CN117541957B - Method, system and medium for generating event solving strategy based on artificial intelligence - Google Patents
Method, system and medium for generating event solving strategy based on artificial intelligence Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a system and a medium for generating an event solving strategy based on artificial intelligence. The method comprises the following steps: identifying abnormal event information from the collected abnormal event video; the abnormal event information at least comprises abnormal event time, abnormal event places, abnormal event characters and abnormal information points; acquiring a historical abnormal video matched with the abnormal event information from a pre-acquired historical video; comprehensively analyzing the abnormal event video and the historical abnormal video to obtain abnormal event context information; the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information; and analyzing the abnormal event context information to obtain an abnormal event solving strategy. The method and the device can analyze and obtain the solving strategy of the abnormal event based on the obtained context information of the abnormal event, thereby improving the event processing effect.
Description
Technical Field
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a method, a system and a medium for generating an event solving strategy based on artificial intelligence.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Along with the continuous development and progress of society, the modernization of a treatment system in China is continuously improved, and the modernization of the primary society is a necessary trend. The basic level of governance is an important component of national governance, and is also an important content for construction of national governance systems and governance capacities.
Currently, for primary management, a manager is usually required to process various events occurring on a primary layer, so that event processing experience of the manager and strain capacity of the manager are very tested. It can be seen that it is difficult to obtain a good event handling effect by handling events depending on manager experience and strain capacity.
Disclosure of Invention
In this context, embodiments of the present invention desire to provide a method, system, and medium for generating an artificial intelligence based event resolution strategy.
In a first aspect of the embodiment of the present invention, there is provided a method for generating an event resolution policy based on artificial intelligence, including:
Identifying abnormal event information from the collected abnormal event video; the abnormal event information at least comprises abnormal event time, abnormal event places, abnormal event characters and abnormal information points;
Acquiring a historical abnormal video matched with the abnormal event information from a pre-acquired historical video;
Comprehensively analyzing the abnormal event video and the historical abnormal video to obtain abnormal event context information; the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information;
and analyzing the abnormal event context information to obtain an abnormal event solving strategy.
In one example of this embodiment, before the identifying the abnormal event information from the collected abnormal event video, the method further includes:
Monitoring the abnormal characteristics of the collected monitoring video in real time;
when abnormal characteristics exist in the monitoring video, determining the acquisition equipment of the monitoring video where the abnormal characteristics are located;
Determining a position coordinate of the acquisition equipment and a first acquisition angle of the acquisition equipment;
Controlling a first unmanned aerial vehicle device and a second unmanned aerial vehicle device to reach a position area where the position coordinates are located, determining a second acquisition angle of the first unmanned aerial vehicle device and determining a third acquisition angle of the second unmanned aerial vehicle device according to the first acquisition angle; wherein the first acquisition angle, the second acquisition angle and the third acquisition angle are all different;
Acquiring a first sub-video at the second acquisition angle by the first unmanned aerial vehicle equipment;
collecting a second sub-video at the third collection angle through the second unmanned aerial vehicle device;
And determining the monitoring video, the first sub-video and the second sub-video as abnormal event videos.
In an embodiment of the present invention, the monitoring the collected monitoring video for abnormal characteristics in real time includes:
Acquiring video frame information of the current moment from the acquired monitoring video; wherein the video frame information at least comprises a current video image and a current video sound;
if the decibel of the current video sound is larger than a preset decibel, determining that abnormal characteristics exist in the monitoring video;
If the decibel of the current video sound is smaller than or equal to the preset decibel, carrying out abnormal recognition on the current video image to obtain an abnormal recognition result;
and if the abnormal recognition result indicates that the abnormal feature exists in the current video image, determining that the abnormal feature exists in the monitoring video.
In an example of this embodiment, the performing anomaly identification on the current video image to obtain an anomaly identification result includes:
performing human body feature recognition on the current video image to obtain a human body feature recognition result;
If the human body characteristic recognition result indicates that the target human body characteristic exists in the current video image, carrying out anomaly analysis on the target human body characteristic to obtain an anomaly analysis result;
And if the abnormal analysis result indicates that the target human body characteristic is an abnormal human body characteristic, determining the abnormal human body characteristic as an abnormal recognition result.
In an example of this embodiment, if the human feature recognition result indicates that the target human feature does not exist in the current video image, the method further includes:
determining a current position point of the current video image acquisition;
obtaining a standard image matched with the current moment and the current position point;
comparing the current video image with the standard image to obtain an environment distinguishing characteristic;
Performing anomaly analysis on the environmental distinguishing characteristics to obtain environmental anomaly analysis results;
And if the environmental abnormality analysis result indicates that the environmental distinguishing feature is an abnormal environmental feature, determining the abnormal environmental feature as an abnormality recognition result.
In one example of this implementation, after the obtaining the abnormal event resolution policy, the method further includes:
Comparing the abnormal event character with the character information to be wanted to obtain a character information comparison result;
if the character information comparison result shows that the abnormal event character is different from the character information to be wanted, executing the step of acquiring a history abnormal video matched with the abnormal event information from the history video acquired in advance;
If the character information comparison result shows that the abnormal event character is the same as the character information to be wanted, comparing the face image of the abnormal event character with the face image to be wanted in the character information to be wanted to obtain a face image comparison result;
And if the face image comparison result shows that the face image of the abnormal event person is the same as the face image to be wanted, determining that the abnormal event person is the wanted person.
In an example of this embodiment, after the determining that the abnormal event person is a wanted person, the method further includes:
determining management department information matched with the abnormal event location;
Sending an arrest instruction containing the wanted person to a target management department corresponding to the management department information, so that an administrator of the target management department carries out arrest on the wanted person according to the arrest instruction.
In an example of this embodiment, after the determining the position coordinates where the collecting device is located and the first collecting angle of the collecting device, the method further includes:
Determining the current distance between the occurrence place of the abnormal feature and the position coordinate of the acquisition equipment; the direction from the occurrence place of the abnormal characteristic to the position coordinate of the acquisition equipment is the same as the first acquisition angle of the acquisition equipment;
Determining a circular area according to the occurrence place of the abnormal feature and the current distance; the midpoint of the circular area is the place where the abnormal feature occurs, and the radius of the circular area is the current distance; the position coordinates of the acquisition equipment are on the boundary of the circular area;
Determining a first positioning point and a second positioning point from the boundary of the circular area; the position coordinates of the acquisition device, the first positioning point and the second positioning point can divide the boundary of the circular area into three equal parts.
In an example of this embodiment, the controlling the first unmanned aerial vehicle device and the second unmanned aerial vehicle device to reach the location area where the location coordinates are located, and determining the second collection angle of the first unmanned aerial vehicle device and determining the third collection angle of the second unmanned aerial vehicle device according to the first collection angle includes:
controlling the first unmanned aerial vehicle equipment to reach the first positioning point, and controlling the second unmanned aerial vehicle equipment to reach the second positioning point;
Determining a direction from the first positioning point to the occurrence place of the abnormal feature as a second acquisition angle, and determining a direction from the second positioning point to the occurrence place of the abnormal feature as a third acquisition angle;
Setting the acquisition direction of the first unmanned aerial vehicle equipment according to the second acquisition angle so that the first unmanned aerial vehicle equipment acquires the image of the occurrence place of the abnormal characteristic;
and setting the acquisition direction of the second unmanned aerial vehicle equipment according to the third acquisition angle so that the second unmanned aerial vehicle equipment acquires the image of the occurrence place of the abnormal characteristic.
In a second aspect of the embodiments of the present invention, there is provided a system for generating an event resolution policy based on artificial intelligence, including:
The identification unit is used for identifying abnormal event information from the collected abnormal event video; the abnormal event information at least comprises abnormal event time, abnormal event places, abnormal event characters and abnormal information points;
the acquisition unit is used for acquiring historical abnormal videos matched with the abnormal event information from the pre-acquired historical videos;
The first analysis unit is used for comprehensively analyzing the abnormal event video and the historical abnormal video to obtain abnormal event context information; the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information;
And the second analysis unit is used for analyzing the abnormal event context information to obtain an abnormal event solving strategy.
In a third aspect of embodiments of the present invention, there is provided a computing device comprising: at least one processor, memory, and input output unit; wherein the memory is for storing a computer program and the processor is for invoking the computer program stored in the memory to perform the method of any of the first aspects.
In a fourth aspect of the embodiments of the present invention, there is provided a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any of the first aspects.
According to the method, the system and the medium for generating the event solving strategy based on the artificial intelligence, the abnormal event information can be identified from the collected abnormal event videos, the historical abnormal videos matched with the abnormal event information can be obtained from the historical videos according to the abnormal event information, further the abnormal event videos and the historical abnormal videos can be comprehensively analyzed to obtain the abnormal event context information of the abnormal event, the solving strategy of the abnormal event can be obtained through analysis based on the obtained abnormal event context information, and therefore the event processing effect is improved.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
FIG. 1 is a flow chart of a method for generating an artificial intelligence based event resolution strategy according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an apparatus for generating an event resolution strategy based on artificial intelligence according to an embodiment of the present invention;
FIG. 3 schematically illustrates a schematic structural diagram of a medium according to an embodiment of the present invention;
FIG. 4 schematically illustrates a structural diagram of a computing device in accordance with embodiments of the present invention.
In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Detailed Description
The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are presented merely to enable those skilled in the art to better understand and practice the invention and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Those skilled in the art will appreciate that embodiments of the invention may be implemented as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the following forms, namely: complete hardware, complete software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
According to the embodiment of the invention, an artificial intelligence-based event solving strategy generation method, an artificial intelligence-based event solving strategy generation system and an artificial intelligence-based event solving strategy generation medium are provided.
It should be noted that any number of elements in the figures are for illustration and not limitation, and that any naming is used for distinction only and not for limitation.
The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments thereof.
Exemplary method
Referring now to fig. 1, fig. 1 is a flowchart illustrating a method for generating an event resolution policy based on artificial intelligence according to an embodiment of the present invention. It should be noted that embodiments of the present invention may be applied to any scenario where applicable.
The flow of the artificial intelligence based event solution strategy generation method according to an embodiment of the present invention shown in fig. 1 includes:
Step S101, identifying abnormal event information from the collected abnormal event video.
In the embodiment of the invention, the abnormal event information at least comprises abnormal event time, abnormal event place, abnormal event person and abnormal information point. The abnormal information points may be abnormal characteristics, which may be abnormal action characteristics executed by an abnormal event person, abnormal environmental characteristics occurring in an abnormal environment, abnormal temperature characteristics occurring in abnormal weather, and the like.
In the embodiment of the present invention, the abnormal event information may further include an abnormal event type, where the abnormal event type may include a person dispute type, a natural disaster type, a damage type of a consumable, and the like, which is not limited in the embodiment of the present invention.
As an alternative embodiment, the following steps may also be performed prior to step S101:
Monitoring the abnormal characteristics of the collected monitoring video in real time;
when abnormal characteristics exist in the monitoring video, determining the acquisition equipment of the monitoring video where the abnormal characteristics are located;
Determining a position coordinate of the acquisition equipment and a first acquisition angle of the acquisition equipment;
Controlling a first unmanned aerial vehicle device and a second unmanned aerial vehicle device to reach a position area where the position coordinates are located, determining a second acquisition angle of the first unmanned aerial vehicle device and determining a third acquisition angle of the second unmanned aerial vehicle device according to the first acquisition angle; wherein the first acquisition angle, the second acquisition angle and the third acquisition angle are all different;
Acquiring a first sub-video at the second acquisition angle by the first unmanned aerial vehicle equipment;
collecting a second sub-video at the third collection angle through the second unmanned aerial vehicle device;
And determining the monitoring video, the first sub-video and the second sub-video as abnormal event videos.
When the monitoring video is monitored, the position of the abnormal feature can be determined, and the two unmanned aerial vehicle devices can be controlled to reach the position, so that the acquisition device at the position, the first unmanned aerial vehicle device and the second unmanned aerial vehicle device can shoot the position from different angles at the same time, and a more comprehensive abnormal event video can be obtained.
As an optional implementation manner, the manner of controlling the first unmanned aerial vehicle device and the second unmanned aerial vehicle device to reach the location area where the location coordinates are located may specifically be:
Determining the current distance of the occurrence place of the abnormal feature from the position coordinates of the acquisition equipment; the direction from the occurrence place of the abnormal characteristic to the position coordinate of the acquisition equipment is the same as the first acquisition angle of the acquisition equipment;
determining a circular area according to the occurrence place and the current distance of the abnormal features; the midpoint of the circular area is the place where the abnormal feature occurs, and the radius of the circular area is the current distance; the position coordinates of the acquisition equipment are on the boundary of the circular area;
determining a first positioning point and a second positioning point from the boundary of the circular area; the position coordinates, the first positioning point and the second positioning point of the acquisition equipment can divide the boundary of the circular area into three parts;
And controlling the first unmanned aerial vehicle device and the second unmanned aerial vehicle device to reach the position region where the position coordinates are located, and determining the second acquisition angle of the first unmanned aerial vehicle device and determining the third acquisition angle of the second unmanned aerial vehicle device according to the first acquisition angle may specifically be:
controlling the first unmanned aerial vehicle equipment to reach a first positioning point, and controlling the second unmanned aerial vehicle equipment to reach a second positioning point;
determining a direction from the first locating point to the occurrence place of the abnormal feature as a second acquisition angle, and determining a direction from the second locating point to the occurrence place of the abnormal feature as a third acquisition angle;
Setting the acquisition direction of the first unmanned aerial vehicle equipment according to the second acquisition angle so that the first unmanned aerial vehicle equipment acquires images of the occurrence place of the abnormal characteristics;
And setting the acquisition direction of the second unmanned aerial vehicle equipment according to the third acquisition angle so that the second unmanned aerial vehicle equipment acquires images of the occurrence place of the abnormal characteristics.
Thereby ensuring that the abnormal characteristics can be acquired from all angles.
For example, when it is detected that there is a person in the monitoring video acquired by the acquisition device that is being quarreling, the plurality of persons in quarreling are determined as abnormal characteristics, and the positions where the plurality of persons are located may be determined as the places where the abnormal characteristics occur, and the current distance of the places where the abnormal characteristics occur from the acquisition device may be determined. Furthermore, a circular area can be determined according to the occurrence place of the abnormal characteristics and the current distance, namely, a plurality of people which are quarreling are positioned at the center position of the circular area, and the acquisition equipment is positioned on the boundary of the circular area; and the first positioning point and the second positioning point can be determined from the boundary of the circular area, at this time, the positions of the first positioning point, the second positioning point and the acquisition equipment can divide the boundary of the circular area into three parts, and the image data of each character in the quarreling can be acquired more comprehensively from three angles by dividing the boundary of the circular area into three parts.
Specifically, the first unmanned aerial vehicle device can be controlled to fly to the first positioning point, and the second acquisition angle of the first unmanned aerial vehicle device is controlled to face the direction of the plurality of people which are quarreling, so that the first unmanned aerial vehicle device can acquire the plurality of people which are quarreling at the same time; the second unmanned aerial vehicle device is further controlled to fly to a second positioning point, and the third collection angle of the second unmanned aerial vehicle device is controlled to be over against the direction of the plurality of people which are quarreling, so that the second unmanned aerial vehicle device can collect the plurality of people which are quarreling at the same time.
Alternatively, when it is detected that an abnormal geographic activity (such as a mountain torrent, an earthquake, a debris flow, etc.) exists in the monitoring video acquired by the acquisition device, the abnormal geographic activity is determined as an abnormal feature, a position where the abnormal geographic activity is located may be determined as an abnormal feature occurrence place, and a current distance from the abnormal feature occurrence place to the acquisition device may be determined. Furthermore, a circular area can be determined according to the occurrence place and the current distance of the abnormal features, namely, the abnormal geographic activity is positioned at the central position of the circular area, and the acquisition equipment is positioned on the boundary of the circular area; and the first positioning point and the second positioning point can be determined from the boundary of the circular area, at the moment, the boundary of the circular area can be equally divided into three parts by the positions of the first positioning point, the second positioning point and the acquisition equipment, and the image data containing abnormal geographic activities can be more comprehensively acquired from three angles by equally dividing the boundary of the circular area into three parts.
Specifically, the first unmanned aerial vehicle device can be controlled to fly to a first positioning point, and the second acquisition angle of the first unmanned aerial vehicle device is controlled to be opposite to the direction of the abnormal geographic activity, so that the first unmanned aerial vehicle device can acquire the abnormal geographic activity; the second unmanned aerial vehicle device is further controlled to fly to a second positioning point, and the third collection angle of the second unmanned aerial vehicle device is controlled to be just opposite to the direction of the abnormal geographic activity, so that the second unmanned aerial vehicle device can collect the abnormal geographic activity.
As an optional implementation manner, the manner of monitoring the collected monitoring video for abnormal characteristics in real time may specifically be:
Acquiring video frame information of the current moment from the acquired monitoring video; wherein the video frame information at least comprises a current video image and a current video sound;
if the decibel of the current video sound is larger than a preset decibel, determining that abnormal characteristics exist in the monitoring video;
If the decibel of the current video sound is smaller than or equal to the preset decibel, carrying out abnormal recognition on the current video image to obtain an abnormal recognition result;
and if the abnormal recognition result indicates that the abnormal feature exists in the current video image, determining that the abnormal feature exists in the monitoring video.
When the decibel of the video sound is too large, the abnormality in the video image is further identified, and an abnormality identification result is determined; if the abnormal characteristics exist in the abnormal identification result, the abnormal characteristics can be considered to exist in the monitoring video; therefore, the method does not need to carry out abnormality identification on each frame of image in the video, and only carries out abnormality identification under the condition of decibel abnormality, thereby improving the efficiency of abnormality identification.
As an optional implementation manner, the method for performing anomaly identification on the current video image to obtain the anomaly identification result may specifically be:
performing human body feature recognition on the current video image to obtain a human body feature recognition result;
If the human body characteristic recognition result indicates that the target human body characteristic exists in the current video image, carrying out anomaly analysis on the target human body characteristic to obtain an anomaly analysis result;
And if the abnormal analysis result indicates that the target human body characteristic is an abnormal human body characteristic, determining the abnormal human body characteristic as an abnormal recognition result.
By implementing the embodiment, when the human body characteristics exist in the video, the human body characteristics can be subjected to abnormal analysis, so that an abnormal analysis result is obtained, the abnormal analysis of the human body characteristics is still carried out under the condition that the human body characteristics do not exist in the video, and the efficiency of carrying out abnormal identification on the human body characteristics is improved.
As an optional implementation manner, if the human body feature recognition result indicates that the target human body feature does not exist in the current video image, the following steps may be further performed:
determining a current position point of the current video image acquisition;
obtaining a standard image matched with the current moment and the current position point;
comparing the current video image with the standard image to obtain an environment distinguishing characteristic;
Performing anomaly analysis on the environmental distinguishing characteristics to obtain environmental anomaly analysis results;
And if the environmental abnormality analysis result indicates that the environmental distinguishing feature is an abnormal environmental feature, determining the abnormal environmental feature as an abnormality recognition result.
By implementing the embodiment, the current video image can be compared with the standard image to obtain the environment distinguishing characteristic, and the environment distinguishing characteristic can be subjected to anomaly analysis, so that the anomaly condition in the environment can be rapidly identified.
Step S102, acquiring a historical abnormal video matched with the abnormal event information from a pre-acquired historical video.
And step S103, comprehensively analyzing the abnormal event video and the historical abnormal video to obtain abnormal event context information.
In the embodiment of the invention, the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information.
And step S104, analyzing the abnormal event context information to obtain an abnormal event solving strategy.
As an alternative embodiment, after step S104, the following steps may also be performed:
Comparing the abnormal event character with the character information to be wanted to obtain a character information comparison result;
if the character information comparison result shows that the abnormal event character is different from the character information to be wanted, executing the step of acquiring a history abnormal video matched with the abnormal event information from the history video acquired in advance;
If the character information comparison result shows that the abnormal event character is the same as the character information to be wanted, comparing the face image of the abnormal event character with the face image to be wanted in the character information to be wanted to obtain a face image comparison result;
And if the face image comparison result shows that the face image of the abnormal event person is the same as the face image to be wanted, determining that the abnormal event person is the wanted person.
By implementing the embodiment, the abnormal event character can be compared with the character information to be seized, so that the success rate of catching the character to be seized is improved.
As an alternative embodiment, after determining that the abnormal event person is a wanted person, the following steps may be further performed:
determining management department information matched with the abnormal event location;
Sending an arrest instruction containing the wanted person to a target management department corresponding to the management department information, so that an administrator of the target management department carries out arrest on the wanted person according to the arrest instruction.
The implementation of the method can send the abnormal event location to the manager of the most matched management department, so that the manager can arrest wanted people as soon as possible, and the arrest success rate of the wanted people is improved.
The method and the device can analyze and obtain the solution strategy of the abnormal event based on the obtained context information of the abnormal event, thereby improving the event processing effect. In addition, the invention can obtain more comprehensive abnormal event videos. In addition, the invention can also improve the efficiency of abnormality identification. In addition, the invention can also improve the efficiency of identifying the abnormality aiming at the human body characteristics. In addition, the invention can also rapidly identify the abnormal conditions in the environment. In addition, the invention can also improve the success rate of capturing the wanted people. In addition, the invention can also improve the arrest success rate of the wanted person.
Exemplary System
Having described the method of an exemplary embodiment of the present invention, a system for generating an artificial intelligence based event resolution strategy according to an exemplary embodiment of the present invention will be described with reference to FIG. 2, the system comprising:
An identifying unit 201, configured to identify abnormal event information from the collected abnormal event video; the abnormal event information at least comprises abnormal event time, abnormal event places, abnormal event characters and abnormal information points;
An acquisition unit 202, configured to acquire a historical abnormal video matched with the abnormal event information from a previously acquired historical video;
the first analysis unit 203 is configured to perform comprehensive analysis on the abnormal event video and the historical abnormal video to obtain abnormal event context information; the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information;
and a second analysis unit 204, configured to analyze the context information of the abnormal event to obtain an abnormal event resolution policy.
Exemplary Medium
Having described the method and system of the exemplary embodiments of the present invention, reference is now made to fig. 3, which illustrates a computer-readable storage medium of the exemplary embodiments of the present invention, and reference is now made to fig. 3, which shows a computer-readable storage medium, an optical disc 30, having a computer program (i.e., a program product) stored thereon that, when executed by a processor, implements the steps described in the above-described method embodiments, such as identifying abnormal event information from collected abnormal event videos; the abnormal event information at least comprises abnormal event time, abnormal event places, abnormal event characters and abnormal information points; acquiring a historical abnormal video matched with the abnormal event information from a pre-acquired historical video; comprehensively analyzing the abnormal event video and the historical abnormal video to obtain abnormal event context information; the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information; analyzing the abnormal event context information to obtain an abnormal event solving strategy; the specific implementation of each step is not repeated here.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
Exemplary computing device
Having described the methods, systems, and media of exemplary embodiments of the present invention, next, a computing device for artificial intelligence based event resolution policy generation of exemplary embodiments of the present invention is described with reference to FIG. 4.
FIG. 4 illustrates a block diagram of an exemplary computing device 40 suitable for use in implementing embodiments of the invention, the computing device 40 may be a computer system or a server. The computing device 40 shown in fig. 4 is merely an example and should not be taken as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 4, components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, a bus 403 that connects the various system components (including the system memory 402 and the processing units 401).
Computing device 40 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computing device 40 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 402 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 4021 and/or cache memory 4022. Computing device 40 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, ROM4023 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4 and commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media), may be provided. In such cases, each drive may be coupled to bus 403 through one or more data medium interfaces. The system memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.
A program/utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 4024 generally perform the functions and/or methodologies of the described embodiments of the present invention.
Computing device 40 may also communicate with one or more external devices 404 (e.g., keyboard, pointing device, display, etc.). Such communication may occur through an input/output (I/O) interface 405. Moreover, computing device 40 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 406. As shown in fig. 4, network adapter 406 communicates with other modules of computing device 40, such as processing unit 401, etc., over bus 403. It should be appreciated that although not shown in fig. 4, other hardware and/or software modules may be used in connection with computing device 40.
The processing unit 401 executes various functional applications and data processing by running a program stored in the system memory 402, for example, identifies abnormal event information from the acquired abnormal event video; the abnormal event information at least comprises abnormal event time, abnormal event places, abnormal event characters and abnormal information points; acquiring a historical abnormal video matched with the abnormal event information from a pre-acquired historical video; comprehensively analyzing the abnormal event video and the historical abnormal video to obtain abnormal event context information; the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information; and analyzing the abnormal event context information to obtain an abnormal event solving strategy. The specific implementation of each step is not repeated here. It should be noted that while several units/modules or sub-units/sub-modules of an artificial intelligence based event resolution strategy generation system are mentioned in the above detailed description, such partitioning is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
In the description of the present invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Furthermore, although the operations of the methods of the present invention are depicted in the drawings in a particular order, this is not required or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
Claims (5)
1. An artificial intelligence based event resolution strategy generation method comprises the following steps:
Identifying abnormal event information from the collected abnormal event video; the abnormal event information at least comprises abnormal event time, abnormal event places, abnormal event characters and abnormal information points;
Acquiring a historical abnormal video matched with the abnormal event information from a pre-acquired historical video;
Comprehensively analyzing the abnormal event video and the historical abnormal video to obtain abnormal event context information; the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information;
Analyzing the abnormal event context information to obtain an abnormal event solving strategy;
and, before the abnormal event information is identified from the collected abnormal event video, the method further comprises:
Monitoring the abnormal characteristics of the collected monitoring video in real time;
when abnormal characteristics exist in the monitoring video, determining the acquisition equipment of the monitoring video where the abnormal characteristics are located;
Determining a position coordinate of the acquisition equipment and a first acquisition angle of the acquisition equipment;
Controlling a first unmanned aerial vehicle device and a second unmanned aerial vehicle device to reach a position area where the position coordinates are located, determining a second acquisition angle of the first unmanned aerial vehicle device and determining a third acquisition angle of the second unmanned aerial vehicle device according to the first acquisition angle; wherein the first acquisition angle, the second acquisition angle and the third acquisition angle are all different;
Acquiring a first sub-video at the second acquisition angle by the first unmanned aerial vehicle equipment;
collecting a second sub-video at the third collection angle through the second unmanned aerial vehicle device;
Determining the monitoring video, the first sub video and the second sub video together as an abnormal event video;
And controlling the first unmanned aerial vehicle device and the second unmanned aerial vehicle device to reach a position area where the position coordinates are located, determining a second acquisition angle of the first unmanned aerial vehicle device and determining a third acquisition angle of the second unmanned aerial vehicle device according to the first acquisition angle, including:
Controlling the first unmanned aerial vehicle equipment to reach a first positioning point and controlling the second unmanned aerial vehicle equipment to reach a second positioning point; the position coordinates of the acquisition equipment, the first positioning point and the second positioning point divide the boundary of the circular area into three parts;
Determining a direction from the first positioning point to the occurrence place of the abnormal feature as a second acquisition angle, and determining a direction from the second positioning point to the occurrence place of the abnormal feature as a third acquisition angle;
Setting the acquisition direction of the first unmanned aerial vehicle equipment according to the second acquisition angle so that the first unmanned aerial vehicle equipment acquires the image of the occurrence place of the abnormal characteristic;
Setting the acquisition direction of the second unmanned aerial vehicle equipment according to the third acquisition angle so that the second unmanned aerial vehicle equipment acquires images of the occurrence place of the abnormal features;
And the real-time monitoring of the abnormal characteristics of the collected monitoring video comprises the following steps:
Acquiring video frame information of the current moment from the acquired monitoring video; wherein the video frame information at least comprises a current video image and a current video sound;
if the decibel of the current video sound is larger than a preset decibel, determining that abnormal characteristics exist in the monitoring video;
If the decibel of the current video sound is smaller than or equal to the preset decibel, carrying out abnormal recognition on the current video image to obtain an abnormal recognition result;
if the abnormal recognition result indicates that abnormal features exist in the current video image, determining that abnormal features exist in the monitoring video;
and performing anomaly identification on the current video image to obtain an anomaly identification result, wherein the anomaly identification result comprises:
performing human body feature recognition on the current video image to obtain a human body feature recognition result;
If the human body characteristic recognition result indicates that the target human body characteristic exists in the current video image, carrying out anomaly analysis on the target human body characteristic to obtain an anomaly analysis result;
if the abnormal analysis result indicates that the target human body characteristic is an abnormal human body characteristic, determining the abnormal human body characteristic as an abnormal recognition result;
If the human body feature recognition result indicates that the target human body feature does not exist in the current video image, the method further comprises:
determining a current position point of the current video image acquisition;
obtaining a standard image matched with the current moment and the current position point;
comparing the current video image with the standard image to obtain an environment distinguishing characteristic;
Performing anomaly analysis on the environmental distinguishing characteristics to obtain environmental anomaly analysis results;
And if the environmental abnormality analysis result indicates that the environmental distinguishing feature is an abnormal environmental feature, determining the abnormal environmental feature as an abnormality recognition result.
2. The method for generating an artificial intelligence based event resolution strategy according to claim 1, further comprising, after said obtaining an abnormal event resolution strategy:
Comparing the abnormal event character with the character information to be wanted to obtain a character information comparison result;
if the character information comparison result shows that the abnormal event character is different from the character information to be wanted, executing the step of acquiring a history abnormal video matched with the abnormal event information from the history video acquired in advance;
If the character information comparison result shows that the abnormal event character is the same as the character information to be wanted, comparing the face image of the abnormal event character with the face image to be wanted in the character information to be wanted to obtain a face image comparison result;
And if the face image comparison result shows that the face image of the abnormal event person is the same as the face image to be wanted, determining that the abnormal event person is the wanted person.
3. The artificial intelligence based event resolution policy generation method of claim 2, after said determining that said abnormal event person is a wanted person, the method further comprising:
determining management department information matched with the abnormal event location;
Sending an arrest instruction containing the wanted person to a target management department corresponding to the management department information, so that an administrator of the target management department carries out arrest on the wanted person according to the arrest instruction.
4. The method for generating an artificial intelligence based event resolution strategy according to claim 2, wherein after determining the position coordinates of the acquisition device and the first acquisition angle of the acquisition device, the method further comprises:
Determining the current distance between the occurrence place of the abnormal feature and the position coordinate of the acquisition equipment; the direction from the occurrence place of the abnormal characteristic to the position coordinate of the acquisition equipment is the same as the first acquisition angle of the acquisition equipment;
Determining a circular area according to the occurrence place of the abnormal feature and the current distance; the midpoint of the circular area is the place where the abnormal feature occurs, and the radius of the circular area is the current distance; the position coordinates of the acquisition equipment are on the boundary of the circular area;
A first anchor point and a second anchor point are determined from the boundary of the circular area.
5. An artificial intelligence based event resolution strategy generation system comprising:
The identification unit is used for identifying abnormal event information from the collected abnormal event video; the abnormal event information at least comprises abnormal event time, abnormal event places, abnormal event characters and abnormal information points;
the acquisition unit is used for acquiring historical abnormal videos matched with the abnormal event information from the pre-acquired historical videos;
The first analysis unit is used for comprehensively analyzing the abnormal event video and the historical abnormal video to obtain abnormal event context information; the abnormal event context information at least comprises abnormal event process information, abnormal event reasons and event responsibility analysis information;
the second analysis unit is used for analyzing the abnormal event context information to obtain an abnormal event solving strategy;
and, the identification unit is further configured to:
Before the abnormal event information is identified from the collected abnormal event videos, carrying out abnormal characteristic real-time monitoring on the collected monitoring videos;
when abnormal characteristics exist in the monitoring video, determining the acquisition equipment of the monitoring video where the abnormal characteristics are located;
Determining a position coordinate of the acquisition equipment and a first acquisition angle of the acquisition equipment;
Controlling a first unmanned aerial vehicle device and a second unmanned aerial vehicle device to reach a position area where the position coordinates are located, determining a second acquisition angle of the first unmanned aerial vehicle device and determining a third acquisition angle of the second unmanned aerial vehicle device according to the first acquisition angle; wherein the first acquisition angle, the second acquisition angle and the third acquisition angle are all different;
Acquiring a first sub-video at the second acquisition angle by the first unmanned aerial vehicle equipment;
collecting a second sub-video at the third collection angle through the second unmanned aerial vehicle device;
Determining the monitoring video, the first sub video and the second sub video together as an abnormal event video;
And the identification unit controls the first unmanned aerial vehicle device and the second unmanned aerial vehicle device to reach the position region where the position coordinates are located, and the mode of determining the second acquisition angle of the first unmanned aerial vehicle device and the third acquisition angle of the second unmanned aerial vehicle device according to the first acquisition angle is specifically as follows:
Controlling the first unmanned aerial vehicle equipment to reach a first positioning point and controlling the second unmanned aerial vehicle equipment to reach a second positioning point; the position coordinates of the acquisition equipment, the first positioning point and the second positioning point divide the boundary of the circular area into three parts;
Determining a direction from the first positioning point to the occurrence place of the abnormal feature as a second acquisition angle, and determining a direction from the second positioning point to the occurrence place of the abnormal feature as a third acquisition angle;
Setting the acquisition direction of the first unmanned aerial vehicle equipment according to the second acquisition angle so that the first unmanned aerial vehicle equipment acquires the image of the occurrence place of the abnormal characteristic;
Setting the acquisition direction of the second unmanned aerial vehicle equipment according to the third acquisition angle so that the second unmanned aerial vehicle equipment acquires images of the occurrence place of the abnormal features;
the mode of the identification unit for monitoring the abnormal characteristics of the collected monitoring video in real time is specifically as follows:
Acquiring video frame information of the current moment from the acquired monitoring video; wherein the video frame information at least comprises a current video image and a current video sound;
if the decibel of the current video sound is larger than a preset decibel, determining that abnormal characteristics exist in the monitoring video;
If the decibel of the current video sound is smaller than or equal to the preset decibel, carrying out abnormal recognition on the current video image to obtain an abnormal recognition result;
if the abnormal recognition result indicates that abnormal features exist in the current video image, determining that abnormal features exist in the monitoring video;
And the identification unit performs anomaly identification on the current video image, and the mode of obtaining the anomaly identification result is specifically as follows:
performing human body feature recognition on the current video image to obtain a human body feature recognition result;
If the human body characteristic recognition result indicates that the target human body characteristic exists in the current video image, carrying out anomaly analysis on the target human body characteristic to obtain an anomaly analysis result;
if the abnormal analysis result indicates that the target human body characteristic is an abnormal human body characteristic, determining the abnormal human body characteristic as an abnormal recognition result;
If the human body characteristic recognition result indicates that the target human body characteristic does not exist in the current video image, determining a current position point acquired by the current video image; obtaining a standard image matched with the current moment and the current position point; comparing the current video image with the standard image to obtain an environment distinguishing characteristic; performing anomaly analysis on the environmental distinguishing characteristics to obtain an environmental anomaly analysis result;
And if the environmental abnormality analysis result indicates that the environmental distinguishing feature is an abnormal environmental feature, determining the abnormal environmental feature as an abnormality recognition result.
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