CN113505713A - Intelligent video analysis method and system based on airport security management platform - Google Patents
Intelligent video analysis method and system based on airport security management platform Download PDFInfo
- Publication number
- CN113505713A CN113505713A CN202110805230.5A CN202110805230A CN113505713A CN 113505713 A CN113505713 A CN 113505713A CN 202110805230 A CN202110805230 A CN 202110805230A CN 113505713 A CN113505713 A CN 113505713A
- Authority
- CN
- China
- Prior art keywords
- information
- abnormal
- obtaining
- video
- airport
- 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.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 88
- 230000002776 aggregation Effects 0.000 claims abstract description 113
- 238000004220 aggregation Methods 0.000 claims abstract description 113
- 230000002159 abnormal effect Effects 0.000 claims abstract description 95
- 206010000117 Abnormal behaviour Diseases 0.000 claims abstract description 83
- 238000012544 monitoring process Methods 0.000 claims abstract description 56
- 238000005192 partition Methods 0.000 claims abstract description 53
- 230000008014 freezing Effects 0.000 claims abstract description 40
- 238000007710 freezing Methods 0.000 claims abstract description 40
- 238000000034 method Methods 0.000 claims description 46
- 238000012423 maintenance Methods 0.000 claims description 21
- 230000006399 behavior Effects 0.000 claims description 11
- 201000010099 disease Diseases 0.000 claims description 10
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 10
- 238000012549 training Methods 0.000 claims description 8
- 238000007405 data analysis Methods 0.000 claims description 6
- 230000001815 facial effect Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 abstract description 7
- 238000007726 management method Methods 0.000 description 93
- 230000000694 effects Effects 0.000 description 12
- 230000008569 process Effects 0.000 description 10
- 230000005540 biological transmission Effects 0.000 description 8
- 230000003139 buffering effect Effects 0.000 description 7
- 238000003062 neural network model Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000010354 integration Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 206010063385 Intellectualisation Diseases 0.000 description 4
- 238000004590 computer program Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 238000000638 solvent extraction Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 208000006673 asthma Diseases 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000007621 cluster analysis Methods 0.000 description 2
- 230000007123 defense Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 208000019622 heart disease Diseases 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000003925 brain function Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 239000007943 implant Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000001483 mobilizing effect Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000005312 nonlinear dynamic Methods 0.000 description 1
- 230000000399 orthopedic effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Alarm Systems (AREA)
Abstract
The invention provides a video intelligent analysis method based on an airport security management platform, which comprises the following steps: according to the first airport security management platform, acquiring first airport monitoring information, dividing a security management area, and acquiring first partition information; obtaining first video information of a first partition; inputting the first video information into an abnormal behavior analysis model to obtain first abnormal behavior information; carrying out position marking on the abnormal place according to the first abnormal behavior information to obtain first positioning information; performing abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image, and judging whether first abnormal information has a first crowd gathering or not; obtaining a first aggregation scale, if present; according to the first positioning information and the first aggregation scale, a first emergency plan is generated and sent to the first airport security management platform for processing, and the technical problem that an airport monitoring system in the prior art is lack of a video analysis method with high feasibility is solved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence correlation, in particular to a video intelligent analysis method and system based on an airport safety management platform.
Background
In airport management work, security is one of the important topics in the aviation industry, including flight security and ground security. Ground safety is the safety management of an airport, and the safety and management in the area range directly relate to the safety of air defense and the normal operation of ground work. It can therefore be seen that airport security management work is particularly important in the aviation industry.
With the continuous and deep development of airport industry informatization and intellectualization, the airport security management platform is gradually developed, and a perfect security system is the premise that the airport is stable, efficient and continuous. In the construction process of the safety precaution system, the view screen monitoring system plays an important role in information acquisition, but because of the complex actual conditions and huge monitoring demand of an airport, the information amount is too huge, so that the emergency capacity of the monitoring system for various conditions is weak, and at present, the safety management is still carried out mainly by manpower and in a mode of taking a management platform as an auxiliary.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the airport monitoring system in the prior art is short of a video analysis method with high feasibility.
Disclosure of Invention
The embodiment of the application provides an intelligent video analysis method and system based on an airport security management platform, and solves the technical problem that an airport monitoring system in the prior art is lack of a video analysis method with high feasibility. The method and the system achieve the aim of partitioning the safety management area according to the integration of the monitoring information in the area by calling the monitoring information of each area of the airport, and reduce the data volume to be processed. And further transmitting the monitored video information of the partitions to an intelligent analysis model, carrying out emergency treatment on the crowd in the partitions if abnormal behaviors exist, and carrying out independent intelligent analysis on each partition, so that the transmission quantity of huge data is reduced, the possibility of landing is enhanced, and the technical effect of the video analysis method with high feasibility is obtained.
In view of the above problems, the embodiments of the present application provide an intelligent management method and system for orthopedic implants.
In a first aspect, an embodiment of the present application provides an intelligent video analysis method based on an airport security management platform, where the method is applied to an airport security management platform, and the platform is intelligently connected to a video freezing device, and the method includes: acquiring first airport monitoring information according to the first airport security management platform; performing safety management area division according to the first airport monitoring information to obtain first partition information; obtaining first video information of the first partition; inputting the first video information into an abnormal behavior analysis model, and obtaining first abnormal behavior information according to the abnormal behavior analysis model; carrying out position marking on an abnormal place according to the first abnormal behavior information to obtain first positioning information; performing abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image; judging whether first abnormal information has a first crowd according to the first frozen image; if the first abnormal information exists in a first aggregation crowd, obtaining a first aggregation scale; generating a first emergency plan according to the first positioning information and the first aggregation scale; and sending the first emergency plan to the first airport safety management platform for emergency treatment.
On the other hand, the embodiment of the present application provides an intelligent video analysis system based on an airport security management platform, wherein the system includes: the first obtaining unit is used for obtaining first airport monitoring information according to the first airport security management platform; a second obtaining unit, configured to perform security management area division according to the first airport monitoring information, and obtain first partition information; a third obtaining unit, configured to obtain first video information of the first partition; a fourth obtaining unit, configured to input the first video information into an abnormal behavior analysis model, and obtain first abnormal behavior information according to the abnormal behavior analysis model; a fifth obtaining unit, configured to perform position marking on an abnormal location according to the first abnormal behavior information, and obtain first positioning information; a sixth obtaining unit, configured to perform abnormal video freezing on the first video information by using a first video freezing device, and obtain a first frozen image; the first judging unit is used for judging whether the first abnormal information has a first crowd according to the first frozen image; a seventh obtaining unit, configured to obtain a first aggregation scale if the first abnormal information exists in a first aggregation crowd; a first generating unit, configured to generate a first emergency plan according to the first positioning information and the first aggregation scale; and the first sending unit is used for sending the first emergency plan to the first airport safety management platform for emergency treatment.
In a third aspect, an embodiment of the present application provides an intelligent video analysis system based on an airport security management platform, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to any one of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the first airport monitoring information is obtained according to the first airport security management platform; performing safety management area division according to the first airport monitoring information to obtain first partition information; obtaining first video information of the first partition; inputting the first video information into an abnormal behavior analysis model, and obtaining first abnormal behavior information according to the abnormal behavior analysis model; carrying out position marking on an abnormal place according to the first abnormal behavior information to obtain first positioning information; performing abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image; judging whether first abnormal information has a first crowd according to the first frozen image; if the first abnormal information exists in a first aggregation crowd, obtaining a first aggregation scale; generating a first emergency plan according to the first positioning information and the first aggregation scale; the first emergency plan is sent to the first airport safety management platform for emergency processing, so that the safety management area is partitioned by calling the monitoring information of each area of the airport according to the integration of the monitoring information in the area, and the data volume to be processed is reduced. And further transmitting the monitored video information of the partitions to an intelligent analysis model, carrying out emergency treatment on the crowd in the partitions if abnormal behaviors exist, and carrying out independent intelligent analysis on each partition, so that the transmission quantity of huge data is reduced, the possibility of landing is enhanced, and the technical effect of the video analysis method with high feasibility is obtained.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flowchart of an intelligent video analysis method based on an airport security management platform according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another intelligent video analysis method based on an airport security management platform according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a method for obtaining a first aggregation index based on data analysis of the first population of aggregations and the second population of aggregations according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a video intelligent analysis system based on an airport security management platform according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: the device comprises a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a first judging unit 17, a seventh obtaining unit 18, a first generating unit 19, a first sending unit 20, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The embodiment of the application provides an intelligent video analysis method and system based on an airport security management platform, and solves the technical problem that an airport monitoring system in the prior art is lack of a video analysis method with high feasibility. The method and the system achieve the aim of partitioning the safety management area according to the integration of the monitoring information in the area by calling the monitoring information of each area of the airport, and reduce the data volume to be processed. And further transmitting the monitored video information of the partitions to an intelligent analysis model, carrying out emergency treatment on the crowd in the partitions if abnormal behaviors exist, and carrying out independent intelligent analysis on each partition, so that the transmission quantity of huge data is reduced, the possibility of landing is enhanced, and the technical effect of the video analysis method with high feasibility is obtained.
Summary of the application
In airport management work, security is one of the important topics in the aviation industry, including flight security and ground security. Ground safety is the safety management of an airport, and the safety and management in the area range directly relate to the safety of air defense and the normal operation of ground work. It can therefore be seen that airport security management work is particularly important in the aviation industry. With the continuous and deep development of airport industry informatization and intellectualization, the airport security management platform is gradually developed, and a perfect security system is the premise that the airport is stable, efficient and continuous. In the construction process of a safety precaution system, a view screen monitoring system plays an important role in information acquisition, but because of the complex actual situation and huge monitoring demand of an airport, the information amount is too huge, so that the emergency capacity of the monitoring system for various situations is weak, safety management is still performed by taking manpower as a main mode and taking a management platform as an auxiliary mode at present, and the airport monitoring system in the prior art is in the technical problem of lacking a video analysis method with strong feasibility.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides an intelligent video analysis method based on an airport security management platform, wherein the method is applied to the airport security management platform, the platform is intelligently connected with a video freezing device, and the method comprises the following steps: acquiring first airport monitoring information according to the first airport security management platform; performing safety management area division according to the first airport monitoring information to obtain first partition information; obtaining first video information of the first partition; inputting the first video information into an abnormal behavior analysis model, and obtaining first abnormal behavior information according to the abnormal behavior analysis model; carrying out position marking on an abnormal place according to the first abnormal behavior information to obtain first positioning information; performing abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image; judging whether first abnormal information has a first crowd according to the first frozen image; if the first abnormal information exists in a first aggregation crowd, obtaining a first aggregation scale; generating a first emergency plan according to the first positioning information and the first aggregation scale; and sending the first emergency plan to the first airport safety management platform for emergency treatment.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides an intelligent video analysis method based on an airport security management platform, where the method is applied to an airport security management platform, and the platform is intelligently connected to a video freezing device, and the method includes:
s100: acquiring first airport monitoring information according to the first airport security management platform;
specifically, the first airport security management platform refers to a platform which is built based on modern information technology, big data and intellectualization and guarantees security and management work in an airport area; the first airport monitoring information refers to information which is acquired by monitoring information acquisition devices distributed at various positions of an airport and is provided for the first airport security management platform to process, and the monitoring information acquisition devices of the monitoring information preferably adopt high-definition intelligent cameras. The first step of building the monitoring system is to call the historical first airport monitoring information and the real-time first airport monitoring information from the first airport security management platform, so as to facilitate subsequent information feedback processing.
S200: performing safety management area division according to the first airport monitoring information to obtain first partition information;
specifically, the security management area refers to all areas in the first airport where security management is required; the first partition information refers to a result of dividing the safety management area according to the first airport monitoring information called from the first airport safety management platform in history and the first airport monitoring information in real time, the preferred division mode is to perform cluster analysis on the first airport monitoring information, and the cluster analysis refers to unsupervised grouping of similar samples in a sample set according to certain mode similarity measurement and clustering algorithm. The safety management area is divided according to the concentration of similar accidents. By partitioning the safety management area and optionally arranging intercommunicating agents in different partitions, the transmission and data volume of information flow are reduced, and the information processing efficiency is improved.
S300: obtaining first video information of the first partition;
specifically, the first partition refers to a certain partition after the security management area is partitioned; the first video information refers to real-time monitoring information acquired by a monitoring information acquisition device in the first partition after the partition. The first video information is only real-time monitoring information in the first partition, and is not interfered by monitoring information of other partitions, so that redundancy of the information and data loss in the transmission process are reduced, and the accuracy of obtaining the information is enhanced.
S400: inputting the first video information into an abnormal behavior analysis model, and obtaining first abnormal behavior information according to the abnormal behavior analysis model;
specifically, the first abnormal behavior information is a screening result of the abnormal behavior of the video information obtained by inputting the first video information into the abnormal behavior analysis model for intelligent analysis, the abnormal behavior analysis model is established on the basis of a neural network model and has the characteristics of the neural network model, wherein the artificial neural network is an abstract mathematical model which is proposed and developed on the basis of modern neuroscience and aims at reflecting the structure and the function of the human brain, the neural network is an operation model and is formed by connecting a large number of nodes (or called neurons) with each other, each node represents a specific output function called an excitation function, the connection between every two nodes represents a weighted value for a signal passing through the connection, called a weighted value, which is equivalent to the memory of the artificial neural network, and the output of the network is in accordance with the connection mode of the network, the abnormal behavior analysis model established based on the neural network model can output accurate first abnormal behavior information, so that the method has strong analysis and calculation capacity and achieves the technical effects of accuracy and high efficiency.
S500: carrying out position marking on an abnormal place according to the first abnormal behavior information to obtain first positioning information;
specifically, the first positioning information refers to that after the first abnormal behavior information concludes that the first video information in the first partition has an abnormal behavior, the position of the abnormal behavior in the first video is defined immediately, and then a specific position determination method is not limited herein according to the position of the first partition corresponding to the position of the abnormal behavior in the first video, because a mature technology exists already. The source of the first abnormal behavior information can be rapidly controlled by acquiring the first positioning information.
S600: performing abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image;
specifically, the first video freezing device refers to a device that freezes a video clip into a high-definition picture when a high-definition anomaly is extracted from the first video information, and the first video freezing device is, by way of non-limiting example: the convolution can be used as a feature extractor in machine learning, so that extracted feature information has centralization and representativeness, and convolution features of abnormal information of the first video information are obtained. Further, the first frozen image refers to a result of freezing the abnormal information of the first video information based on the first video freezing device.
S700: judging whether first abnormal information has a first crowd according to the first frozen image;
s800: if the first abnormal information exists in a first aggregation crowd, obtaining a first aggregation scale;
s900: generating a first emergency plan according to the first positioning information and the first aggregation scale;
s1000: and sending the first emergency plan to the first airport safety management platform for emergency treatment.
Specifically, the first crowd refers to a crowd gathering condition at a position where an abnormal condition occurs, which is obtained according to the first abnormal information and the first positioning information after the first frozen image is uploaded; further, the first aggregation scale refers to a basic condition of the first aggregation population obtained by clustering according to the number of people, density, gender, age, and the like of the first aggregation population without any limitation.
Further, different first emergency plans may be screened according to the first aggregation size and the category of the first abnormal information. By way of example and not limitation: if the first abnormal information is the occurrence of fire, the first emergency plan can be used for evacuating people according to the first positioning information, sounding an alarm, alarming, cutting off a propagation source and mobilizing security personnel in an airport to extinguish a fire. And judging whether the number of the dispatched workers is the decision of an evacuation scheme according to the first aggregation scale. The first emergency protocol is optional: in the early stage, a cloud database formed by experience schemes for emergency treatment of various conditions is used, and the first emergency plan is obtained by screening in the cloud database according to the category of the first abnormal information and the first aggregation scale; and in the later period, on the basis that the data volume reaches the trainable intelligent model, a supervised training neural network model is provided, and when the neural network model reaches a convergence state, the first emergency plan with higher individuation degree can be obtained.
Furthermore, because the first abnormal information is determined and the first emergency plan is also finished in the first partition decision, the airport resources can be coordinated and emergency treatment can be carried out only by uploading the first emergency plan to the first airport security management platform. Because the first airport security management platform only needs to coordinate resources to execute the first emergency plan, and other work is completed in the first partition, compared with the prior art, the information transmission process is reduced, each partition makes a decision independently, the redundancy of decision data volume is reduced, and the decision efficiency is improved.
Further, based on the abnormal video freezing of the first video information by the first video freezing device, a first frozen image is obtained, as shown in fig. 2, the method further includes step S1100:
s1110: obtaining a first screening image by screening the first frozen image, wherein the first screening image is an image containing facial features of an abnormal user;
s1120: acquiring first abnormal user information by constructing data of all images in the first screened image;
s1130: acquiring first user identity information according to the first abnormal user information;
s1140: obtaining first historical disease information according to the first user identity information;
s1150: and generating a second emergency plan according to the first historical disease information and the first emergency plan.
Specifically, the first selected image is selected from the first frozen image information to obtain relatively representative image information. Here, the first abnormal information is taken as the indication of the sudden discomfort condition of a person in the first partition, and the first filtered image filtered from the first frozen image includes the image of the facial feature of the abnormal user. Further, the first abnormal user information refers to that based on the facial feature information of the abnormal user, screening can be performed in a large database to obtain basic information of the abnormal user, including but not limited to: age, sex, name, contact information, past disease history, especially heart disease, asthma, etc. The identity information of the first user refers to the information such as the age, the sex, the name, the contact information of relatives and the like; the first history disease information refers to past disease history, especially heart disease, asthma and the like. Furthermore, the first emergency plan is corrected based on the first historical disease information, and the second emergency plan is obtained. The first frozen image is screened to obtain individual information with strong representativeness, and further, the first emergency plan is corrected according to the individual information, and the obtained second emergency plan can comprehensively process abnormal conditions caused by the first abnormal information, for example, priority can be obtained and processed for individuals with special conditions. The technical effect of improving the individual formulation of the emergency plan is achieved.
Further, based on the obtaining of the first aggregation size if the first abnormal information exists in the first aggregation group, the method step S800 further includes:
s810: according to the first video freezing device, obtaining a second frozen image, wherein the second frozen image is an image of people gathering from a first time node and a second time node;
s820: obtaining a first aggregated population quantity for the first time node;
s830: obtaining a second aggregated population quantity for the second time node;
s840: obtaining a first aggregation index by performing data analysis on the first population of aggregations and the second population of aggregations;
s850: obtaining the first aggregation size based on the first aggregation index.
Specifically, the second frozen image is obtained by freezing the first video information according to the occurrence time interval of the first abnormal information based on the first video freezing device, and a representative frozen image set is the second frozen image, the frozen image includes images of the crowd group that appear at all times of the occurrence time interval of the first abnormal information, and the first time node and the second time node are representative time nodes corresponding to the representative frozen image set, that is, the second frozen image, at these times. Further, the first time node and the second time node are respectively called as the first aggregated population number and the second aggregated population number; the first aggregation index refers to information such as the number of people, crowd density and flowing trend in the first abnormal information area, and the larger the number of people and the crowd density are, the larger the first aggregation scale is; the larger the tendency of the population in which the first abnormal information area flows, the larger the first aggregation size. Further, the first aggregation size is obtained based on the influence relationship between the first aggregation index and the first aggregation size, and an information basis is provided for a further process.
Further, based on said obtaining said first aggregation size according to said first aggregation index, said method step S840 further comprises:
s841: obtaining first place information of the second frozen image;
s842: obtaining a first management level of the first place information;
s843: judging whether the first aggregation index is greater than or equal to a preset aggregation index or not according to the first management level;
s844: if the first aggregation index is larger than or equal to a preset aggregation index, first reminding information is obtained;
s845: and adding the first reminding information to the first emergency plan.
Specifically, the first location information is a location such as a flight area, a freight area, an oil depot area, a terminal building area, or the like, where it is determined based on the second frozen image that the first abnormality information has occurred, and the aggregation index corresponding to the location is different and the first management level set in correspondence with the location is different depending on the size of the crowd requested by the location. Furthermore, the corresponding first management level can be obtained through screening according to the first place information, and the aggregation scale which can be accommodated by the first place and the corresponding preset aggregation index can be obtained according to the first management level. Furthermore, the first aggregation index is compared with the preset aggregation index, such as the number of people, the crowd density and other information, if the first aggregation index exceeds or equals to the preset aggregation index, the aggregation scale is indicated to be out of control, the first reminding information is obtained and added into the first emergency plan, and timely solution is guaranteed.
Further, based on the sending of the first emergency plan to the first airport security management platform for emergency processing, the method step S1000 further includes:
s1010: inputting first security maintenance personnel information of the first airport into the first airport security management platform;
s1020: when the first airport safety management platform receives the first emergency plan information, first real-time positioning information of the first safety maintenance personnel is obtained;
s1030: acquiring a first abnormal area maintainer according to first real-time positioning information of the first safety maintainer and the first positioning information;
s1040: obtaining a first requirement maintenance personnel according to the first emergency plan;
s1050: and sending the first positioning information to the first requirement maintenance personnel.
Specifically, the first security maintenance personnel information of the first airport refers to staff persons, such as airport security, technicians and the like, who can schedule the maintenance of the first airport security; and after the first airport security management platform obtains the first emergency plan, scheduling in the first security maintenance personnel of the first airport according to the maintenance quantity of the security personnel required by the first emergency plan. The specific implementation mode is as follows: firstly, first safety maintenance personnel of all schedulable first airports are obtained, secondly, first real-time positioning information of the first safety maintenance personnel is uploaded, maintenance personnel in the first abnormal area are preferentially scheduled, secondly, according to the quantity of safety maintenance personnel required by the first emergency plan, and then the first positioning information is sent to the first requirement maintenance personnel from near to far with the first positioning information as the reference, so that the first abnormal information is guaranteed to be solved in time.
Further, based on the obtaining of the first aggregation index by performing data analysis on the first aggregation population quantity and the second aggregation population quantity, as shown in fig. 3, the method further includes step S1200:
s1210: obtaining a first time node difference value according to the first time node and the second time node, wherein the first time node difference value corresponds to the first aggregation index;
s1220: obtaining a first execution time according to the first emergency plan;
s1230: performing aggregation propagation aggregation prediction according to the first execution time and the first positioning information to obtain a first propagation index;
s1240: obtaining a first prediction aggregation index according to the first propagation index;
s1250: using the first prediction aggregation index as first assistance data for the first emergency protocol.
Specifically, the first time node difference value refers to a result obtained by subtracting the first time node and the second time node, where the first time node and the second time node are both representative time nodes, and the first abnormal information appearing between the first time difference values is obvious, and sets of data of the first aggregation index corresponding to the first abnormal information appearing between the first time difference values are combined to obtain a correlation between the first time node difference value and the first aggregation index in the first abnormal information. Further, according to the first contingency scheme, the first contingency scheme includes a correlation between the first time node difference and the first aggregation index, and the first execution time is determined according to the emergency degree of the situation. Furthermore, after receiving the first execution time, the coordination resource needs a certain buffering time, and the possible crowd gathering size in the first partition in the buffering time needs to be predicted, so that the targeted measures can be taken; the first propagation index refers to data according to the flow trend and speed information of the crowd and the distance between the crowd and the first positioning information; the first prediction aggregation index is used for comprehensively judging the aggregation trend of the crowd in the first partition in the buffer time of the first execution time by combining the first propagation index. Furthermore, the burst condition occurring in the buffering time interval of the first execution time can be handled in time according to the first prediction aggregation index. Since the buffering time occurs during the execution of the first emergency plan, but the first emergency plan does not involve the information, the first prediction aggregation index can take the emergency situation in the buffering time into account, and the first emergency plan is executed by taking the emergency situation as auxiliary information, thereby achieving the technical effect of stronger real-time performance.
Further, based on the inputting of the first video information into an abnormal behavior analysis model, obtaining first abnormal behavior information according to the abnormal behavior analysis model, the method step S400 further includes:
s410: obtaining a first behavior convolution characteristic according to the first video information;
s420: inputting the first behavior convolution characteristic as first input information into the abnormal behavior analysis model;
s430: the abnormal behavior analysis model is obtained through training of multiple groups of data, wherein the multiple groups of data comprise the first behavior convolution characteristics and identification information for identifying the first abnormal characteristics;
s440: obtaining a first output result of the abnormal behavior analysis model, wherein the first output result is the first abnormal feature;
s450: and obtaining the first abnormal behavior information according to the first abnormal characteristic.
Specifically, convolution can be used as a feature extractor in machine learning, so that the extracted feature information has concentration and representativeness, the convolution feature extraction is carried out on the first video information to obtain the first behavior convolution feature, and further, the abnormal behavior analysis model is also a neural network model, namely, a neural network model in machine learning, which reflects many basic features of human brain functions and is a highly complex nonlinear dynamic learning system. The self-training learning system can continuously carry out self-training learning according to training data, and the multiple groups of data comprise the first behavior convolution characteristic and identification information for identifying the first abnormal characteristic. And continuously self-correcting the abnormal behavior analysis model, and finishing the supervised learning process when the output information of the abnormal behavior analysis model reaches a preset accuracy rate/convergence state. By performing data training on the abnormal behavior analysis model, the abnormal behavior analysis model can process input data more accurately, the output first abnormal characteristic information is more accurate, and further the first abnormal behavior information is summarized based on the first abnormal characteristic information. The technical effects of accurately obtaining data information and improving the intellectualization of the evaluation result are achieved.
To sum up, the video intelligent analysis method and system based on the airport security management platform provided by the embodiment of the application have the following technical effects:
1. the first airport monitoring information is obtained according to the first airport security management platform; performing safety management area division according to the first airport monitoring information to obtain first partition information; obtaining first video information of the first partition; inputting the first video information into an abnormal behavior analysis model, and obtaining first abnormal behavior information according to the abnormal behavior analysis model; carrying out position marking on an abnormal place according to the first abnormal behavior information to obtain first positioning information; performing abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image; judging whether first abnormal information has a first crowd according to the first frozen image; if the first abnormal information exists in a first aggregation crowd, obtaining a first aggregation scale; generating a first emergency plan according to the first positioning information and the first aggregation scale; the first emergency plan is sent to the first airport safety management platform for emergency processing, so that the safety management area is partitioned by calling the monitoring information of each area of the airport according to the integration of the monitoring information in the area, and the data volume to be processed is reduced. And further transmitting the monitored video information of the partitions to an intelligent analysis model, carrying out emergency treatment on the crowd in the partitions if abnormal behaviors exist, and carrying out independent intelligent analysis on each partition, so that the transmission quantity of huge data is reduced, the possibility of landing is enhanced, and the technical effect of the video analysis method with high feasibility is obtained.
2. Since the buffering time occurs during the execution of the first emergency plan, but the first emergency plan does not involve the information, the first prediction aggregation index can take the emergency situation in the buffering time into account, and the first emergency plan is executed by taking the emergency situation as auxiliary information, thereby achieving the technical effect of stronger real-time performance.
Example two
Based on the same inventive concept as the video intelligent analysis method based on the airport security management platform in the foregoing embodiment, as shown in fig. 4, an embodiment of the present application provides a video intelligent analysis system based on the airport security management platform, wherein the system includes:
the first obtaining unit 11 is configured to obtain first airport monitoring information according to a first airport security management platform;
a second obtaining unit 12, where the second obtaining unit 12 is configured to perform security management area division according to the first airport monitoring information, and obtain first partition information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain first video information of the first partition;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to input the first video information into an abnormal behavior analysis model, and obtain first abnormal behavior information according to the abnormal behavior analysis model;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to perform position marking on an abnormal place according to the first abnormal behavior information, so as to obtain first positioning information;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to perform abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image;
a first judging unit 17, where the first judging unit 17 is configured to judge whether the first abnormal information includes a first crowd according to the first frozen image;
a seventh obtaining unit 18, where the seventh obtaining unit 18 is configured to obtain a first aggregation size if the first abnormal information exists in the first aggregation group;
a first generating unit 19, where the first generating unit 19 is configured to generate a first emergency plan according to the first positioning information and the first aggregation scale;
a first sending unit 20, where the first sending unit 20 is configured to send the first emergency plan to the first airport security management platform for emergency processing.
Further, the system further comprises:
an eighth obtaining unit, configured to obtain a first filtered image by filtering the first frozen image, where the first filtered image is an image that includes facial features of an abnormal user;
a ninth obtaining unit, configured to obtain first abnormal user information by performing data construction on all images in the first screened image;
a tenth obtaining unit, configured to obtain first user identity information according to the first abnormal user information;
an eleventh obtaining unit, configured to obtain first historical disease information according to the first user identity information;
and the second generation unit is used for generating a second emergency plan according to the first historical disease information and the first emergency plan.
Further, the system further comprises:
a twelfth obtaining unit, configured to obtain a second frozen image according to the first video freezing device, where the second frozen image is an image of a crowd including a first time node and a second time node;
a thirteenth obtaining unit for obtaining a first aggregated population quantity of the first time node;
a fourteenth obtaining unit, configured to obtain a second aggregated population number of the second time node;
a fifteenth obtaining unit configured to obtain a first aggregation index by performing data analysis on the first aggregation population amount and the second aggregation population amount;
a sixteenth obtaining unit configured to obtain the first aggregation size based on the first aggregation index.
Further, the system further comprises:
a seventeenth obtaining unit configured to obtain first location information of the second frozen image;
an eighteenth obtaining unit configured to obtain a first management level of the first location information;
a second judging unit, configured to judge whether the first aggregation index is greater than or equal to a preset aggregation index according to the first management level;
a nineteenth obtaining unit, configured to obtain first reminding information if the first aggregation index is greater than or equal to a preset aggregation index;
a first adding unit, configured to add the first reminding information to the first emergency plan.
Further, the system further comprises:
the first input unit is used for inputting first safety maintenance personnel information of the first airport into the first airport safety management platform;
a twentieth obtaining unit, configured to obtain first real-time positioning information of the first safety maintenance worker when the first airport safety management platform receives the first emergency plan information;
a twenty-first obtaining unit, configured to obtain a first abnormal area maintainer according to first real-time positioning information of the first safety maintainer and the first positioning information;
a twenty-second obtaining unit, configured to obtain a first requirement maintenance worker according to the first emergency plan;
and the second sending unit is used for sending the first positioning information to the first requirement maintenance personnel.
Further, the system further comprises:
a twenty-third obtaining unit, configured to obtain a first time node difference value according to the first time node and the second time node, where the first time node difference value corresponds to the first aggregation index;
a twenty-fourth obtaining unit, configured to obtain a first execution time according to the first emergency plan;
a twenty-fifth obtaining unit, configured to perform aggregate propagation aggregate prediction according to the first execution time and the first positioning information, and obtain a first propagation index;
a twenty-sixth obtaining unit configured to obtain a first predicted aggregation index according to the first propagation index;
a first setting unit to use the first prediction aggregation index as first auxiliary data of the first emergency plan.
Further, the system further comprises:
a twenty-seventh obtaining unit, configured to obtain a first behavior convolution feature according to the first video information;
a second input unit, configured to input the first behavior convolution feature as first input information into the abnormal behavior analysis model;
a first training unit, configured to train the abnormal behavior analysis model to obtain multiple sets of data, where the multiple sets of data include the first behavior convolution feature and identification information identifying the first abnormal feature;
a first output unit, configured to obtain a first output result of the abnormal behavior analysis model, where the first output result is the first abnormal feature;
a twenty-eighth obtaining unit, configured to obtain the first abnormal behavior information according to the first abnormal feature.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to figure 5,
based on the same inventive concept as the video intelligent analysis method based on the airport security management platform in the foregoing embodiment, the embodiment of the present application further provides a video intelligent analysis system based on the airport security management platform, which includes: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects.
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 3, but this does not mean only one bus or one type of bus.
The communication interface 303 may be any device, such as a transceiver, for communicating with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), a wired access network, and the like.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable Programmable read-only memory (EEPROM), a compact disk read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute the computer-executable instructions stored in the memory 301, so as to implement the video intelligent analysis method based on the airport security management platform provided by the above-mentioned embodiment of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The embodiment of the application provides an intelligent video analysis method based on an airport security management platform, wherein the method is applied to the airport security management platform, the platform is intelligently connected with a video freezing device, and the method comprises the following steps: acquiring first airport monitoring information according to the first airport security management platform; performing safety management area division according to the first airport monitoring information to obtain first partition information; obtaining first video information of the first partition; inputting the first video information into an abnormal behavior analysis model, and obtaining first abnormal behavior information according to the abnormal behavior analysis model; carrying out position marking on an abnormal place according to the first abnormal behavior information to obtain first positioning information; performing abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image; judging whether first abnormal information has a first crowd according to the first frozen image; if the first abnormal information exists in a first aggregation crowd, obtaining a first aggregation scale; generating a first emergency plan according to the first positioning information and the first aggregation scale; and sending the first emergency plan to the first airport safety management platform for emergency treatment. The method and the system achieve the aim of partitioning the safety management area according to the integration of the monitoring information in the area by calling the monitoring information of each area of the airport, and reduce the data volume to be processed. And further transmitting the monitored video information of the partitions to an intelligent analysis model, carrying out emergency treatment on the crowd in the partitions if abnormal behaviors exist, and carrying out independent intelligent analysis on each partition, so that the transmission quantity of huge data is reduced, the possibility of landing is enhanced, and the technical effect of the video analysis method with high feasibility is obtained.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by design of a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations.
Claims (9)
1. An airport security management platform-based intelligent video analysis method is applied to an airport security management platform, and the platform is intelligently connected with a video freezing device, and the method comprises the following steps:
acquiring first airport monitoring information according to the first airport security management platform;
performing safety management area division according to the first airport monitoring information to obtain first partition information;
obtaining first video information of the first partition;
inputting the first video information into an abnormal behavior analysis model, and obtaining first abnormal behavior information according to the abnormal behavior analysis model;
carrying out position marking on an abnormal place according to the first abnormal behavior information to obtain first positioning information;
performing abnormal video freezing on the first video information through a first video freezing device to obtain a first frozen image;
judging whether first abnormal information has a first crowd according to the first frozen image;
if the first abnormal information exists in a first aggregation crowd, obtaining a first aggregation scale;
generating a first emergency plan according to the first positioning information and the first aggregation scale;
and sending the first emergency plan to the first airport safety management platform for emergency treatment.
2. The method of claim 1, wherein the abnormal video freezing of the first video information by the first video freezing device obtains a first frozen image, the method further comprising:
obtaining a first screening image by screening the first frozen image, wherein the first screening image is an image containing facial features of an abnormal user;
acquiring first abnormal user information by constructing data of all images in the first screened image;
acquiring first user identity information according to the first abnormal user information;
obtaining first historical disease information according to the first user identity information;
and generating a second emergency plan according to the first historical disease information and the first emergency plan.
3. The method of claim 1, wherein said obtaining a first aggregate size if said first abnormal information is present in a first aggregate population, further comprises:
according to the first video freezing device, obtaining a second frozen image, wherein the second frozen image is an image of people gathering from a first time node and a second time node;
obtaining a first aggregated population quantity for the first time node;
obtaining a second aggregated population quantity for the second time node;
obtaining a first aggregation index by performing data analysis on the first population of aggregations and the second population of aggregations;
obtaining the first aggregation size based on the first aggregation index.
4. The method of claim 3, wherein said obtaining said first aggregation size is based on said first aggregation index, said method further comprising:
obtaining first place information of the second frozen image;
obtaining a first management level of the first place information;
judging whether the first aggregation index is greater than or equal to a preset aggregation index or not according to the first management level;
if the first aggregation index is larger than or equal to a preset aggregation index, first reminding information is obtained;
and adding the first reminding information to the first emergency plan.
5. The method of claim 1, wherein the sending the first emergency plan to the first airport security management platform for emergency handling, the method further comprising:
inputting first security maintenance personnel information of the first airport into the first airport security management platform;
when the first airport safety management platform receives the first emergency plan information, first real-time positioning information of the first safety maintenance personnel is obtained;
acquiring a first abnormal area maintainer according to first real-time positioning information of the first safety maintainer and the first positioning information;
obtaining a first requirement maintenance personnel according to the first emergency plan;
and sending the first positioning information to the first requirement maintenance personnel.
6. A method as in claim 3, wherein the first aggregation index is obtained by data analysis of the first and second aggregated population numbers, the method further comprising:
obtaining a first time node difference value according to the first time node and the second time node, wherein the first time node difference value corresponds to the first aggregation index;
obtaining a first execution time according to the first emergency plan;
performing aggregation propagation aggregation prediction according to the first execution time and the first positioning information to obtain a first propagation index;
obtaining a first prediction aggregation index according to the first propagation index;
using the first prediction aggregation index as first assistance data for the first emergency protocol.
7. The method of claim 1, wherein the first video information is input into an abnormal behavior analysis model, and first abnormal behavior information is obtained according to the abnormal behavior analysis model, the method further comprising:
obtaining a first behavior convolution characteristic according to the first video information;
inputting the first behavior convolution characteristic as first input information into the abnormal behavior analysis model;
the abnormal behavior analysis model is obtained through training of multiple groups of data, wherein the multiple groups of data comprise the first behavior convolution characteristics and identification information for identifying the first abnormal characteristics;
obtaining a first output result of the abnormal behavior analysis model, wherein the first output result is the first abnormal feature;
and obtaining the first abnormal behavior information according to the first abnormal characteristic.
8. An intelligent video analysis system based on an airport security management platform, wherein the system comprises:
the first obtaining unit is used for obtaining first airport monitoring information according to the first airport security management platform;
a second obtaining unit, configured to perform security management area division according to the first airport monitoring information, and obtain first partition information;
a third obtaining unit, configured to obtain first video information of the first partition;
a fourth obtaining unit, configured to input the first video information into an abnormal behavior analysis model, and obtain first abnormal behavior information according to the abnormal behavior analysis model;
a fifth obtaining unit, configured to perform position marking on an abnormal location according to the first abnormal behavior information, and obtain first positioning information;
a sixth obtaining unit, configured to perform abnormal video freezing on the first video information by using a first video freezing device, and obtain a first frozen image;
the first judging unit is used for judging whether the first abnormal information has a first crowd according to the first frozen image;
a seventh obtaining unit, configured to obtain a first aggregation scale if the first abnormal information exists in a first aggregation crowd;
a first generating unit, configured to generate a first emergency plan according to the first positioning information and the first aggregation scale;
and the first sending unit is used for sending the first emergency plan to the first airport safety management platform for emergency treatment.
9. An intelligent video analysis system based on an airport security management platform comprises: a processor coupled with a memory for storing a program that, when executed by the processor, causes a system to perform the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110805230.5A CN113505713B (en) | 2021-07-16 | 2021-07-16 | Intelligent video analysis method and system based on airport safety management platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110805230.5A CN113505713B (en) | 2021-07-16 | 2021-07-16 | Intelligent video analysis method and system based on airport safety management platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113505713A true CN113505713A (en) | 2021-10-15 |
| CN113505713B CN113505713B (en) | 2022-09-23 |
Family
ID=78013531
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110805230.5A Active CN113505713B (en) | 2021-07-16 | 2021-07-16 | Intelligent video analysis method and system based on airport safety management platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113505713B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114399498A (en) * | 2022-01-19 | 2022-04-26 | 浙大网新系统工程有限公司 | Airport runway surface condition assessment method and system |
| CN115204753A (en) * | 2022-09-14 | 2022-10-18 | 深圳市深信信息技术有限公司 | Intelligent farm trade place behavior monitoring method and system and readable storage medium |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2553592A1 (en) * | 2005-07-28 | 2007-01-28 | Inventio Ag | Method of guiding a user in an environment, particularly in a building |
| CN102280011A (en) * | 2011-06-09 | 2011-12-14 | 无锡国科微纳传感网科技有限公司 | Boundary safeguard alarm disposing method and system therefor |
| WO2014148395A1 (en) * | 2013-03-21 | 2014-09-25 | 株式会社日立国際電気 | Video monitoring system, video monitoring method, and video monitoring device |
| CN105718857A (en) * | 2016-01-13 | 2016-06-29 | 兴唐通信科技有限公司 | Human body abnormal behavior detection method and system |
| CN106094633A (en) * | 2016-07-05 | 2016-11-09 | 张宁 | Civil Aviation Airport terminal safety long-distance Sis Based On Pi Database |
| JP2017038739A (en) * | 2015-08-18 | 2017-02-23 | 株式会社ユニバーサルエンターテインメント | Information processing device, information reading device, and gaming machine |
| CN107862437A (en) * | 2017-10-16 | 2018-03-30 | 中国人民公安大学 | The public domain crowd massing method for early warning and system assessed based on risk probability |
| US20190124134A1 (en) * | 2004-03-16 | 2019-04-25 | Icontrol Networks, Inc. | Generating risk profile using data of home monitoring and security system |
| CN109819208A (en) * | 2019-01-02 | 2019-05-28 | 江苏警官学院 | A kind of dense population security monitoring management method based on artificial intelligence dynamic monitoring |
| CN110751341A (en) * | 2019-10-29 | 2020-02-04 | 浪潮天元通信信息系统有限公司 | Video planning analysis system and method based on Internet of things |
| CN111460917A (en) * | 2020-03-13 | 2020-07-28 | 温州大学大数据与信息技术研究院 | Airport abnormal behavior detection system and method based on multi-mode information fusion |
| CA3063018A1 (en) * | 2018-11-21 | 2020-10-17 | Beijing Yutian Technology Co. Ltd | An intelligent emergency decision support system for emergency communication |
| WO2020211428A1 (en) * | 2019-04-17 | 2020-10-22 | 广东康云科技有限公司 | Park area safety supervisory system and method |
| CN112000043A (en) * | 2020-08-28 | 2020-11-27 | 广州粤建三和软件股份有限公司 | High formwork real-time monitoring method, device, equipment and medium based on block chain |
| WO2021004344A1 (en) * | 2019-07-10 | 2021-01-14 | 平安科技（深圳）有限公司 | Data analysis-based risk identification method and related device |
| CN113051980A (en) * | 2019-12-27 | 2021-06-29 | 华为技术有限公司 | Video processing method, device, system and computer readable storage medium |
| CN113095984A (en) * | 2021-04-28 | 2021-07-09 | 南通市第一人民医院 | Emergency plan generation method and system for emergency of nursing staff |
-
2021
- 2021-07-16 CN CN202110805230.5A patent/CN113505713B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190124134A1 (en) * | 2004-03-16 | 2019-04-25 | Icontrol Networks, Inc. | Generating risk profile using data of home monitoring and security system |
| CA2553592A1 (en) * | 2005-07-28 | 2007-01-28 | Inventio Ag | Method of guiding a user in an environment, particularly in a building |
| CN102280011A (en) * | 2011-06-09 | 2011-12-14 | 无锡国科微纳传感网科技有限公司 | Boundary safeguard alarm disposing method and system therefor |
| WO2014148395A1 (en) * | 2013-03-21 | 2014-09-25 | 株式会社日立国際電気 | Video monitoring system, video monitoring method, and video monitoring device |
| JP2017038739A (en) * | 2015-08-18 | 2017-02-23 | 株式会社ユニバーサルエンターテインメント | Information processing device, information reading device, and gaming machine |
| CN105718857A (en) * | 2016-01-13 | 2016-06-29 | 兴唐通信科技有限公司 | Human body abnormal behavior detection method and system |
| CN106094633A (en) * | 2016-07-05 | 2016-11-09 | 张宁 | Civil Aviation Airport terminal safety long-distance Sis Based On Pi Database |
| CN107862437A (en) * | 2017-10-16 | 2018-03-30 | 中国人民公安大学 | The public domain crowd massing method for early warning and system assessed based on risk probability |
| CA3063018A1 (en) * | 2018-11-21 | 2020-10-17 | Beijing Yutian Technology Co. Ltd | An intelligent emergency decision support system for emergency communication |
| CN109819208A (en) * | 2019-01-02 | 2019-05-28 | 江苏警官学院 | A kind of dense population security monitoring management method based on artificial intelligence dynamic monitoring |
| WO2020211428A1 (en) * | 2019-04-17 | 2020-10-22 | 广东康云科技有限公司 | Park area safety supervisory system and method |
| WO2021004344A1 (en) * | 2019-07-10 | 2021-01-14 | 平安科技（深圳）有限公司 | Data analysis-based risk identification method and related device |
| CN110751341A (en) * | 2019-10-29 | 2020-02-04 | 浪潮天元通信信息系统有限公司 | Video planning analysis system and method based on Internet of things |
| CN113051980A (en) * | 2019-12-27 | 2021-06-29 | 华为技术有限公司 | Video processing method, device, system and computer readable storage medium |
| CN111460917A (en) * | 2020-03-13 | 2020-07-28 | 温州大学大数据与信息技术研究院 | Airport abnormal behavior detection system and method based on multi-mode information fusion |
| CN112000043A (en) * | 2020-08-28 | 2020-11-27 | 广州粤建三和软件股份有限公司 | High formwork real-time monitoring method, device, equipment and medium based on block chain |
| CN113095984A (en) * | 2021-04-28 | 2021-07-09 | 南通市第一人民医院 | Emergency plan generation method and system for emergency of nursing staff |
Non-Patent Citations (1)
| Title |
|---|
| 高宇恒: "机场安全管理成熟度评价研究", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅰ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114399498A (en) * | 2022-01-19 | 2022-04-26 | 浙大网新系统工程有限公司 | Airport runway surface condition assessment method and system |
| CN115204753A (en) * | 2022-09-14 | 2022-10-18 | 深圳市深信信息技术有限公司 | Intelligent farm trade place behavior monitoring method and system and readable storage medium |
| CN115204753B (en) * | 2022-09-14 | 2022-12-02 | 深圳市深信信息技术有限公司 | Intelligent farm trade place behavior monitoring method and system and readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113505713B (en) | 2022-09-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11615695B2 (en) | Coverage agent for computer-aided dispatch systems | |
| CN113505713B (en) | Intelligent video analysis method and system based on airport safety management platform | |
| AU2023258418A1 (en) | Emergency response system | |
| WO2014014470A1 (en) | Problem analysis and priority determination based on fuzzy expert systems | |
| CN111611085B (en) | Yun Bian collaboration-based man-machine hybrid enhanced intelligent system, method and device | |
| EP3217242A1 (en) | Using aircraft data recorded during flight to predict aircraft engine behavior | |
| CN113516244A (en) | Intelligent operation and maintenance method and device, electronic equipment and storage medium | |
| CN114221908A (en) | Dynamic current-limiting fusing processing method and device, computer equipment and storage medium | |
| CN113839799B (en) | Alarm association rule mining method and device | |
| CN117350640A (en) | Project progress management method and system | |
| CN116187675A (en) | Task allocation method, device, equipment and storage medium | |
| CN113946464B (en) | Alarm noise reduction method combining model and experience pre-training and parallel deduction | |
| CN111953533B (en) | Method, device and equipment for regulating and controlling working duration of target network node and storage medium | |
| Retnowardhani et al. | Classify interval range of crime forecasting for crime prevention decision making | |
| EP4315166A1 (en) | Method and system for predicting and classifying event sequences embedded in a knowledge graph | |
| CN113379247A (en) | Modeling method and system of enterprise potential safety hazard tracking model | |
| CN106599435B (en) | Airport day traffic capacity determining method and device | |
| CN110807251A (en) | Network public opinion polarization method and system integrating individual heterogeneity and dynamic dependency | |
| KR102545637B1 (en) | Business management system and method using user terminal | |
| CN118114902A (en) | Intelligent construction management platform based on BIM technology | |
| Shao-Yu et al. | The Study on the Prediction Models of Human Factor Flight Accidents by Combining Fuzzy Clustering Methods and Neural Networks | |
| CN113343280A (en) | Joint learning-based private cloud algorithm model generation method | |
| CN117873825A (en) | Method and device for determining running state of multi-access edge computing node | |
| CN115580539A (en) | Network computing storage scheduling method, system and equipment based on artificial intelligence scene | |
| CN116050801A (en) | Data processing method, device, equipment, medium and product based on software development |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230717 Address after: 2800 Wanyuan Road, Minhang District, Shanghai 201100 Patentee after: SHANGHAI YUNSAI ZHILIAN INFORMATION TECHNOLOGY Co.,Ltd. Address before: Room 513, floor 5, No. 2016, Yishan Road, Minhang District, Shanghai 201103 Patentee before: SHANGHAI SAGA ELECTRONICS TECHNOLOGY CO.,LTD. |