阅读说明：本技术 一种用于矿井灾后救援场景的目标定位方法 (Target positioning method for mine post-disaster rescue scene ) 是由 胡青松 张赫男 陈艳 李世银 孙彦景 于 2020-12-29 设计创作，主要内容包括：本发明公开了一种用于矿井灾后救援场景的目标定位方法,先获取待评价节点的邻居节点信息、环境因素、节点剩余能量,然后利用上述数据构建判断矩阵并进行一致性检验,最终获得通过检验的判断矩阵；建立待评价节点的特征矩阵,并计算出规范化特征矩阵,将通过一致性检验的判断矩阵和规范化特征矩阵联合起来构造权重矩阵,进而确定理想解和反理想解,并计算待评价节点分别与理想解和反理想解的欧式距离,利用该距离计算出待评价节点与理想解的贴进度,将贴近度作为待评价节点漂移的可信度,接着选择多个可信度较大的待评价节点作为最终的信标节点,最后根据确定的信标节点采用已知定位算法完成目标定位工作,从而有效保证目标定位的精度。(The invention discloses a target positioning method for a mine post-disaster rescue scene, which comprises the steps of firstly obtaining neighbor node information, environmental factors and node residual energy of a node to be evaluated, then utilizing the data to construct a judgment matrix and carrying out consistency check, and finally obtaining the judgment matrix passing the check; establishing a feature matrix of a node to be evaluated, calculating a normalized feature matrix, combining a judgment matrix passing consistency check with the normalized feature matrix to construct a weight matrix, further determining an ideal solution and an anti-ideal solution, calculating Euclidean distances between the node to be evaluated and the ideal solution and the anti-ideal solution respectively, calculating the closeness between the node to be evaluated and the ideal solution by using the distances, taking the closeness as the reliability of the drift of the node to be evaluated, selecting a plurality of nodes to be evaluated with higher reliability as final beacon nodes, and finally finishing target positioning work by adopting a known positioning algorithm according to the determined beacon nodes, thereby effectively ensuring the precision of target positioning.)

1. A target positioning method for a mine post-disaster rescue scene is characterized by comprising the following specific steps:

the method comprises the steps that firstly, a beacon node which is likely to drift after a mine disaster is used as a node to be evaluated, and neighbor node information, environmental factors and node residual energy of the node to be evaluated are obtained, wherein the neighbor node information comprises distance variation of the neighbor node and the node to be evaluated and the number of the neighbor nodes; the environmental factors include temperature, humidity, and wind speed; the distance variation between the neighbor nodes and the nodes to be evaluated is calculated by a known relative positioning method, the number of the neighbor nodes and the node residual energy are obtained by information exchange between the neighbor nodes, and the environmental factors are obtained by measurement;

step two, according to the data obtained in the step one, a judgment matrix is constructed and consistency inspection is carried out, and finally the judgment matrix passing the inspection is obtained;

and step three, establishing a feature matrix of the node to be evaluated, calculating a normalized feature matrix, combining the judgment matrix and the normalized feature matrix obtained in the step two to construct a weight matrix, further determining an ideal solution and an anti-ideal solution, calculating Euclidean distances between the node to be evaluated and the ideal solution and between the node to be evaluated and the anti-ideal solution, calculating the degree of closeness between the node to be evaluated and the ideal solution by using the obtained Euclidean distances, taking the degree of closeness as the drift credibility of the node to be evaluated, sequencing the nodes to be evaluated from high to low according to the credibility, selecting a plurality of nodes to be evaluated with high credibility as final beacon nodes, and finally completing target positioning work by adopting a known positioning algorithm according to the determined beacon nodes.

2. The target positioning method for the mine post-disaster rescue scene as recited in claim 1, wherein the second step specifically comprises:

firstly, constructing a hierarchical model: the target layer is a reliability judgment matrix of the node to be evaluated; the criterion layer includes neighbor node information B₁Environmental factor B₂Node residual energy B₃(ii) a Factor layer, namely node drift influence factor, including distance variation C between neighbor node and node to be evaluated₁Number of neighbor nodes C₂Temperature C₃Humidity C₄Wind speed C₅And node residual energy C₆A total of 6 items;

secondly, constructing judgment matrixes of all layers: if satisfy a_ij>0，a_ij＝1/a_jiWherein 1 is less than or equal to i, j, k is less than or equal to n, then A is a positive reciprocal matrix, wherein a_ijIs the ith row and the jth column element of the matrix A; satisfies a_ija_jk＝a_ikWherein, the positive and reciprocal matrix A with 1 being equal to or less than i, j, k being equal to or less than n is called as a consistency matrix; firstly, determining the importance of various factors in each layer, comparing the factors with each other according to the table 1 to determine the weight, and constructing B₁、B₂、B₃Credibility judgment matrix P of nodes to be evaluated₁，C₁、C₂To B₁Is determined by the matrix P₂，C₃、C₄、C₅To B₂Is determined by the matrix P₃，C₆To B₃Is determined by the matrix P₄(ii) a Wherein, P₁、P₂、P₃And P₄Is matrix A ═ a_ij]Concretization of (1);

TABLE 1 judge matrix Scale

③ to judge the matrix P₁And P₃And (3) carrying out consistency check: determining matrix consistency by a consistency ratio C_RThe characterization method comprises the following calculation method shown in formula (1):

wherein C is_IIs an index of the degree of inconsistency; gamma is an average random consistency index, see table 2; lambda [ alpha ]_maxJudging the maximum eigenvalue of the matrix; lambda is the order of the judgment matrix; if C is present_R<0.1, the matrix is considered to meet the requirement of consistency; otherwise, the matrix does not meet the consistency requirement, the weight of each factor needs to be determined again, and the judgment matrix is constructed again;

fourthly, calculating the single-level weight: p₄Only one factor is contained, and the weight is 1; calculating satisfies P_xW＝λ_max1Taking the characteristic root and the characteristic vector of W, wherein x is 1,2 or 3, and taking a corresponding value if a certain matrix is calculated; lambda [ alpha ]_max1Is a matrix P₁W is λ_max1The component of W is the single-level weight of the corresponding element;

TABLE 2 average random consistency index

Fifthly, total hierarchical weight: all factors C₁～C₆The total weight N of the factor is obtained by multiplying the weight occupied by the factor in the criterion layer by the weight of the criterion layer in the target layer; the total weight occupied by 6 factors is recorded as N ═ N₁,n₂,n₃,n₄,n₅,n₆]The subscript of n represents the serial number of the node reliability influence factor and has the same meaning as the subscript of C; and N is a matrix with 6 rows and 1 column, so that the construction of the judgment matrix is completed.

3. The target positioning method for the mine post-disaster rescue scene as recited in claim 1, wherein the third step specifically comprises:

I. assuming that n nodes are provided, and each node has 6 credibility influence factors, then a characteristic matrix D can be established by using the formula (2);

wherein i 1, 2.. multidot.m, j 1, 2.. multidot.n; i represents the ith influence factor, and is the same as the influence factor of the analytic hierarchy process; j represents the jth node;

II, calculating a normalized vector r by using the formula (3)_ijForming a normalized matrix R as shown in formula (4);

III, constructing a weight normalization matrix V by using the formula (5), wherein the weight normalization value is V_ij；

IV, determining a positive ideal solution by using the formula (6), wherein the positive ideal solution of the jth node isThe negative ideal solution is determined by equation (7), and the negative ideal solution of the jth node is

V, calculating a distance scale: calculating n-dimensional Euclidean distance S from the node to be evaluated to the ideal solution and the anti-ideal solution by the formula (8) and the formula (9)⁺And S^-；

VI, calculating the closeness of the ideal solution by using the formula (10)And is

And taking the closeness as the drift credibility of the nodes to be evaluated, sorting the nodes to be evaluated from high to low according to the credibility, selecting a plurality of nodes to be evaluated with high credibility as final beacon nodes, and finally finishing target positioning work by adopting a known positioning algorithm according to the determined beacon nodes.

Technical Field

The invention relates to a target positioning method, in particular to a target positioning method for a mine post-disaster rescue scene.

Background

Coal is the main energy in China, and coal mining is threatened by accidents such as gas and roof for a long time. After a coal mine accident occurs, the accurate determination of the position of the trapped person is the key for improving the emergency response speed and the rescue effect. Only by determining the accurate position of the trapped person, the rescue work can be purposefully carried out. The mine target positioning generally adopts a positioning method based on ranging, and the positioning method uses beacon nodes distributed at various known positions of a mine as references to determine the positions of target nodes. However, a coal mine accident may cause part of the beacon nodes to be damaged, and the rest of the beacon nodes may deviate from the original position along with the movement of the coal rock mass, which is referred to as beacon drift.

Once beacon drift occurs, the position of the beacon node becomes unknown, thereby causing the mine positioning system to lose a reference position from which to rely on positioning. If the beacon position before the drift is still used for positioning, the positioning error is inevitably increased or even completely wrong. For convenience, the target location after the beacon drift occurs is called relocation, so in order to ensure the target location accuracy after the mine disaster, it is a research direction of the industry how to select a node without drift or with small drift amount as a new beacon node, that is, how to determine a relocation beacon node.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides the target positioning method for the mine post-disaster rescue scene, which can determine that no node drifts or the drift amount is as small as possible after the mine disaster, and uses the node as a new beacon node to perform subsequent target positioning, thereby effectively ensuring the target positioning precision.

In order to achieve the purpose, the invention adopts the technical scheme that: a target positioning method for a mine post-disaster rescue scene comprises the following specific steps:

the method comprises the steps that firstly, a beacon node which is likely to drift after a mine disaster is used as a node to be evaluated, and neighbor node information, environmental factors and node residual energy of the node to be evaluated are obtained, wherein the neighbor node information comprises distance variation of the neighbor node and the node to be evaluated and the number of the neighbor nodes; the environmental factors include temperature, humidity, and wind speed; the distance variation between the neighbor nodes and the nodes to be evaluated is calculated by a known relative positioning method, the number of the neighbor nodes and the node residual energy are obtained by information exchange between the neighbor nodes, and the environmental factors are obtained by measurement;

step two, according to the data obtained in the step one, a judgment matrix is constructed and consistency inspection is carried out, and finally the judgment matrix passing the inspection is obtained;

and step three, establishing a feature matrix of the node to be evaluated, calculating a normalized feature matrix, combining the judgment matrix and the normalized feature matrix obtained in the step two to construct a weight matrix, further determining an ideal solution and an anti-ideal solution, calculating Euclidean distances between the node to be evaluated and the ideal solution and between the node to be evaluated and the anti-ideal solution, calculating the degree of closeness between the node to be evaluated and the ideal solution by using the obtained Euclidean distances, taking the degree of closeness as the drift credibility of the node to be evaluated, sequencing the nodes to be evaluated from high to low according to the credibility, selecting a plurality of nodes to be evaluated with high credibility as final beacon nodes, and finally completing target positioning work by adopting a known positioning algorithm according to the determined beacon nodes. .

Further, the second step is specifically:

firstly, constructing a hierarchical model: the target layer is a reliability judgment matrix of the node to be evaluated; the criterion layer includes neighbor node information B₁Environmental factor B₂Node residual energy B₃(ii) a Factor layer, namely node drift influence factor, including distance variation C between neighbor node and node to be evaluated₁Number of neighbor nodes C₂Temperature C₃Humidity C₄Wind speed C₅And node residual energy C₆A total of 6 items;

secondly, constructing judgment matrixes of all layers: if satisfy a_ij>0，a_ij＝1/a_jiWherein 1 is less than or equal to i, j, k is less than or equal to n, then A is a positive reciprocal matrix, wherein a_ijIs the ith row and the jth column element of the matrix A; satisfies a_ija_jk＝a_ikWherein, the positive and reciprocal matrix A with 1 being equal to or less than i, j, k being equal to or less than n is called as a consistency matrix; firstly, determining the importance of various factors in each layer, comparing the factors with each other according to the table 1 to determine the weight, and constructing B₁、B₂、B₃Credibility judgment matrix P of nodes to be evaluated₁，C₁、C₂To B₁Is determined by the matrix P₂，C₃、C₄、C₅To B₂Is determined by the matrix P₃，C₆To B₃Is determined by the matrix P₄(ii) a Wherein, P₁、P₂、P₃And P₄Is matrix A ═ a_ij]Concretization of (1);

TABLE 1 judge matrix Scale

③ to judge the matrix P₁And P₃And (3) carrying out consistency check: the judgment matrix with the order of 3 or more does not strictly meet the requirement of complete consistency, and the more serious the inconsistency is, the larger the judgment error is. However, the judgment matrix is difficult to completely meet the consistency, so that the judgment is performed by setting a specific consistency index, when the consistency of the judgment matrix meets the index requirement, the judgment matrix meeting the consistency is considered to meet the requirement, and the judgment matrix meeting the consistency is called as a consistency matrix. Determining matrix consistency by a consistency ratio C_RThe characterization method comprises the following calculation method shown in formula (1):

wherein C is_IIs an index of the degree of inconsistency; gamma is an average random consistency index, see table 2; lambda [ alpha ]_maxJudging the maximum eigenvalue of the matrix; lambda is the order of the judgment matrix; if C is present_R<0.1, the matrix is considered to meet the requirement of consistency; otherwise, the matrix does not meet the consistency requirement, the weight of each factor needs to be determined again, and the judgment matrix is constructed again;

fourthly, calculating the single-level weight: p₄Only one factor is contained, and the weight is 1; calculating satisfies P_xW＝λ_max1Taking the characteristic root and the characteristic vector of W, wherein x is 1,2 or 3, and taking a corresponding value if a certain matrix is calculated; lambda [ alpha ]_max1Is a matrix P₁W is λ_max1The component of W is the single-level weight of the corresponding element;

TABLE 2 average random consistency index

Fifthly, total hierarchical weight: all factors C₁～C₆The total weight N of the factor is obtained by multiplying the weight occupied by the factor in the criterion layer by the weight of the criterion layer in the target layer; for example, if the weight occupied by the distance variation of the neighboring node in the neighboring node information is w11, and the weight occupied by the distance variation of the neighboring node in the node reliability determination matrix is w1, the weight occupied by the distance variation of the neighboring node in the total rank is n₁W11 w 1. The total weight occupied by 6 factors is recorded as N ═ N₁,n₂,n₃,n₄,n₅,n₆]The subscript of n represents the serial number of the node reliability influence factor and has the same meaning as the subscript of C; and N is a matrix with 6 rows and 1 column, so that the construction of the judgment matrix is completed.

Further, the third step is specifically:

I. assuming that n nodes are provided, and each node has 6 credibility influence factors, then a characteristic matrix D can be established by using the formula (2);

wherein i 1, 2.. multidot.m, j 1, 2.. multidot.n; i represents the ith influence factor, and is the same as the influence factor of the analytic hierarchy process; j represents the jth node;

II, calculating a normalized vector r by using the formula (3)_ijComposition normalizationThe matrix R is shown as a formula (4);

III, constructing a weight normalization matrix V by using the formula (5), wherein the weight normalization value is V_ij；

IV, determining a positive ideal solution by using the formula (6), wherein the positive ideal solution of the jth node isThe negative ideal solution is determined by equation (7), and the negative ideal solution of the jth node is

V, calculating a distance scale: calculating n-dimensional Euclidean distance S from the node to be evaluated to the ideal solution and the anti-ideal solution by the formula (8) and the formula (9)⁺And S^-；

VI, calculating the closeness of the ideal solution by using the formula (10)And is

And taking the closeness as the drift credibility of the nodes to be evaluated, sorting the nodes to be evaluated from high to low according to the credibility, selecting a plurality of nodes to be evaluated with high credibility as final beacon nodes, and finally finishing target positioning work by adopting a known positioning algorithm according to the determined beacon nodes.

Compared with the prior art, the method comprises the steps of firstly obtaining neighbor node information, environmental factors and node residual energy of a node to be evaluated, then utilizing the data to construct a judgment matrix and carrying out consistency inspection, and finally obtaining the judgment matrix passing the inspection; then, establishing a feature matrix of the node to be evaluated, calculating a normalized feature matrix, combining a judgment matrix passing consistency check with the normalized feature matrix to construct a weight matrix, further determining an ideal solution and an anti-ideal solution, calculating Euclidean distances between the node to be evaluated and the ideal solution and the anti-ideal solution respectively, calculating the approach degree between the node to be evaluated and the ideal solution by using the distances, taking the approach degree as the drift credibility of the node to be evaluated, sequencing the nodes to be evaluated from large to small according to the credibility, selecting a plurality of nodes to be evaluated with high credibility as final beacon nodes, and finally finishing target positioning work by adopting a known positioning algorithm according to the determined beacon nodes. Therefore, the invention can determine that no node drifts or the drift amount is as small as possible after the mine disaster, and the node is used as a new beacon node for subsequent target positioning, thereby effectively ensuring the precision of target positioning.

Drawings

FIG. 1 is an overall flow diagram of the present invention;

FIG. 2 is a schematic diagram of a hierarchical analysis model of the decision matrix according to the present invention.

Detailed Description

The present invention will be further explained below.

As shown in fig. 1, the method comprises the following specific steps:

the method comprises the steps that firstly, a beacon node which is likely to drift after a mine disaster is used as a node to be evaluated, and neighbor node information, environmental factors and node residual energy of the node to be evaluated are obtained, wherein the neighbor node information comprises distance variation of the neighbor node and the node to be evaluated and the number of the neighbor nodes; the environmental factors include temperature, humidity, and wind speed; the distance variation between the neighbor nodes and the nodes to be evaluated is calculated by a known relative positioning method, the number of the neighbor nodes and the node residual energy are obtained by information exchange between the neighbor nodes, and the environmental factors are obtained by measurement;

step two, according to the data obtained in the step one, a judgment matrix is constructed and consistency inspection is carried out, and finally the judgment matrix passing the inspection is obtained;

and step three, establishing a feature matrix of the node to be evaluated, calculating a normalized feature matrix, combining the judgment matrix and the normalized feature matrix obtained in the step two to construct a weight matrix, further determining an ideal solution and an anti-ideal solution, calculating Euclidean distances between the node to be evaluated and the ideal solution and between the node to be evaluated and the anti-ideal solution, calculating the degree of closeness between the node to be evaluated and the ideal solution by using the obtained Euclidean distances, taking the degree of closeness as the drift credibility of the node to be evaluated, sequencing the nodes to be evaluated from high to low according to the credibility, selecting a plurality of nodes to be evaluated with high credibility as final beacon nodes, and finally completing target positioning work by adopting a known positioning algorithm according to the determined beacon nodes. .

Further, the second step is specifically:

firstly, constructing a hierarchical model: the hierarchical analysis model comprises a target layer, a criterion layer and a factor layer, wherein the target layer is to be analyzedEvaluating a reliability judgment matrix of the nodes; the criterion layer includes neighbor node information B₁Environmental factor B₂Node residual energy B₃(ii) a Factor layer, namely node drift influence factor, including distance variation C between neighbor node and node to be evaluated₁Number of neighbor nodes C₂Temperature C₃Humidity C₄Wind speed C₅And node residual energy C₆A total of 6 entries, as shown in FIG. 2;

secondly, constructing judgment matrixes of all layers: if satisfy a_ij>0，a_ij＝1/a_jiWherein 1 is less than or equal to i, j, k is less than or equal to n, then A is a positive reciprocal matrix, wherein a_ijIs the ith row and the jth column element of the matrix A; satisfies a_ija_jk＝a_ikWherein, the positive and reciprocal matrix A with 1 being equal to or less than i, j, k being equal to or less than n is called as a consistency matrix; firstly, determining the importance of various factors in each layer, comparing the factors with each other according to the table 1 to determine the weight, and constructing B₁、B₂、B₃Credibility judgment matrix P of nodes to be evaluated₁，C₁、C₂To B₁Is determined by the matrix P₂，C₃、C₄、C₅To B₂Is determined by the matrix P₃，C₆To B₃Is determined by the matrix P₄(ii) a Wherein, P₁、P₂、P₃And P₄Is matrix A ═ a_ij]Concretization of (1);

TABLE 3 judge matrix Scale

Firstly, constructing an upper triangular matrix, comparing the ith factor with the jth factor, obtaining the proportion of the ith factor and the jth factor according to the table 1, and taking the proportion as an element a of the ith row and the jth column of the judgment matrix_ijIt is obvious that the diagonal element a_iiAll are 1. And then, obtaining the values of the rest elements of the matrix from the upper triangular matrix according to the positive and negative matrix rule. According to the method, the 4 judgment matrixes P are constructed in sequence₁、P₂、P₃And P₄. Wherein P is₄Since it contains only one element, its weight is 1.

③ to judge the matrix P₁And P₃And (3) carrying out consistency check: the judgment matrix with the order of 3 or more does not strictly meet the requirement of complete consistency, and the more serious the inconsistency is, the larger the judgment error is. However, the judgment matrix is difficult to completely meet the consistency, so that the judgment is performed by setting a specific consistency index, when the consistency of the judgment matrix meets the index requirement, the judgment matrix meeting the consistency is considered to meet the requirement, and the judgment matrix meeting the consistency is called as a consistency matrix. Determining matrix consistency by a consistency ratio C_RThe characterization method comprises the following calculation method shown in formula (1):

wherein C is_IIs an index of the degree of inconsistency; gamma is an average random consistency index, see table 2; lambda [ alpha ]_maxJudging the maximum eigenvalue of the matrix; lambda is the order of the judgment matrix; if C is present_R<0.1, the matrix is considered to meet the requirement of consistency; otherwise, the matrix does not meet the consistency requirement, the weight of each factor needs to be determined again, and the judgment matrix is constructed again;

fourthly, calculating the single-level weight: p₄Only one factor is contained, and the weight is 1; calculating satisfies P_xW＝λ_max1Taking the characteristic root and the characteristic vector of W, wherein x is 1,2 or 3, and taking a corresponding value if a certain matrix is calculated; lambda [ alpha ]_max1Is a matrix P₁W is λ_max1The component of W is the single-level weight of the corresponding element;

TABLE 4 average random consistency index

Fifthly, total hierarchical weight: all factors C₁～C₆Is multiplied by the weight of the factor in the criterion layerObtaining the weight of the target layer; for example, if the weight occupied by the distance variation of the neighboring node in the neighboring node information is w11, and the weight occupied by the distance variation of the neighboring node in the node reliability determination matrix is w1, the weight occupied by the distance variation of the neighboring node in the total rank is n₁W11 w 1. The total weight occupied by 6 factors is recorded as N ═ N₁,n₂,n₃,n₄,n₅,n₆]The subscript of n represents the serial number of the node reliability influence factor and has the same meaning as the subscript of C; and N is a matrix with 6 rows and 1 column, so that the construction of the judgment matrix is completed.

Further, the third step is specifically:

I. assuming that n nodes are provided, and each node has 6 credibility influence factors, then a characteristic matrix D can be established by using the formula (2);

wherein i 1, 2.. multidot.m, j 1, 2.. multidot.n; i represents the ith influence factor, and is the same as the influence factor of the analytic hierarchy process; j represents the jth node;

II, calculating a normalized vector r by using the formula (3)_ijForming a normalized matrix R as shown in formula (4);

III, constructing a weight normalization matrix V by using the formula (5), wherein the weight normalization value is V_ij；

IV, determining the positive ideal solution, j node by using the formula (6)Definitely ideal solution isThe negative ideal solution is determined by equation (7), and the negative ideal solution of the jth node is

V, calculating a distance scale: calculating n-dimensional Euclidean distance S from the node to be evaluated to the ideal solution and the anti-ideal solution by the formula (8) and the formula (9)⁺And S^-；

VI, calculating the closeness of the ideal solution by using the formula (10)And is

Because the idealized beacons have two, one is a positive ideal beacon or optimal beacon and the other is a negative ideal beacon or worst beacon. The beacon node with the highest credibility is closest to the optimal beacon node and farthest from the worst beacon node; therefore, the closeness is used as the reliability of the drifting of the nodes to be evaluated, the nodes to be evaluated are sorted from high to low according to the reliability, then a plurality of nodes to be evaluated with high reliability are selected as final beacon nodes, and finally target positioning work is completed according to the determined beacon nodes by adopting a known positioning algorithm.