprises a third acquisition module, a fourth acquisition module and a processing module.
Specifically, the third acquiring module is configured to acquire information related to all front-end personnel stealing the vehicle case before the first acquiring module 501 acquires face information of active front-end personnel in the target area within the detection period;
the system comprises a fourth acquisition module, a third acquisition module and a fourth acquisition module, wherein the fourth acquisition module is used for acquiring an active personnel information set, and the active personnel information set stores the information of personnel with an active track in a detection period in a target area;
The processing module is used for respectively matching the information of the front personnel with the active personnel information set so as to determine the active front personnel of the target area in the detection period.
On the basis of the above embodiment, optionally, the sending module 503 is specifically configured to obtain an activity track of the target active forensic person, determine, according to the activity track, a target address where the target active forensic person frequently appears, and send the personal data of the target active forensic person and the target address to a user terminal held by a case handling person.
Optionally, the vehicle comprises an electric vehicle, a motorcycle or a bicycle.
In one embodiment, a data processing apparatus is provided, the schematic structure of which may be as shown in fig. 6. The device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the device is configured to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the device is used for storing data involved in the data processing. The network interface of the device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data processing method.
It will be appreciated by persons skilled in the art that the structure shown in fig. 6 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and does not constitute a limitation of the apparatus to which the present inventive arrangements are applied, and that a particular apparatus may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.
In one embodiment, a data processing apparatus is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
Acquiring face information of active forensics personnel in a detection period of a target area, wherein the active forensics personnel are personnel with vehicle theft history behaviors and activity tracks in the target area in the detection period;
Acquiring behavior data of target active forensic personnel entering and exiting a specific place in the detection period according to the face information of the active forensic personnel and snapshot data of the personnel entering and exiting the specific place in the detection period, wherein the specific place is located in the target area;
And when the target active front personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data, sending information of the target active front personnel to a user terminal.
Optionally, the behavior data includes a number of times that the behavior data is presented at the particular location, a riding status when the behavior data is presented at the particular location, and an attribute characteristic of the riding vehicle if the riding vehicle is present.
In one embodiment, the processor when executing the computer program further performs the steps of matching face information of the active forensic personnel with snapshot data of personnel entering and exiting a specific place in the detection period to obtain the occurrence times of target active forensic personnel occurring in the specific place in the detection period, and performing scene snapshot picture structural analysis on the matched target snapshot data to obtain the riding state of the target active forensic personnel when entering and exiting the specific place each time and the attribute characteristics of a vehicle ridden by the target active forensic personnel when the riding vehicle exists.
In one embodiment, the processor further performs the steps of determining the number of rides of the target active front personnel under a preset condition according to the riding state of the target active front personnel when the number of occurrences is greater than or equal to a preset number of occurrences each time the target active front personnel enters and exits the specific place, judging whether the attribute characteristics of the target active front personnel on which the vehicle is ridden each time are the same or not if the number of rides is greater than or equal to the preset number of rides, and determining that the target active front personnel is a suspicious person involved in vehicle theft if the attribute characteristics are not the same, wherein the preset condition is that the vehicle is ridden when the target active front personnel enters the specific place, and the preset number of rides is less than or equal to the preset number of occurrences when the target active front personnel leaves the specific place.
In one embodiment, the processor when executing the computer program further performs the steps of obtaining information concerning all forecourts of theft of the vehicle case, obtaining an active personnel information set, matching the information of each forecourt with the active personnel information set respectively to determine active forecourt personnel of the target area in the detection period, wherein the active personnel information set stores information of personnel of the target area with an activity track in the detection period.
In one embodiment, the processor further performs the steps of acquiring an activity track of the target active forensic person, determining a target address where the target active forensic person frequently appears according to the activity track, and sending the personal data of the target active forensic person and the target address to a user terminal held by a case handling person.
Optionally, the vehicle comprises an electric vehicle, a motorcycle or a bicycle.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Acquiring face information of active forensics personnel in a detection period of a target area, wherein the active forensics personnel are personnel with vehicle theft history behaviors and activity tracks in the target area in the detection period;
Acquiring behavior data of target active forensic personnel entering and exiting a specific place in the detection period according to the face information of the active forensic personnel and snapshot data of the personnel entering and exiting the specific place in the detection period, wherein the specific place is located in the target area;
And when the target active front personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data, sending information of the target active front personnel to a user terminal.
The data processing device, the device and the storage medium provided in the foregoing embodiments may execute the data processing method provided in any embodiment of the present application, and have the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in the above embodiments may be found in the data processing method provided in any of the embodiments of the present application.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. A method of data processing, comprising:
acquiring information of all forerunner personnel with vehicle theft cases from a database of a public security system;
Acquiring an event personnel information set according to the traffic ticket buying information, the travel accommodation information, the internet bar registration information and the medical clinic registration information of the personnel in the detection period of the target area, wherein the event personnel information set comprises the information of the personnel with the event track in the detection period of the target area;
matching the information of each forerunner with the information set of the active staff to determine the active forerunner in the detection period, wherein the active forerunner is a staff with the history of vehicle theft and the activity track in the target area in the detection period;
acquiring certificate information of the active forensic personnel from a database of a public security system, and extracting face information of the active forensic personnel from the certificate information;
Acquiring behavior data of target active forensics personnel entering and exiting a specific place in the detection period according to face information of the active forensics personnel and snapshot data of personnel entering and exiting the specific place in the detection period, wherein the specific place is positioned in the target area and is related to vehicle theft, and the behavior data comprises the times of occurrence in the specific place, riding states when entering and exiting the specific place and attribute characteristics of a riding vehicle under the condition of the riding vehicle;
And when the target active front personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data, sending information of the target active front personnel to a user terminal.
2. The method according to claim 1, wherein the obtaining behavior data of the target active forensic person who enters and exits the specific place in the detection period according to the face information of the active forensic person and the snapshot data of the person who enters and exits the specific place in the detection period includes:
Matching the face information of the active forensic personnel with snapshot data of personnel entering and exiting a specific place in the detection period to obtain the occurrence times of target active forensic personnel in the specific place in the detection period;
And carrying out scene snapshot picture structural analysis on the matched target snapshot data to acquire the riding state of the target active front personnel when the target active front personnel enter and exit the specific place each time and the attribute characteristics of the riding vehicle of the target active front personnel under the condition of the riding vehicle.
3. The method of claim 1, wherein said determining from said behavioral data that said target active front office worker is a suspect involved in theft of a vehicle comprises:
When the number of times of occurrence in the specific place is greater than or equal to the preset number of times of occurrence, determining the number of times of riding of the target active forensic personnel under the preset condition according to the riding state of the target active forensic personnel when the target active forensic personnel enter the specific place each time, wherein the preset condition is that riding vehicles exist when entering the specific place and no riding vehicles exist when leaving the specific place;
If the riding times are greater than or equal to preset riding times, judging whether the attribute characteristics of the riding vehicles of the target active forensics are the same or not, wherein the preset riding times are less than or equal to preset occurrence times;
if the target active forensics are different, determining that the target active forensics are suspicious persons related to vehicle theft.
4. A method according to any one of claims 1 to 3, characterized in that said sending information of the target active forensic person to a user terminal comprises:
Acquiring the activity track of the target active front personnel;
according to the activity track, determining a target address where the target active front personnel frequently appear;
and sending the personal data of the target active forensic personnel and the target address to a user terminal held by a case handling personnel.
5. A method according to any one of claims 1 to 3, wherein the vehicle comprises an electric vehicle, a motorcycle or a bicycle.
6. A data processing apparatus, comprising:
the third acquisition module is used for acquiring information of all forerunner personnel with vehicle theft cases from a database of the public security system;
The fourth acquisition module is used for acquiring an event personnel information set according to the traffic ticket buying information, the travel industry accommodation information, the internet bar registration information and the medical clinic registration information of the personnel in the detection period of the target area, wherein the event personnel information set comprises the information of the personnel with the event track in the detection period of the target area;
The processing module is used for matching the information of each front department personnel with the active personnel information set to determine the active front department personnel of the target area in the detection period, wherein the active front department personnel are the personnel with the history of vehicle theft and the activity track in the target area in the detection period;
The first acquisition module is used for acquiring the certificate information of the active forensic personnel from a database of the public security system and extracting the face information of the active forensic personnel from the certificate information;
The second acquisition module is used for acquiring behavior data of target active forensic personnel entering and exiting the specific place in the detection period according to the face information of the active forensic personnel and the snapshot data of the personnel entering and exiting the specific place in the detection period, wherein the specific place is positioned in the target area and is related to vehicle theft, and the behavior data comprises the times of occurrence in the specific place, the riding state when the specific place is entered and exiting and the attribute characteristics of the riding vehicle when the riding vehicle exists;
And the sending module is used for sending the information of the target active forensic personnel to the user terminal when the target active forensic personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data.
7. A data processing device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 5.
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| CN106339428B (en) * | 2016-08-16 | 2019-08-23 | 东方网力科技股份有限公司 | Suspect's personal identification method and device based on video big data |
| US11080975B2 (en) * | 2018-06-29 | 2021-08-03 | Baidu Usa Llc | Theft proof techniques for autonomous driving vehicles used for transporting goods |
| WO2021102760A1 (en) * | 2019-11-27 | 2021-06-03 | 深圳云天励飞技术有限公司 | Method and apparatus for analyzing behavior of person, and electronic device |
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| CN111950471B (en) * | 2020-08-14 | 2024-02-13 | 杭州海康威视系统技术有限公司 | Target object identification method and device |
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