CN113918563A - Method and device for determining deployment control information, storage medium and electronic device - Google Patents
Method and device for determining deployment control information, storage medium and electronic device Download PDFInfo
- Publication number
- CN113918563A CN113918563A CN202111139040.0A CN202111139040A CN113918563A CN 113918563 A CN113918563 A CN 113918563A CN 202111139040 A CN202111139040 A CN 202111139040A CN 113918563 A CN113918563 A CN 113918563A
- Authority
- CN
- China
- Prior art keywords
- real
- data
- abnormal
- time data
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000002159 abnormal effect Effects 0.000 claims abstract description 132
- 238000004590 computer program Methods 0.000 claims description 18
- 206010000117 Abnormal behaviour Diseases 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 3
- 238000012545 processing Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 9
- 238000011835 investigation Methods 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 6
- 238000004140 cleaning Methods 0.000 description 6
- 238000007726 management method Methods 0.000 description 5
- 230000002547 anomalous effect Effects 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 238000012216 screening Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 241001622623 Coeliadinae Species 0.000 description 1
- 238000012351 Integrated analysis Methods 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000002354 daily effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004801 process automation Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 208000008918 voyeurism Diseases 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisions for transferring data to distant stations, e.g. from a sensing device
- G06K17/0029—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisions for transferring data to distant stations, e.g. from a sensing device the arrangement being specially adapted for wireless interrogation of grouped or bundled articles tagged with wireless record carriers
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Networks & Wireless Communication (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Alarm Systems (AREA)
Abstract
The embodiment of the invention provides a method and a device for determining control information, a storage medium and an electronic device, wherein the method comprises the following steps: determining whether an object included in the real-time data is abnormal or not by using the real-time data acquired in the target area to obtain an object state of the object; determining the probability of abnormal objects in a target area in a preset time period by using N object states corresponding to N objects in N real-time data in the preset time period; and determining deployment information for deploying the target area based on the probability of the abnormal object. By the method and the device, the problem of inaccurate control of the abnormal object in the related technology is solved, and the effects of accurately analyzing the abnormal object and accurately controlling the target area are achieved.
Description
Technical Field
The embodiment of the invention relates to the field of data processing, in particular to a method and a device for determining deployment and control information, a storage medium and an electronic device.
Background
In recent years, with improvement of living standards and technological progress of people, automobiles also become common transportation tools for people to go out daily, and great convenience is brought to life and work of people. On the other hand, however, automobiles provide opportunities for people to escape from hit-and-run, drive-and-track, etc., while providing convenience for people's life and work. If the travel information of the vehicles can be found, the method provides great convenience for policemen to find a certain person. With the development of internet technology and information processing technology, intelligent analysis and processing of various information become popular research content in the industry. In addition, the traffic equipment in all areas is greatly increased nowadays, and the construction of traffic information engineering also forms a mature information acquisition technology of vehicles. In the prior art, vehicle data acquisition equipment at each gate collects vehicle passing data at each gate and stores the data in a relational database or dispersed unrelated text data. If the number of the passing vehicles is acquired, two ways are available: manually making statistics and making statistics from relational data.
However, when the feature information of the vehicle passing through the gate collected by the collecting device is incomplete, for example, the brand of the vehicle and the shielding of the license plate are not clearly seen, the incomplete feature is not necessarily caused by human factors, but may be caused by the shooting direction, angle, light and the like of the collecting device, or the situation that the recording personnel only clearly memorize a part of the license plate, the color, the brand and the like of the vehicle at that time. When the storage amount of the vehicle passing data is so large, it is common to find a vehicle with insufficient characteristics as a sea fishing needle. The search range of the vehicle passing data is narrowed through screening, the subsequent workload can be reduced by times and hundreds of times, and the search cost is saved. In the prior art, frequent vehicle passing is to count the number of times that a vehicle passes through a certain place (a gate), screen out/capture vehicle data according to preset conditions by the number of times that the vehicle passes through the vehicle, so as to narrow the search range, and screen out a vehicle passing data set reaching a certain number of vehicle passing times, namely called as frequent vehicle passing.
In the prior art, the bayonet data exists in a relational database or is dispersed into text data which is not related, and in the huge data volume, manual statistics for finding frequent vehicle passing data is obviously unrealistic (the data volume is huge, and data is found manually according to a certain rule); the relational database stores data according to a form of a crossed two-dimensional table, the storage cost is increased due to large amount of stored data, and the target vehicle passing data cannot be quickly inquired and positioned due to traversing of huge data stored in the two-dimensional table, so that the real-time inquiry of the frequent vehicle passing data cannot be realized. The simple query also needs a waiting time of several minutes, and the speed of counting the frequent vehicle passing data is reduced, so that the actual real-time query and counting purposes are far from being met.
In view of the above technical problems, no effective solution has been proposed in the related art.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining control information, a storage medium and an electronic device, which are used for at least solving the problem of inaccurate control of abnormal objects in the related art.
According to an embodiment of the present invention, there is provided a method for determining deployment information, including: determining whether an object included in real-time data is abnormal or not by using the real-time data acquired in a target area to obtain an object state of the object, wherein the real-time data includes data information associated with the object, and the object state includes an abnormal state, and the abnormal state is used for triggering management and control information for managing and controlling the target area; determining the probability of the abnormal object in the target area in a preset time period by using N object states corresponding to N objects included in N real-time data in the preset time period, wherein N is a natural number greater than or equal to 1, and the N real-time data are used for representing N data counted in real time in the preset time period; and determining deployment information for deploying the target area based on the probability of the abnormal object.
According to another embodiment of the present invention, there is provided a control information determination apparatus including: a first determining module, configured to determine, by using real-time data acquired in a target area, whether an object included in the real-time data is abnormal, to obtain an object state of the object, where the real-time data includes data information associated with the object, and the object state includes an abnormal state, and the abnormal state is used to trigger control information for controlling the target area; a second determining module, configured to determine, by using N object states corresponding to N objects included in N real-time data within a preset time period, a probability that an abnormal object occurs in the target area within the preset time period, where N is a natural number greater than or equal to 1, and the N real-time data are used to represent N data counted in real time within the preset time period; and the third determining module is used for determining the deployment information for deploying the target area based on the probability of the abnormal object.
In an exemplary embodiment, the first determining module includes one of: a first determining unit, configured to determine that the object is in an abnormal state when it is determined from the real-time data that the object has abnormal behavior in the target area; a second determination unit configured to determine that the object is in an abnormal state when it is determined from the real-time data that the object information of the object matches the object information of a target abnormal object; and the third determining unit is used for determining that the object is in an abnormal state under the condition that the accumulated frequency of the object appearing in the target area is determined to be greater than a first preset frequency from the real-time data.
In an exemplary embodiment, the second determining module includes: a first recording unit, configured to record real-time data in which an abnormal object occurs in the N pieces of real-time data, and obtain a first number of the abnormal objects occurring within the preset time period; a fourth determining unit, configured to determine, using the first number and the N, a probability that the abnormal object occurs in the target area within the preset time period.
In an exemplary embodiment, the apparatus further includes: a fourth determining module, configured to determine, by using N object states corresponding to N objects included in N real-time data within a preset time period, a data latitude in the M acquired real-time data before determining a probability that the target region has an abnormal object within the preset time period, where M is a natural number greater than or equal to N; and the first merging module is used for merging the data with the same latitude in the M real-time data to obtain the N real-time data.
In an exemplary embodiment, the apparatus further includes: the first obtaining module is configured to obtain real-time data before determining whether an object included in the real-time data is abnormal, using the real-time data obtained in a target area, where the real-time data includes at least one of: non-motor vehicle information associated with the object acquired by the Radio Frequency Identification (RFID) device, attribute information of the object, MAC address information of the object acquired by the Media Access Control (MAC) device, and motor vehicle information associated with the object.
In an exemplary embodiment, the apparatus further includes: the first recording module is configured to record the real-time data into a data table according to a data latitude to which the real-time data belongs after the real-time data is acquired, where the data latitude includes at least one of: non-motor vehicle information, attribute information of the object, MAC address information, and motor vehicle information.
In an exemplary embodiment, the apparatus further includes: the second recording module is configured to determine whether an object included in the real-time data is abnormal or not by using the real-time data acquired in the target area, and after the object state of the object is obtained, record the abnormal state of the object into the data table in the case that the object is an abnormal object, so as to determine a probability that the abnormal object occurs in the target area within the preset time period.
According to a further embodiment of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
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 method and the device, whether the object included in the real-time data is abnormal or not is determined by utilizing the real-time data acquired in the target area, so that the object state of the object is obtained, wherein the real-time data includes data information related to the object, the object state includes an abnormal state, and the abnormal state is used for triggering management and control information for managing and controlling the target area; determining the probability of abnormal objects in a target region in a preset time period by using N object states corresponding to N objects in N real-time data in the preset time period, wherein N is a natural number greater than or equal to 1, and the N real-time data are used for representing N data counted in real time in the preset time period; and determining deployment information for deploying the target area based on the probability of the abnormal object. The method achieves the purpose of analyzing the abnormal object in multiple dimensions, and can accurately analyze the probability of the abnormal object in the target area. Therefore, the problem of inaccurate control of the abnormal object in the related technology can be solved, and the effects of accurately analyzing the abnormal object and accurately controlling the target area are achieved.
Drawings
Fig. 1 is a block diagram of a hardware structure of a mobile terminal of a method for determining deployment control information according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of determining arming information according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a Geohash region according to an embodiment of the present invention;
FIG. 4 is an overall flow diagram according to an embodiment of the invention;
FIG. 5 is a raw data cleansing flow diagram according to an embodiment of the present invention;
FIG. 6 is a flow diagram of data summarization according to an embodiment of the present invention;
FIG. 7 is a flow diagram of data processing for objects that frequently cross a Geohash region, according to an embodiment of the invention;
FIG. 8 is a flowchart illustrating a process of processing Geohash region frequent crossing checkpoints data according to an embodiment of the present invention;
FIG. 9 is a flow chart of determining an abnormal object according to an embodiment of the present invention;
fig. 10 is a block diagram of a configuration of a device for determining deployment information 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 operation on a mobile terminal, fig. 1 is a hardware structure block diagram of the mobile terminal of a method for determining deployment and control information according to an 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 to store a computer program, for example, a software program and a module of application software, such as a computer program corresponding to the determination method of the deployment control information 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 for receiving or transmitting 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), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In this embodiment, a method for determining deployment information is provided, and fig. 2 is a flowchart of the method for determining deployment information according to the embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, determining whether an object included in real-time data is abnormal or not by using the real-time data acquired in a target area to obtain an object state of the object, wherein the real-time data includes data information associated with the object, the object state includes an abnormal state, and the abnormal state is used for triggering management and control information for managing and controlling the target area;
step S204, determining the probability of abnormal objects in the target area in the preset time period by using N object states corresponding to N objects included in N real-time data in the preset time period, wherein N is a natural number greater than or equal to 1, and the N real-time data are used for representing N data counted in real time in the preset time period;
step S206, determining deployment information for deploying the target area based on the probability of the abnormal object.
The execution subject of the above steps may be a terminal, but is not limited thereto.
The embodiment includes, but is not limited to, being applied to a scene for checking abnormal objects, for example, a traffic intersection, a train station, and the like.
In the present embodiment, the object includes, but is not limited to, a pedestrian, and the object in the abnormal state includes, but is not limited to, a certain kind of person. The real-time data includes, but is not limited to, face information appearing in the target area, a model of the vehicle, a license plate number, information of a non-motor vehicle, information of a motor vehicle, and the like.
In this embodiment, the target area includes, but is not limited to, a Geohash area, the Geohash is a division of a geographic space, and is set by human customization, and the bayonet device, the MAC device, and the RFID device in the space are managed in a unified manner. For example, as shown in fig. 3, each gate, RFID radio frequency device, and MAC acquisition device in a city are installed in each traffic in the city, and a Geohash area is formed by artificially dividing a monitoring area on a map, so that data acquired in the area can be analyzed and an alarm can be given in advance.
In the present embodiment, the management and control information includes, but is not limited to, information that triggers the deployment of police force in the target area, such as alarm information; the deployment information includes, but is not limited to, information for police deployment of the target area, such as deployment of police cars, police officers, etc.
For example, the present embodiment acquires 10 pieces of real-time data during a day, wherein 5 pieces of real-time data trigger an alarm. The probability of an abnormal object occurring within the target area during the time of day is 50%. A certain police force is deployed in the target area according to the probability.
Through the steps, whether an object included in real-time data is abnormal or not is determined by utilizing the real-time data acquired in the target area, and an object state of the object is obtained, wherein the real-time data includes data information related to the object, the object state includes an abnormal state, and the abnormal state is used for triggering management and control information for managing and controlling the target area; determining the probability of abnormal objects in a target region in a preset time period by using N object states corresponding to N objects in N real-time data in the preset time period, wherein N is a natural number greater than or equal to 1, and the N real-time data are used for representing N data counted in real time in the preset time period; and determining deployment information for deploying the target area based on the probability of the abnormal object. The method achieves the purpose of analyzing the abnormal object in multiple dimensions, and can accurately analyze the probability of the abnormal object in the target area. Therefore, the problem of inaccurate control of the abnormal object in the related technology can be solved, and the effects of accurately analyzing the abnormal object and accurately controlling the target area are achieved.
In an exemplary embodiment, the determining, by using the real-time data acquired in the target area, an object abnormality included in the real-time data, and obtaining an object state of the object, includes one of:
s1, determining that the object is in an abnormal state under the condition that the object is determined to have abnormal behavior in the target area from the real-time data; the abnormal behaviors include, but are not limited to, behaviors such as stealing, peeping, stealing things and the like;
s2, determining that the object is in an abnormal state when the object information of the object is matched with the object information of the target abnormal object from the real-time data; the target abnormal object comprises a certain person;
and S3, determining that the object is in an abnormal state under the condition that the accumulated number of times that the object appears in the target area is determined to be larger than the first preset number of times from the real-time data. For example, a person who frequently enters and exits at a certain gate may be determined as an abnormal state.
In an exemplary embodiment, determining, by using N object states corresponding to N objects included in N real-time data within a preset time period, a probability that an abnormal object occurs in a target region within the preset time period includes:
s1, recording the real-time data of the abnormal objects in the N real-time data to obtain a first number of the abnormal objects in a preset time period;
and S2, determining the probability of the abnormal object in the target area within the preset time period by using the first number and the N.
In this embodiment, for example, 10 pieces of real-time data are acquired during a day, wherein 5 pieces of real-time data trigger an alarm. The probability of an abnormal object occurring within the target area during the time of day is 50%. A certain police force is deployed in the target area according to the probability.
In an exemplary embodiment, before determining the probability of the abnormal object occurring in the target region within the preset time period by using N object states corresponding to N objects included in N real-time data within the preset time period, the method further includes:
s1, determining the data latitude in the M acquired real-time data, wherein M is a natural number which is greater than or equal to N;
and S2, merging the data with the same latitude in the M real-time data to obtain N real-time data.
In this embodiment, the influence of the same data with different dimensions on the analysis result is mainly handled, for example, the data is merged into a wide table, and people, vehicles, RFIDs, MACs in the same bayonet interval time (interval time is set manually) range can merge data related to the attributes of people into one piece of data, and perform data type marking, and the like.
In this embodiment, for example, the association relationship between data of different dimensions includes: the method comprises the following steps that people and electric vehicles are searched for the electric vehicles in an electric vehicle record library through relevant information of the people; checking related information of the personnel in a personnel basic library through the information of the electric vehicle; human and automobile: searching the motor vehicle of the person in a motor vehicle record library through the related information of the person; checking the related information of the personnel in a personnel basic library through the motor vehicle information; object and MAC: and positioning is carried out through the MAC address, the range of the MAC address appears in the control circle, and personnel examination is carried out on the range of the track of the MAC address.
In an exemplary embodiment, before determining whether an object included in the real-time data is abnormal using the real-time data acquired in the target region, the method further includes:
s1, acquiring real-time data, wherein the real-time data comprises at least one of the following data: non-motor vehicle information associated with the object, acquired by the Radio Frequency Identification (RFID) device, attribute information of the object, MAC address information of the object, acquired by the Media Access Control (MAC) device, motor vehicle information associated with the object.
In this embodiment, for example, the gate, the MAC acquisition device, and the RFID radio frequency device in the Geohash area perform various types of data acquisition.
In an exemplary embodiment, after acquiring the real-time data, the method further comprises:
s1, recording the real-time data into a data table according to the data latitude to which the real-time data belongs, wherein the data latitude comprises at least one of the following: non-motor vehicle information, attribute information of the object, MAC address information, motor vehicle information.
In this embodiment, the method further includes processing the N real-time data, for example, deleting abnormal data in the data information to obtain the cleaning data, where the abnormal data includes at least one of: data with incomplete information in the data information, data which cannot be modified in the data information, and data with repeated information in the data information.
In this embodiment, the data in the data information may be cleaned in batches, for example, data that cannot be completed is deleted, data with wrong format is deleted, duplicate data is deleted, and the like. And merging the data information with the same data latitude in the cleaning data information to obtain summarized data.
In this embodiment, the influence of the same data with different dimensions on the analysis result is mainly handled, for example, the data is merged into a wide table, and people, vehicles, RFIDs, MACs in the same bayonet interval time (interval time is set manually) range can merge data related to the attributes of people into one piece of data, and perform data type marking, and the like.
In this embodiment, for example, the association relationship between data of different dimensions includes: the method comprises the following steps that people and electric vehicles are searched for the electric vehicles in an electric vehicle record library through relevant information of the people; checking related information of the personnel in a personnel basic library through the information of the electric vehicle; human and automobile: searching the motor vehicle of the person in a motor vehicle record library through the related information of the person; checking the related information of the personnel in a personnel basic library through the motor vehicle information; object and MAC: and positioning is carried out through the MAC address, the range of the MAC address appears in the control circle, and personnel examination is carried out on the range of the track of the MAC address.
In one exemplary embodiment, determining whether an anomalous object is included in the target region based on the summary data includes:
s1, determining the times of occurrence of object data information, vehicle data information, RFID data information and MAC data information in the summarized data at a bayonet arranged in a target area in a preset time period to obtain first data;
s2, determining a high-frequency bayonet in the target area based on the first data to obtain a target bayonet;
s3, determining the occurrence frequency of object data information, vehicle data information, RFID data information and MAC data information in the summarized data in the target area within a preset time period to obtain second data;
s4, determining a high-frequency area in the target area based on the second data to obtain a target area;
s5, it is determined whether an abnormal object is included in the target mount and the target region.
In this embodiment, for example, in the Geohash region, the number of times that each vehicle, each person, each RFID, and the MAC pass through each gate is counted, and ranking data of each gate and each Geohash ranking data are respectively obtained according to the number ranking. And (5) counting ranking data of each object in the checkpoint and the region by adopting an integral system.
In one exemplary embodiment, determining whether an anomalous object is included in the target region based on the summary data includes:
s1, determining the times of the object data information, the vehicle data information, the RFID data information and the MAC data information in the summarized data appearing at a target bayonet arranged in a target area within a preset time period to obtain third data;
s2, determining the occurrence frequency of object data information, vehicle data information, RFID data information and MAC data information in the summarized data in a target area in the target area within a preset time period to obtain fourth data;
and S3, determining whether the target area comprises the abnormal object by using the third data and the fourth data.
In this embodiment, for example, the Geohash area includes a plurality of checkpoints, RFID devices, and counts the number of passing of each car, each person, and each RFID device in each checkpoint, and ranks the data by using an integral system, accumulates data for N days, and counts data information of each checkpoint, where each region is ranked earlier.
In one exemplary embodiment, determining whether an anomalous object is included in the target region based on the summary data includes:
s1, comparing the summarized data with abnormal objects stored in a preset database to obtain a comparison result;
and S2, determining whether the target area comprises the abnormal object or not based on the comparison result.
In an exemplary embodiment, determining whether the target region includes an abnormal object based on the comparison result includes one of:
s1, determining that the target area comprises the abnormal object under the condition that the summary data comprises the object matched with the stored abnormal object;
and S2, determining that the abnormal object is included in the target area under the condition that the objects matched with the stored abnormal object are not included in the summarized data and the frequency of the objects included in the summarized data appearing in the target area is greater than a preset threshold value.
In this embodiment, for example, the summarized data is determined, the data entering the data stream is compared with the case library data, if some kind of object is subjected to control circle deployment, the message is pushed to a public security dispatching center, and a public security automatic dispatching system dispatches surrounding public security police officers to go to the control circle deployment for field investigation;
if no object of a certain type exists, comparing the stream data with the case database data, judging whether the stream data has the object of the certain type, if the object of the certain type exists and the object frequently appears in the control circle, the level of the control circle is increased, the control circle is a key attention area, and the analyzed data is pushed to a local public security office platform;
if no object of a certain class exists, whether the total value of the objects in the area exceeds a set threshold value or not is judged, if the total value exceeds the set threshold value, the deployment control circle sends a grade rise, and the public security automatic dispatching system dispatches the surrounding public security police officers to go to the deployment control circle.
In one exemplary embodiment, the method further comprises:
s1, when the abnormal object is included in the determined target area, determining the area range of the abnormal object;
s2, determining the area range as an abnormal range to monitor the abnormal object within the abnormal range.
In this embodiment, for example, a gate of a desk is screened, custom date data analysis is performed on data of the desk, a public security officer can screen out objects frequently appearing in the desk in the near day, and after the desk, data with frequency reduction appears in the desk, and suspect objects are checked. Data in the control arrangement circle is analyzed through historical data for multiple days, data of objects exceeding a threshold value appearing in the control arrangement circle or a bayonet are extracted, information is pushed to a local public security big data platform, the grade of a certain person appearing in the control arrangement circle is raised, and a regional public security police officer is informed and dispatched to conduct investigation on the control arrangement circle.
In an exemplary embodiment, after determining whether an object included in the real-time data is abnormal by using the real-time data acquired in the target area and obtaining an object state of the object, the method further includes:
and S1, recording the abnormal state of the object into a data table to determine the probability of the abnormal object in the target area within the preset time period in the case that the object is the abnormal object.
The invention is illustrated below with reference to specific examples:
in the embodiment, personnel data are extracted and vehicle license plate information is acquired by using each bayonet in a city, data acquisition is performed on a non-motor vehicle by using an RFID radio frequency device, data acquisition is performed on MAC address information by using an MAC acquisition device, classified data cleaning is performed on the acquired data, the data is gathered after cleaning, the data information is performed again, after cleaning, frequent algorithm model analysis is performed on a preset area, the data meeting the requirements is alarmed to a public security officer, and the public security officer can control and prevent the area in advance.
In this embodiment, the Geohash area is a geographic space partition, and is set by an artificial user, the bayonet device, the MAC device, and the RFID device in the space are managed in a unified manner, as shown in fig. 3, each bayonet in a city, an RFID radio frequency device, and an MAC acquisition device are installed in each traffic in the city, the Geohash area is formed by artificially dividing the monitoring area on a map, and data acquired by the area can be analyzed to give an alarm in advance, and a specific flow is shown in fig. 4, and the method includes the following steps:
s401: and a bayonet, an MAC acquisition device and an RFID radio frequency device in the Geohash area are used for acquiring various data.
S402: the stored data is washed in batches according to the rules, as shown in FIG. 5;
s403: and (3) summarizing and cleaning the batch-processed data again, mainly processing the influence of the same data with different dimensions on the analysis result, and processing the rule: data are merged into a wide table, personnel, vehicles, RFID and MAC in the same bayonet interval time (interval time is set manually) range can merge personnel attribute related data into one piece of data, and data type marking is carried out, for example, people and electric vehicles: searching the electric vehicle in an electric vehicle record library according to the related information of people; checking related information of the personnel in a personnel basic library through the information of the electric vehicle; human and automobile: searching the motor vehicle of the person in a motor vehicle record library through the related information of the person; checking the related information of the personnel in a personnel basic library through the motor vehicle information; object and MAC: and positioning is carried out through the MAC address, the range of the MAC address appears in the control circle, and personnel examination is carried out on the range of the track of the MAC address.
The present embodiment includes the following scenarios:
scene one:
the person rides the electric vehicle with three riders and passes through the intersection, the face recognition device collects information of three riders at 2020-10-2312: 00:00, the RFID collection device at the same intersection collects information of three riders of the electric vehicle at 2020-10-2312: 00:02 (the set interval threshold time is 2 seconds), the electric vehicle records registration information is three riders, the two pieces of data are combined into one piece and written into a wide table, and the type of the strip data is marked, wherein the type is as follows: personnel, RFID.
Scene two:
if the registered information of the electric vehicle is not three persons, the two data are not merged and are written into the wide table as two data to mark, the data of the person marks a mark, and the data of the electric vehicle marks an RFID, as shown in FIG. 6.
S404: performing spark model algorithm data processing, and performing data statistics in 2 dimensions;
dimension one: the object frequently passes through the Geohash area;
the Geohash area comprises a plurality of bayonets, RFID and MAC acquisition equipment, the number of times that each vehicle, each person, each RFID and MAC pass through each bayonet is counted, ranking data of each bayonet and ranking data of each Geohash are respectively obtained according to quantity sequencing. And (5) counting ranking data of each object in the checkpoint and the region by adopting an integral system. (both the ranking and score presented in the scheme can be configured) as shown in FIG. 7;
dimension two: the Geohash area frequently passes people, vehicles, RFID (radio frequency identification devices) and MAC (media access control) and comprises a plurality of bayonets, RFID and MAC equipment, wherein each area is counted every day, the number of the passing of each vehicle, each person and each RFID in each bayonet is counted, the data is ranked and processed by adopting an integral system, the data of N days is accumulated, and the data information of each bayonet, which is ranked at the front, in each area is counted. (both the ranking and score presented in the scheme can be configured) as shown in FIG. 8;
s405: analyzing a flink algorithm model and preventing a control arrangement circumplan strategy;
judging the collected data, comparing the data entering the data stream with the case database data, if a certain type of object exists, arranging a control circle, pushing the message to a public security dispatching center, dispatching surrounding public security police officers by a public security automatic dispatching system, and going to the arrangement control circle to carry out on-site investigation;
if no object of a certain type exists, comparing the stream data with the case database data, judging whether the stream data has the object of the certain type, if the object of the certain type exists and the object frequently appears in the control circle, the level of the control circle is increased, the control circle is a key attention area, and the analyzed data is pushed to a local public security office platform;
if no object of a certain class exists, whether the total value of the objects in the area exceeds a set threshold value or not is judged, if the total value exceeds the set threshold value, the deployment control circle issues a grade rise, the public security automatic scheduling system schedules the peripheral public security police officers to go to the deployment control circle, and field order maintenance is carried out, as shown in fig. 9;
s406: analyzing spark offline data and analyzing data after a case;
and (4) screening a gate of the emergency place by using the data processed by spark in the S404, performing custom date data analysis on the data of the emergency place, screening objects frequently appearing in the emergency place in the near day by a public security officer, and performing suspect object investigation on the data of which the frequency is reduced in the emergency place after the emergency.
Data in the control arrangement circle is analyzed through historical data for multiple days, data of objects exceeding a threshold value appearing in the control arrangement circle or a bayonet are extracted, information is pushed to a local public security big data platform, the grade of a certain person appearing in the control arrangement circle is raised, and a regional public security police officer is informed and dispatched to conduct investigation on the control arrangement circle.
In summary, the embodiment performs the integrated analysis on the data of four dimensions of personnel, vehicles, MAC and RFID, and compared with the single analysis on the vehicle, the analyzed data has a reference value.
The embodiment provides a unified big data pool by providing a big data storage pool for converging global structured data in the main domain, and effectively supports various big data analysis, query and deployment and control services.
In the embodiment, a flink framework is adopted to extract and judge original data, the conditions of a control area are mastered in real time by relying on an AI (intelligent information technology) algorithm, and for a control circle with abnormal conditions, a surrounding public security police officer is scheduled by relying on an automatic scheduling system to carry out on-site investigation, so that the control circle is mastered in real time by full process automation;
this embodiment uses mainstream system distributed frame hadoop, carries out AI intelligence algorithm analysis to historical data through spark, and the back is sent out to the case, can carry out suspect object investigation from a plurality of dimensions, reduces police's investigation scope, improves police's efficiency of handling a case, obtains guaranteeing through speed and stability simultaneously.
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 deployment and control information is further provided, where the device is used to implement the foregoing embodiments and preferred embodiments, and details are not repeated for what has been described. 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. 10 is a block diagram of a configuration of an organization information determination apparatus according to an embodiment of the present invention, and as shown in fig. 10, the apparatus includes:
a first determining module 1002, configured to determine, by using real-time data acquired in a target area, whether an object included in the real-time data is abnormal, to obtain an object state of the object, where the real-time data includes data information associated with the object, and the object state includes an abnormal state, and the abnormal state is used to trigger control information for controlling the target area;
a second determining module 1004, configured to determine, by using N object states corresponding to N objects included in N real-time data within a preset time period, a probability that an abnormal object occurs in a target region within the preset time period, where N is a natural number greater than or equal to 1, and the N real-time data are used to represent N data counted in real time within the preset time period;
a third determining module 1006, configured to determine deployment information for deploying the target area based on the probability of the abnormal object.
In an exemplary embodiment, the first determining module includes one of:
a first determining unit, configured to determine that the object is in an abnormal state when it is determined from the real-time data that the object has abnormal behavior in the target area;
a second determination unit configured to determine that the object is in an abnormal state when it is determined from the real-time data that the object information of the object matches the object information of a target abnormal object;
and the third determining unit is used for determining that the object is in an abnormal state under the condition that the accumulated frequency of the object appearing in the target area is determined to be greater than a first preset frequency from the real-time data.
In an exemplary embodiment, the second determining module includes:
a first recording unit, configured to record real-time data in which an abnormal object occurs in the N pieces of real-time data, and obtain a first number of the abnormal objects occurring within the preset time period;
a fourth determining unit, configured to determine, using the first number and the N, a probability that the abnormal object occurs in the target area within the preset time period.
In an exemplary embodiment, the apparatus further includes:
a fourth determining module, configured to determine, by using N object states corresponding to N objects included in N real-time data within a preset time period, a data latitude in the M acquired real-time data before determining a probability that the target region has an abnormal object within the preset time period, where M is a natural number greater than or equal to N;
and the first merging module is used for merging the data with the same latitude in the M real-time data to obtain the N real-time data.
In an exemplary embodiment, the apparatus further includes:
the first obtaining module is configured to obtain real-time data before determining whether an object included in the real-time data is abnormal, using the real-time data obtained in a target area, where the real-time data includes at least one of: non-motor vehicle information associated with the object acquired by the Radio Frequency Identification (RFID) device, attribute information of the object, MAC address information of the object acquired by the Media Access Control (MAC) device, and motor vehicle information associated with the object.
In an exemplary embodiment, the apparatus further includes:
the first recording module is configured to record the real-time data into a data table according to a data latitude to which the real-time data belongs after the real-time data is acquired, where the data latitude includes at least one of: non-motor vehicle information, attribute information of the object, MAC address information, and motor vehicle information.
In an exemplary embodiment, the apparatus further includes:
the second recording module is configured to determine whether an object included in the real-time data is abnormal or not by using the real-time data acquired in the target area, and after the object state of the object is obtained, record the abnormal state of the object into the data table in the case that the object is an abnormal object, so as to determine a probability that the abnormal object occurs in the target area within the preset time period.
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 is arranged to perform the steps of any of the above-mentioned method embodiments when executed.
In the present embodiment, the above-described computer-readable storage medium may be configured to store a computer program for executing the above steps.
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.
In an exemplary embodiment, the processor may be configured to execute the above steps by a computer program.
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 for determining deployment control information is characterized by comprising the following steps:
determining whether an object included in real-time data is abnormal or not by using the real-time data acquired in a target area to obtain an object state of the object, wherein the real-time data includes data information associated with the object, the object state includes an abnormal state, and the abnormal state is used for triggering management and control information for managing and controlling the target area;
determining the probability of the abnormal object in the target area in a preset time period by using N object states corresponding to N objects in N real-time data in the preset time period, wherein N is a natural number greater than or equal to 1, and the N real-time data are used for representing N data counted in real time in the preset time period;
and determining deployment information for deploying the target area based on the probability of the abnormal object.
2. The method of claim 1, wherein determining an object anomaly included in the real-time data using the real-time data obtained in the target region to obtain an object state of the object comprises one of:
determining that the object is in an abnormal state when the object is determined to have abnormal behavior in the target area from the real-time data;
determining that the object is in an abnormal state if it is determined from the real-time data that the object information of the object matches the object information of a target abnormal object;
and under the condition that the accumulated times of the object appearing in the target area is determined to be more than a first preset time from the real-time data, determining that the object is in an abnormal state.
3. The method according to claim 1, wherein determining the probability of the abnormal object occurring in the target region within a preset time period by using N object states corresponding to N objects included in N real-time data within the preset time period comprises:
recording real-time data of abnormal objects in the N pieces of real-time data to obtain a first number of the abnormal objects in the preset time period;
and determining the probability of the abnormal object in the target area within the preset time period by using the first number and the N.
4. The method according to claim 1, wherein before determining the probability of the target region having the abnormal object within the preset time period by using N object states corresponding to N objects included in N real-time data within the preset time period, the method further comprises:
determining a data latitude in the acquired M real-time data, wherein M is a natural number greater than or equal to N;
and combining the data with the same latitude in the M real-time data to obtain the N real-time data.
5. The method of claim 1, wherein prior to determining whether an object included in the real-time data is abnormal using real-time data acquired in a target region, the method further comprises:
obtaining the real-time data, wherein the real-time data comprises at least one of: non-motor vehicle information associated with the object collected by a Radio Frequency Identification (RFID) device, attribute information of the object, MAC address information of the object collected by a Media Access Control (MAC) device, and motor vehicle information associated with the object.
6. The method of claim 5, wherein after acquiring the real-time data, the method further comprises:
recording the real-time data into a data table according to the data latitude to which the real-time data belongs, wherein the data latitude comprises at least one of the following: non-motor vehicle information, attribute information of the object, MAC address information, motor vehicle information.
7. The method of claim 6, wherein after determining whether an object included in the real-time data is abnormal using the real-time data acquired in the target area and obtaining the object status of the object, the method further comprises:
and in the case that the object is an abnormal object, recording the abnormal state of the object into the data table to determine the probability of the abnormal object in the target area within the preset time period.
8. An apparatus for determining deployment information, comprising:
the device comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining whether an object included in real-time data is abnormal or not by using the real-time data acquired in a target area to obtain an object state of the object, the real-time data includes data information related to the object, the object state includes an abnormal state, and the abnormal state is used for triggering management and control information for managing and controlling the target area;
a second determining module, configured to determine, by using N object states corresponding to N objects included in N real-time data within a preset time period, a probability that an abnormal object occurs in the target region within the preset time period, where N is a natural number greater than or equal to 1, and the N real-time data are used to represent N data counted in real time within the preset time period;
and the third determining module is used for determining the deployment information for deploying the target area based on the probability of the abnormal object.
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 method of 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111139040.0A CN113918563A (en) | 2021-09-27 | 2021-09-27 | Method and device for determining deployment control information, storage medium and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111139040.0A CN113918563A (en) | 2021-09-27 | 2021-09-27 | Method and device for determining deployment control information, storage medium and electronic device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113918563A true CN113918563A (en) | 2022-01-11 |
Family
ID=79236480
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111139040.0A Pending CN113918563A (en) | 2021-09-27 | 2021-09-27 | Method and device for determining deployment control information, storage medium and electronic device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113918563A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114495502A (en) * | 2022-01-29 | 2022-05-13 | 青岛海信网络科技股份有限公司 | Method and device for determining abnormal driving exploration area |
-
2021
- 2021-09-27 CN CN202111139040.0A patent/CN113918563A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114495502A (en) * | 2022-01-29 | 2022-05-13 | 青岛海信网络科技股份有限公司 | Method and device for determining abnormal driving exploration area |
| CN114495502B (en) * | 2022-01-29 | 2023-11-28 | 青岛海信网络科技股份有限公司 | Determination method and device for abnormal driving exploration area |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| DE112018004325T5 (en) | SYSTEMS AND METHODS FOR RISK ANALYSIS | |
| CN110298254A (en) | A kind of analysis method and system for personnel's abnormal behaviour | |
| CN110866642A (en) | Security monitoring method and device, electronic equipment and computer readable storage medium | |
| CN104167095B (en) | Based on the vehicle behavior pattern checking method of smart city | |
| CN107832196A (en) | A kind of monitoring device and monitoring method for real-time logs anomalous content | |
| CN110705477A (en) | Behavior analysis method and apparatus, electronic device, and computer storage medium | |
| CN111930868A (en) | Big data behavior trajectory analysis method based on multi-dimensional data acquisition | |
| CN109949569A (en) | A kind of traffic violation data auditing system and its implementation | |
| CN111291682A (en) | Method and device for determining target object, storage medium and electronic device | |
| CN110969405A (en) | Mobile GIS-based forest protection positioning and attendance checking calculation method | |
| CN111291216B (en) | Method and system for analyzing foothold based on face structured data | |
| CN110727805A (en) | Community knowledge graph construction method and system | |
| CN109460938A (en) | A kind of city intelligent central control system and its method | |
| CN112800910A (en) | Communication machine room maintenance operation efficiency evaluation method and system | |
| CN111062562A (en) | Community grid service linkage disposal control method and system | |
| CN114841712B (en) | Method and device for determining illegal operation state of network appointment vehicle tour and electronic equipment | |
| CN113794819A (en) | Intelligent management method, system, device and medium for epidemic prevention place | |
| CN113918563A (en) | Method and device for determining deployment control information, storage medium and electronic device | |
| CN112132994A (en) | Monitoring method and system for analyzing driver behavior based on real-time data | |
| CN111476685A (en) | Behavior analysis method, device and equipment | |
| CN109992604B (en) | Land pursuit and seizing informatization system and seizing method | |
| CN110990455A (en) | Method and system for identifying house properties by big data | |
| CN116935659B (en) | High-speed service area bayonet vehicle auditing system and method thereof | |
| CN112528901A (en) | Vehicle aggregation alarm method and system based on big data | |
| CN112381414A (en) | Auxiliary alarm management system based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |