阅读说明：本技术 在单向强连通通信网络中建立双随机通信矩阵的分布式方法与系统 (Distributed method and system for establishing double random communication matrixes in unidirectional strong communication network ) 是由 李方圆 张起源 刘艳红 秦家虎 马麒超 霍本岩 杨磊 吴振龙 于 2021-07-30 设计创作，主要内容包括：本发明公开了一种在单向强连通通信网络中建立双随机通信矩阵的分布式方法及系统,方法包括根据单向强连通通信网络的强连通图计算出通信网络的通信拓扑结构；根据通信网络的通信拓扑结构计算出通信网络中所有的环路；在通信网络中的所有环路中计算出包含所有节点的最大环路并求和及单位化,得到单向强连通通信网络的双随机通信矩阵。本发明只需要给出含有一个最大环的强连通的通信网络,不需要其他条件,就可以为通信网络建立双随机通信矩阵,这相比于其他使通信网络双随机化的方法适用性更好,使用范围更加广泛。(The invention discloses a distributed method and a distributed system for establishing a double random communication matrix in a one-way strong communication network, wherein the method comprises the steps of calculating a communication topological structure of the communication network according to a strong communication diagram of the one-way strong communication network; calculating all loops in the communication network according to the communication topological structure of the communication network; and calculating the maximum loop including all nodes in all loops in the communication network, summing and unitizing to obtain the double random communication matrix of the unidirectional strong communication network. The invention can establish the double random communication matrix for the communication network only by providing the strongly-connected communication network with the largest ring without other conditions, and compared with other methods for double randomization of the communication network, the method has better applicability and wider application range.)

1. A distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network, comprising:

s1, calculating the communication topological structure of the communication network according to the strong communication diagram of the unidirectional strong communication network;

s2, calculating all loops in the communication network according to the communication topological structure of the communication network;

and S3, calculating the maximum loop including all nodes in all loops in the communication network, summing and unitizing to obtain the double random communication matrix of the unidirectional strong communication network.

2. Distributed method for establishing a dual random communication matrix in a unidirectional strongly connected communication network according to claim 1,

each node in the communication network is assumed to have a unique identifier, denoted as { p, q }; the identifiers of all nodes have an order relation capable of comparing sizes; the directed edges from the node p to the node q are represented by (p, q), and the transmitting variable S is respectively arranged at the node p_pReceiving variable R_pLocal variable L_pAnd a temporary storage variable P_p；

Step S1 includes:

s11, respectively setting the transmission variable S of the node p_pReceiving variable R_pLocal variable L_pAnd a temporary storage variable P_pIs initialized to S_p[0]、R_p[0]、P_p[0]、L_p[0]The initialization formula is as follows:

wherein the content of the first and second substances,representing an empty set, and ← representing a valuation operation;

s12, recording the data received by the node p in the k step as R_p[k]Is provided with

Wherein if there is a directed edge (p, q) in the communication topology, T_p,qIs the complete set, otherwise T_p,qIs an empty set; n is a radical of_pRepresenting an introspection neighbor set of a node p, n represents intersection operation, and u represents union operation;

s13, the data R received by the node p in the k step_p[k]Recording the processed variable P to a temporary storage variable P_pTo obtain P_p[k]The processing formula is as follows:

wherein (β, α) represents the set R_p[k]Any of (1);

s14, according to the received data R_p[k]Updating local variable of node p to L_p[k]The update formula is as follows:

L_p[k]←L_p[k-1]∪P_p[k]

s15, recording the data sent by the node p in the k step as S_p[k]Then, then

S_p[k]←P_p[k]；

S16, if k is less than r, jumping to S12, otherwise executing S2, wherein r is the radius of the strong communication graph.

3. The distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to claim 2, wherein the step S2 comprises:

s21, according to the local variable L_p[r]A set I of identifiers of the communication topology is established, wherein,

I←{α|(β,α)∈L_p(r)}

setting matrix D ═ (0)_n×nIs an n multiplied by n dimensional zero matrix, n is the number of elements in the identifier set I;

s22, sorting the identifier set I into a list C based on a sorting algorithm, wherein the length of the list C is n, so that a mapping relation f: C → { 1.. multidot.n } is established, and if so, the mapping relation f: C → { 1.. multidot.n } is provided

f(C_i)＝i

Wherein, C_iIs the ith element in list C;

s23, for node C_iE.g. I, looking for loop O (C)_i) The loop starting point is node C_i(ii) a The operation of recording the list head is H: C → I, then

H([α,C′])＝α

Wherein C' is an arbitrary list, giving O (C)_i) Assigned initial value of [ C']The following relationships are given:

s24, if there is only one (C)_j,H(O(C_i)))∈L_i(r), i.e. node H (O (C)_i) Only one directed edge away from the node, then

If present (C)_j,H(O(C_i)))∈L_i(r) and (C)_j′,H(O(C_i)))∈L_i(r), and C_j≠C_j′I.e. node H (O (C)_i) At least two directed edges away from the node, then the loops are recorded as follows:

s25, if C_j＝C_iAnd if there is C_j′Then C is_j′＝C_iI.e. starting point is C_iEnd point is also C_iThen find out the node C_iAll loops O (C) as starting points_i,..); otherwise, it jumps to step S24.

4. The distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to claim 3, wherein the step S3 comprises:

s31, establishing a adjacency matrix B of the loop, having

Wherein, for any i, j ∈ { 1., n }, if any, (C)_j,C_i)∈O(C_i,..), let B_i,jIf not, let B_i,j＝0；

S32, if the matrix B is full rank, i.e., rank (B) ═ n, then

D←D+B；

S33, if C_i≠C_nThen, then

C_i←C_i+1

Jumping to step S23, otherwise executing step S34;

s34, obtaining a double random communication matrix D through summation and unity calculation, wherein the calculation formula is as follows:

alternatively, the first and second electrodes may be,

5. distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to any of claims 1-4, characterized in that before step S1, the method further comprises:

and constructing a strong communication graph of the one-way strong communication network to be processed, and acquiring the address information of each node in the strong communication graph.

6. A distributed system for establishing dual random communication matrices in a unidirectional strongly connected communication network, comprising:

the topological structure calculating module is used for calculating the communication topological structure of the communication network according to the strong connection diagram of the unidirectional strong connection communication network;

the loop calculation module is used for calculating all loops in the communication network according to the communication topological structure of the communication network;

and the double-random communication matrix generation module is used for calculating the maximum loop containing all nodes in all loops in the communication network, summing and unitizing the maximum loop to obtain the double-random communication matrix of the unidirectional strong communication network.

7. The distributed system for establishing dual random communication matrices in a unidirectional strong connectivity communication network of claim 6,

each node in the communication network is assumed to have a unique identifier, denoted as { p, q }; the identifiers of all nodes have an order relation capable of comparing sizes; the directed edges from the node p to the node q are represented by (p, q), and the transmitting variable S is respectively arranged at the node p_pTo connectYield variable R_pLocal variable L_pAnd a temporary storage variable P_p；

The calculating the communication topological structure of the communication network according to the strong connection diagram of the unidirectional strong connection communication network comprises the following steps:

s11, respectively setting the transmission variable S of the node p_pReceiving variable R_pLocal variable L_pAnd a temporary storage variable P_pIs initialized to S_p[0]、R_p[0]、P_p[0]、L_p[0]The initialization formula is as follows:

wherein the content of the first and second substances,representing an empty set, and ← representing a valuation operation;

s12, recording the data received by the node p in the k step as R_p[k]Is provided with

Wherein if there is a directed edge (p, q) in the communication topology, T_p,qIs the complete set, otherwise T_p,qIs an empty set; n is a radical of_pRepresenting the degree of entry of a node pThe neighbor set, n represents intersection operation, and u represents union operation;

s13, the data R received by the node p in the k step_p[k]Recording the processed variable P to a temporary storage variable P_pTo obtain P_p[k]The processing formula is as follows:

wherein (β, α) represents the set R_p[k]Any of (1);

s14, according to the received data R_p[k]Updating local variable of node p to L_p[k]The update formula is as follows:

L_p[k]←L_p[k-1]∪P_p[k]

s15, recording the data sent by the node p in the k step as S_p[k]Then, then

S_p[k]←P_p[k]；

And S16, if k is less than r, jumping to step S12, otherwise, calculating all loops in the communication network according to the communication topological structure of the communication network, wherein r is the radius of the strong communication graph.

8. The distributed system for establishing dual random communication matrices in a unidirectional strong connectivity communication network of claim 7,

the calculating all loops in the communication network according to the communication topology of the communication network includes:

s21, according to the local variable L_p[r]A set I of identifiers of the communication topology is established, wherein,

I←{α|(β,α)∈L_p(r)}

setting matrix D ═ (0)_n×nIs an n multiplied by n dimensional zero matrix, n is the number of elements in the identifier set I;

s22, sorting the identifier set I into a list C based on a sorting algorithm, wherein the length of the list C is n, so that a mapping relation f: C → { 1.. multidot.n } is established, and if so, the mapping relation f: C → { 1.. multidot.n } is provided

f(C_i)＝i

Wherein, C_iIs the ith element in list C;

s23, for node C_iE.g. I, looking for loop O (C)_i) The loop starting point is node C_i(ii) a The operation of recording the list head is H: C → I, then

H([α,C′])＝α

Wherein C' is an arbitrary list, giving O (C)_i) Assigned initial value of [ C']The following relationships are given:

s24, if there is only one (C)_j,H(O(C_i)))∈L_i(r), i.e. node H (O (C)_i) Only one directed edge away from the node, then

If present (C)_j,H(O(C_i)))∈L_i(r) and (C)_j′,H(O(C_i)))∈L_i(r), and C_j≠C_j′I.e. node H (O (C)_i) At least two directed edges away from the node, then the loops are recorded as follows:

s25, if C_j＝C_iAnd if there is C_j′Then C is_j′＝C_iI.e. starting point is C_iEnd point is also C_iThen find out the node C_iAll loops O (C) as starting points_i,..); otherwise, it jumps to step S24.

9. The distributed system for establishing dual random communication matrices in a unidirectional strong connectivity communication network of claim 8,

the step of calculating the maximum loop including all nodes in all loops in the communication network, summing and unitizing the maximum loop to obtain the double random communication matrix of the unidirectional strong communication network comprises the following steps:

s31, establishing a adjacency matrix B of the loop, having

Wherein, for any i, j ∈ { 1., n }, if any, (C)_j,C_i)∈O(C_i,..), let B_i,jIf not, let B_i,j＝0；

S32, if the matrix B is full rank, i.e., rank (B) ═ n, then

D←D+B；

S33, if C_i≠C_nThen, then

C_i←C_i+1

Jumping to step S23, otherwise executing step S34;

s34, obtaining a double random communication matrix D through summation and unity calculation, wherein the calculation formula is as follows:

alternatively, the first and second electrodes may be,

10. the distributed system for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to any one of claims 6-9, further comprising:

and the strong connection graph building module is used for building a strong connection graph of the one-way strong connection communication network to be processed before the communication topological structure of the communication network is calculated according to the strong connection graph of the one-way strong connection communication network, and acquiring the address information of each node in the strong connection graph.

Technical Field

The invention relates to the technical field of multi-agent systems, in particular to a distributed method and a distributed system for establishing a double random communication matrix in a one-way strong-connectivity communication network.

Background

Related applications of multi-agent systems generally rely on an average consensus algorithm to coordinate the behavior of multiple nodes, a prerequisite for using the average consensus algorithm is that a dual random communication matrix has been established in the system. However, in the initial stage of multi-agent system deployment, the dual random communication matrix does not exist naturally, and appropriate weights need to be set for the received data and the transmitted data respectively through an appropriate node negotiation mechanism, so that the dual random communication matrix is obtained finally. That is, at the beginning of the deployment of the multi-agent system, no good negotiation mechanism is established in the multi-agent system, and it is difficult to set appropriate weights for the received data and the transmitted data, respectively, so that it is difficult to obtain a dual random communication matrix, and therefore, the method of setting weights for the received data and the transmitted data to make the communication network dual-randomization has poor applicability in practical applications.

Disclosure of Invention

The invention aims to at least solve the technical problems in the prior art, particularly creatively provides a distributed method and a distributed system for establishing a double-random communication matrix in a unidirectional strong-communication network, and effectively solves the problem that the method for setting weights for received data and sent data to enable the communication network to be double-randomized in the prior art has poor applicability in practical application.

To achieve the above object, according to a first aspect of the present invention, there is provided a distributed method for establishing dual random communication matrices in a unidirectional strongly connected communication network, the method comprising the steps of:

s1, calculating the communication topological structure of the communication network according to the strong communication diagram of the unidirectional strong communication network;

s2, calculating all loops in the communication network according to the communication topological structure of the communication network;

and S3, calculating the maximum loop including all nodes in all loops in the communication network, summing and unitizing to obtain the double random communication matrix of the unidirectional strong communication network.

Preferably, each node in the communication network is assumed to have a unique identifier, denoted as { p, q }; the identifiers of all nodes have an order relation capable of comparing sizes; the directed edges from the node p to the node q are represented by (p, q), and the transmitting variable S is respectively arranged at the node p_pReceiving variable R_pLocal variable L_pAnd a temporary storage variable P_p；

Step S1 includes:

s11, respectively setting the transmission variable S of the node p_pReceiving variable R_pLocal variable L_pAnd a temporary storage variable P_pIs initialized to S_p[0]、R_p[0]、P_p[0]、L_p[0]The initialization formula is as follows:

wherein the content of the first and second substances,the empty set is represented by the number of empty sets,← denotes assignment calculation;

s12, recording the data received by the node p in the k step as R_p[k]Is provided with

Wherein if there is a directed edge (p, q) in the communication topology, T_p,qIs the complete set, otherwise T_p,qIs an empty set; n is a radical of_pRepresenting an introspection neighbor set of a node p, n represents intersection operation, and u represents union operation;

s13, the data R received by the node p in the k step_p[k]Recording the processed variable P to a temporary storage variable P_pTo obtain P_p[k]The processing formula is as follows:

wherein (β, α) represents the set R_p[k]Any of (1);

s14, according to the received data R_p[k]Updating local variable of node p to L_p[k]The update formula is as follows:

L_p[k]←L_p[k-1]∪P_p[k]

s15, recording the data sent by the node p in the k step as S_p[k]Then, then

S_p[k]←P_p[k]；

S16, if k is less than r, jumping to S12, otherwise executing S2, wherein r is the radius of the strong communication graph.

Preferably, step S2 includes:

s21, according to the local variable L_p[r]A set I of identifiers of the communication topology is established, wherein,

I←{α|(β,α)∈L_p(r)}

setting matrix D ═ (0)_n×nIs an n multiplied by n dimensional zero matrix, n is the number of elements in the identifier set I;

s22, sorting the identifier set I into a list C based on a sorting algorithm, wherein the length of the list C is n, so that a mapping relation f: C → { 1.. multidot.n } is established, and if so, the mapping relation f: C → { 1.. multidot.n } is provided

f(C_i)＝i

Wherein, C_iIs the ith element in list C;

s23, for node C_iE.g. I, looking for loop O (C)_i) The loop starting point is node C_i(ii) a The operation of recording the list head is H: C → I, then

H([α,C′])＝α

Wherein C' is an arbitrary list, giving O (C)_i) Assigned initial value of [ C']The following relationships are given:

s24, if there is only one (C)_j,H(O(C_i)))∈L_i(r), i.e. node H (O (C)_i) Only one directed edge away from the node, then

If present (C)_j,H(O(C_i)))∈L_i(r) and (C)_j′,H(O(C_i)))∈L_i(r), and C_j≠C_j′I.e. node H (O (C)_i) At least two directed edges away from the node, then the loops are recorded as follows:

s25, if C_j＝C_iAnd if there is C_j′Then C is_j′＝C_iI.e. starting point is C_iEnd point is also C_iThen find out the node C_iAll loops O (C) as starting points_i,..); otherwise, it jumps to step S24.

Preferably, step S3 includes:

s31, establishing a adjacency matrix B of the loop, having

Wherein, for any i, j ∈ { 1., n }, if any, (C)_j,C_i)∈O(C_i,..), let B_i,jIf not, let B_i,j＝0；

S32, if the matrix B is full rank, i.e., rank (B) ═ n, then

D←D+B；

S33, if C_i≠C_nThen, then

C_i←C_i+1

Jumping to step S23, otherwise executing step S34;

s34, obtaining a double random communication matrix D through summation and unity calculation, wherein the calculation formula is as follows:

alternatively, the first and second electrodes may be,

preferably, before step S1, the method further comprises:

and constructing a strong communication graph of the one-way strong communication network to be processed, and acquiring the address information of each node in the strong communication graph.

According to a second aspect of the present invention, the present invention further provides a distributed system for establishing dual random communication matrices in a unidirectional strong connectivity communication network, the system comprising:

the topological structure calculating module is used for calculating the communication topological structure of the communication network according to the strong connection diagram of the unidirectional strong connection communication network;

the loop calculation module is used for calculating all loops in the communication network according to the communication topological structure of the communication network;

and the double-random communication matrix generation module is used for calculating the maximum loop containing all nodes in all loops in the communication network, summing and unitizing the maximum loop to obtain the double-random communication matrix of the unidirectional strong communication network.

Preferably, each node in the communication network is assumed to have a unique identifier, denoted as { p, q }; the identifiers of all nodes have an order relation capable of comparing sizes; the directed edges from the node p to the node q are represented by (p, q), and the transmitting variable S is respectively arranged at the node p_pReceiving variable R_pLocal variable L_pAnd a temporary storage variable P_p；

The calculating the communication topological structure of the communication network according to the strong connection diagram of the unidirectional strong connection communication network comprises the following steps:

s11, respectively setting the transmission variable S of the node p_pReceiving variable R_pLocal variable L_pAnd a temporary storage variable P_pIs initialized to S_p[0]、R_p[0]、P_p[0]、L_p[0]The initialization formula is as follows:

wherein the content of the first and second substances,representing an empty set, and ← representing a valuation operation;

s12, recording the data received by the node p in the k step as R_p[k]Is provided with

Wherein if there is a directed edge (p, q) in the communication topology, T_p,qIs the complete set, otherwise T_p,qIs an empty set; n is a radical of_pRepresenting an introspection neighbor set of a node p, n represents intersection operation, and u represents union operation;

s13, the data R received by the node p in the k step_p[k]Recording the processed variable P to a temporary storage variable P_pTo obtain P_p[k]The processing formula is as follows:

wherein (β, α) represents the set R_p[k]Any of (1);

s14, according to the received data R_p[k]Updating local variable of node p to L_p[k]The update formula is as follows:

L_p[k]←L_p[k-1]∪P_p[k]

s15, recording the data sent by the node p in the k step as S_p[k]Then, then

S_p[k]←P_p[k]；

And S16, if k is less than r, jumping to step S12, otherwise, calculating all loops in the communication network according to the communication topological structure of the communication network, wherein r is the radius of the strong communication graph.

Preferably, the calculating all loops in the communication network according to the communication topology of the communication network includes:

s21, according to the local variable L_p[r]Set of identifiers I for establishing a communication topology, whichIn (1),

I←{α|(β,α)∈L_p(r)}

setting matrix D ═ (0)_n×nIs an n multiplied by n dimensional zero matrix, n is the number of elements in the identifier set I;

s22, sorting the identifier set I into a list C based on a sorting algorithm, wherein the length of the list C is n, so that a mapping relation f: C → { 1.. multidot.n } is established, and if so, the mapping relation f: C → { 1.. multidot.n } is provided

f(C_i)＝i

Wherein, C_iIs the ith element in list C;

s23, for node C_iE.g. I, looking for loop O (C)_i) The loop starting point is node C_i(ii) a The operation of recording the list head is H: C → I, then

H([α,C′])＝α

Wherein C' is an arbitrary list, giving O (C)_i) Assigned initial value of [ C']The following relationships are given:

s24, if there is only one (C)_j,H(O(C_i)))∈L_i(r), i.e. node H (O (C)_i) Only one directed edge away from the node, then

If present (C)_j,H(O(C_i)))∈L_i(r) and (C)_j′,H(O(C_i)))∈L_i(r), and C_j≠C_j′I.e. node H (O (C)_i) At least two directed edges away from the node, then the loops are recorded as follows:

s25, if C_j＝C_iAnd if there is C_j′Then C is_j′＝C_iI.e. starting point is C_iEnd point is also C_iThen find out the node C_iAll loops O (C) as starting points_i,..); otherwise, it jumps to step S24.

Preferably, the calculating, summing, and unitizing the maximum loops including all nodes in all loops in the communication network to obtain the dual random communication matrix of the unidirectional strong connectivity communication network includes:

s31, establishing a adjacency matrix B of the loop, having

Wherein, for any i, j ∈ { 1., n }, if any, (C)_j,C_i)∈O(C_i,..), let B_i,jIf not, let B_i,j＝0；

S32, if the matrix B is full rank, i.e., rank (B) ═ n, then

D←D+B；

S33, if C_i≠C_nThen, then

C_i←C_i+1

Jumping to step S23, otherwise executing step S34;

s34, obtaining a double random communication matrix D through summation and unity calculation, wherein the calculation formula is as follows:

alternatively, the first and second electrodes may be,

preferably, the distributed system for establishing the dual random communication matrix in the unidirectional strong connectivity communication network further comprises:

a strong connection graph constructing module, configured to construct a strong connection graph of the one-way strong connection communication network to be processed before calculating a communication topology structure of the communication network according to the strong connection graph of the one-way strong connection communication network, and obtain address information of each node in the strong connection graph

According to the scheme, the invention provides a distributed method and a system for establishing a double random communication matrix in a unidirectional strong communication network, wherein the method comprises the steps of calculating a communication topological structure of the communication network according to a strong communication diagram of the unidirectional strong communication network; calculating all loops in the communication network according to the communication topological structure of the communication network; and calculating the maximum loop including all nodes in all loops in the communication network, summing and unitizing to obtain the double random communication matrix of the unidirectional strong communication network. The invention can establish the double random communication matrix for the communication network only by providing the strongly-connected communication network with the largest ring without other conditions, and compared with other methods for double randomization of the communication network, the method has better applicability and wider application range. The problem that the method for setting the weight of the received data and the weight of the sent data to enable the communication network to be double-randomized in the prior art is poor in applicability in practical application is effectively solved. The method and the system are also suitable for establishing the dual random communication matrix in the unidirectional strong communication balanced communication network and the bidirectional communication network. The present invention may be applied to the junior stages of multi-agent system deployment.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a flow chart of a distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to a preferred embodiment of the present invention;

FIG. 2 is a flow diagram of a distributed method for establishing dual random communication matrices in a unidirectional strongly connected communication network in accordance with an embodiment of the present invention;

fig. 3 is a schematic structural diagram of a distributed system for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to a preferred embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Interpretation of terms:

multi-agent system: the system is a group system which is composed of a plurality of autonomous individuals and has certain overall functions through mutual information communication and interaction among the individuals.

Unidirectional strongly connected communication network: the topological graph representing the communication relationship among the nodes in the network is a one-way and strongly connected communication network.

Unidirectional strong connectivity balanced communication network: the topological graph representing the communication relationship among the nodes in the network is a one-way, strongly connected and balanced communication network. Wherein balance means that the out-degree and the in-degree of each node are equal.

Two-way connectivity communication network: the topology graph representing the communication relationships between nodes in the network is a bi-directional and connected communication network (also called a non-directional communication network).

Dual random communication matrices: the adjacent matrix corresponding to the communication topological graph is a double random matrix, namely each element in the matrix is a real number not less than zero, and the summation of each row and each column is 1.

Strong connection diagram: in the directed graph, if there is a path from v1 to v2 and from v2 to v1 for each pair of vertices v1 and v2, the graph is said to be a strong-connected graph.

Communication topology: the communication topology refers to a topological graph representing communication relationships among nodes in a communication network.

Loop circuit: end-to-end paths in the topology map.

Maximum loop: the loops of the topological graph comprise the loops with the most nodes.

As shown in fig. 1, which is a flowchart of a distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to a preferred embodiment of the present invention, the method may include the following steps:

s101, calculating a communication topological structure of the communication network according to a strong communication graph of the unidirectional strong communication network;

in the initial stage of multi-agent system deployment, the dual random communication matrix is not naturally existed, so that the dual random communication matrix needs to be established in the unidirectional strong connection communication network. Firstly, a communication topology structure of a communication network needs to be found according to a pre-constructed strong connection diagram of a to-be-processed unidirectional strong connection communication network, which is a basis for finding a loop in the communication network. Specifically, each node may find the communication topology of the communication network in an iterative manner by using a distributed method.

S102, calculating all loops in the communication network according to the communication topological structure of the communication network;

after finding the communication topology of the communication network, all loops in the communication network need to be found according to the obtained communication topology. In particular, all loops in the communication network can be found in an iterative manner.

And S103, calculating the maximum loop including all nodes in all loops in the communication network, summing and unitizing to obtain a double random communication matrix of the unidirectional strong communication network.

And finally, finding out the maximum loop containing all nodes from all loops according to all loops in the communication network, and calculating according to the maximum loop by a summing and unitizing method to obtain the double random communication matrix of the unidirectional strong communication network.

In summary, in the present embodiment, a distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network is provided, where a communication topology of the communication network is first calculated according to a strong connectivity graph of the unidirectional strong connectivity communication network; then calculating all loops in the communication network according to the communication topological structure of the communication network; and finally, calculating the maximum loop including all the nodes in all the loops in the communication network, summing and unitizing to obtain the double random communication matrix of the unidirectional strong communication network. The invention can establish the double random communication matrix for the communication network only by providing the strongly-connected communication network with the largest ring without other conditions, and compared with other methods for double randomization of the communication network, the method has better applicability and wider application range.

In some other embodiments of the present invention, based on the above embodiments, the method may further include the following steps:

before step S101, a strong connectivity graph of a to-be-processed unidirectional strong connectivity communication network is constructed, and address information of each node in the strong connectivity graph is acquired.

The following describes in detail a distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to the present invention with specific examples:

fig. 2 is a flow chart of a distributed method for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to an embodiment of the present invention. Assuming that each node in the communication network has a unique identifier, denoted as p, q, the identifier of the node may specifically be a MAC address orIP address and other parameters capable of representing node identity uniqueness; the identifiers of all nodes have strict order relation, namely the identifiers of all nodes can be compared in size; the directed edges from the node p to the node q are represented by (p, q), and the transmitting variable S is respectively arranged at the node p_p(Send), receiving variable R_p(Receive), local variable L_p(Local) and a scratch variable P_p(Provisional). The specific steps for establishing the dual random communication matrix are as follows:

step 1, respectively transmitting variable S of node p_pReceiving variable R_pLocal variable L_pAnd a temporary storage variable P_pIs initialized to S_p[0]、R_p[0]、P_p[0]、L_p[0]The initialization formula is as follows:

wherein the content of the first and second substances,representing an empty set, and ← representing a valuation operation;

step 2, recording the data received by the node p in the k step as R_p[k]Is provided with

R_p[k]←∪_q∈Nq(T_p,q∩S_q[k])

Wherein if there is a directed edge (p, q) in the communication topology, T_p,qIs the complete set, otherwise T_p,qIs an empty collector；N_pRepresenting an introspection neighbor set of a node p, n represents intersection operation, and u represents union operation;

step 3, receiving the data R received by the node p in the k step_p[k]Recording the processed variable P to a temporary storage variable P_pTo obtain P_p[k]The processing formula is as follows:

wherein (β, α) represents the set R_p[k]Any of (1);

step 4, according to the received data R_p[k]Updating local variable of node p to L_p[k]The update formula is as follows:

L_p[k]←L_p[k-1]∪P_p[k]

step 5, recording the data sent by the node p in the k step as S_p[k]Then, then

S_p[k]←P_p[k]；

Step 6, if k is less than r, jumping to step S12, otherwise, executing step 7, wherein r is the radius of the strong communication graph;

step 7, according to the local variable L_p[r]A set I of identifiers of the communication topology is established, wherein,

I←{α|(β,α)∈L_p(r)}

setting matrix D ═ (0)_n×nIs an n multiplied by n dimensional zero matrix, n is the number of elements in the identifier set I;

step 8, sorting the identifier set I into a list C based on a sorting algorithm, wherein the length of the list C is n, so that a mapping relation f: C → { 1.. multidot.n } is established, and then

f(C_i)＝i

Wherein, C_iIs the ith element in list C;

step 9, to node C_iE.g. I, looking for loop O (C)_i) The loop starting point is node C_i(ii) a The operation of recording the list head is H: C → I, then

H([α,C′])＝α

Wherein C' is an arbitrary list, giving O (C)_i) Assigned initial value of [ C']The following relationships are given:

step 10, if there is a unique (C)_j,H(O(C_i)))∈L_i(r), i.e. node H (O (C)_i) Only one directed edge away from the node, then

If present (C)_j,H(O(C_i)))∈L_i(r) and (C)_j′,H(O(C_i)))∈L_i(r), and C_j≠C_j′I.e. node H (O (C)_i) At least two directed edges away from the node, then the loops are recorded as follows:

step 11, if C_j＝C_iAnd if there is C_j′Then C is_j′＝C_iI.e. starting point is C_iEnd point is also C_iThen find out the node C_iAll loops O (C) as starting points_i,..); otherwise, jumping to step 10;

step 12, establishing a adjacency matrix B of the loop, having

Wherein, for any i, j ∈ { 1., n }, if any, (C)_j,C_i)∈O(C_i,..), let B_i,jIf not, let B_i,j＝0；

Step 13, if the matrix B is full rank, i.e., rank (B) ═ n, then

D←D+B；

Step 14, if C_i≠C_nThen, then

C_i←C_i+1

Jumping to step 9, otherwise executing step 15;

step 15, obtaining a dual random communication matrix D through summation and unitized calculation, wherein the calculation formula is as follows:

alternatively, the first and second electrodes may be,

as shown in fig. 3, a schematic structural diagram of a distributed system for establishing a dual random communication matrix in a unidirectional strong connectivity communication network according to a preferred embodiment of the present invention, the system may include:

a topology calculation module 201, configured to calculate a communication topology of a communication network according to a strong connectivity graph of a unidirectional strong connectivity communication network;

in the initial stage of multi-agent system deployment, the dual random communication matrix is not naturally existed, so that the dual random communication matrix needs to be established in the unidirectional strong connection communication network. Firstly, a communication topology structure of a communication network needs to be found according to a pre-constructed strong connection diagram of a to-be-processed unidirectional strong connection communication network, which is a basis for finding a loop in the communication network. Specifically, each node may find the communication topology of the communication network in an iterative manner by using a distributed method.

A loop calculation module 202, configured to calculate all loops in the communication network according to a communication topology of the communication network;

after finding the communication topology of the communication network, all loops in the communication network need to be found according to the obtained communication topology. In particular, all loops in the communication network can be found in an iterative manner.

And the double-random communication matrix generation module 203 is configured to calculate a maximum loop including all nodes in all loops in the communication network, sum and unitize the maximum loop to obtain a double-random communication matrix of the unidirectional strong communication network.

And finally, finding out the maximum loop containing all nodes from all loops according to all loops in the communication network, and calculating according to the maximum loop by a summing and unitizing method to obtain the double random communication matrix of the unidirectional strong communication network.

In summary, in the embodiment, a distributed system for establishing a dual random communication matrix in a unidirectional strong connectivity communication network is provided, and a communication topology structure of the communication network is calculated by a topology structure calculation module according to a strong connectivity graph of the unidirectional strong connectivity communication network; calculating all loops in the communication network according to the communication topological structure of the communication network by a loop calculation module; and calculating the maximum loop containing all nodes in all loops in the communication network through a double-random communication matrix generation module, summing and unitizing to obtain the double-random communication matrix of the unidirectional strong communication network. The invention can establish the double random communication matrix for the communication network only by providing the strongly-connected communication network with the largest ring without other conditions, and compared with other methods for double randomization of the communication network, the method has better applicability and wider application range.

In some other embodiments of the present invention, on the basis of the above embodiments, the system may further include:

and the strong connection graph building module is used for building a strong connection graph of the one-way strong connection communication network to be processed before the communication topological structure of the communication network is calculated according to the strong connection graph of the one-way strong connection communication network, and acquiring the address information of each node in the strong connection graph.

The working principle of the distributed system for establishing the dual random communication matrix in the unidirectional strong communication network disclosed in this embodiment is the same as the working principle of the distributed method for establishing the dual random communication matrix in the unidirectional strong communication network, and is not described herein again.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.