CN111898259A - An ontology-based method for collision detection of large-scale event events - Google Patents

Abstract

The invention provides a large-scale activity event conflict detection method based on an ontology. According to the method, aiming at the application scene of the commonality of the large-scale activities, the large-scale activities are defined as triples through the objects of the large-scale activities, the flows of the large-scale activities and the rules of the large-scale activities; defining objects of large activities by personnel, facilities, time, space, volunteers, audiences, venues and control points; defining a specific flow of the large-scale activity through links of the large-scale activity; further constructing object data attributes of volunteer class, audience class, venue class and control point class; and establishing a time conflict detection rule, a space conflict detection rule and an attribute conflict detection rule, and sequentially detecting whether the large-scale activities conflict or not according to the rules. The invention realizes the method for detecting the conflict of the large-scale activity events based on the body, solves the problems of overall planning and management of the large-scale activity, and has the advantages of rapidness, effectiveness, repeatability, expandability and robustness.

Description

An Ontology-Based Conflict Detection Method for Large Activity Events

technical field

The invention relates to the technical field of information system integration, in particular to an ontology-based large-scale activity event conflict detection method.

Background technique

Large-scale events involve factors such as venues, personnel, facilities, and event processes. Organization, management, and scheduling are complex. After planning and design, repeated simulations, drills, and manual judgments are required to find out and confirm whether there are conflicts in terms of personnel, time, and venue.

The existing event models for large-scale activities are generally divided into two categories: large-scale activity models based on a macro-perspective and large-scale activity models based on a micro-perspective. Large-scale activity models based on a macroscopic perspective are based on the whole and are represented by geometric figures. The model studies crowd movement as a whole, and crowd movement is often represented as a flow in a graph, ignoring differences and interactions between individuals. It mainly includes fluid dynamics model and potential energy field model. The large-scale activity model based on a microscopic perspective is based on individual characteristics, and focuses on describing the influence of individual psychological factors, between individuals, and between individuals and the environment in the dynamic process of evacuation. There are three main types: social force model, cellular automata model, and agent-based model. These models focus on the evacuation of crowds, and are not yet capable of overall planning and management of large-scale events, nor can they automatically detect potential conflicts by formulating rules.

Therefore, in order to solve the above-mentioned technical problems, the present invention provides a new technology.

SUMMARY OF THE INVENTION

In order to solve the standardized design and automatic conflict detection of large-scale activity events, the present invention provides an ontology-based collision detection method for large-scale activity events.

An ontology-based large-scale event conflict detection method, which includes the following steps:

Step 1: For the common application scenarios of large-scale activities, define large-scale activities as triples through the objects of large-scale activities, the process of large-scale activities, and the rules of large-scale activities;

Step 2: Define the objects of large-scale activities through the personnel class, facility class, time class, and space class;

Step 3: Define the specific process of the large-scale event through the links of the large-scale event;

Step 4: Construct object classes through volunteer class, audience class, venue class, and control point class, and further construct the object data attributes of volunteer class, audience class, venue class, and control point class;

Step 5: establish a time conflict detection rule, a space conflict detection rule and an attribute conflict detection rule, and sequentially detect whether there is a conflict in a large-scale event according to the time conflict detection rule, the space conflict detection rule and the attribute conflict detection rule respectively;

Preferably, the large-scale activities described in step 1 are:

Event={Object,Flow,Rule}

Among them, Object is the object of large-scale activities; Flow is the specific process of large-scale activities, which consists of a series of links; Rule is the conflict detection rules of large-scale activities, and the conflict detection rules include time conflict detection rules, space conflict detection rules, and attribute conflicts. detection rules;

Preferably, the object of the large-scale activity is defined by the personnel class, the facility class, the time class and the space class in step 2, that is, the object of the large-scale activity described in step 1 is:

Object=Human∪Facility∪Time∪Space

Among them, Human is a personnel class, Facility is a facility class, Time is a time class, and Space is a space class;

The subclasses of the human class include: the staff class Staff, the volunteer class Volunteer, the performer class Performer, and the audience class Audience;

The subclasses of the facility class Facility include: a fixed facility class Facility_fix and a mobile facility class Facility_mobile;

The subclasses of the fixed facility class Facility_fix include: Venues class Venues, parking lot class ParkingLot, entry and exit route class Route, and control point class Spot;

The time class Time includes a time point class Instant and a time period class Interval;

The time period class is expressed by a pair of time points: interval={beginTime, endTime}, where beginTime∈Instant, endTime∈Instant, beginTime<endTime.

The space class Space includes: a space point subclass Point, a space line subclass Line;

The spatial point subclass Point, expressed as a coordinate point (x, y), is used to represent the control point subclass Spot in the activity;

The space line subclass Line, expressed as a coordinate point string {(x ₁ , y ₁ ), (x ₂ , y ₂ ),...(x _n , y _n )}, is used to represent the entry and exit paths in the activity subclass Route;

Preferably, the specific process of the large-scale event is defined through the links of the large-scale event in step 3, that is, the specific process of the large-scale event in step 1 is:

Flow={Activity _i |i∈[1,n]}

Among them, n is the number of links in the specific process of large-scale activities, and Activity _i is the i-th link in the specific process of large-scale activities;

The i-th link in the specific process of the large-scale event includes: personnel, facilities, time, and space;

The i-th link in the specific process of large-scale activities is expressed as:

Activity _i = {Human, Facility, Time, Space}

Activity _i represents the human object Activity _i ^Human involved in the link Activity _i , the behavior completed by the facility Activity _i ^Facility at the specified time Activity _i ^Time and the specified space Activity _i ^Space ;

In the specific process of a large-scale event, there may be links performed by the same person in different time periods, or there may be links performed by different people at the same time, and there may be operations performed by different people in the same space or at different times.

Preferably, the object class described in step 4 is Object _DP , which is specifically defined as:

Among them, Volunteer is the volunteer class described in step 1, Audience is the audience class described in step 1, Venues is the venue class described in step 1, Spot is the control point class described in step 1, and Object is the object of the large-scale event described in step 2 ;

The object data attribute described in step 4 is used to describe the characteristics of the object class itself, and is specifically defined as

Data_Properties={speedInWalking,count,VenuesP}

Among them, Data Properties are the object data properties of volunteers, audiences, venues, and control points;

speedInWalking is used to describe the walking speed of volunteers and spectators, specifically defined as:

speedInWalking＝{Volunteer _{speedInWalking} ,Audience _{speedInWalking} }

Volunteer _{speedInWalking} means the walking speed of the volunteers, Audience _{speedInWalking} means the walking speed of the audience;

count is used to describe the number of volunteer classes, audience classes, and control point classes, and is specifically defined as:

count={Volunteer _count ,Audience _count ,Spot _count }

Volunteer _count means the number of volunteers, Audience _count means the number of audiences, and Spot _count means the number of control points;

VenuesP is used to describe the number of entrances and exits of Venues, the maximum number of spectators that can be accommodated, and the security check time required for each person to enter, specifically defined as:

VenuesP={Venues _{numberOfSecurityChannels} , Venues _{maximumOccupancy} , Venues _{timeforSecurityCheck} }

Venues _{numberOfSecurityChannels} indicates the number of entrances and exits of Venues of the venue;

Venues _{maximumOccupancy} indicates the maximum number of spectators the venue Venues can accommodate;

Venues _{timeforSecurityCheck} indicates the security check time for each person entering the Venues venue;

Preferably, the time conflict detection rule described in step 5 is composed of a first time conflict detection rule and a second time conflict detection rule:

The first time conflict detection rule is used to judge whether the execution time of the ith link Activity _i (where i∈[1,n], n is the number of links in the specific flow of the large-scale activity) in the specific process of the large-scale activity is sufficient. , the specific detection method is:

The audience walks from the parking lot to the gymnasium as the ith actual link Activity _i in the specific process of the large-scale event, and the audience Audience involved is Activity _i ^Human . As defined in step 4, Audience _{speedInWalking} means the walking speed of the audience, from the parking lot to The walking space path line of the gymnasium is Activity _i ^Space , and the time interval for people walking from the parking lot to the gymnasium is Activity _i ^Time , as defined in step 1, interval={beginTime, endTime}, where beginTime∈Instant, endTime∈Instant , beginTime<endTime.

If the actual time required for the audience to walk is less than the time allocated by the plan, that is, if (lengthOf(line)/Audience _{speedInWalking} )<(interval _endTime -interval _startTime ) is established, there is a conflict in the planned activity link Activity _i , where lengthOf(line ) represents calculating the length of the space path line in Activity _i , interval _endTime represents the end time of Activity _i , and interval _startTime represents the start time of Activity _i ;

Venue spectators enter the security check activity link Activity _i , the target venue Venues is Activity _i ^Facility , and the planned and allocated time interval is Activity _i ^Time , as defined in step 1, interval={beginTime, endTime}, where beginTime∈Instant, endTime∈Instant , beginTime _< endTime;

As defined in step 4, Venues _{maximumOccupancy} means the maximum number of spectators that the Venues of the venue can accommodate, Venues _{numberOfSecurityChannels} means the number of entrances and exits of the Venues of the venue, and Venues _{timeforSecurityCheck} means the security check time required for each person entering the Venues of the venue;

If the actual time required for the maximum number of spectators to enter the venue is less than the time allocated by the plan, that is, if (Venues _{maximumOccupancy} ×Venues _{timeforSecurityCheck} /Venues _{numberOfSecurityChannels} )<(interval _endTime -interval _startTime ) is established, the planned Activity _i has a conflict;

The second time conflict detection rule is used to determine all Activity _i and Activity _j in the specific flow Flow of the large-scale activity (where i∈[1,n], j∈[1,n], and i≠j, n is the When the number of links in the specific process of the large-scale event is the same as the human and the object Human, whether there is an intersection of the time planned and allocated;

In the specific process Flow used to detect the entire large-scale activity, when the same person Human needs to perform multiple activities, whether there is a time allocation conflict.

If there are two links Activity _i and Activity _j in the specific flow of large-scale activities, where Activity _i ∈ Flow, Activity _j ∈ Flow, i∈[1,n], j∈[1,n], and i≠j, n is the number of links in the specific process of the large-scale activity, and there is a conflict in the time allocation of personnel in Activity _i and Activity _j , that is, if:

成立，则Activity_i和Activity_j存在时间分配冲突，其中，如步骤3中定义，Activity_i ^Human表示环节Activity_i涉及的人员Human_i，Activity_j ^Human表示环节Activity_j涉及的人员 Human_j，Activity_i ^Time表示环节Activity_i涉及的时间周期Time_i，Activity_j ^Time表示环节Activity_j涉及的时间周期Time_j。

If established, there is a time allocation conflict between Activity _i and Activity _j . As defined in step 3, Activity _i ^Human represents the person Human _{i involved in the link Activity i ,} Activity _j ^Human represents the person Human _j involved in the link Activity _j , and Activity _i ^Time represents the time period Time _i involved in the link Activity _i , and Activity _j ^Time represents the time period Time _j involved in the link Activity _j .

The space conflict detection rules described in step 5 are:

Determine all Activity _i and Activity _j in the specific flow of the large-scale activity (where i∈[1,n], j∈[1,n], and i≠j, n is the number of links in the specific flow of the large-scale activity) When there is an intersection between the planned and allocated time Time, check whether the planned allocated space Space has an intersection.

If there are two links Activity _i and Activity _j in the specific flow of large-scale activities, where Activity _i ∈ Flow, Activity _j ∈ Flow, i∈[1,n], j∈[1,n], and i≠j,n For the number of links in the specific process of the large-scale activity, if the time and space in Activity _i and Activity _j overlap at the same time, there will be conflicts in space allocation between these two or more links. i.e. if:

成立，则Activity_i和Activity_j存在空间分配冲突，其中，如步骤4中定义，Activity_i ^Time表示环节Activity_i涉及的时间周期Time_i，Activity_j ^Time表示环节Activity_j涉及的时间周期Time_j，Activity_i ^Space表示环节Activity_i涉及的空间Space_i，Activity_j ^Space表示环节Activity_j涉及的空间Space_j。

If established, there is a space allocation conflict between Activity _i and Activity _j . As defined in step 4, Activity _i ^Time represents the time period Time _i involved in the link Activity _i , Activity _j ^Time represents the time period Time _j involved in the link Activity _j , and Activity _i ^Space represents the space Space _i involved in the link Activity _i , and Activity _j ^Space represents the space Space _j involved in the link Activity _j .

The attribute conflict detection rule described in step 5 is to use the data attribute of the object to judge whether there is a conflict in the activity arrangement, and the specific method is:

Determine whether the number of spectators is greater than the maximum number of people the venue can accommodate;

If the number of spectators is greater than the maximum number that the venue can accommodate, there is an attribute conflict;

Specifically, as defined in step 4, Audience _count means the number of audiences, Venues _{maximumOccupancy} means the maximum number of audiences Venues can accommodate,

That is, if (Audience _{countOfAudience} > Venues _{maximumOccupancy} ) is established, there is an attribute conflict;

Determine whether the number of volunteers is large and the number of control points that need to arrange volunteers;

If the number of volunteers is less than the number of control points that need to arrange volunteers, there is an attribute conflict;

Specifically, as defined in step 4, Volunteer _count represents the number of volunteer classes, and Spot _count represents the number of control point classes, that is, if (Volunteer _{countOfVolunteer} > Spot _countOfSpots ) is established, there is an attribute conflict.

The invention realizes an ontology-based large-scale activity event conflict detection method, which can realize the simulation of the large-scale activity event process flow and the automatic detection of various conflicts in the activity according to the application target field.

After inspection, the modeling method proposed by the present invention can overcome the defect that most of the existing event models for large-scale activities are only biased towards crowd evacuation, and solve the problem of overall planning and management for the application target field.

The large-scale event event modeling and conflict detection method established by the invention is fast and effective, and has repeatability, scalability and robustness, and has extremely important significance for improving the scientificity of large-scale event planning and management.

Description of drawings

FIG. 1 is a flow chart of the method of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The present invention takes holding the opening ceremony of a large-scale sports event as a specific embodiment, and in conjunction with accompanying drawing 1, the present invention is further described in detail:

Step 1: For the common application scenarios of large-scale activities, define large-scale activities as triples through the objects of large-scale activities, the process of large-scale activities, and the rules of large-scale activities;

The large-scale activities described in step 1 are:

Event={Object,Flow,Rule}

Among them, Object is the object of large-scale activities; Flow is the specific process of large-scale activities, which consists of a series of links; Rule is the conflict detection rules of large-scale activities, and the conflict detection rules include time conflict detection rules, space conflict detection rules, and attribute conflicts. detection rules;

Step 2: Define the objects of large-scale activities through the personnel class, facility class, time class, and space class;

Use Protégé or other ontology modeling software to achieve.

In step 2, the objects of large-scale activities are defined through personnel class, facility class, time class and space class, that is, the objects of large-scale activities described in step 1 are:

Object=Human∪Facility∪Time∪Space

Among them, Human is a personnel class, Facility is a facility class, Time is a time class, and Space is a space class;

The subclasses of the human class include: the staff class Staff, the volunteer class Volunteer, the performer class Performer, and the audience class Audience;

The subclasses of the facility class Facility include: a fixed facility class Facility_fix and a mobile facility class Facility_mobile;

The subclasses of the fixed facility class Facility_fix include: Venues class Venues, parking lot class ParkingLot, entry and exit route class Route, and control point class Spot;

The time class Time includes a time point class Instant and a time period class Interval;

The time period class is expressed by a pair of time points: interval={beginTime, endTime}, where beginTime∈Instant, endTime∈Instant, beginTime<endTime.

The space class Space includes: a space point subclass Point, a space line subclass Line;

The spatial point subclass Point, expressed as a coordinate point (x, y), is used to represent the control point subclass Spot in the activity;

The space line subclass Line, expressed as a string of coordinate points {(x ₁ , y ₁ ), (x ₂ , y ₂ ),...(x _n , y _n )}, is used to represent the entry and exit paths in the activity subclass Route;

Step 3: Define the specific process of the large-scale event through the links of the large-scale event;

Use Protégé or other ontology modeling software to achieve.

In step 3, the specific process of the large-scale event is defined through the links of the large-scale event, that is, the specific process of the large-scale event in step 1 is:

Flow={Activity _i |i∈[1,n]}

Among them, n is the number of links in the specific process of large-scale activities, and Activity _i is the i-th link in the specific process of large-scale activities;

The i-th link in the specific process of the large-scale event includes: personnel, facilities, time, and space;

The i-th link in the specific process of large-scale activities is expressed as:

Activity _i = {Human, Facility, Time, Space}

Activity _i represents the human object Activity _i ^Human involved in the link Activity _i , the behavior completed by the facility Activity _i ^Facility at the specified time Activity _i ^Time and the specified space Activity _i ^Space ;

In the specific process of a large-scale event, there may be links performed by the same person in different time periods, or there may be links performed by different people at the same time, and there may be operations performed by different people in the same space or at different times.

Step 4: Construct object classes through volunteer class, audience class, venue class, and control point class, and further construct the object data attributes of volunteer class, audience class, venue class, and control point class;

Use Protégé or other ontology modeling software to achieve.

The object class described in step 4 is Object _DP , which is specifically defined as:

Among them, Volunteer is the volunteer class described in step 1, Audience is the audience class described in step 1, Venues is the venue class described in step 1, Spot is the control point class described in step 1, and Object is the object of the large-scale event described in step 2 ;

The object data attribute described in step 4 is used to describe the characteristics of the object class itself, and is specifically defined as:

Data_Properties={speedInWalking,count,VenuesP}

Among them, Data Properties are the object data properties of volunteers, audiences, venues, and control points;

speedInWalking is used to describe the walking speed of volunteers and spectators, specifically defined as:

speedInWalking＝{Volunteer _{speedInWalking} ,Audience _{speedInWalking} }

Volunteer _{speedInWalking} means the walking speed of the volunteers, Audience _{speedInWalking} means the walking speed of the audience;

count is used to describe the number of volunteer classes, audience classes, and control point classes, and is specifically defined as:

count={Volunteer _count ,Audience _count ,Spot _count }

Volunteer _count means the number of volunteers, Audience _count means the number of audiences, and Spot _count means the number of control points;

VenuesP is used to describe the number of entrances and exits of Venues, the maximum number of spectators that can be accommodated, and the security check time required for each person to enter, specifically defined as:

VenuesP={Venues _{numberOfSecurityChannels} , Venues _{maximumOccupancy} , Venues _{timeforSecurityCheck} }

Venues _{numberOfSecurityChannels} indicates the number of entrances and exits of Venues of the venue;

Venues _{maximumOccupancy} indicates the maximum number of spectators the venue Venues can accommodate;

Venues _{timeforSecurityCheck} indicates the security check time for each person entering the Venues venue;

Step 5: establish a time conflict detection rule, a space conflict detection rule and an attribute conflict detection rule, and sequentially detect whether there is a conflict in a large-scale event according to the time conflict detection rule, the space conflict detection rule and the attribute conflict detection rule respectively;

It is implemented based on the Semantic Web Rule Language (SWRL) in Protégé or other ontology modeling software.

The time conflict detection rule described in step 5 is composed of a first time conflict detection rule and a second time conflict detection rule:

The first time conflict detection rule is used to judge whether the execution time of the ith link Activity _i (where i∈[1,n], n is the number of links in the specific flow of the large-scale activity) in the specific process of the large-scale activity is sufficient. , the specific detection method is:

The audience walks from the parking lot to the gymnasium as the ith actual link Activity _i in the specific process of the large-scale event, and the audience Audience involved is Activity _i ^Human . As defined in step 4, Audience _{speedInWalking} means the walking speed of the audience, from the parking lot to The walking space path line of the gymnasium is Activity _i ^Space , and the time interval for people walking from the parking lot to the gymnasium is Activity _i ^Time , as defined in step 1, interval={beginTime, endTime}, where beginTime∈Instant, endTime∈Instant , beginTime<endTime.

If the actual time required for the audience to walk is less than the time allocated by the plan, that is, if (lengthOf(line)/Audience _{speedInWalking} )<(interval _endTime -interval _startTime ) is established, there is a conflict in the planned activity link Activity _i , where lengthOf(line ) represents calculating the length of the space path line in Activity _i , interval _endTime represents the end time of Activity _i , and interval _startTime represents the start time of Activity _i ;

Venue spectators enter the security check activity link Activity _i , the target venue Venues is Activity _i ^Facility , and the planned time interval is Activity _i ^Time , as defined in step 1, interval={beginTime, endTime}, where beginTime∈Instant, endTime∈Instant , beginTime _< endTime;

As defined in step 4, Venues _{maximumOccupancy} means the maximum number of spectators that the Venues of the venue can accommodate, Venues _{numberOfSecurityChannels} means the number of entrances and exits of the Venues of the venue, and Venues _{timeforSecurityCheck} means the security check time required for each person entering the Venues of the venue;

If the actual time required for the maximum number of spectators to enter the venue is less than the planned allocation time, that is, if (Venues _{maximumOccupancy} ×Venues _{timeforSecurityCheck} /Venues _{numberOfSecurityChannels} )<(interval _endTime -interval _startTime ) is established, the planned Activity _i has a conflict;

The second time conflict detection rule is used to determine all Activity _i and Activity _j in the specific flow Flow of the large-scale activity (where i∈[1,n], j∈[1,n], and i≠j, n is the When the number of links in the specific process of the large-scale event is the same as the human and the object, whether there is an intersection of the time planned and allocated;

In the specific process Flow used to detect the entire large-scale activity, when the same person Human needs to perform multiple activities, whether there is a time allocation conflict.

If there are two links Activity _i and Activity _j in the specific flow of large-scale activities, where Activity _i ∈ Flow, Activity _j ∈ Flow, i∈[1,n], j∈[1,n], and i≠j, n is the number of links in the specific process of the large-scale activity, and there is a conflict in the time allocation of personnel in Activity _i and Activity _j , that is, if:

成立，则Activity_i和Activity_j存在时间分配冲突，其中，如步骤3中定义，Activity_i ^Human表示环节Activity_i涉及的人员Human_i，Activity_j ^Human表示环节Activity_j涉及的人员 Human_j，Activity_i ^Time表示环节Activity_i涉及的时间周期Time_i，Activity_j ^Time表示环节Activity_j涉及的时间周期Time_j。

If established, there is a time allocation conflict between Activity _i and Activity _j . As defined in step 3, Activity _i ^Human represents the person Human _{i involved in the link Activity i ,} Activity _j ^Human represents the person Human _j involved in the link Activity _j , and Activity _i ^Time represents the time period Time _i involved in the link Activity _i , and Activity _j ^Time represents the time period Time _j involved in the link Activity _j .

The space conflict detection rules described in step 5 are:

Determine all Activity _i and Activity _j in the specific flow of large-scale activities (where i∈[1,n], j∈[1,n], and i≠j, n is the number of links in the specific flow of the large-scale activity) When there is an intersection between the planned and allocated time Time, check whether the planned allocated space Space has an intersection.

If there are two links Activity _i and Activity _j in the specific flow of large-scale activities, where Activity _i ∈ Flow, Activity _j ∈ Flow, i∈[1,n], j∈[1,n], and i≠j,n For the number of links in the specific process of the large-scale activity, if the time and space in Activity _i and Activity _j overlap at the same time, there will be conflicts in space allocation between these two or more links. i.e. if:

成立，则Activity_i和Activity_j存在空间分配冲突，其中，如步骤4中定义，Activity_i ^Time表示环节Activity_i涉及的时间周期Time_i，Activity_j ^Time表示环节Activity_j涉及的时间周期Time_j，Activity_i ^Space表示环节Activity_i涉及的空间Space_i，Activity_j ^Space表示环节Activity_j涉及的空间Space_j。

If established, there is a space allocation conflict between Activity _i and Activity _j . As defined in step 4, Activity _i ^Time represents the time period Time _i involved in the link Activity _i , Activity _j ^Time represents the time period Time _j involved in the link Activity _j , and Activity _i ^Space represents the space Space _i involved in the link Activity _i , and Activity _j ^Space represents the space Space _j involved in the link Activity _j .

The attribute conflict detection rule described in step 5 is to use the data attribute of the object to judge whether there is a conflict in the activity arrangement, and the specific method is:

Determine whether the number of spectators is greater than the maximum number of people the venue can accommodate;

If the number of spectators is greater than the maximum number that the venue can accommodate, there is an attribute conflict;

Specifically, as defined in step 4, Audience _count refers to the number of audiences, and Venues _{maximumOccupancy} refers to the maximum number of audiences that Venues can accommodate. That is, if (Audience _{countOfAudience} > Venues _{maximumOccupancy} ) is established, there is an attribute conflict;

Determine whether the number of volunteers is large and the number of control points that need to arrange volunteers;

If the number of volunteers is less than the number of control points that need to arrange volunteers, there is an attribute conflict;

Specifically, as defined in step 4, Volunteer _count represents the number of volunteer classes, and Spot _count represents the number of control point classes, that is, if (Volunteer _{countOfVolunteer} > Spot _countOfSpots ) is established, there is an attribute conflict.

Step 6: Create an instance and implement it with Protégé or other ontology modeling software

According to the actual arrangement of specific large-scale activities, create instances of various objects, and assign values to the data attributes of each instance according to the actual situation.

Step 7: Execute rule reasoning to check the classes, attributes and instances of the created ontology, detect time, space and attribute conflicts in the large-scale activity arrangement instance, and use Protégé or Jena and other inference engines in other ontology modeling software to realize.

Through the execution of the inference engine, the time, space and attribute conflicts between large-scale activity instances are automatically detected and obtained, which provides a basis for the optimization of large-scale activities.

The technical solution of the present invention is not limited to the opening ceremonies of large-scale sports events listed above, but also includes various other large-scale events.

The above-mentioned embodiments only represent the embodiments of the present invention, and the descriptions thereof are specific and detailed, but should not be construed as limiting the scope of the patent of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can also be made, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims (6)

1. A method for detecting conflict of large-scale activity events based on an ontology is characterized by comprising the following steps:

step 1: according to the application scene of the commonality of the large-scale activities, the large-scale activities are defined as triples through the objects of the large-scale activities, the flow of the large-scale activities and the rules of the large-scale activities;

step 2: defining objects of large activities through personnel classes, facility classes, time classes and space classes;

and step 3: defining a specific flow of the large-scale activity through links of the large-scale activity;

and 4, step 4: constructing object classes through volunteers, audiences, venues and control points, and further constructing object data attributes of the volunteers, the audiences, the venues and the control points;

and 5: and establishing a time conflict detection rule, a space conflict detection rule and an attribute conflict detection rule, and sequentially detecting whether the large-scale activities conflict or not according to the time conflict detection rule, the space conflict detection rule and the attribute conflict detection rule respectively.

2. The ontology-based large-scale activity event conflict detection method of claim 1, wherein: step 1, the large-scale activities are as follows:

Event＝{Object,Flow,Rule}

wherein, Object is the Object of large-scale activity; flow is a specific Flow of a large-scale activity and consists of a series of links; rule is a conflict detection Rule of large-scale activities, and the conflict detection Rule comprises a time conflict detection Rule, a space conflict detection Rule and an attribute conflict detection Rule.

3. The ontology-based large-scale activity event conflict detection method of claim 1, wherein: step 2, defining the object of the large activity through a personnel class, a facility class, a time class and a space class, namely the object of the large activity in step 1 is as follows:

Object＝Human∪Facility∪Time∪Space

wherein, Human is a personnel class, Facility is a Facility class, Time is a Time class, and Space is a Space class;

the subclasses of Human classes Human include: staff class Staff, Volunteer class Volunter and Performer class Performer, Audience class Audio;

subclasses of the Facility-class Facility include: fixed Facility type Facility _ fix and mobile Facility type Facility _ mobile;

the subclass of fixed-Facility class Facility _ fix includes: venues, parkingLot, access path and control point;

the Time class Time comprises a Time point class Instant and a Time period class Interval;

the time period class is expressed by a pair of time points: interval ═ { beginTime, endTime }, where beginTime belongs to Instant, endTime belongs to Instant, beginTime < endTime;

the Space class Space includes: a space Point subclass Point and a space Line subclass Line;

the space Point subclass Point is expressed as a coordinate Point (x, y) and is used for representing a control Point subclass Spot in the activity;

the space Line subclass Line is expressed as a coordinate point string { (x)₁,y₁),(x₂,y₂),…(x_n,y_n) Represents the access field path subclass Route in the event.

4. The ontology-based large-scale activity event conflict detection method of claim 1, wherein: step 3, defining a specific flow of the large-scale activity through links of the large-scale activity, namely the specific flow of the large-scale activity in step 1 is as follows:

Flow＝{Activity_i|i∈[1,n]}

wherein n is the number of links in the specific flow of the large-scale Activity, Activity_iThe ith link in the specific flow of the large-scale activity;

the ith link in the specific process of the large-scale activity comprises the following steps: personnel, facilities, time, space;

the ith link in the specific flow of the large-scale activity is represented as:

Activity_i＝{Human,Facility,Time,Space}

Activity_irepresenting Activity in a link_iThe person object involved in

At a specified time

And designating space

Use facility

A completed action;

links executed by the same person in different time periods can exist in the specific flow of the large-scale activity, links executed by different persons in the same time period can also exist, and operations executed by different persons in the same space or different times can also exist.

5. The ontology-based large-scale activity event conflict detection method of claim 1, wherein: step 4, the Object class is Object_DPSpecifically defined as:

Object_DP＝{Volunteer,Audience,Venues,Spot}，

wherein, Volunter is the Volunteer class in step 1, Audio is the Audience class in step 1, Venues is the venue class in step 1, Spot is the control point class in step 1, and Object is the Object of the large-scale activity in step 2;

step 4, the object data attribute is used for describing the self characteristics of the object class and is specifically defined as

Data_Properties＝{speedInWalking,count,VenuesP}

Wherein, the Data Properties are object Data attributes of volunteer class, audience class, venue class and control point class;

speedInWalking is used for describing walking speeds of volunteers and audiences, and is specifically defined as:

speedInWalking＝{Volunteer_{speedInWalking},Audience_{speedInWalking}}

Volunteer_{speedInWalking}that is, the walking speed of the volunteer, Audio_{speedInWalking}I.e. representing the walking speed of the viewer;

the count is used for describing the number of volunteer classes, audience classes and control point classes, and is specifically defined as:

count＝{Volunteer_count,Audience_count,Spot_count}

Volunteer_countthat is, the number of volunteers, Audio_countI.e. the number of persons representing the audience class, Spot_countI.e. representing the number of control point classes;

VenuesP is used to describe the number of entrances and exits of Venues in Venues, the number of audiences which can be accommodated at most, and the security check time which is required for each person to enter, and is specifically defined as follows:

VenuesP＝{Venues_{numberOfSecurityChannels},Venues_{maximumOccupancy},Venues_{timeforSecurityCheck}}

Venues_{numberOfSecurityChannels}the number of entrances and exits of Venues is represented;

Venues_{maximumOccupancy}indicating that the Venues can accommodate the maximum number of audiences in the venue;

Venues_{timeforSecurityCheck}representing the amount of screening time spent by each person entering Venues.

6. The ontology-based large-scale activity event conflict detection method of claim 1, wherein: the time conflict detection rule of step 5 is composed of a first time conflict detection rule and a second time conflict detection rule:

the first time conflict detection rule is used for judging the Activity of the ith link in the specific flow of the large-scale Activity_i(where i ∈ [1, n ]]N is the number of links in the specific flow of the large-scale activity), and the specific detection method is as follows:

the i-th actual link Activity in the concrete process that the spectator walks from the parking lot to the gymnasium as a large-scale Activity_iThe Audience Audiology is Activity_i ^HumanAudio, as defined in step 4_{speedInWalking}I.e. representing the walking speed of the spectator, and the walking space route line from the parking lot to the gym is Activity_i ^SpaceThe time interval allocated from the walking of the personnel from the parking lot to the planning of the gymnasium is Activity_i ^TimeAs defined in step 1, interval ═ { beginTime, endTime }, where beginTime is equivalent to Instant, endTime is equivalent to Instant, beginTime is equivalent to Instant<endTime；

If the actual time required for the viewer to walk is less than the time allocated for planning, i.e., (length of)/Audio_{speedInWalking})<(interval_endTime-interval_startTime) If yes, the Activity of the planned Activity link is established_iThere is a conflict where length of (line) represents computing Activity_iLength of line of hollow space path, interval_endTimeRepresenting Activity_iEnd time of, interval_startTimeRepresenting Activity_iThe start time of (c);

activity of security check Activity for entrance of stadium audience_iThe Venues of the target venue is Activity_i ^FacilityThe time interval of the planning allocation is Activity_i ^TimeAs defined in step one, interval ═ { beginTime, endTime }, where beginTime is equivalent to Instant, endTime is equivalent to Instant, beginTime is equivalent to Instant_<endTime；

Venues, as defined in step 4_{maximumOccupancy}I.e. the number of Venues in the venue that can accommodate the maximum audience_{numberOfSecurityChannels}I.e. the number of entrances and exits, V, of Venuesenues_{timeforSecurityCheck}I.e., representing the security check time spent by each person entering Venues;

if the actual time required for the maximum number of spectators to enter is less than the time allocated for the program, i.e., (Venues)_{maximumOccupancy}×Venues_{timeforSecurityCheck}/Venues_{numberOfSecurityChannels})<(interval_endTime-interval_startTime) If yes, the Activity planned_iA conflict exists;

the second time conflict detection rule is used for judging all Activities in the specific Flow of the large-scale Activity_iAnd Activity_j(where i ∈ [1, n ]]，j∈[1,n]And i ≠ j, n is the number of links in the specific flow of the large-scale activity), whether the Time allocated by planning has intersection or not is the same under the condition that Human objects are the same;

the method is used for detecting whether time distribution conflict exists or not when the same person Human needs to execute multiple link activities in the specific Flow of the whole large-scale Activity;

if two links Activity in the Flow of the specific Flow of the large-scale Activity_iAnd Activity_jWherein Activity_i∈Flow，Activity_j∈Flow，i∈[1,n]，j∈[1,n]And i ≠ j, n is the number of links in the specific flow of the large-scale Activity, Activity_iAnd Activity_jThere is a time allocation conflict for the person, i.e. if:

if true, Activity_iAnd Activity_jThere is a time allocation conflict where Activity, as defined in step 3, is_i ^HumanRepresenting link Activity_iPeople involved Human_i，Activity_j ^HumanRepresenting link Activity_jPeople involved Human_j，Activity_i ^TimeRepresenting link Activity_iTime period involved_i，Activity_j ^TimeRepresenting link Activity_jTime of interestPeriodic Time_j；

And 5, the spatial conflict detection rule is as follows:

judging all Activities in the specific Flow of the large-scale Activity_iAnd Activity_j(where i ∈ [1, n ]]，j∈[1,n]And i ≠ j, n is the number of links in the specific flow of the large-scale activity), and whether Space allocated by planning has intersection or not is determined while intersection exists in the Time allocated by planning;

if the Activity of two links in the specific Flow of the large-scale Activity_iAnd Activity_jWherein Activity_i∈Flow，Activity_j∈Flow，i∈[1,n]，j∈[1,n]And i ≠ j, n is the number of links in the specific flow of the large-scale Activity, Activity_iAnd Activity_jThe time and the space in the process have intersection at the same time, and the two or more links have conflict in space distribution; namely if:

if true, Activity_iAnd Activity_jThere is a space allocation conflict where Activity, as defined in step 4, is_i ^TimeRepresenting link Activity_iTime period involved_i，Activity_j ^TimeRepresenting link Activity_jTime period involved_j，Activity_i ^SpaceRepresenting link Activity_iSpace involved_i，Activity_j ^SpaceRepresenting link Activity_jSpace involved_j；

Step 5, the attribute conflict detection rule is to determine whether a conflict exists in the activity schedule by using the data attribute of the object, and the specific method is as follows:

judging whether the number of audiences is larger than the maximum number of audiences which can be accommodated in the venue;

if the number of audiences is greater than the maximum number of people that the venue can accommodate, then there is an attribute conflict;

specifically, Audio science as defined in step 4_countI.e. the number of people representing the audience class, Venues_{maximumOccupancy}I.e. the number of Venues that can accommodate the audience at the most,

that is if (audion)_{countOfAudience}＞Venues_{maximumOccupancy}) If yes, the attribute conflict exists;

judging whether the number of the volunteers is large or not and the number of the control points of which the volunteers need to be arranged;

if the number of the volunteers is less than the number of the control points needing to arrange the volunteers, an attribute conflict exists;

specifically, as defined in step 4, Volunter_countI.e. representing the number of volunteers, Spot_countI.e. representing the number of control point classes, i.e. if (Volunter)_{countOfVolunteer}＞Spot_countOfSpots) If so, there is an attribute conflict.

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