CN112529407B - Multi-dimensional quantitative dynamic deduction evaluation method for power grid emergency - Google Patents
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Abstract
The invention provides a multidimensional quantitative dynamic deduction evaluation method for a power grid emergency, and relates to the technical field of power safety. The invention provides a novel method for calculating the influence quantity of index factors by constructing a multidimensional quantization evaluation index plane directed graph of a power grid emergency, acquiring multidimensional quantization indexes and index weights of the power grid emergency by adopting a directed graph and clustering combined algorithm, and utilizing a deduction engine to deduce and display the power grid emergency, so that the accuracy of the power grid emergency index factor on result deduction can be obviously improved.
Description
Technical Field
The invention relates to the technical field of electric power safety, in particular to a multidimensional quantitative dynamic deduction evaluation method for power grid emergency events.
Background
At present, the power system in China is characterized by large-scale extra-high voltage alternating current-direct current hybrid connection and massive access of new energy, power grid emergencies are not effectively eliminated, the related links of the power grid emergencies are many, the generation and evolution mechanisms are complex, and the evolution process has high uncertainty and interlocking dynamics. In order to prevent and effectively deal with the power grid emergency and realize scientific, quick, accurate and efficient emergency disposal capability, a power grid emergency multidimensional quantitative dynamic inversion evaluation technology is provided.
Based on the power grid emergency multi-dimensional quantitative dynamic inversion evaluation technology, intelligent analysis, quantitative inversion and evaluation verification of a coupling-driven evolution process under the power grid emergency situation can be realized, self-diagnosis and dynamic correction of evolution and emergency treatment situation study and judgment can be realized, and loss caused by the power grid emergency is reduced to the minimum degree.
Disclosure of Invention
The purpose of the invention is realized by the following technical scheme.
The power grid emergency multidimensional quantitative dynamic deduction evaluation method comprises the following steps:
step 1: construction of multidimensional quantitative evaluation index plane directed graph of power grid emergency
1) Acquiring a multidimensional quantitative evaluation index of the power grid emergency; the indexes comprise power grid facility site environment indexes, power grid facility indexes, power grid rescue goods and materials indexes, power grid rescue personnel indexes and power grid system emergency capacity indexes.
Further, the grid facility site environment indicators include: temperature index, humidity index, wind index, geological structure index, air visibility index, and the like; the grid facility metrics include: the method comprises the following steps of (1) power grid facility type indexes, power grid facility position indexes, power grid facility quantity indexes, power grid facility height indexes and the like; the power grid rescue goods and materials indexes comprise: the method comprises the following steps of (1) obtaining rescue goods type indexes, rescue goods quantity indexes, rescue position and power grid facility distance indexes and the like; the power grid rescuer indexes comprise: the method comprises the following steps of (1) providing a rescuer type index, a rescuer number index, a rescuer age index, a rescuer scheduling index and the like; the power grid system emergency capacity index comprises: the system communication line number index, the system communication speed index, the system storage capacity index, the system maximum throughput index and the like;
2) Obtaining the corresponding relation between the indexes and the emergency evolution result in the historical emergency evolution process of the power grid
Further, the corresponding relation between the index and the emergency evolution result in the historical emergency evolution process of the power grid is pre-stored data;
wherein E (t) represents the evolution result of the emergency event along with the time t, f (u) i T) represents the index u i Influence vector of time t on grid installation, G (u) i ,u j T) represents the index u j T versus index u over time i The influence coefficient of (a); n represents the number of indices.
3) Dynamic adjustment of f (u) i T) vector value, calculating different f (u) i T) the standard deviation of E (t) under the vector value is taken, the standard deviation of E (t) is compared with a preset grading threshold value, and an index u is obtained i Rank value u in terms of importance i ， y ；
Dynamic adjustment of G (u) i ,u j T) coefficient value taking, calculating different G (u) i ,u j T) standard deviation of E (t) under coefficient value, comparing the standard deviation of E (t) with a preset grading threshold value to obtain an index u j For the index u 1 To u j-1 ，u j-1 To u n Influence vector X of j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) Wherein x is j,j+1 Representative index u j For the index u j+1 The influence value of (d);
4) Obtaining an index u i Is normalized, and an index u is calculated i Normalized valueu i，x ；
5) U in index i，x Value as x-axis coordinate, in the index u i，y The value is taken as a y-axis coordinate and is (u) i，x ,u i，y ) Projected into a rectangular plane coordinate system to form an index u i The discrete point group of (2); by vector X j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) Each index u is constructed with (j =1.. N) as an edge i A bidirectional directed graph of (i =1.. N);
step 2: obtaining the multidimensional quantitative evaluation index and index weight of the power grid emergency
6) Acquiring a multi-dimensional quantization index of the power grid emergency, and searching an index discrete point in the plane index coordinate system;
7) Clustering discrete point groups searched in a plane rectangular coordinate system, and calculating a clustering center to obtain each clustering center point (u) c，x ,u c，y ) (ii) a In each clustering region range, calculating discrete points (u) in the clustering region range i，x ,u i，y ) To the center of the cluster (u) c，x ,u c，y ) Of Euclidean distance d (c,i) Comparing the Euclidean distance with a preset distance grade threshold value to obtain an index weight lambda (i，1) ；
8) Calculating index weight lambda of each discrete point according to the bidirectional directed graph (i，2) ；
Wherein, κ 1 And kappa 2 Are respectively a weighting coefficient; kappa 1 And kappa 2 0.6 and 0.4 can be taken respectively;
9) Obtaining an index u i Weight value of (2) < lambda > i ；
λ i ＝λ (i，1) +λ (i，2) ；
And step 3: controlling the dynamic deduction engine to obtain the deduction result and display it
1) Performing deduction engine input on indexes and corresponding weights corresponding to the index discrete points searched in the plane rectangular coordinate system;
2) Controlling a deduction engine to calculate and form a deduction simulation result;
J(w 1 ,w 2 ...w m )＝F(u i ,λ i )
wherein J (w) 1 ,w 2 ...w m ) Representing deduction, e.g. w 1 Representing 6 levels of wind; w is a 1 Representing a shortage of line maintenance personnel.
Furthermore, the deduction engine provides a manual control interface, and can manually control starting, pausing and ending;
3) The deduction engine sets a modification interface, and a user can manually add or modify the index u i And λ i The system adjusts u according to the user in real time i And λ i Displaying a deduction result;
4) And storing the deduction process and the result of the dynamic deduction engine into a power grid historical emergency evolution process database.
The invention has the technical effects that:
1) Index factors of various power grid emergencies are comprehensively calculated, a novel index factor influence quantity calculation method is provided, and the accuracy of deduction of results by the index factors of the power grid emergencies can be remarkably improved;
2) The numerical quantity of each index, the influence among the indexes and the influence of each index on the emergency result are comprehensively considered, index design and weight calculation are carried out, and the accuracy of the calculation of the influence of index factors is further improved;
3) The deduction engine is constructed, so that a user can dynamically adjust index factors and weights according to actual situation factors, materials can be dynamically modified and allocated, real-time modification of indexes can be performed according to predicted environmental data, the deduction engine performs corresponding real-time dynamic adjustment display, and technical support is provided for decision making of scheduling personnel.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart of a specific example of a multidimensional quantitative dynamic deduction evaluation method for a grid emergency in the implementation 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.
According to the embodiment of the invention, the power grid emergency multidimensional quantitative dynamic deduction evaluation method comprises the following steps:
step 1: construction of multidimensional quantitative evaluation index plane directed graph of power grid emergency
1) Acquiring a multidimensional quantitative evaluation index of the power grid emergency; the indexes comprise a power grid facility site environment index, a power grid facility index, a power grid rescue material index, a power grid rescue personnel index and a power grid system emergency capacity index.
Further, the grid facility site environment indicators include: a temperature index, a humidity index, a wind power index, a geological structure index, an air visibility index; the grid facility metrics include: the type index of the power grid facility, the position index of the power grid facility and the power grid facility; the power grid rescue goods and materials indexes comprise: the method comprises the following steps of (1) obtaining a rescue material type index, a rescue material quantity index and a rescue position and power grid facility distance index; the power grid rescuer indexes comprise: the method comprises the following steps of (1) providing a rescuer type index, a rescuer number index, a rescuer age index and a rescuer scheduling index; the power grid system emergency capacity index comprises: a system communication line number index, a system communication speed index, a system storage capacity index and a system;
2) Obtaining the corresponding relation between the indexes and the evolution results of the sudden events in the historical sudden event evolution process of the power grid
Further, the corresponding relation between the index and the emergency evolution result in the historical emergency evolution process of the power grid is pre-stored data;
wherein E (t) represents the evolution result of the emergency event along with the time t, f (u) i T) represents the index u i Vector of influence of time t on the grid installation, G (u) i ,u j T) represents the index u j T versus index u over time i The influence coefficient of (a); n represents the number of indices.
3) Dynamic adjustment of f (u) i T) vector value, calculating different f (u) i T) the standard deviation of E (t) under the vector value is taken, the standard deviation of E (t) is compared with a preset grading threshold value, and an index u is obtained i Rank value u in terms of importance i ， y ；
Dynamic adjustment of G (u) i ,u j T) coefficient value taking, calculating different G (u) i ,u j And t) taking the value of the standard deviation of the E (t), comparing the standard deviation of the E (t) with a preset grading threshold value, and acquiring an index u j For the index u 1 To u j-1 ，u j-1 To u n Influence vector X of j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) Wherein x is j,j+1 Representative index u j For the index u j+1 The influence value of (d);
4) Obtaining an index u i Is normalized, and an index u is calculated i Normalized value u i ， x ；
5) U in index i，x Value as x-axis coordinate, in the index u i，y The value is taken as a y-axis coordinate and is (u) i，x ,u i，y ) Projected into a rectangular plane coordinate system to form an index u i The discrete point group of (2); in the direction ofQuantity X j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) (j =1.. N) constructing each index u for the edge i A bidirectional directed graph of (i =1.. N);
and 2, step: obtaining the multidimensional quantitative evaluation index and index weight of the power grid emergency
6) Acquiring a multi-dimensional quantization index of the power grid emergency, and searching an index discrete point in the plane index coordinate system;
7) Clustering discrete point groups searched in a plane rectangular coordinate system, and calculating a clustering center to obtain each clustering center point (u) c，x ,u c，y ) (ii) a In each clustering region range, calculating discrete points (u) in the clustering region range i，x ,u i，y ) To the center of the cluster (u) c，x ,u c，y ) Of Euclidean distance d (c,i) Comparing the Euclidean distance with a preset distance grade threshold value to obtain an index weight lambda (i，1) ；
8) Calculating index weight lambda of each discrete point according to the bidirectional directed graph (i，2) ；
Wherein, κ 1 And kappa 2 Are respectively a weighting coefficient; kappa 1 And kappa 2 0.6 and 0.4 can be taken respectively;
4) Obtaining an index u i Weight value of lambda i ；
λ i ＝λ (i，1) +λ (i，2) ；
And step 3: dynamic deduction engine control, obtaining deduction result and displaying
1) Performing deduction engine input on indexes and corresponding weights corresponding to the index discrete points searched in the plane rectangular coordinate system;
2) Controlling a deduction engine to calculate and form a deduction simulation result;
J(w 1 ,w 2 ...w m )＝F(u i ,λ i )
wherein J (w) 1 ,w 2 ...w m ) Representing derived results, e.g. w 1 Representing 6 levels of wind; w is a 1 Representing a shortage of line maintenance personnel.
Furthermore, the deduction engine provides a manual control interface, and can manually control starting, pausing and ending;
3) The deduction engine sets a modification interface, and a user can manually add or modify the index u i And λ i The system adjusts u according to the user in real time i And λ i Displaying a deduction result; if the user dynamically adjusts the number of people for maintaining the line, the deduction engine dynamically adjusts the deduction result for the user to check.
4) And storing the deduction process and the result of the dynamic deduction engine into a power grid historical emergency evolution process database.
The power grid emergency multidimensional quantitative dynamic deduction and evaluation system comprises: the system comprises an index construction module, an index extraction module and a dynamic deduction module. Wherein:
an index construction module: and constructing a multidimensional quantitative evaluation index plane directed graph of the power grid emergency. 1) Acquiring a multidimensional quantitative evaluation index of the power grid emergency; the indexes comprise power grid facility site environment indexes, power grid facility indexes, power grid rescue goods and materials indexes, power grid rescue personnel indexes and power grid system emergency capacity indexes.
Further, the grid facility site environment indicators include: a temperature index, a humidity index, a wind power index, a geological structure index, an air visibility index; the grid facility metrics include: the type index of the power grid facility, the position index of the power grid facility and the power grid facility; the power grid rescue goods and materials indexes comprise: the method comprises the following steps of (1) obtaining a rescue material type index, a rescue material quantity index and a rescue position and power grid facility distance index; the power grid rescuer indexes comprise: the method comprises the following steps of (1) providing a rescuer type index, a rescuer number index, a rescuer age index and a rescuer scheduling index; the power grid system emergency capacity indexes comprise: a system communication line number index, a system communication speed index, a system storage capacity index and a system;
2) Acquiring the corresponding relation between the indexes and the emergency evolution result in the historical emergency evolution process of the power grid,
further, the corresponding relation between the index and the emergency evolution result in the historical emergency evolution process of the power grid is pre-stored data;
wherein E (t) represents the evolution result of the emergency event along with the time t, f (u) i T) represents the index u i Vector of influence of time t on the grid installation, G (u) i ,u j T) represents the index u j T versus index u over time i The influence coefficient of (a); n represents the number of indices.
3) Dynamic adjustment of f (u) i T) vector value, calculating different f (u) i T) the standard deviation of E (t) under the vector value is taken, the standard deviation of E (t) is compared with a preset grading threshold value, and an index u is obtained i Rank value u in terms of importance i ， y ；
Dynamic adjustment of G (u) i ,u j T) coefficient value, calculating different G (u) i ,u j T) standard deviation of E (t) under coefficient value, comparing the standard deviation of E (t) with a preset grading threshold value to obtain an index u j For the index u 1 To u j-1 ，u j-1 To u n Influence vector X of j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) Wherein x is j,j+1 Representative index u j For the index u j+1 The influence value of (c);
4) Obtaining an index u i Is normalized, and the index u is calculated i Normalized value u i，x ；
5) U in index i，x The value is taken as x-axis coordinate and is indicated by the index u i，y The value is taken as a y-axis coordinate and is (u) i，x ,u i，y ) Projected into a rectangular plane coordinate system to form an index u i The discrete point group of (2); by vector X j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) Each index u is constructed with (j =1.. N) as an edge i A bidirectional directed graph of (i =1.. N);
an index extraction module: obtaining multidimensional quantization index and index weight of power grid emergency
6) Acquiring a multi-dimensional quantitative index of the power grid emergency, and searching an index discrete point in the plane index coordinate system;
7) Clustering is carried out aiming at the discrete point groups searched in the plane rectangular coordinate system, and clustering centers are calculated to obtain each clustering center point (u) c，x ,u c，y ) (ii) a In each clustering region range, calculating discrete points (u) in the clustering region range i，x ,u i，y ) To the center of the cluster (u) c，x ,u c，y ) Of Euclidean distance d (c,i) Comparing the Euclidean distance with a preset distance grade threshold value to obtain an index weight lambda (i，1) ；
8) Calculating index weight lambda of each discrete point according to the bidirectional directed graph (i，2) ；
Wherein, κ 1 And kappa 2 Respectively are weighting coefficients; kappa 1 And kappa 2 0.6 and 0.4 can be taken respectively;
4) Obtaining an index u i Weight value of (2) < lambda > i ；
λ i ＝λ (i，1) +λ (i，2) ；
A dynamic deduction module: controlling the dynamic deduction engine to obtain the deduction result and display it
1) Performing deduction engine input on indexes and corresponding weights corresponding to the index discrete points searched in the plane rectangular coordinate system;
2) Controlling a deduction engine to calculate and form a deduction simulation result;
J(w 1 ,w 2 ...w m )＝F(u i ,λ i )
wherein J (w) 1 ,w 2 ...w m ) Representing derived results, e.g. w 1 Representing 6 levels of wind; w is a 1 Representing a shortage of line maintenance personnel.
Furthermore, the deduction engine provides a manual control interface, and can manually control starting, pausing and ending;
3) The deduction engine sets a modification interface, and the user can manually add or modify the index u i And λ i The system adjusts u in real time according to the user i And λ i Displaying a deduction result; if the number of the line maintenance personnel is insufficient and is displayed on the deduction engine interface, manually changing the number of the line maintenance personnel by a user, and automatically deducing and displaying the modified result by the deduction engine;
4) And storing the deduction process and the result of the dynamic deduction engine into a power grid historical emergency evolution process database.
The invention has the technical effects that:
1) Index factors of various power grid emergencies are comprehensively calculated, a novel index factor influence quantity calculation method is provided, and accuracy of deduction of results by the index factors of the power grid emergencies can be remarkably improved;
2) The numerical quantity of each index, the influence among the indexes and the influence of each index on the emergency result are comprehensively considered, index design and weight calculation are carried out, and the accuracy of the calculation of the influence of index factors is further improved;
3) The deduction engine is constructed, so that a user can dynamically adjust index factors and weights according to actual situation factors, materials can be dynamically modified and allocated, real-time modification of indexes can be performed according to predicted environmental data, the deduction engine performs corresponding real-time dynamic adjustment display, and technical support is provided for decision making of scheduling personnel.
While the invention has been described with reference to specific preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the following claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (4)
1. A multidimensional quantitative dynamic deduction evaluation method for power grid emergencies is characterized by comprising the following steps:
step 1: constructing a multidimensional quantitative evaluation index plane directed graph of the power grid emergency;
step 2: acquiring a multi-dimensional quantitative evaluation index and an index weight of a power grid emergency;
and step 3: the dynamic deduction engine controls to obtain and display a deduction result;
step 1, constructing a multidimensional quantitative evaluation index plane digraph of the power grid emergency, which comprises the following steps:
1) Acquiring a multidimensional quantitative evaluation index of the power grid emergency; the indexes comprise a power grid facility site environment index, a power grid facility index, a power grid rescue material index, a power grid rescue personnel index and a power grid system emergency capacity index;
2) Acquiring a corresponding relation between indexes and an emergency evolution result in the historical emergency evolution process of the power grid;
the corresponding relation between the index and the emergency evolution result in the historical emergency evolution process of the power grid is pre-stored data;
wherein E (t) represents the evolution result of the emergency event along with the time t, f (u) i T) represents the index u i Influence vector of time t on grid installation, G (u) i ,u j T) represents the index u j T versus index u over time i The influence coefficient of (c); n represents the number of indexes;
3) Dynamic adjustment of f (u) i T) vector value, calculating different f (u) i T) the standard deviation of E (t) under the vector value is taken, the standard deviation of E (t) is compared with a preset grading threshold value, and an index u is obtained i Rank value u in terms of importance i，y ；
Dynamic adjustment of G (u) i ,u j T) coefficient value taking, calculating different G (u) i ,u j T) standard deviation of E (t) under coefficient value, comparing the standard deviation of E (t) with a preset grading threshold value to obtain an index u j For the index u 1 To u j-1 ，u j-1 To u n Influence vector X of j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) Wherein x is j,j+1 Representative index u j For the index u j+1 The influence value of (d);
4) Obtaining an index u i Is normalized, and an index u is calculated i Normalized value u i，x ；
5) U in index i，x The value is taken as x-axis coordinate and is indicated by the index u i，y The value is taken as a y-axis coordinate, expressed by (u) i，x ,u i，y ) Projected into a rectangular plane coordinate system to form an index u i The discrete point group of (2); by vector X j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) Each index u is constructed with (j =1.. N) as an edge i I =1.. N, bidirectional directed graph.
2. The power grid emergency multidimensional quantitative dynamic deduction and evaluation method according to claim 1, wherein: the multidimensional quantitative evaluation index of the power grid emergency comprises the following steps:
the power grid facility field environment indexes comprise: a temperature index, a humidity index, a wind index, a geological structure index, an air visibility index; the grid facility metrics include: the method comprises the following steps of (1) obtaining a power grid facility type index, a power grid facility position index, a power grid facility quantity index and a power grid facility height index; the power grid rescue goods and materials indexes comprise: the method comprises the following steps of (1) obtaining rescue material type indexes, rescue material quantity indexes and rescue position and power grid facility distance indexes; the power grid rescuer indexes comprise: the method comprises the following steps of (1) providing a rescuer type index, a rescuer number index, a rescuer age index and a rescuer scheduling index; the power grid system emergency capacity index comprises: the system communication line number index, the system communication speed index, the system storage capacity index and the system maximum throughput index.
3. The power grid emergency multidimensional quantitative dynamic deduction and evaluation method according to claim 1, wherein: the step 3 specifically includes:
1) Performing deduction engine input on indexes and corresponding weights corresponding to index discrete points searched in a plane rectangular coordinate system;
2) Controlling a deduction engine to calculate and form a deduction simulation result;
3) The deduction engine sets a modification interface, and the user can manually add or modify the index u i And corresponding weight value lambda i The system adjusts the index u according to the user in real time i And corresponding weight value lambda i Displaying a deduction result;
4) And storing the deduction process and the result of the dynamic deduction engine into a power grid historical emergency evolution process database.
4. A power grid emergency multidimensional quantification dynamic deduction and evaluation system is characterized by comprising: index construction module, index extraction module, dynamic deduction module, wherein:
the index construction module is used for constructing a multidimensional quantitative evaluation index plane directed graph of the power grid emergency;
the index extraction module is used for acquiring a multi-dimensional quantitative evaluation index and an index weight of the power grid emergency;
the dynamic deduction module is used for controlling the dynamic deduction engine to obtain and display a deduction result;
the index construction module is used for constructing a multidimensional quantitative evaluation index plane directed graph of the power grid emergency, and comprises the following steps:
1) Acquiring a multidimensional quantitative evaluation index of the power grid emergency; the indexes comprise a power grid facility site environment index, a power grid facility index, a power grid rescue material index, a power grid rescue personnel index and a power grid system emergency capacity index;
2) Acquiring a corresponding relation between an index and an emergency evolution result in the historical emergency evolution process of the power grid, wherein the corresponding relation between the index and the emergency evolution result in the historical emergency evolution process of the power grid is pre-stored data;
wherein E (t) represents the evolution result of the emergency event along with the time t, f (u) i T) represents the index u i Influence vector of time t on grid installation, G (u) i ,u j T) represents the index u j T versus index u over time i The influence coefficient of (c); n represents the number of indexes;
3) Dynamic adjustment of f (u) i T) vector value, calculating different f (u) i T) taking the value of the vector, comparing the standard deviation of the E (t) with a preset grading threshold value, and acquiring an index u i Rank value u in terms of importance i，y ；
Dynamic adjustment of G (u) i ,u j T) coefficient value taking, calculating different G (u) i ,u j And t) taking the value of the standard deviation of the E (t), comparing the standard deviation of the E (t) with a preset grading threshold value, and acquiring an index u j For the index u 1 To u j-1 ，u j-1 To u n Influence vector X of j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) Wherein x is j,j+1 Representative index u j For the index u j+1 The influence value of (d);
4) Obtaining an index u i Is normalized, and an index u is calculated i Normalized value u i，x ；
5) U in index i，x The value is taken as x-axis coordinate and is indicated by the index u i，y The value is taken as a y-axis coordinate, expressed by (u) i，x ,u i，y ) Projected into a rectangular plane coordinate system to form an index u i The discrete point group of (2); by vector X j (x j,1 ,x j,2 ...x j,j-1 ,x j,j+1 ,x j,n ) (j =1.. N) constructing each index u for the edge i N, i =1.
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