CN115169930B - Event security method and system - Google Patents
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Abstract
The application provides a method and a system for event security, which are particularly applied to the field of image processing and comprise the steps of collecting video data of a match area and a non-match area in an event venue in real time; obtaining the current competition stage; if so, acquiring personnel information in the non-competition area; and dividing a plurality of sub-regions; obtaining initial weight values of a plurality of sub-areas according to historical event data; inputting video data of a competition area into a first anomaly monitoring model to obtain first anomaly information; calculating actual weight values of a plurality of sub-regions; acquiring sub-video data corresponding to a high actual weight value; inputting the video data and the sub-video data of the non-competition area into a second anomaly monitoring model to obtain second anomaly information; and distributing security events according to the first abnormal information and the second abnormal information. Therefore, the stability of the event venue is maintained, and the security efficiency is improved.
Description
Technical Field
The present application relates to the field of image processing, and more particularly, to a method and system for event security.
Background art
In recent years, the number of sports events sponsored and held by China is gradually increased, and participants of international events are numerous and have differences in culture, custom and sources, which brings great examination and challenge to security work.
In the prior art, security levels are usually determined according to the number of personnel in the venue, the trending degree of events and the like, and security plans are made according to different security levels.
Therefore, how to make a security scheme capable of coping with the diversity and uncertainty of events and improve the security efficiency is an urgent technical problem to be solved.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a system for security of competition, which take the region dimension and the time dimension into the influence factors of a security plan, fully consider the influence of conflict generated in a competition region on the security of a non-competition region, and provide a coping scheme based on the influence factors. Therefore, the stability of the event venue is maintained, and the security efficiency is improved. The specific technical scheme is as follows:
in a first aspect of an embodiment of the present invention, there is provided a method for event security, the method including: dividing the event venue into a competition area and a non-competition area; acquiring video data of the competition area and the non-competition area in real time; obtaining the stage of the event where the event venue is located at the current moment according to the event arrangement; the competition stage comprises pre-competition, competition and post-competition; if the competition stage is pre-competition or post-competition, respectively inputting the video data of the competition area and the video data of the non-competition area into a first abnormity monitoring model and a second abnormity monitoring model to obtain first abnormity information and second abnormity information; if the competition stage is competition, acquiring personnel information in the non-competition area; dividing the non-competition area into a plurality of sub-areas according to the personnel information; the plurality of sub-regions correspond to a plurality of sub-video data; obtaining initial weight values of the plurality of sub-areas according to historical event data of the event venue; inputting the video data of the competition area into a first anomaly monitoring model to obtain first anomaly information; obtaining actual weight values of the plurality of sub-regions according to the first abnormal information; sorting the actual weight values from large to small, and acquiring K sub-video data corresponding to the first K actual weight values; inputting the video data of the non-competition area and the K pieces of sub-video data into a second anomaly monitoring model to obtain second anomaly information; and distributing security events according to the first abnormal information and the second abnormal information.
Optionally, the personnel information includes a number of viewing events, seating information, and support team information.
Optionally, the obtaining initial weight values of the plurality of subareas according to the historical event data of the event venue includes: collecting historical event data of the event venue; counting the abnormal times and abnormal degrees of each position in the historical event data; wherein the degree of abnormality includes mild, moderate and severe; summarizing the abnormal times and the abnormal degree of each position in each sub-area to obtain the abnormal times and the abnormal degree of each sub-area; if n positions exist in the ith sub-region, determining an initial weight value of each sub-region according to the following formula:
wherein, w i Initial weight value, f, representing the ith sub-region i,j And p i,j Respectively representing the abnormal times and abnormal degrees of the jth position of the ith sub-area, p i,j The values of {1,2,3} correspond to the mild, moderate and severe degrees of the abnormality, respectively.
Optionally, inputting the video data of the competition area into a first anomaly monitoring model to obtain first anomaly information, where the method includes: inputting the video data of the competition area into a convolutional neural network model to obtain first abnormal information; the first abnormality information includes an abnormality body 1 and an abnormality body 2; wherein, the abnormal subjects comprise coaches, team members and referees.
Optionally, the obtaining the actual weight values of the multiple sub-regions according to the first abnormal information includes: determining an adjustment factor k according to the abnormal subject 1 and the abnormal subject 2; determining the actual weight value of each sub-region according to the following formula:
Q i ＝w i ×k
wherein Q is i Actual weight value, w, representing the ith sub-region i Representing the initial weight value of the ith sub-region.
Optionally, the determining an adjustment factor k according to the abnormal subject 1 and the abnormal subject 2 includes: the adjustment factor k is calculated according to the following formula:
k＝A×B
wherein A represents an abnormal subject 1, and when the abnormal subject 1 is a referee, a coach or a team member, the values of A are respectively 0.5, 1 and 1.5; and B represents an abnormal subject 2, and when the abnormal subject 2 is a referee, a coach or a team member, the values of B are respectively 0.5, 1 and 1.5.
Optionally, the inputting the video data of the non-match area and the K pieces of sub-video data into a second anomaly monitoring model to obtain second anomaly information includes: inputting the video data of the non-competition area and the K sub-video data into an improved convolutional neural network model to obtain second abnormal information; the second abnormality information includes an abnormality sub-area 1 and an abnormality sub-area 2.
Optionally, the allocating a security event according to the first abnormal information and the second abnormal information includes: carrying out emergency plan on the first abnormal information and the second abnormal information; and visually displaying the emergency plan and distributing security events.
In yet another aspect of an embodiment of the present invention, there is provided an event security system, including: the area dividing module is used for dividing the event venue into a competition area and a non-competition area; the video acquisition module is used for acquiring video data of the competition area and the non-competition area in real time; the event stage acquisition module is used for acquiring the event stage of the event venue at the current moment according to the event arrangement; the competition stage comprises before competition, during competition and after competition; the anomaly monitoring module is used for respectively inputting the video data of the competition area and the video data of the non-competition area into a first anomaly monitoring model and a second anomaly monitoring model to obtain first anomaly information and second anomaly information if the competition stage is pre-competition or post-competition; if the competition stage is competition, acquiring personnel information in the non-competition area; dividing the non-competition area into a plurality of sub-areas according to the personnel information; the plurality of sub-regions correspond to a plurality of sub-video data; obtaining initial weight values of the plurality of sub-areas according to historical event data of the event venue; inputting the video data of the competition area into a first anomaly monitoring model to obtain first anomaly information; obtaining actual weight values of the plurality of sub-regions according to the first abnormal information; sorting the actual weight values from large to small, and acquiring K sub-video data corresponding to the first K actual weight values; inputting the video data of the non-competition area and the K pieces of sub-video data into a second anomaly monitoring model to obtain second anomaly information; and the security module is used for distributing security events according to the first abnormal information and the second abnormal information.
Optionally, the personnel information includes a number of viewing events, seating information, and support team information.
Optionally, the obtaining initial weight values of the plurality of subareas according to the historical event data of the event venue includes: collecting historical event data of the event venue; counting the abnormal times and abnormal degrees of each position in the historical event data; wherein the degree of abnormality includes mild, moderate and severe; summarizing the abnormal times and the abnormal degree of each position in each sub-area to obtain the abnormal times and the abnormal degree of each sub-area; if n positions exist in the ith sub-region, determining an initial weight value of each sub-region according to the following formula:
wherein w i Initial weight value, f, representing the ith sub-region i,j And p i,j Respectively representing the abnormal times and abnormal degrees of the jth position of the ith sub-area, p i,j The values of {1,2,3} correspond to the mild, moderate and severe degrees of the abnormality, respectively.
Optionally, inputting the video data of the competition area into a first anomaly monitoring model to obtain first anomaly information, where the method includes: inputting the video data of the competition area into a convolutional neural network model to obtain first abnormal information; the first abnormality information includes an abnormality body 1 and an abnormality body 2; wherein, the abnormal subjects comprise coaches, team members and referees.
Optionally, the obtaining the actual weight values of the multiple sub-regions according to the first abnormal information includes: determining an adjustment factor k according to the abnormal subject 1 and the abnormal subject 2; determining the actual weight value of each sub-region according to the following formula:
Q i ＝w i ×k
wherein Q is i Actual weight value, w, representing the ith sub-region i Representing the initial weight value of the ith sub-region.
Optionally, the determining an adjustment factor k according to the abnormal subject 1 and the abnormal subject 2 includes: the adjustment factor k is calculated according to the following formula:
k＝A×B
wherein, A represents an abnormal subject 1, and when the abnormal subject 1 is a referee, a coach or a team member, the value of A is 0.5, 1 or 1.5 respectively; and B represents an abnormal subject 2, and when the abnormal subject 2 is a referee, a coach or a team member, the values of B are respectively 0.5, 1 and 1.5.
Optionally, the inputting the video data of the non-match area and the K pieces of sub-video data into a second anomaly monitoring model to obtain second anomaly information includes: inputting the video data of the non-competition area and the K sub-video data into an improved convolutional neural network model to obtain second abnormal information; the second abnormality information includes an abnormality sub-area 1 and an abnormality sub-area 2.
Optionally, the security module is further configured to: performing an emergency plan on the first abnormal information and the second abnormal information; and visually displaying the emergency plan and distributing security events.
Has the advantages that:
the invention provides a security method and a security system for a competition, which take the regional dimension and the time dimension into the influence factors of a security plan, and fully consider the influence of conflicts generated in a competition area on the security of a non-competition area; specifically, video data of a competition area and a non-competition area in a competition venue are collected in real time; obtaining the current competition stage; if the video data of the competition area and the video data of the non-competition area are input into the first anomaly monitoring model and the second anomaly monitoring model respectively before or after the competition, so that first anomaly information and second anomaly information are obtained; if so, acquiring personnel information in the non-competition area; and dividing a plurality of sub-regions; obtaining initial weight values of a plurality of sub-areas according to historical event data; inputting video data of a competition area into a first anomaly monitoring model to obtain first anomaly information; calculating actual weight values of a plurality of sub-regions; acquiring sub-video data corresponding to a high actual weight value; inputting the video data and the sub-video data of the non-competition area into a second anomaly monitoring model to obtain second anomaly information; and distributing security events according to the first abnormal information and the second abnormal information. Therefore, the stability of the event venue is maintained, and the security efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a method for securing an event according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of an abnormal information determination method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an event security system according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a method and a system for event security, which comprises the steps of collecting video data of a competition area and a non-competition area in an event venue in real time; obtaining the current competition stage; if so, acquiring personnel information in the non-competition area; dividing a plurality of sub-regions; obtaining initial weight values of a plurality of subregions according to historical event data; inputting video data of a competition area into a first anomaly monitoring model to obtain first anomaly information; calculating actual weight values of a plurality of sub-regions; acquiring sub-video data corresponding to a high actual weight value; inputting the video data and the sub-video data of the non-competition area into a second anomaly monitoring model to obtain second anomaly information; and distributing security events according to the first abnormal information and the second abnormal information. Therefore, the stability of the event venue is maintained, and the security efficiency is improved.
The event security method and the event security system can be specifically integrated in electronic equipment, and the electronic equipment can be equipment such as a terminal and a server. The terminal can be a light field camera, a vehicle-mounted camera, a mobile phone, a tablet Computer, an intelligent Bluetooth device, a notebook Computer, or a Personal Computer (PC) and other devices; the server may be a single server or a server cluster composed of a plurality of servers.
It can be understood that the event security method and system of the embodiment may be executed on the terminal, may also be executed on the server, and may also be executed by both the terminal and the server. The above examples should not be construed as limiting the present application.
Fig. 1 shows a schematic flow chart of the event security method provided in the embodiment of the present application, please refer to fig. 1, which specifically includes the following steps:
s110, dividing the event venue into a competition area and a non-competition area.
Wherein the non-playing area may be an auditorium, a secure corridor, etc.
And S120, collecting video data of the competition area and the non-competition area in real time.
The computer device receives video data collected by a mobile camera, and the video can be transmitted through a fifth generation mobile communication technology and also can be transmitted through a wifi network.
S130, obtaining the stage of the event where the event venue is located at the current moment according to the event arrangement.
Wherein, the competition period comprises before competition, during competition and after competition.
And S140, determining first abnormal information and second abnormal information according to the event stage.
The specific determination method of the abnormal information will be described in detail in the next embodiment, which is not described herein again.
Alternatively, the abnormal information may be collision information, violation information, erroneous judgment information, or the like.
S150, distributing security events according to the first abnormal information and the second abnormal information.
Optionally, performing an emergency plan on the first abnormal information and the second abnormal information; and visually displaying the emergency plan and distributing security events.
Therefore, the regional dimension and the time dimension are brought into the influence factors of the security plan, the stability of the event venue can be maintained, and the security efficiency is improved.
Fig. 2 shows a schematic flowchart of an abnormal information determining method provided in an embodiment of the present application, please refer to fig. 2, which specifically includes the following steps:
and S210, determining the current event stage.
And S220, if the competition stage is pre-competition or post-competition, respectively inputting the video data of the competition area and the video data of the non-competition area into a first abnormity monitoring model and a second abnormity monitoring model to obtain first abnormity information and second abnormity information.
Wherein the first anomaly monitoring model and the second anomaly monitoring model may be convolutional neural network models.
And S230, if the competition stage is a competition, dividing the non-competition area into a plurality of sub-areas.
Specifically, collecting personnel information in the non-match area; and dividing the non-competition area into a plurality of sub-areas according to the personnel information. Wherein each sub-region corresponds to a sub-video data.
Alternatively, the person information may include the number of times of watching events, seat information, and support team information.
S240, obtaining initial weight values of the plurality of sub-areas according to historical event data of the event venue.
In one embodiment, step S240 may specifically include the following steps:
and S241, collecting historical event data of the event venue.
And S242, counting the abnormal times and abnormal degrees of each position in the historical event data.
Wherein the degree of abnormality includes mild, moderate and severe.
And S243, summarizing the abnormal times and the abnormal degree of each position in each sub-area to obtain the abnormal times and the abnormal degree of each sub-area.
S244, if there are n positions in the ith sub-region, determining an initial weight value of each sub-region according to the following formula:
wherein, w i Initial weight value, f, representing the ith sub-region i,j And p i,j Respectively representing the abnormal times and abnormal degrees of the jth position of the ith sub-area, p i,j The values of {1,2,3} correspond to the mild, moderate and severe degrees of the abnormality, respectively.
And S250, inputting the video data of the competition area into a first abnormity monitoring model to obtain first abnormity information.
Specifically, video data of the competition area is input into a convolutional neural network model to obtain first abnormal information; the first abnormality information includes an abnormality body 1 and an abnormality body 2.
Wherein, the abnormal subjects comprise coaches, team members and referees.
And S260, obtaining the actual weight values of the plurality of sub-areas according to the first abnormal information.
In one embodiment, step S260 may specifically include the following steps:
and S261, determining an adjustment factor k according to the abnormal subject 1 and the abnormal subject 2.
Specifically, the adjustment factor k is calculated according to the following formula:
k＝A×B
wherein A represents an abnormal subject 1, and when the abnormal subject 1 is a referee, a coach or a team member, the values of A are respectively 0.5, 1 and 1.5; and B represents an abnormal subject 2, and when the abnormal subject 2 is a referee, a coach or a team member, the values of B are respectively 0.5, 1 and 1.5.
S262, determining the actual weight value of each sub-area according to the following formula:
Q i ＝w i ×k
wherein Q is i Representing the actual weight value of the ith sub-region, w i Representing the initial weight value of the ith sub-region.
S270, sorting the actual weight values from large to small, and obtaining K sub-video data corresponding to the first K actual weight values.
This allows a plurality of sub-areas in which abnormality is likely to occur in the non-game area to be identified from the history data, and these sub-areas to be input as a model in step S280.
S280, inputting the video data of the non-competition area and the K sub-video data into a second anomaly monitoring model to obtain second anomaly information.
Specifically, the video data of the non-competition area and the K sub-video data are input into an improved convolutional neural network model to obtain second abnormal information; the second abnormality information includes an abnormality sub-area 1 and an abnormality sub-area 2.
In the embodiment, different exception processing is carried out on different event stages, the influence of the exception generated in the game area on the safety of the non-game area is fully considered, the concepts of the exception frequency, the exception degree, the exception main body and the exception subarea are introduced, and more accurate exception information can be determined through calculation.
In order to implement the above method embodiment, this embodiment further provides an event security system, as shown in fig. 3, where the system includes:
an area dividing module 310 for dividing the event venue into a competition area and a non-competition area;
a video capture module 320 for capturing video data of the match area and the non-match area in real time;
the event phase obtaining module 330 is configured to obtain, according to event arrangement, an event phase at which the event venue is located at the current moment; the competition stage comprises pre-competition, competition and post-competition;
an anomaly monitoring module 340, configured to, if the event stage is pre-event or post-event, input the video data of the event area and the video data of the non-event area into a first anomaly monitoring model and a second anomaly monitoring model respectively to obtain first anomaly information and second anomaly information;
if the competition stage is competition, acquiring personnel information in the non-competition area; dividing the non-competition area into a plurality of sub-areas according to the personnel information; the plurality of sub-regions correspond to a plurality of sub-video data; obtaining initial weight values of the plurality of sub-areas according to historical event data of the event venue; inputting the video data of the competition area into a first anomaly monitoring model to obtain first anomaly information; obtaining actual weight values of the plurality of sub-regions according to the first abnormal information; sorting the actual weight values from large to small, and acquiring K sub-video data corresponding to the first K actual weight values; and inputting the video data of the non-competition area and the K pieces of sub-video data into a second anomaly monitoring model to obtain second anomaly information.
And a security module 350, configured to allocate a security event according to the first abnormal information and the second abnormal information.
Optionally, the personnel information includes a number of viewing events, seating information, and support team information.
Optionally, the obtaining initial weight values of the plurality of sub-areas according to the historical event data of the event venue includes: collecting historical event data of the event venue; counting the abnormal times and abnormal degrees of each position in the historical event data; wherein the degree of abnormality includes mild, moderate and severe; summarizing the abnormal times and the abnormal degree of each position in each sub-area to obtain the abnormal times and the abnormal degree of each sub-area; if n positions exist in the ith sub-region, determining an initial weight value of each sub-region according to the following formula:
wherein, w i Initial weight value, f, representing the ith sub-region i,j And p i,j Respectively representing the number of abnormalities and degree of abnormality of the jth position of the ith sub-region, p i,j The values of {1,2,3} correspond to the mild, moderate and severe degrees of the abnormality, respectively.
Optionally, inputting the video data of the competition area into a first anomaly monitoring model to obtain first anomaly information, where the method includes: inputting the video data of the competition area into a convolutional neural network model to obtain first abnormal information; the first abnormality information includes an abnormality body 1 and an abnormality body 2; wherein the abnormal subjects comprise coaches, players and referees.
Optionally, the obtaining the actual weight values of the multiple sub-regions according to the first abnormal information includes: determining an adjustment factor k according to the abnormal subject 1 and the abnormal subject 2; determining the actual weight value of each sub-region according to the following formula:
Q i ＝w i ×k
wherein Q i Representing the actual weight value of the ith sub-region, w i Representing the initial weight value of the ith sub-region.
Optionally, the determining an adjustment factor k according to the abnormal subject 1 and the abnormal subject 2 includes: the adjustment factor k is calculated according to the following formula:
k＝A×B
wherein A represents an abnormal subject 1, and when the abnormal subject 1 is a referee, a coach or a team member, the values of A are respectively 0.5, 1 and 1.5; and B represents an abnormal subject 2, and when the abnormal subject 2 is a referee, a coach or a team member, the values of B are respectively 0.5, 1 and 1.5.
Optionally, the inputting the video data of the non-match area and the K pieces of sub-video data into a second anomaly monitoring model to obtain second anomaly information includes: inputting the video data of the non-competition area and the K sub-video data into an improved convolutional neural network model to obtain second abnormal information; the second abnormality information includes an abnormality sub-area 1 and an abnormality sub-area 2.
Optionally, the security module 350 is further configured to: carrying out emergency plan on the first abnormal information and the second abnormal information; and visually displaying the emergency plan and distributing security events.
The system brings the regional dimension and the time dimension into the influence factors of the security plan, can maintain the stability of the event venue, and improves the security efficiency.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the modules/units/sub-units/components in the above-described apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into 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 computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures, and moreover, the terms "first," "second," "third," etc. are used merely to distinguish one description from another, and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of event security, the method comprising:
dividing the event venue into a competition area and a non-competition area;
acquiring video data of the competition area and the non-competition area in real time;
obtaining the stage of the event in which the event venue is located at the current moment according to the event arrangement; the competition stage comprises pre-competition, competition and post-competition;
if the competition stage is pre-competition or post-competition, respectively inputting the video data of the competition area and the video data of the non-competition area into a first abnormity monitoring model and a second abnormity monitoring model to obtain first abnormity information and second abnormity information;
if the competition stage is competition, acquiring personnel information in the non-competition area; dividing the non-competition area into a plurality of sub-areas according to the personnel information; the plurality of sub-regions correspond to a plurality of sub-video data;
obtaining initial weight values of the plurality of sub-areas according to historical event data of the event venue;
inputting the video data of the competition area into a first anomaly monitoring model to obtain first anomaly information;
obtaining actual weight values of the plurality of sub-regions according to the first abnormal information;
sorting the actual weight values from large to small, and acquiring K sub-video data corresponding to the first K actual weight values;
inputting the video data of the non-competition area and the K pieces of sub-video data into a second anomaly monitoring model to obtain second anomaly information;
and distributing security events according to the first abnormal information and the second abnormal information.
2. A method for event security according to claim 1, wherein the personnel information comprises the number of viewing events, seating information and support team information.
3. The event security method of claim 2, wherein obtaining the initial weight values of the plurality of sub-regions according to the historical event data of the event venue comprises:
collecting historical event data of the event venue;
counting the abnormal times and abnormal degrees of each position in the historical event data; wherein the degree of abnormality includes mild, moderate and severe;
summarizing the abnormal times and the abnormal degree of each position in each sub-area to obtain the abnormal times and the abnormal degree of each sub-area;
if n positions exist in the ith sub-region, determining an initial weight value of each sub-region according to the following formula:
wherein, w i Initial weight value, f, representing the ith sub-region i,j And p i,j Respectively representing the abnormal times and abnormal degrees of the jth position of the ith sub-area, p i,j The value of (d) is {1,2,3}, corresponding to the mild, moderate and severe degrees of the abnormality, respectively.
4. A method as claimed in claim 3, wherein inputting video data of the playing area into a first anomaly monitoring model to obtain first anomaly information comprises:
inputting the video data of the competition area into a convolutional neural network model to obtain first abnormal information; the first abnormality information includes an abnormality body 1 and an abnormality body 2;
wherein, the abnormal subjects comprise coaches, team members and referees.
5. The event security method according to claim 4, wherein the obtaining the actual weight values of the plurality of sub-regions according to the first anomaly information comprises:
determining an adjustment factor k according to the abnormal subject 1 and the abnormal subject 2;
determining the actual weight value of each sub-region according to the following formula:
Q i ＝w i ×k
wherein Q is i Representing the actual weight value of the ith sub-region, w i Representing the initial weight value of the ith sub-region.
6. The event security method of claim 5, wherein determining the adjustment factor k from the abnormal subject 1 and the abnormal subject 2 comprises:
the adjustment factor k is calculated according to the following formula:
k＝A×B
wherein A represents an abnormal subject 1, and when the abnormal subject 1 is a referee, a coach or a team member, the values of A are respectively 0.5, 1 and 1.5; and B represents an abnormal subject 2, and when the abnormal subject 2 is a referee, a coach or a team member, the values of B are respectively 0.5, 1 and 1.5.
7. The event security method of claim 1, wherein the inputting the video data of the non-competition area and the K sub-video data into a second anomaly monitoring model to obtain second anomaly information comprises:
inputting the video data of the non-competition area and the K sub-video data into an improved convolutional neural network model to obtain second abnormal information; the second abnormality information includes an abnormality sub-area 1 and an abnormality sub-area 2.
8. The event security method of claim 1, wherein the assigning security events according to the first exception information and the second exception information comprises:
carrying out emergency plan on the first abnormal information and the second abnormal information;
and visually displaying the emergency plan and distributing security events.
9. A system for securing a game, the system comprising:
the area dividing module is used for dividing the event venue into a competition area and a non-competition area;
the video acquisition module is used for acquiring video data of the competition area and the non-competition area in real time;
the event stage acquisition module is used for acquiring the event stage of the event venue at the current moment according to the event arrangement; the competition stage comprises pre-competition, competition and post-competition;
the anomaly monitoring module is used for respectively inputting the video data of the competition area and the video data of the non-competition area into a first anomaly monitoring model and a second anomaly monitoring model to obtain first anomaly information and second anomaly information if the competition stage is pre-competition or post-competition;
if the competition stage is competition, acquiring personnel information in the non-competition area; dividing the non-competition area into a plurality of sub-areas according to the personnel information; the plurality of sub-regions correspond to a plurality of sub-video data;
obtaining initial weight values of the plurality of sub-areas according to historical event data of the event venue;
inputting the video data of the competition area into a first anomaly monitoring model to obtain first anomaly information;
obtaining actual weight values of the plurality of sub-regions according to the first abnormal information;
sorting the actual weight values from large to small to obtain K sub-video data corresponding to the first K actual weight values;
inputting the video data of the non-competition area and the K pieces of sub-video data into a second anomaly monitoring model to obtain second anomaly information;
and the security module is used for distributing security events according to the first abnormal information and the second abnormal information.
10. The event security system of claim 9, wherein the security module is further configured to:
carrying out emergency plan on the first abnormal information and the second abnormal information;
and visually displaying the emergency plan and distributing security events.
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