CN114282778B - Collaborative monitoring method for traffic situation under road emergency - Google Patents
Collaborative monitoring method for traffic situation under road emergency Download PDFInfo
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Abstract
The invention relates to a collaborative monitoring method for traffic situation under road emergency, belonging to the field of road safety monitoring. The method comprises the following steps: constructing a node network based on a traffic network, determining indexes related to traffic situations under road emergencies and constructing an initial data set; when a certain node monitors a road emergency, uploading emergency data to a server as an event source node, and acquiring monitoring data of a plurality of strong association nodes of the source node according to the road network association by the server, and constructing a dynamic data set and a static data set of the event source node by combining an initial data set; the server weights the dynamic data set and the static data set to obtain the influence value of the event on each strong association node, so as to perform situation reasoning on the road emergency; and sending monitoring instructions to be coordinated to each strong association node according to the reasoning result. The invention can effectively improve the traffic safety emergency efficiency, reduce the influence of road emergencies on society and ensure the trip safety.
Description
Technical Field
The invention belongs to the field of road safety monitoring, and relates to a collaborative monitoring method for traffic situation under road emergency.
Background
Along with the networked construction of urban traffic, a highway cooperative command and dispatch cloud platform is researched and developed in multiple places, and a road safety barrier mainly comprising people prevention, physical prevention and technical prevention is constructed. The platform is integrated with a linkage mechanism, so that an integrated dispatching mode of multiparty participation of traffic police, road administration, obstacle clearance, rescue and the like is formed. In the event handling process, the system intelligently associates information such as videos, weather, personnel, vehicles and the like of the event places, automatically generates event handling groups, and shares the event information to each party by one key, so that the recording and backtracking of the event process are realized, the cooperative scheduling party is changed into multiple parties from one party, and the multiparty cooperative efficiency is effectively improved.
However, the research of expert scholars at home and abroad in the field of collaborative monitoring and early warning of road safety is relatively less, and meanwhile, as the road environment situation is more and more complicated, the abnormal event of road traffic is more and more frequent, and the application of the efficient and rapid collaborative control method in the situation monitoring of road emergency is necessary. The road emergency monitoring and early warning is a main aspect in traffic situation monitoring. The method is one of the necessary conditions for ensuring the smooth urban traffic by timely finding out and eliminating road emergencies and timely issuing road network blocking information.
Therefore, in the emergency treatment stage of the emergency in the field of road safety management, the road data is efficiently processed and analyzed, timely and effectively predicted and reliably researched, emergency treatment decisions are quickly made, the emergency treatment capacity is improved, and the method is applied to various scenes such as road traffic accident escape path prediction, rescue and relief route planning, ambulance road selection and the like, and provides assistance for the road emergency situation monitoring of relevant management personnel.
Disclosure of Invention
In view of the above, the invention constructs a model of traffic situation indexes and node influence degrees under the road emergency, and aims to provide a collaborative monitoring method for traffic situations under the road emergency. When the road emergency is monitored, predicting the node with the same road emergency situation at the next moment, and monitoring, distributing and controlling the node in advance, so that the traffic safety monitoring and early warning are facilitated.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a collaborative monitoring method for traffic situation under road emergency event specifically comprises the following steps:
s1: constructing a node network G based on a traffic network, determining indexes related to traffic situation under a road emergency, and constructing an initial data set A;
S2: when a certain node monitors a road emergency, uploading emergency data to a server as an event source node, and acquiring monitoring data of a plurality of strong association nodes of the source node according to the road network association by the server, and constructing a dynamic data set and a static data set of the event source node by combining an initial data set A;
S3: the server weights the dynamic data set and the static data set to obtain the influence value of the event on each strong association node, so as to perform situation reasoning on the road emergency;
s4: and sending monitoring instructions to be coordinated to each strong association node according to the reasoning result.
Further, in step S1, a node network G based on the traffic network is constructed, which specifically includes: the monitoring node equipment is arranged at the traffic intersection and used for collecting road emergency information; meanwhile, the server is used as a master node and is communicated with all monitoring node equipment and used for analyzing the nodes affected by the emergency; abstracting a traffic network into an unowned undirected graph, namely a node network graph G according to the geographic position relation among the monitoring node devices;
the constructed node network graph G is as follows:
G＝[V,E,W]
Wherein V is a set of nodes V i, n total nodes; v 0 is a master node, v a is a monitoring node, a=1, …, n; e is a set of edges, E ij E represents a connection path of the monitoring nodes v i and v j; w is a set of association degrees;
The node association matrix W is constructed as follows:
Wherein w ij represents the association degree between the node v i and the node v j, and whether the monitoring nodes are connected by edges or not is used for judging the association between the nodes, i=1, 2,3, …, n, j=1, 2,3, …, n;
An edge is connected between the node v i and the node v j, and if no other node exists in the connected edge, the association degree is 1, which represents that the node v i is strongly associated with the node v j, namely, the node v j is a strongly associated node of the node v i, and vice versa; if node v i is connected with node v j by an edge, and the connected edge contains n-1 nodes, the degree of association is n, which represents that node v i is weakly associated with node v j, i.e., node v j is a weakly associated node of node v i, and vice versa; if no edge connection exists between the node v i and the node v j, the association degree is 0, which represents that the node v i is not associated with the node v j;
The invention simplifies the traffic network into a node network diagram, and constructs a node association matrix according to the association of each monitoring node. In the node network diagram, the server is in communication connection with all node devices, so that when the server is used as a main node in the node network, the server is in strong association with all nodes and can be regarded as a father node of all monitoring nodes, and the server is not needed to be considered when a node association matrix is constructed.
Further, in step S1, determining an index related to the situation of the road emergency, and constructing an initial data set a, which specifically includes: defining static indexes and dynamic indexes of traffic situation under the road emergency, and determining r static indexes and m dynamic indexes, wherein the static indexes can comprise road length, total width of a vehicle road, the number of traffic lights placed between two monitoring nodes, the number of crossroads, the number of key areas passing by and the like. Dynamic indicators may include traffic flow, direction of movement, speed of movement, ambient temperature, humidity, wind speed, weather, congestion level, etc.; because the positions of all the monitoring nodes are determined, the data of the static indexes can be acquired and stored in the server before the situation of the road emergency is monitored;
Constructing an initial data set A, wherein the initial data set is a set of static index data of all nodes;
A＝{A1,…,Ar}
Wherein a k1 is the node set of the kth 1 static index, k1=1, 2, …, r.
Further, in step S2, a static data set and a dynamic data set of the event source node are constructed, which specifically includes the following steps:
S21: when a certain monitoring node v i monitors a road emergency, defining the monitoring node as an event source node, uploading emergency data to a server, and waiting for calculation processing of the server;
The road emergency data D uploaded by the monitoring node v i is as follows:
D＝[d1,d2,d3,…,dn]
D 1 is id information of a monitoring node for monitoring the road emergency, d a is road emergency data information, such as license plate information, vehicle speed, driving direction and the like of a target vehicle, and a=2, 3,4, … and n;
S22: according to the relevance of the event source node v i and other nodes, the server searches a node v j which is strongly related to the event source node v i, acquires situation data of the strongly related nodes v j after the road emergency is monitored, and the acquired monitoring data of the strongly related nodes v j are dynamic index data of the node equipment;
The monitoring data E j of the strongly associated node v j of the event source node v i is:
Wherein e j is id information of the strong association node v j, The strong association node v j, which is the event source node v i, is the kth 2 dynamic index value, k2=1, 2, …, m at this time;
S23: the server constructs a static data set B of the event source node according to the uploaded road emergency data D and by combining the node incidence matrix W and the initial data set A;
The static data set B of the constructed event source node is as follows:
Wherein, For the static index values of the event source node v i and the node v j about the kth 1 road, k1=1, 2, …, r, j=1, 2, …, n;
S24: the server processes the data E j of the strong association node v j to obtain a dynamic data set C of the event source node;
C j is the dynamic data of the strong association node v j processed by the server, and the processed dynamic data of the strong association nodes are converged to construct a dynamic data set C of the event source node. The constructed dynamic data set C of the event source node is as follows:
Wherein, K2=1, 2, …, m, j=1, 2, …, n for the kth 2 dynamic index data of the strongly associated node v j with respect to the event source node v i; the dynamic data set C is only a data set obtained by processing dynamic index data between the event source node v i and the strong association node v j, and the weak association node and the unassociated node do not participate in calculation in the dynamic data set C of the event source node, so that the dynamic index data between the event source node v i and the unassociated node and between the event source node v i and the weak association node can be directly assigned to 0; /(I)The expression of (2) is: /(I)
Further, in step S3, situation reasoning is performed on the road emergency, which specifically includes the following steps:
S31: the server calculates an influence value f j of the monitored emergency on the node v j;
S32: the server constructs a node influence score set F i according to the influence value F j, and the obtained node influence score set F i is: f i＝{f1,…,fn };
S33: the server calculates the probability p j that the node v j is likely to monitor the same kind of road emergency at the next moment according to the node influence score set F i;
the calculation formula of the monitoring probability p j of the node v j is as follows:
according to the monitoring probability P j of the node v j, and forming a node monitoring probability set P i of the event source node v i, the node monitoring probability set P i is constructed as follows: p i＝{p1,…,pn }.
Further, the step S31 specifically includes the steps of:
S311: giving different weights to the static index and the dynamic index according to the influence degree of the road emergency, and obtaining a static weight alpha and a dynamic weight beta;
The static weight alpha and the dynamic weight beta are constructed as follows:
α＝{α1,…,αr}
β＝{β1,…,βm}
Where α k1 represents the kth 1 static weight, k1=1, 2, …, r; β k2 represents the kth 2 dynamic weight, k2=1, 2, …, m;
S312: weighting the static weight alpha and the dynamic weight beta with the corresponding parameter values respectively to obtain the action intensity values of the event source nodes on other nodes, namely the influence value f j; the action intensity value reflects the influence degree of the event source node on other nodes, and the larger the intensity value is, the larger the influence degree of the event source node on the node is after the situation of the road emergency is monitored;
The action intensity value is obtained by weighting according to a static data set B and a dynamic data set C of an event source node and combining a static weight alpha and a dynamic weight beta, and the calculation formula is as follows:
further, in step S4, a monitoring instruction that needs to be coordinated is sent to each strong association node according to the reasoning result, which specifically includes the following steps:
S41: dividing different monitoring levels q j according to the monitoring probability p j, dividing the monitoring levels into three levels of strong cooperation, weak cooperation and no cooperation, and sending cooperative monitoring instructions of different levels to each strong association node;
When the monitoring probability p j is 0, the node v j is not affected by the traffic situation of the road emergency, the traffic situation of the road emergency is not required to be monitored, and the cooperative rank q j =0 is no cooperative rank; when the monitoring probability p j is not 0 and is not the maximum value in all the monitoring probabilities, the road emergency traffic situation is proved to have a certain influence on the node v j, the road emergency traffic situation needs to be monitored, and the cooperative rank q j =1 is a weak cooperative rank; when the monitoring probability p j is the maximum value, the fact that the road emergency traffic situation has the greatest influence on the node v j in the nodes related to the event source node v i is indicated that the road emergency traffic situation needs to be monitored in a key way, and the cooperative grade q j =2 is a strong cooperative grade;
S42: according to different collaboration levels, the server sends different monitoring instructions; the larger the synergy level value, the more monitoring functions the node needs to turn on. Aiming at the nodes with different collaboration grades, the server determines the priority of the sending instruction according to the numerical value of the collaboration grade, and sends a monitoring instruction to each node to complete the collaborative monitoring function.
The invention has the beneficial effects that: the invention can effectively improve the traffic safety emergency efficiency, reduce the influence of road emergencies on social economy and people life, and ensure the travel safety of citizens.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a block diagram of a multi-node collaborative monitoring network for an event source node;
FIG. 2 is a schematic diagram of a region selection of a multi-node collaborative monitoring method;
FIG. 3 is a flow chart of the collaborative monitoring method of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
In this embodiment, the road emergency is mainly a vehicle collision accident, a red light running accident, overspeed driving, ambulance rescue route selection, and the like. The type and number of road emergencies are not limited in this embodiment. In this embodiment, the static indexes collected by the monitoring nodes may include a road length, a total width of a vehicle road, the number of traffic lights placed between two monitoring nodes, the number of intersections, the number of key areas passing by, and the like. Dynamic indicators may include traffic flow, direction of movement, speed of movement, ambient temperature, humidity, wind speed, weather, congestion level, etc. The classification of the moving object may refer to a kind of the moving object, such as a person, a car, an object, and the like. The static index and the dynamic index are not limited in this embodiment, as long as the collaborative monitoring can be satisfied.
Referring to fig. 1-3, fig. 1 is a multi-node collaborative monitoring network for an event source node.
After the node monitors the road emergency, the node is used as an event source node to upload event related data to a server; after the server receives the road emergency data, the server calculates the nodes forming strong association relation with the event source nodes according to the data, as shown in fig. 2, the nodes having strong association with the event source nodes form a collaborative monitoring area, and the server communicates with the nodes; the strong association nodes sequentially provide monitoring data of the time period of the occurrence of the road emergency to the server; and the server processes and calculates the provided monitoring data and sends monitoring instructions to be completed to a plurality of strongly associated nodes of the event source node.
The cooperative monitoring method for traffic situation under road emergency is explained in detail below.
Fig. 3 is a flowchart of the collaborative monitoring method for a situation of a road emergency, as shown in fig. 3, the method specifically includes the following steps:
s1: constructing a node network G based on a traffic network, determining indexes related to the situation of a road emergency, and constructing an initial data set A, wherein the method specifically comprises the following steps of:
S11: the monitoring node equipment is arranged at the traffic intersection and used for collecting traffic situation information of the road emergency. Meanwhile, the server is used as a master node and is communicated with all monitoring node equipment for analyzing the nodes affected by the abnormality. Abstracting a traffic network into an unowned undirected graph G according to the geographical position relation among the monitoring node devices;
the constructed node network graph G is as follows:
G＝[V,E,W]
V＝[v0,v1,…,vn]
i＝0,1,…,n
Wherein V is a set of nodes V i, n total nodes; v 0 is a master node, v a is a monitoring node, a=1, …, n.
E is a collection of edges, E ij E represents the connection path of the monitoring nodes v i and v j.
W is a set of association degrees, W ij epsilon W represents the association degrees of nodes v i and v j, and whether the intersections where all monitoring node devices are located are connected by a direct edge or not is used for judging the association between the nodes.
The node association matrix W is constructed as follows:
Where w ij represents the association of node v i with node v j, i=1, 2,3, …, n, j=1, 2,3, …, n.
Node v i is geographically connected to node v j by at least one edge that does not pass through other nodes, representing that node v i is associated with node v j; if node v i and node v j need to be connected through transit by other nodes, there is no directly connected edge, representing node v i is not associated with node v j. If there is an association between two nodes v i,vj, w ij is 1, and node v i and node v j are associated nodes to each other. If the two nodes are not associated, w ij is 0 and node v i and node v j are not associated nodes.
The invention simplifies the traffic network into a node network diagram, and constructs a node association matrix according to the association of each monitoring node. In the node network diagram, the master node has communication connection with all nodes, namely, the representative master node has association with all nodes and can be regarded as a father node of all monitoring nodes, so that the master node is not considered when constructing the node association matrix.
S12: defining static indexes and dynamic indexes of traffic situation under the road emergency; determining that r static indexes exist; there are determined m dynamic indexes. Because the positions of all the monitoring nodes are determined, the data of the static indexes can be acquired and stored in the server before the road emergency is monitored.
S13: constructing an initial data set A, wherein the initial data set is a set of static index data of all nodes;
A＝{A1,…,Ar}
Wherein a k1 is the node set of the kth 1 static index, k1=1, 2, …, r.
The static index of the present embodiment mainly includes a vehicle flow rate (vehicle/minute), an illumination intensity (lx), and the like.
The following illustrates the construction of the initial data set a, assuming that 4 monitoring nodes are installed in the area, the road emergency defines one type of running red light behavior of the vehicle, and the static indexes are respectively determined to be the line length (m) between the nodes and the number (2) of traffic lights between the nodes, and r=2.
A＝{A1,A2}
The values of the line lengths of the node 1, the node 2, the node 3 and the node 4 are respectively 200 (m), 0 (m) and 160 (m).
The values of the line lengths of the node 2, the node 1, the node 3 and the node 4 are respectively 200 (m), 400 (m) and 80 (m).
The values of the line lengths of the node 3, the node 1, the node 2 and the node 4 are respectively 0 (m), 400 (m) and 0 (m).
The values of the line lengths of the node 4, the node 1, the node 2 and the node 3 are 160 (m), 80 (m) and 0 (m), respectively.
The traffic lights of the node 1, the node 2, the node 3 and the node 4 have the values of 3 (pieces), 0 (pieces) and 4 (pieces) respectively.
The traffic lights of the node 2, the node 1, the node 3 and the node 4 have the values of 3 (pieces), 3 (pieces) and 1 (piece) respectively.
The traffic lights of the node 3, the node 1, the node 2 and the node 4 have the values of 0 (one), 3 (one) and 0 (one) respectively.
The traffic lights of the node 4, the node 1, the node 2 and the node 3 have the values of 4 (one), 1 (one) and 0 (one) respectively.
S2: when a node monitors a road emergency, the node is used as an event source node to upload emergency data to a server, and the server acquires monitoring data of a plurality of strong association nodes of the source node according to the road network association and constructs a dynamic and static data set of the event source by combining an initial data set A, and the method specifically comprises the following steps:
S21: when a certain monitoring node v i monitors a road emergency, defining the monitoring node as an event source node, uploading emergency data to a server, and waiting for calculation processing of the server;
The road emergency data D uploaded by the monitoring node are as follows:
D＝[d1,d2,d3,…,dn]
Wherein d 1 is id information of a monitoring node monitoring the road emergency, d a (a=2, 3,4, …, n) is data information carried when the monitoring node monitors the road emergency, and d a (a=2, 3,4, …, n) can be defined by itself.
S22: according to the relevance of the event source node v i and other nodes, the server searches a node v j which is strongly related to the event source node v i, acquires road emergency data of the strongly related nodes v j after the emergency situation is monitored, and the acquired monitoring data of the strongly related nodes v j are road emergency dynamic index data at the node equipment.
Each strongly associated node v j monitoring data E j of the event source node v i is:
Wherein e j is id information of the strongly associated node, The strongly associated node v j, which is the event source node v i, is now the kth 2 dynamic index value.
S23: and the server constructs a static data set B of the event source node according to the uploaded road emergency data D and combining the node association matrix W and the initial data set A.
The static data set B of the constructed event source node is as follows:
Wherein, The static index values for the event source node v i and the node v j (j=1, 2, …, n) for the kth 1 road.
The following illustrates the construction of the static data set B of the event source node, and in combination with the initial data set a in this embodiment, it is assumed that the event source node is v 1, and the static data set B of the corresponding event source node is as follows:
Wherein 200 of the first row and the second column of the static data set B of the event source node indicates that the data value of the event source node v 1 and the node v 2 about the first static index, i.e. the inter-node line length, is 200m.
S24: the server processes the data E j of the strong association node v j to obtain a dynamic data set C of the event source node;
C j is the dynamic data of the strong association node v j processed by the server, and the processed dynamic data of the strong association nodes are converged to construct a dynamic data set C of the event source node. The constructed dynamic data set C of the event source node is as follows:
Wherein, K2=1, 2, …, m, j=1, 2, …, n for the kth 2 dynamic index data of the strongly associated node v j with respect to the event source node v i; the dynamic data set C is only a data set obtained by processing dynamic index data between the event source node v i and the strong association node v j, and the weak association node and the unassociated node do not participate in calculation in the dynamic data set C of the event source node, so that the dynamic index data between the event source node v i and the unassociated node and between the event source node v i and the weak association node can be directly assigned to 0;
The expression of (2) is:
S3: the server weights the dynamic data set and the static data set to obtain the influence value of the event on each strong association node, so as to perform situation reasoning on the road emergency, and the method specifically comprises the following steps:
s31: the server calculates an influence value f j of the monitored emergency on the node v j, and specifically includes the following steps:
S311: giving different weights to the static index and the dynamic index according to the influence degree of the traffic situation of the road emergency, and obtaining a static weight alpha and a dynamic weight beta;
The static weight alpha and the dynamic weight beta are constructed as follows:
α＝{α1,…,αr}
β＝{β1,…,βm}
Where α k1 represents the kth 1 static weight, k1=1, 2, …, r; β k2 represents the kth 2 dynamic weight, k2=1, 2, …, m.
S312: weighting the static weight alpha and the dynamic weight beta with corresponding parameter values respectively to obtain the action intensity value of the event source node on other nodes, wherein the action intensity value reflects the influence degree of the event source node on other nodes, and the larger the intensity value is, the larger the influence degree of the event source node on the node is after the road safety event occurs; the action intensity is obtained by combining the static weight alpha and the dynamic weight beta according to the static data set B and the dynamic data set C of the event source node, and the calculation formula is as follows:
S32: the server constructs a node influence score set F i according to the influence value, and the obtained node influence score set F i is:
Fi＝{f1,…,fn}
S33: the server calculates the probability p j that the node v j may monitor the situation of the road emergency at the next moment according to the node influence score set F i.
The monitoring probability p j of the node v j is calculated as follows:
according to the monitoring probability P j of the node v j, and forming a node monitoring probability set P i of the event source node v i, the node monitoring probability set P i is constructed as follows:
Pi＝{p1,…,pn}
s4: sending a monitoring instruction to be coordinated to each strong association node according to the reasoning result, comprising the following steps:
S41: dividing different monitoring grades g j according to the monitoring probability p j, dividing the monitoring grades into three grades of strong cooperation and weak cooperation, and sending cooperative monitoring instructions of different grades to each strong association node;
When the monitoring probability p j is 0, the node v j is not affected by the traffic situation of the road emergency, the traffic situation of the road emergency is not required to be monitored, and the cooperative rank g j =0 is no cooperative rank; when the monitoring probability p j is not 0 and is not the maximum value in all the monitoring probabilities, the road emergency traffic situation is proved to have a certain influence on the node v j, the road emergency traffic situation needs to be monitored, and the cooperative rank g j =1 is a weak cooperative rank; when the monitoring probability p j is the maximum value, it is indicated that the road emergency traffic situation has the greatest influence on the node v j in the nodes related to the event source node v i, and the road emergency traffic situation needs to be monitored in a key way, and the cooperative grade g j =2 is a strong cooperative grade.
S42: according to different collaboration levels, the server sends different monitoring instructions. The larger the synergy level value, the more monitoring functions the node needs to turn on. Aiming at the nodes with different collaboration grades, the server determines the priority of the sending instruction according to the numerical value of the collaboration grade, and sends a monitoring instruction to each node to complete the collaborative monitoring function.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (4)
1. The collaborative monitoring method for traffic situation under road emergency is characterized by comprising the following steps:
s1: constructing a node network G based on a traffic network, determining indexes related to traffic situation under a road emergency, and constructing an initial data set A;
in step S1, a node network G based on a traffic network is constructed, which specifically includes: the monitoring node equipment is arranged at a traffic intersection; the server is used as a main node and is communicated with all monitoring node equipment; abstracting a traffic network into an unowned undirected graph, namely a node network graph G according to the geographic position relation among the monitoring node devices;
the constructed node network graph G is as follows:
G＝[V,E,W]
Wherein V is a set of nodes V i, n total nodes; v 0 is a master node, v a is a monitoring node, a=1, …, n; e is a set of edges, E ij E represents a connection path of the monitoring nodes v i and v j; w is a set of association degrees;
The node association matrix W is constructed as follows:
Wherein w ij represents the degree of association of node v i with node v j, i=1, 2,3, …, n, j=1, 2,3, …, n;
An edge is connected between the node v i and the node v j, and if no other node exists in the connected edge, the association degree is 1, which represents that the node v i is strongly associated with the node v j, namely, the node v j is a strongly associated node of the node v i, and vice versa; if node v i is connected with node v j by an edge, and the connected edge contains n-1 nodes, the degree of association is n, which represents that node v i is weakly associated with node v j, i.e., node v j is a weakly associated node of node v i, and vice versa; if no edge connection exists between the node v i and the node v j, the association degree is 0, which represents that the node v i is not associated with the node v j;
In step S1, determining an index related to traffic situation under a road emergency, and constructing an initial data set a, which specifically includes: defining static indexes and dynamic indexes of traffic situation under the road emergency; determining r static indexes and m dynamic indexes; the data of the static index is acquired and stored in the server before the road emergency is monitored;
Constructing an initial data set A, wherein the initial data set is a set of static index data of all nodes;
A＝{A1,…,Ar}
Wherein a k1 is the node set of the kth 1 static index, k1=1, 2, …, r;
S2: when a certain node monitors a road emergency, uploading emergency data to a server as an event source node, and acquiring monitoring data of a plurality of strong association nodes of the source node according to the road network association by the server, and constructing a dynamic data set and a static data set of the event source node by combining an initial data set A;
in step S2, a dynamic data set and a static data set of the event source node are constructed, which specifically includes the following steps:
S21: when a certain monitoring node v i monitors a road emergency, defining the monitoring node as an event source node, uploading emergency data to a server, and waiting for calculation processing of the server;
The road emergency data D uploaded by the monitoring node v i is as follows:
D＝[d1,d2,d3,…,dn]
Wherein d 1 is id information of a monitoring node monitoring the road emergency, d a is road emergency data information, a=2, 3,4, …, n;
S22: according to the relevance of the event source node v i and other nodes, the server searches a node v j which is strongly related to the event source node v i, acquires situation data of the strongly related nodes v j after the road emergency is monitored, and the acquired monitoring data of the strongly related nodes v j are dynamic index data of the node equipment;
The data E j of each strongly associated node v j of the event source node v i is:
Wherein e j is id information of the strong association node v j, The strong association node v j, which is the event source node v i, is the kth 2 dynamic index value, k2=1, 2, …, m at this time;
S23: the server constructs a static data set B of the event source node according to the uploaded road emergency data D and by combining the node incidence matrix W and the initial data set A;
The static data set B of the constructed event source node is as follows:
Wherein, For the static index values of the event source node v i and the node v j about the kth 1 road, k1=1, 2, …, r, j=1, 2, …, n;
S24: the server processes the data E j of the strong association node v j to obtain a dynamic data set C of the event source node;
C j is dynamic data of the strong association node v j processed by the server, and the processed dynamic data of the strong association nodes are converged to form a dynamic data set C of the event source node; the constructed dynamic data set C of the event source node is as follows:
Wherein, K2=1, 2, …, m, j=1, 2, …, n for the kth 2 dynamic index data of the strongly associated node v j with respect to the event source node v i; the dynamic data set C is only a data set obtained by processing dynamic index data between the event source node v i and the strong association node v j, and the weak association node and the unassociated node do not participate in calculation in the dynamic data set C of the event source node, so that the dynamic index data between the event source node v i and the unassociated node and between the event source node v i and the weak association node are directly assigned to 0; /(I)The expression of (2) is:
S3: the server weights the dynamic data set and the static data set to obtain the influence value of the event on each strong association node, so as to perform situation reasoning on the road emergency;
s4: and sending monitoring instructions to be coordinated to each strong association node according to the reasoning result.
2. The collaborative monitoring method according to claim 1, wherein in step S3, situation reasoning is performed on a road emergency, and the method specifically comprises the steps of:
S31: the server calculates an influence value f j of the monitored emergency on the node v j;
S32: the server constructs a node influence score set F i according to the influence value F j, and the obtained node influence score set F i is: f i＝{f1,…,fn };
S33: the server calculates the probability p j that the node v j is likely to monitor the same kind of road emergency at the next moment according to the node influence score set F i;
the calculation formula of the monitoring probability p j of the node v j is as follows:
according to the monitoring probability P j of the node v j, and forming a node monitoring probability set P i of the event source node v i, the node monitoring probability set P i is constructed as follows: p i＝{p1,…,pn }.
3. The collaborative monitoring method according to claim 2, wherein step S31 specifically comprises the steps of:
S311: giving different weights to the static index and the dynamic index according to the influence degree of the road emergency, and obtaining a static weight alpha and a dynamic weight beta;
The static weight alpha and the dynamic weight beta are constructed as follows:
α＝{α1,…,αr}
β＝{β1,…,βm}
Where α k1 represents the kth 1 static weight, k1=1, 2, …, r; β k2 represents the kth 2 dynamic weight, k2=1, 2, …, m;
S312: weighting the static weight alpha and the dynamic weight beta with the corresponding parameter values respectively to obtain the action intensity values of the event source nodes on other nodes, namely the influence value f j;
The action intensity value is obtained by weighting according to a static data set B and a dynamic data set C of an event source node and combining a static weight alpha and a dynamic weight beta, and the calculation formula is as follows:
4. The collaborative monitoring method according to claim 2, wherein in step S4, a monitoring instruction requiring collaboration is sent to each strong association node according to the reasoning result, specifically comprising the steps of:
S41: dividing different monitoring levels q j according to the monitoring probability p j, dividing the monitoring levels into three levels of strong cooperation, weak cooperation and no cooperation, and sending a cooperation monitoring instruction to each strong association node;
When the monitoring probability p j is 0, the situation of the road emergency is not required to be monitored, and the cooperative level q j =0 is no cooperative level; when the monitoring probability p j is not 0 and is not the maximum value in all the monitoring probabilities, the situation of the road emergency needs to be monitored, and the cooperative level q j =1 is a weak cooperative level; when the monitoring probability p j is the maximum value, the situation of the road emergency needs to be monitored in a key way, and the cooperative level q j =2 is a strong cooperative level;
S42: according to different collaboration levels, the server sends different monitoring instructions; and the server determines the priority of the sending instruction according to the numerical value of the cooperative grade, and sends a monitoring instruction to each node to complete the function of cooperative monitoring.
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