CN113537130A - Data processing method, device, equipment and storage medium - Google Patents
Data processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application relates to a data processing method, a data processing device, data processing equipment and a storage medium. The method comprises the following steps: acquiring face information of active president personnel in a target area in a detection period; wherein, the active predecessor is a person with vehicle theft history; acquiring behavior data of target active predecessor personnel who come in and go out of a specific place in a detection period according to face information of the active predecessor personnel and snapshot data of personnel who come in and go out of the specific place in the detection period; and when the target active president is determined to be suspicious personnel related to the vehicle theft according to the behavior data, sending the information of the target active president to the user terminal. The method mainly analyzes active foreigners with vehicle theft historical behaviors, pre-warns suspicious vehicle theft, and realizes the conversion of case detection thinking from case to person, thereby greatly improving the detection efficiency of the vehicle theft case and simultaneously reducing the occurrence rate of the vehicle theft case.
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 today's social life, theft incidents occur frequently. For example, similar electric vehicles have been high in theft cases due to the reasons that the similar electric vehicles have small technical unlocking difficulty, numerous targets and are easy to select, the stolen goods are fast to sell, the crime is made in a group-fleeing mode, the law lag striking effect is poor, and the like.
In the traditional technology, after a theft case occurs, a case clerk conducts frame-by-frame analysis on video data in a time period by calling video data around the place where the theft case occurs and combining the occurrence time of the theft case reported by a loser so as to lock suspicious personnel who implement the theft behavior. However, under the condition of limited primary police force, police force resources are often the bottleneck of the case detection, and the detection efficiency of the theft case is seriously influenced.
Disclosure of Invention
Based on this, it is necessary to provide a data processing method, apparatus, device and storage medium for solving the technical problem of low detection efficiency of the conventional method for the theft case.
In a first aspect, an embodiment of the present application provides a data processing method, including:
acquiring face information of active president personnel in a target area in a detection period; wherein, the active predecessor personnel are personnel with vehicle theft historical behaviors and activity tracks in the target area in the detection period;
acquiring behavior data of target active president people who come in and go out of the specific place in the detection period according to the face information of the active president people and the snapshot data of people who come in and go out of the specific place in the detection period; wherein the particular locale is within the target area;
and when the target active predecessor is determined to be suspicious people related to vehicle theft according to the behavior data, sending information of the target active predecessor to a user terminal.
In a second aspect, an embodiment of the present application provides a data processing apparatus, including:
the first acquisition module is used for acquiring the face information of active predecessors in a detection time period of a target area; wherein, the active predecessor personnel are personnel with vehicle theft historical behaviors and activity tracks in the target area in the detection period;
the second acquisition module is used for acquiring behavior data of target active president people who come in and go out of a specific place in the detection period according to the face information of the active president people and snapshot data of people who come in and go out of the specific place in the detection period; wherein the particular locale is within the target area;
and the sending module is used for sending the information of the target active predecessor personnel to the user terminal when the target active predecessor 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 executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the data processing method provided in the first aspect of the embodiment of the present application.
According to the technical scheme, behavior data of the target active president personnel who come in and go out of the specific place in the detection period are acquired according to the face information of the active president personnel in the target area in the detection period and snapshot data of the personnel who come in and go out of the specific place in the detection period, and when the target active president personnel are determined to be suspicious personnel related to the stolen vehicle according to the behavior data, information of the target active president personnel is sent to the user terminal so as to remind the clerk to perform key investigation on the target active president personnel. According to the technical scheme, active foreigners with vehicle theft historical behaviors are subjected to key analysis, suspicious persons of vehicle theft are warned in advance, the change of case investigation thinking from case to person is achieved, the investigation efficiency of the vehicle theft case is greatly improved, and meanwhile the occurrence rate of the vehicle theft case is reduced.
Drawings
Fig. 1 is a schematic flowchart of a data processing method according to an embodiment of the present application;
fig. 2 is another schematic flow chart of a data processing method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating the determination of active president persons according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a process for sending information of a target active president according to an embodiment of the present application;
fig. 5 is a schematic structural 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 device 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 are further described in detail by the following embodiments in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that the execution subject of the method embodiments described below may be a data processing apparatus, and the apparatus may be implemented as part of 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 computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a vehicle-mounted navigation terminal), a mobile phone, and the like, and the server may be an independent server or a server cluster composed of a plurality of servers. The method embodiments described below are described taking as an example the execution subject being a data processing device.
Fig. 1 is a schematic flowchart 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 president personnel in a target area in a detection time period.
The target area may be understood as a geographical area for vehicle theft analysis, and the target area may be divided into administrative districts, such as nationwide, provincial, city, district/county, township, and the like. Of course, the target area may be divided by communities. In practical application, the target area can be set according to actual requirements. The detection period can be understood as a time domain range oriented to vehicle theft analysis, the granularity of the division can be months, weeks, days, hours and the like, and the detection period can be set correspondingly based on actual needs, such as setting the detection period to be a week and the like. Alternatively, the vehicle may be an electric vehicle, a motorcycle, or a bicycle.
The active predecessor personnel are personnel who have vehicle theft historical behaviors and have activity tracks in a target area within a detection period. Generally, vehicle theft cases are mostly made by scofflaw and offender, so that the tracks of predecessors with activity tracks in a target area in a detection period can be focused on, and suspicious persons related to the vehicle theft cases can be locked by analyzing the tracks through judging. In practical application, certificate information of predecessor personnel of a vehicle theft case is stored in a database of the public security system, so that the data processing equipment can acquire the certificate information of active predecessor personnel of a target area in a detection period from historical past cases stored in the database and extract face information of the active predecessor personnel from the certificate information.
S102, acquiring behavior data of the target active president people who come in and go out of the specific place in the detection period according to the face information of the active president people and the snapshot data of the people who come in and go out of the specific place in the detection period.
The specific place is located in the target area, and the specific place can be a vehicle stolen spot, such as a used vehicle trading market. The target active president refers to an active president who comes in and goes out of a specific place within a detection period. Generally, monitoring devices (such as cameras) are installed at an entrance and an interior of a specific place, and the monitoring devices can shoot people and vehicles entering and exiting the specific place to form snapshot data.
After the face information of the pre-active person is acquired, the data processing device may match the face information of the pre-active person with snapshot data of persons coming in and going out of a specific place within a detection period to determine whether the pre-active person is present in the specific place within the detection period. If so, it may be determined that the active predecessor is present at the particular location within the detection time period, and the active predecessor present at the particular location may be determined to be the target active predecessor. And meanwhile, acquiring the behavior data of the target active president personnel 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: extracting first face features of active predecessors from face information of the active predecessors, extracting second face features of people in snapshot data (wherein the face features can be facial features and facial features), calculating feature similarity of the first face features and the second face features, and determining that the face information of the active predecessors is matched in the snapshot data if the feature similarity exceeds a preset threshold.
S103, when the target active president personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data, sending information of the target active president personnel to a user terminal.
Since the target active president person passes through a specific place in the detection period, it is necessary to perform an emphasis analysis on the whereabouts of the target active president person. After obtaining behavioral data of the target active predecessor, it may be determined from the behavioral data whether the target active predecessor is a suspect involved in a theft of the vehicle. And if so, sending the information of the target active president personnel to the user terminal. For example, information of the target active president is transmitted to a user terminal held by a clerk in the target area.
Taking the example that the behavior data includes information such as facial expressions and behavior, when the facial expressions of the subjects before the target activity are tense and the behavior is sneak, the subjects before the target activity can be determined as suspicious persons related to the vehicle theft.
According to the data processing method provided by the embodiment of the application, behavior data of the target active president personnel who come in and go out of the specific place in the detection period are obtained according to the face information of the active president personnel in the target area in the detection period and snapshot data of the personnel who come in and go out of the specific place in the detection period, and when the target active president personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data, the information of the target active president personnel is sent to the user terminal so as to remind the case handling personnel to carry out key investigation on the target active president personnel. According to the technical scheme, active foreigners with vehicle theft historical behaviors are subjected to key analysis, suspicious vehicle theft personnel are early warned, and the change of case investigation thinking from case to person is realized, so that the investigation efficiency of the vehicle theft case is greatly improved, and the occurrence rate of the vehicle theft case is reduced.
In one embodiment, the behavior data of the target active predecessor may optionally include the number of times of appearance at a particular location, riding status when entering or exiting the particular location, and attribute characteristics of the riding vehicle in the presence of the riding vehicle. The attribute characteristics 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 optional embodiments of S102, and S204 to S206 described below are optional embodiments of S103. As shown in fig. 2, the method may include:
s201, acquiring face information of active president personnel in the target area in a detection time period.
S202, matching the face information of the active predecessor personnel with snapshot data of personnel coming in and going out of a specific place in the detection time period to obtain the occurrence frequency of the target active predecessor personnel appearing in the specific place in the detection time period.
The data processing equipment can match the face information of the active president personnel with snapshot data of personnel coming in and going out of a specific place in a detection period, if the face information is matched with the snapshot data, the active president personnel can be determined to be in the specific place in the detection period, the active president personnel in the specific place are determined to be target active president personnel, and the matched target snapshot data are filed in an activity track record of the target active president 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 record of the target active president, and determining the number as the occurrence frequency of the target active president in a specific place.
S203, carrying out scene snapshot picture structuralized analysis on the matched target snapshot data to acquire the riding state of the target active predecessor when the target active predecessor goes in and out of the specific place at each time and the attribute characteristics of the vehicle ridden by the target active predecessor under the condition that the riding vehicle exists.
In order to obtain deeper key information, scene snapshot picture structural analysis can be performed on the matched target snapshot data, namely structural analysis is performed on target objects in the target snapshot data, and semantic description of texts is performed, wherein the semantic description comprises pedestrian structuring, vehicle structuring, man-riding structuring and the like. The human riding structuralization is to perform structuralization processing and identification on the riding pedestrians in the target snapshot data, and the structuralization processing includes structuralization processing of clothing, vehicle characteristics and the like of the riders. Through the scene snapshot picture structured analysis of the target snapshot data, whether a riding vehicle exists or not when the target active predecessor goes in and out of a specific place every time can be obtained, and 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 predecessor can be obtained under the condition that the riding vehicle exists.
Exemplarily, it is assumed that the number of occurrences of the target active predecessor in the specific location in the detection period is 5, and after the structural analysis of the scene snapshot picture is performed on the target snapshot data, it can be obtained that no riding vehicle exists when the target active predecessor goes in and out of the specific location twice before, no riding vehicle exists when the target active predecessor goes into the specific location three times later, and vehicle attribute characteristics such as a vehicle type, a vehicle color, a vehicle lamp shape and the like of the riding vehicle when the target active predecessor goes into the specific location three times later are different.
S204, when the occurrence frequency is larger than or equal to a preset occurrence frequency, determining the riding frequency of the target active predecessor under the condition that the target active predecessor meets the preset condition according to the riding state of the target active predecessor when the target active predecessor goes in and out of the specific place each time.
The preset conditions are that a riding vehicle exists when the vehicle enters a specific place and no riding vehicle exists when the vehicle leaves the specific place. In practical application, the preset occurrence times can be correspondingly set based on actual service requirements.
After behavior data of the target active predecessor personnel are obtained, whether the occurrence frequency of the target active predecessor personnel in a specific place in a detection time period is larger than or equal to a preset occurrence frequency is judged, and if yes, the riding frequency of the target active predecessor personnel meeting the preset condition is determined according to the riding state of the target active predecessor personnel in and out of the specific place each time. And if not, analyzing the track of the next target active predecessor in a specific place. Wherein the riding state is used for indicating whether the target active predecessor has a riding vehicle.
Continuing with the example in S203, it is assumed that the number of occurrences of the target-activity predecessor in the specific location within the detection period is 5, and meanwhile, it is assumed that the preset number of occurrences is 2. Because the occurrence frequency of the target active predecessor in the specific place is greater than the preset occurrence frequency, the riding frequency of the target active predecessor meeting the preset condition can be further determined based on the riding state of the target active predecessor in and out of the specific place each time. The riding vehicles are not arranged in the first two times of entering and exiting the specific place, and the riding vehicles are arranged in the last three times of entering the specific place and are not arranged in the last three times of leaving the specific place. Therefore, the number of rides by the target active predecessor who satisfies the preset condition may be determined to be 3.
And S205, if the riding times are more than or equal to the preset riding times, judging whether the attribute characteristics of the vehicles ridden by the target active predecessors every time are the same.
Wherein the preset riding times are less than or equal to the preset occurrence times. Similarly, in practical applications, the preset riding times can be set correspondingly based on actual business requirements.
After the riding times of the target active predecessor are obtained under the preset condition, whether the riding times are larger than or equal to the preset riding times is judged, and if yes, whether 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 predecessor each time are the same is further judged. If the target is the same as the target, analyzing the track of the next target active predecessor in a specific place; if not, the following step S206 is executed.
S206, determining that the target active president personnel are suspicious personnel related to vehicle theft, and sending information of the target active president personnel to the user terminal.
In this embodiment, the matched target snapshot data is subjected to scene snapshot picture structured analysis to extract deeper key information, such as the number of occurrences of the target-active predecessor in a specific place, the riding state of each time when the target-active predecessor goes in or goes out of the specific place, and vehicle attribute characteristics such as the type, color, shape and the like of a riding vehicle in the presence of the riding vehicle. Meanwhile, whether the target active predecessor is a suspicious person related to vehicle theft is determined by combining the occurrence frequency of the target active predecessor in a specific place, the riding state of the target active predecessor in the specific place each time and vehicle attribute characteristics such as the vehicle type, the vehicle color, the vehicle lamp shape and the like of the vehicle ridden under the condition that the vehicle is ridden, and the accuracy of an analysis result is improved by carrying out judgment analysis on deeper behavior data, so that the accuracy of early warning is improved.
In one embodiment, a specific process of determining active presidential persons for a target area within a detection period is also provided. On the basis of the foregoing embodiment, as shown in fig. 3, before the foregoing S101, the method may further include:
s301, acquiring information of all predecessors related to the stolen vehicle case.
Generally, vehicle theft cases are populated with scofflaw and offender cases, and therefore, all predecessors involved in a vehicle theft case may be of significant concern. In practical applications, the database of the public security system stores information of the predecessors of the vehicle theft case, so that the data processing device can acquire information of all predecessors related to the vehicle theft case from historical past cases stored in the database.
And S302, acquiring an activity personnel information set.
The activity personnel information is stored with information of personnel with activity tracks in the target area in the detection period in a centralized manner. The method includes the steps that activity track data information of personnel in a target area in a detection time period is comprehensively gathered, wherein the activity track data information comprises transportation travel ticket purchasing information, travel and lodging information, internet bar registration information, medical outpatient service registration information and the like, the activity tracks of the personnel in the target area are drawn according to the time sequence, and the activity tracks are stored in an activity personnel information set.
And S303, respectively matching the information of each president with the active personnel information set to determine active president personnel in the target area in the detection period.
The active president is a president having an activity track in the target area in the detection period. After information of all foreheads involved in the vehicle case theft and the activity personnel information set are obtained, the information of each forehead is matched with the activity personnel information set respectively, and foreheads with activity tracks in a target area in a detection period are screened out, namely, active foreheads are determined. Subsequently, the trajectory of the partially active predecessor is analyzed with emphasis to investigate suspicious persons involved in vehicle theft.
After the suspicious person related to the vehicle theft is determined, on the basis of the above embodiment, optionally as shown in fig. 4, the process of sending the information of the target active president person to the user terminal may include:
s401, obtaining the activity track of the target active president personnel.
After determining that the target active predecessor is suspicious people related to vehicle theft, obtaining the activity track of the target active predecessor from the activity personnel information set.
S402, determining the target address frequently appearing in the target active president personnel according to the activity track.
After the activity track of the target active president personnel is obtained, the target address which frequently appears in the target active president personnel can be determined by analyzing the activity track. For example, based on the time in the activity trace, it may be determined that the place of occurrence in the evening is the usual residence of the target active president. Therefore, when the data processing equipment sends the personal data of the target active president personnel to the user terminal, the target address which often appears in the target active president personnel can be sent to the user terminal held by the handling personnel, so that the target active president personnel can be accurately caught.
And S403, sending the personal data of the target active president and the target address to a user terminal held by a clerk.
In the embodiment, the information of all the foreigners related to the stolen vehicle case is matched with the active personnel information set to determine the active foreigners in the target area in the detection period, the active foreigners are focused on the motion track of the active foreigners, the redundant data are reduced, the analysis range of the vehicle stolen case is narrowed, and the detection efficiency of the stolen case is further improved. Meanwhile, after the fact that the target active president is suspicious related to vehicle theft is determined, the personal data of the target active president and the frequently-appearing target address are sent to the user terminal held by the counter staff, accurate capture of the target active president is achieved, and the vehicle theft case 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 obtaining module 501, a second obtaining module 502 and a sending module 503.
Specifically, the first obtaining module 501 is configured to obtain face information of an active predecessor in a detection period of a target area; wherein, the active predecessor personnel are personnel with vehicle theft historical behaviors and activity tracks in the target area in the detection period;
the second obtaining module 502 is configured to obtain behavior data of a target active president person who comes in and goes out of a specific place within the detection period according to the face information of the active president person and snapshot data of a person who comes in and goes out of the specific place within the detection period; wherein the particular locale is within the target area;
the sending module 503 is configured to send information of the target active predecessor to the user terminal when it is determined that the target active predecessor is a suspicious person related to vehicle theft according to the behavior data.
The data processing device provided by the embodiment of the application acquires behavior data of target active president personnel who come in and go out of a specific place in a detection period according to face information of the active president personnel in the target area in the detection period and snapshot data of personnel who come in and go out of the specific place in the detection period, and sends information of the target active president personnel to a user terminal when the target active president personnel are determined to be suspicious personnel related to stealing of a vehicle according to the behavior data so as to remind a clerk to perform key investigation on the target active president personnel. According to the technical scheme, active foreigners with vehicle theft historical behaviors are subjected to key analysis, suspicious persons of vehicle theft are warned in advance, the change of case investigation thinking from case to person is achieved, the investigation efficiency of the vehicle theft case is greatly improved, and meanwhile the occurrence rate of the vehicle theft case is reduced.
Optionally, the behavioral data includes a number of times of occurrence at the particular location, a riding state when entering or exiting the particular location, and an attribute characteristic of a riding vehicle in the presence of the riding vehicle.
On the basis of the foregoing embodiment, optionally, the second obtaining module 502 is specifically configured to match the face information of the pre-activity subject person with snapshot data of a person who comes in and goes out of a specific place in the detection period, so as to obtain the occurrence frequency of the target pre-activity subject person who appears in the specific place in the detection period; and carrying out scene snapshot picture structured analysis on the matched target snapshot data to acquire the riding state of the target active predecessor when the target active predecessor goes in and out the specific place at each time and the attribute characteristics of the riding vehicle of the target active predecessor under the condition that the riding vehicle exists.
On the basis of the foregoing embodiment, optionally, the apparatus further includes: and an analysis module.
Specifically, the analysis module is configured to determine, when the occurrence number is greater than or equal to a preset occurrence number, the riding number of the target active predecessor meeting a preset condition according to the riding state of the target active predecessor when the target active predecessor goes in and out of the specific place each time; if the riding times are larger than or equal to the preset riding times, judging whether the attribute characteristics of the riding vehicles of the target active predecessor are the same each time; if not, determining that the target active predecessor is suspicious people related to vehicle theft; the preset conditions are that a riding vehicle exists when the vehicle enters the specific place and no riding vehicle exists when the vehicle leaves the specific place; the preset riding times are less than or equal to the preset occurrence times.
On the basis of the foregoing embodiment, optionally, the apparatus further includes: the device comprises a third acquisition module, a fourth acquisition module and a processing module.
Specifically, the third obtaining module is configured to obtain information of all predecessors related to the vehicle case theft before the first obtaining module 501 obtains face information of active predecessors in the target area in the detection period;
the fourth acquisition module is used for acquiring an activity personnel information set; the activity personnel information is stored with information of personnel with activity tracks in the target area in the detection time period in a centralized manner;
the processing module is used for respectively matching the information of each president with the active personnel information set so as to determine active president personnel in the target area in the detection period.
On the basis of the foregoing embodiment, optionally, the sending module 503 is specifically configured to obtain an activity track of the target active president person; determining target addresses frequently appearing in the target active president personnel according to the activity track; and sending the personal data of the target active president personnel and the target address to a user terminal held by a clerk.
Optionally, the vehicle comprises an electric vehicle, a motorcycle or a bicycle.
In one embodiment, a data processing apparatus is provided, a schematic structural diagram 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the device is used for storing data involved in the data processing process. 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.
Those skilled in the art will appreciate that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation on the devices to which the present application may be applied, and that a particular device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a data processing apparatus comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the following steps when executing the computer program:
acquiring face information of active president personnel in a target area in a detection period; wherein, the active predecessor personnel are personnel with vehicle theft historical behaviors and activity tracks in the target area in the detection period;
acquiring behavior data of target active president people who come in and go out of the specific place in the detection period according to the face information of the active president people and the snapshot data of people who come in and go out of the specific place in the detection period; wherein the particular locale is within the target area;
and when the target active predecessor is determined to be suspicious people related to vehicle theft according to the behavior data, sending information of the target active predecessor to a user terminal.
Optionally, the behavioral data includes a number of times of occurrence at the particular location, a riding state when entering or exiting the particular location, and an attribute characteristic of a riding vehicle in the presence of the riding vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of: matching the face information of the active predecessor personnel with snapshot data of personnel who come in and go out of a specific place in the detection period so as to obtain the occurrence frequency of the target active predecessor personnel appearing in the specific place in the detection period; and carrying out scene snapshot picture structured analysis on the matched target snapshot data to acquire the riding state of the target active predecessor when the target active predecessor goes in and out the specific place at each time and the attribute characteristics of the riding vehicle of the target active predecessor under the condition that the riding vehicle exists.
In one embodiment, the processor, when executing the computer program, further performs the steps of: when the occurrence times are more than or equal to preset occurrence times, determining the riding times of the target active predecessor under the condition of meeting preset conditions according to the riding state of the target active predecessor when the target active predecessor goes in and out of the specific place each time; if the riding times are larger than or equal to the preset riding times, judging whether the attribute characteristics of the riding vehicles of the target active predecessor are the same each time; if not, determining that the target active predecessor is suspicious people related to vehicle theft; the preset conditions are that a riding vehicle exists when the vehicle enters the specific place and no riding vehicle exists when the vehicle leaves the specific place; the preset riding times are less than or equal to the preset occurrence times.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining information relating to all prior persons who stolen the vehicle case; acquiring an active personnel information set; matching the information of each president with the active personnel information set respectively to determine active president personnel in the target area within the detection time period; the activity personnel information is stored with information of personnel with activity tracks in the target area in the detection period in a centralized manner.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring the activity track of the target active president personnel; determining target addresses frequently appearing in the target active president personnel according to the activity track; and sending the personal data of the target active president personnel and the target address to a user terminal held by a clerk.
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 president personnel in a target area in a detection period; wherein, the active predecessor personnel are personnel with vehicle theft historical behaviors and activity tracks in the target area in the detection period;
acquiring behavior data of target active president people who come in and go out of the specific place in the detection period according to the face information of the active president people and the snapshot data of people who come in and go out of the specific place in the detection period; wherein the particular locale is within the target area;
and when the target active predecessor is determined to be suspicious people related to vehicle theft according to the behavior data, sending information of the target active predecessor to a user terminal.
The data processing device, the equipment and the storage medium provided in the above embodiments can execute the data processing method provided in any embodiment of the present application, and have corresponding functional modules and beneficial effects for executing the method. For technical details that are not described in detail in the above embodiments, reference may be made to a data processing method provided in any embodiment of the present application.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile 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 DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A data processing method, comprising:
acquiring face information of active president personnel in a target area in a detection period; wherein, the active predecessor personnel are personnel with vehicle theft historical behaviors and activity tracks in the target area in the detection period;
acquiring behavior data of target active president people who come in and go out of the specific place in the detection period according to the face information of the active president people and the snapshot data of people who come in and go out of the specific place in the detection period; wherein the particular locale is within the target area;
and when the target active predecessor is determined to be suspicious people related to vehicle theft according to the behavior data, sending information of the target active predecessor to a user terminal.
2. The method of claim 1, wherein the behavioral data includes a number of occurrences at the particular location, a ride status when entering or exiting the particular location, and a property characteristic of a ride vehicle if present.
3. The method according to claim 2, wherein the obtaining behavior data of the target active predecessor person who visits the specific location within the detection period according to the face information of the active predecessor person and the snapshot data of the person who visits the specific location within the detection period comprises:
matching the face information of the active predecessor personnel with snapshot data of personnel who come in and go out of a specific place in the detection period so as to obtain the occurrence frequency of the target active predecessor personnel appearing in the specific place in the detection period;
and carrying out scene snapshot picture structured analysis on the matched target snapshot data to acquire the riding state of the target active predecessor when the target active predecessor goes in and out the specific place at each time and the attribute characteristics of the riding vehicle of the target active predecessor under the condition that the riding vehicle exists.
4. The method of claim 2, wherein said determining from said behavioral data that said target active predecessor is a suspect involved in a stolen vehicle comprises:
when the occurrence times are more than or equal to preset occurrence times, determining the riding times of the target active predecessor under the condition of meeting preset conditions according to the riding state of the target active predecessor when the target active predecessor goes in and out of the specific place each time; the preset conditions are that a riding vehicle exists when the vehicle enters the specific place and no riding vehicle exists when the vehicle leaves the specific place;
if the riding times are larger than or equal to the preset riding times, judging whether the attribute characteristics of the riding vehicles of the target active predecessor are the same each time; wherein the preset riding times are less than or equal to the preset occurrence times;
and if not, determining that the target active predecessor is suspicious personnel related to the stolen vehicle.
5. The method according to any one of claims 1 to 4, wherein before the obtaining face information of active predecessors of the target region within the detection period, the method further comprises:
obtaining information relating to all prior persons who stolen the vehicle case;
acquiring an active personnel information set; the activity personnel information is stored with information of personnel with activity tracks in the target area in the detection time period in a centralized manner;
and matching the information of each president with the active personnel information set respectively to determine active presidents in the target area within the detection period.
6. The method according to any one of claims 1 to 4, wherein the sending information of the target active president to a user terminal comprises:
acquiring the activity track of the target active president personnel;
determining target addresses frequently appearing in the target active president personnel according to the activity track;
and sending the personal data of the target active president personnel and the target address to a user terminal held by a clerk.
7. The method of any one of claims 1 to 4, wherein the vehicle comprises an electric vehicle, a motorcycle, or a bicycle.
8. A data processing apparatus, comprising:
the first acquisition module is used for acquiring the face information of active predecessors in a detection time period of a target area; wherein, the active predecessor personnel are personnel with vehicle theft historical behaviors and activity tracks in the target area in the detection period;
the second acquisition module is used for acquiring behavior data of target active president people who come in and go out of a specific place in the detection period according to the face information of the active president people and snapshot data of people who come in and go out of the specific place in the detection period; wherein the particular locale is within the target area;
and the sending module is used for sending the information of the target active predecessor personnel to the user terminal when the target active predecessor personnel are determined to be suspicious personnel related to vehicle theft according to the behavior data.
9. A data processing device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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