CN112885101A - Method and device for determining abnormal equipment, storage medium and electronic device - Google Patents
Method and device for determining abnormal equipment, storage medium and electronic device Download PDFInfo
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- CN112885101A CN112885101A CN202110342518.3A CN202110342518A CN112885101A CN 112885101 A CN112885101 A CN 112885101A CN 202110342518 A CN202110342518 A CN 202110342518A CN 112885101 A CN112885101 A CN 112885101A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
Abstract
The embodiment of the invention provides a method, a device, a storage medium and an electronic device for determining abnormal equipment, wherein the method comprises the following steps: acquiring snapshot data obtained by snapshot equipment for snapshot of a target object; determining abnormal data which meet a preset condition and are included in the snapshot data; an abnormal device is determined from the snapshot devices based on the abnormal data. According to the invention, the problem of low accuracy of data analysis results caused by the fact that abnormal equipment cannot be accurately determined in the related technology is solved, and the effects of accurately determining the abnormal equipment and improving the accuracy of data analysis are achieved.
Description
Technical Field
The embodiment of the invention relates to the field of communication, in particular to a method and a device for determining abnormal equipment, a storage medium and an electronic device.
Background
With the development of the security industry, various types of monitoring bayonet snapshot equipment on roads are increased rapidly, so that the space-time trajectory data volume is greatly improved, and convenience is brought to traffic analysis. However, due to the increase of the number of the checkpoint positions, some influence is also caused to the traffic data analysis.
On one hand, there are many old bayonet point locations, which may be migrated in the reconstruction and extension process, but the bayonet longitude and latitude are not modified. On the other hand, the bayonet point location equipment of each manufacturer may have data delay and other situations when accessing the unified platform, and the time correction of the bayonet equipment is usually inaccurate. Finally, the situation that the identification of the snap shot pictures at the bayonet is inaccurate due to the fuzzy snap shot and failure of the snap shot caused by severe weather exists. All of the above conditions affect the result of the traffic data analysis. It is therefore necessary to perform a checkpoint quality survey prior to data analysis, to identify and avoid such situations.
Therefore, the problem that the accuracy of the data analysis result is low due to the fact that abnormal equipment cannot be accurately determined exists in the related technology.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a storage medium and an electronic device for determining abnormal equipment, which are used for at least solving the problem that the accuracy of a data analysis result is low due to the fact that the abnormal equipment cannot be determined accurately in the related technology.
According to an embodiment of the present invention, there is provided a method of determining an abnormal device, including: acquiring snapshot data obtained by snapshot equipment for snapshot of a target object; determining abnormal data which meet a preset condition and are included in the snapshot data; and determining abnormal equipment from the snapshot equipment based on the abnormal data.
According to another embodiment of the present invention, there is provided an apparatus for determining an abnormal device, including: the acquisition module is used for acquiring snapshot data obtained by the snapshot equipment for snapshot of the target object; the first determination module is used for determining abnormal data which meet a preset condition and are included in the snapshot data; and the second determining module is used for determining abnormal equipment from the snapshot equipment based on the abnormal data.
According to yet another embodiment of the invention, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by a processor, implements the steps of the method as set forth in any of the above.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, the snapshot data obtained by the snapshot device for snapshotting the target object is obtained, the abnormal data meeting the preset conditions in the snapshot data is determined, and the abnormal device is determined from the snapshot device according to the abnormal data. Abnormal data can be determined by analyzing the snapshot data, and abnormal equipment can be accurately determined according to the abnormal data. Therefore, the problem that the accuracy of the data analysis result is low due to the fact that abnormal equipment cannot be determined accurately in the related technology can be solved, and the effects of determining the abnormal equipment accurately and improving the accuracy of data analysis are achieved.
Drawings
Fig. 1 is a block diagram of a hardware configuration of a mobile terminal of a method of determining an abnormal device according to an embodiment of the present invention;
FIG. 2 is a flow diagram of a method of determining an anomalous device in accordance with an embodiment of the present invention;
fig. 3 is a schematic view of a probe architecture of an anomaly device included in a probe snapshot apparatus according to an embodiment of the present invention;
FIG. 4 is a flowchart of a snapshot device that determines a time-corrected exception according to an exemplary embodiment of the present invention;
FIG. 5 is a flowchart of a snapshot device that determines latitude and longitude errors in accordance with an exemplary embodiment of the present invention;
FIG. 6 is a flowchart of a snapshot device that determines recognition errors according to an exemplary embodiment of the present invention;
fig. 7 is a block diagram of an apparatus for determining an abnormal device according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking an example of the method running on a mobile terminal, fig. 1 is a hardware structure block diagram of the mobile terminal of the method for determining an abnormal device according to the embodiment of the present invention. As shown in fig. 1, the mobile terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, wherein the mobile terminal may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used for storing a computer program, for example, a software program and a module of application software, such as a computer program corresponding to the method for determining an abnormal device in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In the present embodiment, a method for determining an abnormal device is provided, and fig. 2 is a flowchart of the method for determining an abnormal device according to the embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, capturing snapshot data obtained by capturing a target object by a capturing device;
step S204, determining abnormal data which meets a preset condition and is included in the snapshot data;
step S206, determining abnormal equipment from the capturing equipment based on the abnormal data.
In the above embodiment, the vehicle, pedestrian, etc. passing through can be monitored by arranging the gate on the road. Install the equipment of taking a candid photograph on the bayonet socket, the equipment of taking a candid photograph can carry out the image snapshot to vehicle, pedestrian etc. through the bayonet socket to reach the purpose of monitoring vehicle, pedestrian through the bayonet socket. The capturing device may be a camera. The capturing device may capture its field of view at predetermined time intervals. Or when the target object appears in the visual field area, the target object is captured to obtain capture data. Wherein the target object may be a vehicle, a person, or the like. After the snapshot data are obtained, the snapshot data can be analyzed to determine abnormal data, and then abnormal equipment is determined according to the abnormal data. The snapshot data may include snapshot time when the snapshot device takes a snapshot of the target object, an ID of the snapshot device, location information of the snapshot device, time when the snapshot data is stored in the database, and the like. The anomaly data may include data of temporal anomalies, data of positional anomalies of the capturing device, data identifying anomalies. Therefore, the abnormal device may include a capturing device in which there is a time correction abnormality, a capturing device in which latitude and longitude are wrong, and a capturing device in which an error is recognized.
In the above embodiment, a schematic diagram of a probing architecture of an abnormal apparatus included in a probing snapshot apparatus may be seen in fig. 3, as shown in fig. 3, the abnormal apparatus included in the probing snapshot apparatus, that is, the overall bayonet quality probing may include: time correction and exploration of the gate, longitude and latitude error exploration of the point location of the gate and snapshot error problem exploration of the point location of the gate. According to the method and the device, the abnormal data are determined through the space-time data of the target object, and the checkpoint position with the quality problem can be rapidly screened out through the abnormal data. The time correction device comprises time correction abnormity, longitude and latitude abnormity and abnormal bayonet equipment for recognizing. The average speed of the target object passing through the point positions of the two bayonets can be calculated according to the time and space information, and therefore whether the bayonets are abnormal in longitude and latitude or not is judged according to the abnormal condition of the average speed. The abnormal rate of the overall target object of each gate can be counted to judge whether the gate has an abnormal recognition problem.
Along with the development in the security protection field, various monitoring bayonet snapshot devices are arranged on the road at present, and the quality of the bayonet snapshot devices is difficult to probe due to the fact that the bayonet snapshot devices can be of different brands and are installed in different time. The snapshot device provides analysis data for traffic data analysis, and the abnormal gate snapshot device with a fault provides wrong data to influence the analysis result of the traffic data. Therefore, when the traffic data is analyzed, the quality of the analyzed traffic data can be firstly detected, namely, the abnormal data is determined, and after the abnormal data is eliminated, the data analysis is carried out, so that the problem of low accuracy of the traffic data analysis result caused by the quality problem of the snapshot device is solved.
Optionally, the main body of the above steps may be a background processor, or other devices with similar processing capabilities, and may also be a machine integrated with at least an image acquisition device and a data processing device, where the image acquisition device may include a graphics acquisition module such as a camera, and the data processing device may include a terminal such as a computer and a mobile phone, but is not limited thereto.
According to the invention, the snapshot data obtained by the snapshot device for snapshotting the target object is obtained, the abnormal data meeting the preset conditions in the snapshot data is determined, and the abnormal device is determined from the snapshot device according to the abnormal data. Abnormal data can be determined by analyzing the snapshot data, and abnormal equipment can be accurately determined according to the abnormal data. Therefore, the problem that the accuracy of the data analysis result is low due to the fact that abnormal equipment cannot be determined accurately in the related technology can be solved, and the effects of determining the abnormal equipment accurately and improving the accuracy of data analysis are achieved.
In one exemplary embodiment, determining abnormal data included in the snapshot data that satisfies a predetermined condition includes: determining the snapshot time of each snapshot data included in the snapshot data and the storage time for storing each snapshot data into a target database; determining the difference value between the capturing time and the storage time corresponding to each capturing data as a first time difference corresponding to each capturing data; and determining the corresponding snapshot data with the first time difference larger than a preset time length as the abnormal data meeting the preset condition. In this embodiment, the abnormal data may be time abnormal data, and when determining whether the snapshot data is the time abnormal data, the snapshot time of each snapshot data may be first obtained, and the storage time of each snapshot data stored in the target database may be first obtained, and it is determined whether each snapshot data is the abnormal data by determining the precedence relationship between the snapshot time and the storage time. For example, when the target object is a vehicle, taking vehicle trajectory data as an example, the capturing device ID in each record of the trajectory data is acquired as a cluster ID, and the capturing time of the vehicle and the warehousing time of the vehicle (corresponding to the above-mentioned storage time) in the record are acquired at the same time. Generally, the vehicle is captured firstly and then enters the garage, so that the capturing time should be earlier than the warehousing time, otherwise, the time correction of the bayonet device is not accurate. Therefore, the time difference between the snapshot time cap _ time and the warehousing time create _ time may be calculated, and if the time difference is a positive value and is greater than the time threshold θ, that is, the cap _ time-create _ time > -, it is determined that the data is abnormal data. That is, the record of the snapshot device generating the data has the condition of inaccurate time correction. The time threshold θ is a preset time period, and the preset time period may be determined according to an actual situation, for example, 30 minutes, 40 minutes, and the like, which is not limited in the present invention.
In the above embodiment, after the abnormal device is determined, the time difference condition of each record generated by the abnormal device may be counted, then clustering is performed according to each bayonet device, the number of inaccurate timing counts of each bayonet point location device is counted, and a bayonet point location device list with the total number greater than a threshold value is returned. The flowchart of the snapshot device for determining the time correction abnormality can be seen in fig. 4.
In one exemplary embodiment, determining abnormal data included in the snapshot data that satisfies a predetermined condition includes: determining first position information of the target object based on first snapshot data included in the snapshot data, wherein the first snapshot data is data generated after the first snapshot device takes a snapshot of the target object; determining second position information of the target object based on second snapshot data included in the snapshot data, wherein the second snapshot data is generated after the second snapshot device takes a snapshot of the target object, and the first snapshot data and the second snapshot data are adjacent data in the snapshot time; determining a target distance based on the first location information and the second location information; determining a first snapshot time at which the first snapshot data is generated and a second snapshot time at which the second snapshot data is generated; determining a second time difference between the first snapshot time and the second snapshot time; determining a travel speed of the target object based on the target distance and the second time difference; determining the abnormal data satisfying the predetermined condition based on the first and second snapshot data in a case where the travel speed is outside a predetermined speed interval. In this embodiment, the abnormal data may also be data of abnormal position of the capturing device, that is, longitude and latitude abnormal data. When the longitude and latitude abnormal data are determined, whether the longitude and latitude are abnormal or not can be judged according to the speed by calculating the speed of the target object. Specifically, for each target object, the trajectory data of the target object may be determined according to the identification information of the target object and the snapshot data. For example, when the target object is a vehicle, the snapshot device takes a snapshot of the target object, and license plate information and snapshot time of the target object can be obtained. Therefore, the snapshot data can be sequenced according to the license plate information and the snapshot time of the target object, and the track information of the target object is determined. And (4) taking the sequenced track data of each license plate number, and calculating the speed between the front record and the rear record.
In the above embodiment, two adjacent capturing devices that capture the target object sequentially may be determined according to the trajectory data. That is, the two capturing devices are devices that generate two adjacent data in the trajectory data, and the geographic positions of the two capturing devices may be adjacent or not adjacent. The snapshot device can trigger the snapshot operation when the target object enters the visual field range of the target object, and therefore the latitude and longitude information of the snapshot device can be determined as the latitude and longitude information of the target device. That is, the first location information and the second location information may be latitude and longitude information of a capturing device that captures the target object. After the latitude and longitude information is determined, the distance between the two capturing devices, namely the target distance, can be determined according to the latitude and longitude information. The calculation method is as follows:
dist_angle(A,B)＝sin(latA*π/180)*sin(latB*π/180)+
cos(latA*π/180)*cos(latB*π/180)*cos((longA-long2)*π/180)
dist(A,B)＝6371000*acos(dist_angle)。
and calculating the time difference time _ dis (A, B) of two front records and two rear records, namely the second time difference:
time_dis(A,B)＝cap_timeA-cap_timeB。
here, the cap _ timeA may represent the second capturing time, and the cap _ timeB may represent the first capturing time. dist _ angle is a cosine value of an included angle between two pieces of capturing equipment and is a middle variable, latA is the latitude of the second capturing equipment, latB is the latitude of the first capturing equipment, longA is the longitude of the second capturing equipment, and longB is the longitude of the first capturing equipment.
In the above embodiment, after the target distance and the second time difference are determined, the distance may be divided by the time difference, and then converted into km/h to determine the speed of the target object: speed (a, B) — (dist (a, B)/time _ dis (a, B)) × 3.6, and if the calculated speed is outside the predetermined speed interval, the abnormal data is further determined from the first snapshot data and the second snapshot data. For example, if the calculated speed is greater than a given maximum speed threshold or less than a minimum speed threshold, it may be determined that the speed is abnormal, and in the case of a speed abnormality, further abnormal data may be determined from the first snapshot data and the second snapshot data.
In the above embodiment, when it is determined that the driving speed of the target object is outside the predetermined speed interval, the error number of the first capturing device and the error number of the second capturing device may be increased by one, the above process is performed on the trajectory data of each target object, and then the total error number of each capturing device and the total number of vehicles passing captured by each capturing device are aggregated and counted according to each capturing device. And (4) obtaining the error rate of each capturing device according to the calculated total error number and the total vehicle passing number captured by each capturing device, wherein the error rate is the card port error number/the card port total vehicle passing number. And filtering the checkpoints according to the fact that the error rate is greater than a given probability threshold and the error number is greater than a given number threshold, and generating a suspected longitude and latitude inventory table of the checkpoint equipment.
In one exemplary embodiment, determining the abnormality data satisfying the predetermined condition based on the first snapshot data and the second snapshot data includes: determining whether the first snapshot time and the second snapshot time are abnormal or not; determining the second snapshot data as the abnormal data meeting the predetermined condition under the condition that the first snapshot time is determined to have abnormality and the second snapshot time is determined not to have abnormality; determining the first snapshot data as the abnormal data satisfying the predetermined condition, if it is determined that there is no abnormality in the first snapshot time and there is an abnormality in the second snapshot time; under the condition that the first snapshot time and the second snapshot time are not abnormal, determining third snapshot data adjacent to the first snapshot data and fourth snapshot data adjacent to the second snapshot data, wherein the third snapshot data are different from the second snapshot data, and the fourth snapshot data are different from the first snapshot data; determining the abnormal data satisfying the predetermined condition based on the third and fourth snapshot data. In the present embodiment, when the calculated traveling speed of the target object is outside the predetermined speed interval, it may be caused by inaccurate capturing time by the capturing device, and therefore, when determining abnormal data, it is necessary to exclude the data with inaccurate capturing time. The method can eliminate the inaccurate time correction checkpoint position list generated by exploring the time and space timing problem of the checkpoint position from the suspected longitude and latitude list table because the speed is influenced by two factors of time and distance, and the generated suspected longitude and latitude list of the checkpoint position can also be caused by inaccurate time.
In one exemplary embodiment, determining the abnormality data satisfying the predetermined condition based on the third snapshot data and the fourth snapshot data includes: determining whether the third snapshot data and the fourth snapshot data have an abnormality; determining the first snapshot data as the abnormal data meeting the predetermined condition under the condition that the third snapshot data is determined not to have abnormality; determining that the second snapshot data is the abnormal data meeting the predetermined condition when it is determined that the fourth snapshot data is not abnormal. In this embodiment, after the data with inaccurate snapshot time is eliminated, the data with inaccurate data caused by other data inaccuracy is also required to be eliminated. The generated suspected longitude and latitude list table of the bayonet point location has the possibility of inaccurate error rate, the reason is that two bayonets are mutually influenced, if one bayonet has the longitude and latitude error, the adjacent bayonets are influenced by the suspected longitude and latitude list table, the calculated speed of the adjacent bayonets is also in a preset speed area, and the error rate of the normal bayonets of the longitude and latitude is increased, so the bayonets are sorted according to the error rate and the error number, the wrong longitude and latitude bayonets are taken according to the sequence and are placed into a new table, and meanwhile, other bayonets influenced by the bayonets are removed and sequentially operated to generate the bayonet longitude and. That is, in the trajectory data, if the third snapshot data adjacent to the first snapshot data is abnormal data, the third snapshot data may affect the first snapshot data, so that the first snapshot data is suspected abnormal data. A flowchart of the snapshot apparatus for determining the longitude and latitude errors can be seen in fig. 5.
In one exemplary embodiment, determining abnormal data included in the snapshot data that satisfies a predetermined condition includes: clustering the snapshot data based on the snapshot object categories to divide the snapshot data into a plurality of categories; determining a first amount of data included in each category, respectively; determining data included in the categories of which the first number is smaller than a first threshold as the abnormal data satisfying a predetermined condition. In this embodiment, the capturing apparatus can capture all the captured objects passing through its field of view, and recognize the identification information of the captured objects. When the snap-shot object is a vehicle, the identification information may be a license plate. Therefore, the snapshot data can include a plurality of license plate information, the snapshot objects can be classified according to the license plate information, and the data with the same license plate information is determined as data of one category. And respectively determining the quantity of the data included in each category, and when the quantity is determined to be smaller than a first threshold value, considering the data as abnormal data. Taking vehicle track data as an example, counting the number of times of each license plate number in the vehicle track; if the number of times of occurrence of the vehicle number is less than or equal to two times, the number plate is considered to be possibly not a real number plate, and the number plate is caused by recognition errors of the snapshot equipment. Therefore, the data can be determined as abnormal data. After the abnormal data is determined, the error number of the checkpoint equipment through which the license plate number passes can be increased by one. That is, the number of errors of the snapshot apparatus that recognized the license plate number is increased by 1. And carrying out aggregation statistics on each checkpoint equipment, and carrying out statistics on the total number of identification errors and the total number of vehicles passing through the checkpoint. And calculating the identification error rate of the checkpoint equipment, wherein the error rate is the total number of checkpoint errors/total number of checkpoint vehicles passing, namely error _ rate is count (error)/count (all). And generating a checkpoint equipment identification error list. A flowchart of determining the snapshot device that identified the error can be seen in fig. 6.
In an exemplary embodiment, determining an abnormal device from the capturing devices based on the abnormal data includes: determining a second amount of the abnormal data generated by each device included in the capturing device; determining devices generating the second number of abnormal data greater than a second threshold as abnormal devices. In this embodiment, after determining the abnormal data, the capturing device that generates the data cannot be directly determined as the abnormal device. A second number of abnormal data generated by each device within a predetermined time may be counted, and in a case where it is determined that the second number is greater than a second threshold value, the device may be determined as an abnormal device. Initially, the error number of each snapshot device may be recorded as 0, when each error data is generated, the error number is incremented by one, the error number of each snapshot device in a predetermined time is counted, and an abnormal snapshot device is determined according to the error number. It should be noted that the error count of each snapshot device is respectively counted according to the abnormal data type, that is, the snapshot time error count, the position information error count, and the identification data error count of each snapshot device may be counted.
In the above embodiment, the third amount of the abnormal data of each type generated by each capturing device may also be determined according to the abnormal data, the error rate of generating the abnormal data of each type is respectively determined, and when the error rate is greater than the preset error rate threshold, the capturing device is determined as the abnormal device of the type.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a device for determining an abnormal device is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 7 is a block diagram of an apparatus for determining an abnormal device according to an embodiment of the present invention, as shown in fig. 7, the apparatus including:
the acquisition module 72 is configured to acquire snapshot data obtained by the snapshot device snapping the target object;
a first determining module 74, configured to determine abnormal data included in the snapshot data, where the abnormal data satisfies a predetermined condition;
a second determining module 76, configured to determine an abnormal device from the capturing devices based on the abnormal data.
In an exemplary embodiment, the first determining module 74 may determine abnormal data included in the snapshot data, which satisfies a predetermined condition, by: determining the snapshot time of each snapshot data included in the snapshot data and the storage time for storing each snapshot data into a target database; determining the difference value between the capturing time and the storage time corresponding to each capturing data as a first time difference corresponding to each capturing data; and determining the corresponding snapshot data with the first time difference larger than a preset time length as the abnormal data meeting the preset condition.
In an exemplary embodiment, the first determining module 74 may determine abnormal data included in the snapshot data, which satisfies a predetermined condition, by: determining first position information of the target object based on first snapshot data included in the snapshot data, wherein the first snapshot data is data generated after the first snapshot device takes a snapshot of the target object; determining second position information of the target object based on second snapshot data included in the snapshot data, wherein the second snapshot data is generated after the second snapshot device takes a snapshot of the target object, and the first snapshot data and the second snapshot data are adjacent data in the snapshot time; determining a target distance based on the first location information and the second location information; determining a first snapshot time at which the first snapshot data is generated and a second snapshot time at which the second snapshot data is generated; determining a second time difference between the first snapshot time and the second snapshot time; determining a travel speed of the target object based on the target distance and the second time difference; determining the abnormal data satisfying the predetermined condition based on the first and second snapshot data in a case where the travel speed is outside a predetermined speed interval.
In an exemplary embodiment, the first determination module 74 may determine the abnormal data satisfying the predetermined condition based on the first snapshot data and the second snapshot data by: determining whether the first snapshot time and the second snapshot time are abnormal or not; determining the second snapshot data as the abnormal data meeting the predetermined condition under the condition that the first snapshot time is determined to have abnormality and the second snapshot time is determined not to have abnormality; determining the first snapshot data as the abnormal data satisfying the predetermined condition, if it is determined that there is no abnormality in the first snapshot time and there is an abnormality in the second snapshot time; under the condition that the first snapshot time and the second snapshot time are not abnormal, determining third snapshot data adjacent to the first snapshot data and fourth snapshot data adjacent to the second snapshot data, wherein the third snapshot data are different from the second snapshot data, and the fourth snapshot data are different from the first snapshot data; determining the abnormal data satisfying the predetermined condition based on the third and fourth snapshot data.
In an exemplary embodiment, the first determining module 74 may determine the abnormal data satisfying the predetermined condition based on the third snapshot data and the fourth snapshot data by: determining whether the third snapshot data and the fourth snapshot data have an abnormality; determining the first snapshot data as the abnormal data meeting the predetermined condition under the condition that the third snapshot data is determined not to have abnormality; determining that the second snapshot data is the abnormal data meeting the predetermined condition when it is determined that the fourth snapshot data is not abnormal.
In an exemplary embodiment, the first determining module 74 may determine abnormal data included in the snapshot data, which satisfies a predetermined condition, by: clustering the snapshot data based on the snapshot object categories to divide the snapshot data into a plurality of categories; determining a first amount of data included in each category, respectively; determining data included in the categories of which the first number is smaller than a first threshold as the abnormal data satisfying a predetermined condition.
In an exemplary embodiment, the second determining module 76 may determine an abnormal device from the capturing devices based on the abnormal data by: determining a second amount of the abnormal data generated by each device included in the capturing device; determining devices generating the second number of abnormal data greater than a second threshold as abnormal devices.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method as set forth in any of the above.
In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.
It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and they may be implemented using program code executable by the computing devices, such that they may be stored in a memory device and executed by the computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into various integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of determining an anomalous device, comprising:
acquiring snapshot data obtained by snapshot equipment for snapshot of a target object;
determining abnormal data which meet a preset condition and are included in the snapshot data;
and determining abnormal equipment from the snapshot equipment based on the abnormal data.
2. The method according to claim 1, wherein determining abnormal data included in the snapshot data that satisfies a predetermined condition includes:
determining the snapshot time of each snapshot data included in the snapshot data and the storage time for storing each snapshot data into a target database;
determining the difference value between the capturing time and the storage time corresponding to each capturing data as a first time difference corresponding to each capturing data;
and determining the corresponding snapshot data with the first time difference larger than a preset time length as the abnormal data meeting the preset condition.
3. The method according to claim 1, wherein determining abnormal data included in the snapshot data that satisfies a predetermined condition includes:
determining first position information of the target object based on first snapshot data included in the snapshot data, wherein the first snapshot data is data generated after the first snapshot device takes a snapshot of the target object;
determining second position information of the target object based on second snapshot data included in the snapshot data, wherein the second snapshot data is generated after the second snapshot device takes a snapshot of the target object, and the first snapshot data and the second snapshot data are adjacent data in the snapshot time;
determining a target distance based on the first location information and the second location information;
determining a first snapshot time at which the first snapshot data is generated and a second snapshot time at which the second snapshot data is generated;
determining a second time difference between the first snapshot time and the second snapshot time;
determining a travel speed of the target object based on the target distance and the second time difference;
determining the abnormal data satisfying the predetermined condition based on the first and second snapshot data in a case where the travel speed is outside a predetermined speed interval.
4. The method according to claim 3, wherein determining the anomaly data that satisfies the predetermined condition based on the first snapshot data and the second snapshot data comprises:
determining whether the first snapshot time and the second snapshot time are abnormal or not;
determining the second snapshot data as the abnormal data meeting the predetermined condition under the condition that the first snapshot time is determined to have abnormality and the second snapshot time is determined not to have abnormality;
determining the first snapshot data as the abnormal data satisfying the predetermined condition, if it is determined that there is no abnormality in the first snapshot time and there is an abnormality in the second snapshot time;
under the condition that the first snapshot time and the second snapshot time are not abnormal, determining third snapshot data adjacent to the first snapshot data and fourth snapshot data adjacent to the second snapshot data, wherein the third snapshot data are different from the second snapshot data, and the fourth snapshot data are different from the first snapshot data; determining the abnormal data satisfying the predetermined condition based on the third and fourth snapshot data.
5. The method according to claim 4, wherein determining the abnormality data that satisfies the predetermined condition based on the third snapshot data and the fourth snapshot data includes:
determining whether the third snapshot data and the fourth snapshot data have an abnormality;
determining the first snapshot data as the abnormal data meeting the predetermined condition under the condition that the third snapshot data is determined not to have abnormality;
determining that the second snapshot data is the abnormal data meeting the predetermined condition when it is determined that the fourth snapshot data is not abnormal.
6. The method according to claim 1, wherein determining abnormal data included in the snapshot data that satisfies a predetermined condition includes:
clustering the snapshot data based on the snapshot object categories to divide the snapshot data into a plurality of categories;
determining a first amount of data included in each category, respectively;
determining data included in the categories of which the first number is smaller than a first threshold as the abnormal data satisfying a predetermined condition.
7. The method of claim 1, wherein determining an abnormal device from the snap-shot devices based on the abnormal data comprises:
determining a second amount of the abnormal data generated by each device included in the capturing device;
determining devices generating the second number of abnormal data greater than a second threshold as abnormal devices.
8. An apparatus for determining an abnormal device, comprising:
the acquisition module is used for acquiring snapshot data obtained by the snapshot equipment for snapshot of the target object;
the first determination module is used for determining abnormal data which meet a preset condition and are included in the snapshot data;
and the second determining module is used for determining abnormal equipment from the snapshot equipment based on the abnormal data.
9. A computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 7.
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