CN108563863A - The energy consumption calculation and dispatching method of City Rail Transit System - Google Patents
The energy consumption calculation and dispatching method of City Rail Transit System Download PDFInfo
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Abstract
The present invention provides a kind of energy consumption calculation of City Rail Transit System and dispatching methods.This method includes：This method includes：It is constituted based on City Rail Transit System, builds urban track traffic energy consumption system Multi-Layered Network Model；The topological attribute and data attribute of calculate node include degree, betweenness and the cluster coefficients of node as input quantity, wherein topological attribute, and data attribute includes the energy consumption and node strength of node；The topological attribute and data attribute of node are merged using OWA operators, obtain the weight of City Rail Transit System interior joint；Based on theory is emerged in large numbers, state is emerged in large numbers to urban rail system energy efficiency in conjunction with the weight of node using Choquet integrals, i.e. efficiency characterization parameter is calculated；Based on urban rail system energy efficiency characterization parameter, metro operation system energy efficiency is regulated and controled.For complicated City Rail Transit System, this method, which can be realized, calculates urban rail system energy efficiency, and combines the Reasonable Regulation And Control of calculated efficiency realization metro operation system energy efficiency.
Description
Technical field
The present invention relates to rail traffic energy consumption calculation technical field more particularly to a kind of energy consumptions of City Rail Transit System
Calculating and dispatching method.
Background technology
With the development of urban track traffic, the huge energy consumption problem of City Rail Transit System has also obtained extensive pass
Note.In the research for having City Rail Transit System efficiency, there has been no scholars to go to grind using network model from the angle of system
Study carefully urban track traffic efficiency, strategy and foundation are provided for the raising of system entirety efficiency.
Network Science is an emerging intersection for specializing in the qualitative and quantitative rule of complication system in nature and society
Science has been applied to various science and engineering field.By developing for a long time, Network Science has formd system
Section's framework and theoretical system.The research focus of complex network model gradually turns to multitiered network from single network in recent years.Multilayer
Development of the network model as single layer network model, what can not be portrayed single layer network includes heterogeneous nodes and heterogeneous side
Network carry out modeling analysis, therefore, the Multi-Layered Network Model research branch new as a comparison of Network Science also begins to
By focus of attention and application, carrying out modeling to City Rail Transit System efficiency with Multi-Layered Network Model has very important meaning
Justice.
Invention content
The embodiment provides a kind of energy consumption calculation of City Rail Transit System and dispatching methods, existing to solve
There is the shortcomings that technology.
A kind of energy consumption calculation and dispatching method of City Rail Transit System, including：
Step 1: according to the constituent relation of City Rail Transit System, comprehensive energy consumption data, structure urban track traffic system
The energy consumption network model of system；
Step 2: the operation energy consumption data by analyzing City Rail Transit System, in conjunction with the urban track traffic of structure
The efficiency network model of system calculates the topological attribute and data attribute of City Rail Transit System interior joint；
Step 3: being merged to the topological attribute and data attribute of node using OWA operators, urban track traffic is obtained
The importance of system interior joint；
Step 4: based on theory is emerged in large numbers, combine the importance of node to City Rail Transit System using Choquet integrals
Efficiency characterization parameter calculated；
Step 5: the efficiency characterization parameter based on City Rail Transit System, to the efficiency of City Rail Transit System into
Row regulation and control.
Further, the constituent relation according to City Rail Transit System, comprehensive energy consumption data build city rail
The energy consumption network model of pipeline transportation system, including：
1.1, the layer in the energy consumption network model of structure City Rail Transit System, the layer are referred to same structure etc.
Grade or Performance Level subsystem and its between interaction relationship set, according to the division principle by upper layer to lower layer to city
All layers in city's Rail Transit System are divided, and L is the set in the energy consumption network model middle level of City Rail Transit System；
It 1.2, will be relatively independent with having of forming of particular kind of relationship by a certain amount of part in City Rail Transit System
Function and the subsystem abstraction of energy consumption are node, and V is the set of the energy consumption network model interior joint of City Rail Transit System；
1.3, existing interaction relationship between the subsystem in City Rail Transit System and subsystem energy consumption is taken out
As to connect side, using Pearson's coefficient between subsystem and subsystem energy consumption data as the weight on side, E hands over for city rail
The set on side in way system energy consumption network model, the calculation formula of the weight w on side are：
In formula：
N --- sample size；
ai,bi--- it is respectively the statistical value of two variables.
Further, the operation energy consumption data by analyzing City Rail Transit System, in conjunction with the city of structure
The efficiency network model of Rail Transit System calculates the topological attribute and data attribute of City Rail Transit System interior joint, packet
It includes：
2.1, the central calculation formula of the degree of topological attribute interior joint i is：
In formula：
--- node vLi,jDegree centrality；
--- with node vLi,jThe quantity on the side being connected directly；
| V | --- the quantity of node set V interior joints；
2.2, the calculation formula of the betweenness of topological attribute interior joint i is：
In formula：
--- pass through node between arbitrary node pairShortest path number；
JS --- the summation of all shortest path numbers between node pair；
2.3, the calculation formula of the cluster coefficients of topological attribute interior joint i is：
In formula：
--- nodeThe existing number of edges between its neighbor node；
--- nodeTotal number of edges that may be present between its neighbor node；
2.4, the energy consumption of data attribute interior joint i is obtained by energy consumption data；
2.5, the calculation formula of the node strength of data attribute interior joint i is：
In formula：
--- with nodeFor the weight on the side of beginning or end.
Further, described that the topological attribute and data attribute of node are merged using OWA operators, obtain city
The importance of Rail Transit System interior joint, including：
3.1, the attribute data of investigation and each node being calculated is built into node evaluation attribute matrix into row set；
3.2, specification is carried out to the attribute data in node evaluation attribute matrix according to attribute data normalizing；
3.3, attribute data is ranked up according to the size of the attribute data values of the node after standardization；
3.4, the attribute data values of node are assembled using OWA operators, obtains the importance value of each node.
Further, it is described according to attribute data normalizing to the attribute data in node evaluation attribute matrix into
Professional etiquette model, including：
The attribute type for the attribute data being arranged in node evaluation attribute matrix include profit evaluation model, cost type, fixed, partially
Release, interval type and deviation interval type, wherein profit evaluation model attribute refers to that the attribute data values the big then better；Cost type attribute refers to
Attribute data values are smaller then better；Fixed attribute refers to attribute data values closer to some fixed value αjIt is then better；Deviation type
Attribute refers to that attribute data values more deviate some fixed value betajIt is then better；Interval type attribute refers to attribute data values closer to fixed
SectionIt is then better；It refers to that attribute data values more deviate fixed interval to deviate interval type attributeIt is then better；
Standardization processing is carried out to attribute data as follows：
If attribute value is profit evaluation model, enable
In formula：
maxaj--- maximum attribute value in jth Column Properties data；
If attribute value is cost type, enable
In formula：
minaj--- minimum attribute value in jth Column Properties data；
If attribute value is fixed, enable
If attribute value is deviation type, enable
If attribute value is interval type, enable
If attribute value is to deviate interval type, enable
Further, described that the attribute data values of node are assembled using OWA operators, obtain the weight of each node
Angle value is wanted, including：
The attribute data values of node are assembled using OWA operators, obtain the importance value of each nodeImportance
ValueCalculation formula it is as follows：
Wherein, brijThe sequence being ranked up according to the descending sequence of attribute value for the attribute after node standardizes
As a result, brijIn i refer to which node, j refer to which attribute, m refer to the total of the attribute data for assembly
Number, γ is the weighing vector of OWA operators, is determined to the weighing vector of OWA operators using least variance method, and the calculating of γ is public
Formula is as follows：
Wherein Disp (γ) indicates that the degree that attribute is used on an equal basis, orness (γ) indicate that done mixing operation is grasped with or
The similarity degree of work.
Further, described based on theory is emerged in large numbers, combine the importance of node to city rail using Choquet integrals
The efficiency characterization parameter of traffic system is calculated, including：
The importance value for the node being calculated is normalized, the knot after the importance value of node is normalized
The section of Shapley value of the fruit as node, Shapley values is 0-1；
By establishing using Marchal entropys as the optimal model of object function, the fuzzy mearue of each set of node is calculated, it is described
It is as follows as the optimal model of object function using Marchal entropys：
In formula
| S | it is the gesture of property set S；
The efficiency state of emerging in large numbers of node is calculated using Choquet integrals, the definition of Choquet integrals is：If gλ
For the λ fuzzy mearues being defined on P (X), f is defined in the non-negative real value measurable function on X, then f is about gλIt is discrete
Choquet integrates (c) ∫ fdgλIt is defined as follows shown in formula：
In formula：
i——f(xi) vector transformation so that 0≤f (x1)≤L≤f(xn)；
Xi=(x1,x2,L,xn), and f (x0)=0.
Further, the efficiency characterization parameter based on City Rail Transit System, to City Rail Transit System
Efficiency regulated and controled, including：
The property integrated using Choquet, seeks the efficiency characterization parameter of subsystem and the energy of City Rail Transit System
The functional relation between characterization parameter is imitated, the efficiency importance of each subsystem is determined according to the functional relation；
According to the formula that Choquet is integrated, work as gλ(Ai) it is known in the case of, the efficiency of City Rail Transit System characterizes
Parameter ∫ fdgλWith the efficiency characterization parameter f (x of subsystemi) between functional relation be shown below：
According to the maxima and minima of investigation energy consumption data, the energy consumption threshold of subsystem obtained by energy consumption data is determined
Value determines energy consumption data not according to the energy consumption threshold value of subsystem obtained by the efficiency characterization parameter of subsystem and energy consumption data
The efficiency characterization parameter threshold value of obtainable subsystem；Efficiency importance and energy consumption data according to each subsystem is unavailable
The efficiency characterization parameter threshold value of subsystem determines the efficiency regulation and control method and strategy of City Rail Transit System.
The embodiment of the present invention utilizes multitiered network mould it can be seen from the technical solution that embodiments of the invention described above provide
Type carries out modeling analysis to City Rail Transit System, is emerged in large numbers based on efficiency in conjunction with theoretical proposed with fuzzy integral method is emerged in large numbers
The efficiency regulation and control method and strategy of the City Rail Transit System of state.This method can be realized to City Rail Transit System
Efficiency is calculated, and carries out Reasonable Regulation And Control to the efficiency of City Rail Transit System.
Description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment
Attached drawing be briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill of field, without having to pay creative labor, other are can also be obtained according to these attached drawings
Attached drawing.
Fig. 1 is a kind of realization of the energy consumption calculation and dispatching method of City Rail Transit System provided in an embodiment of the present invention
Principle schematic；
Fig. 2 is a kind of processing of the energy consumption calculation and dispatching method of City Rail Transit System provided in an embodiment of the present invention
Flow chart；
Fig. 3 is a kind of energy consumption Multi-Layered Network Model schematic diagram of City Rail Transit System provided in an embodiment of the present invention；
Fig. 4 is a kind of abstract graph of the efficiency network model of City Rail Transit System provided in an embodiment of the present invention.
Specific implementation mode
For ease of the understanding to the embodiment of the present invention, done further by taking several specific embodiments as an example below in conjunction with attached drawing
Explanation, and each embodiment does not constitute the restriction to the embodiment of the present invention.
Embodiment one
In order to which City Rail Transit System energy efficiency management provides theoretical foundation, the present invention provides a kind of metro operation systems
Efficiency is calculated to be considered complicated interaction relationship between urban track traffic energy consumption system, utilizes with regulation and control method, this method
Multi-Layered Network Model carries out modeling analysis to City Rail Transit System, combines emerge in large numbers theoretical and fuzzy integral side on this basis
Method calculates the efficiency state of emerging in large numbers of City Rail Transit System, finally proposes the city that state is emerged in large numbers based on efficiency
Rail Transit System efficiency regulates and controls method and strategy.
The realization principle of the energy consumption calculation and dispatching method of a kind of City Rail Transit System provided in an embodiment of the present invention
Schematic diagram is as shown in Figure 1, specifically process flow is as shown in Fig. 2, include following processing steps：
Step 1, the constituent relation according to City Rail Transit System, comprehensive energy consumption data, structure urban track traffic system
The energy consumption network model of system, is as follows：
1.1, the layer in the energy consumption network model of structure City Rail Transit System, the layer are referred to same structure etc.
Grade or Performance Level subsystem and its between interaction relationship set.According to the division principle by upper layer to lower layer to city
All layers in city's Rail Transit System are divided, and L is the set in the energy consumption network model middle level of City Rail Transit System.
Fig. 3 is a kind of energy consumption Multi-Layered Network Model schematic diagram of City Rail Transit System provided in an embodiment of the present invention.
It 1.2, will be relatively independent with having of forming of particular kind of relationship by a certain amount of part in City Rail Transit System
Function and the subsystem abstraction of energy consumption are node.V is the set of the energy consumption network model interior joint of City Rail Transit System.
1.3, existing interaction relationship between the subsystem in City Rail Transit System and subsystem energy consumption is taken out
As to connect side, using Pearson's coefficient between subsystem and subsystem energy consumption data as the weight on side, E hands over for city rail
The set on side in way system energy consumption network model, the calculation formula of the weight w on side are：
In formula：
N --- sample size；
ai,bi--- it is respectively the statistical value of two variables.
Step 2: by the operation energy consumption data analysis to City Rail Transit System, handed in conjunction with the city rail of structure
The energy consumption network model of way system, the topological attribute and data attribute of calculate node.
2.1, the central calculation formula of the degree of topological attribute interior joint i is：
In formula：
--- nodeDegree centrality；
--- with nodeThe quantity on the side being connected directly；
| V | --- the quantity of node set V interior joints.
2.2, the calculation formula of the betweenness of topological attribute interior joint i is：
In formula：
--- pass through node between arbitrary node pairShortest path number；
JS --- the summation of all shortest path numbers between node pair.
2.3, the calculation formula of the cluster coefficients of topological attribute interior joint i is：
In formula：
--- nodeThe existing number of edges between its neighbor node；
--- nodeTotal number of edges that may be present between its neighbor node.
2.4, the energy consumption of data attribute interior joint i is obtained by energy consumption data.
2.5, the calculation formula of the node strength of data attribute interior joint i is：
In formula：
--- with nodeFor the weight on the side of beginning or end.
Step 3: utilizing OWA (Ordered Weighted arithmetic average operator, Ordered Weighted Averaging) operator pair
The topological attribute and data attribute of node are merged, and the importance of City Rail Transit System interior joint is obtained.
3.1, structure assessment objective attribute target attribute matrix.IfFor the property set of evaluation goal, wherein | η | be
The number of property set.For the arbitrary element o in evaluation goal Oi, i ∈ | O |, equal available attributes ηj, j ∈ | η | it is surveyed
Degree, obtains oiAbout ηjAttribute value aij.All elements in evaluation goal are estimated, obtain all elements about η's
Attribute value, to constitute attribute matrix As of the evaluation goal O about property set η,As shown in table 1.
1 attribute matrix of table
3.2, attribute data standardizes.The generally profitable type of attribute type, cost type, fixed, deviation type, interval type and
Deviate interval type etc., wherein profit evaluation model attribute refers to that the attribute data values the big then better；Cost type attribute refers to attribute data values
It is smaller then better；Fixed attribute refers to attribute data values closer to some fixed value αjIt is then better；Deviation type attribute refers to belonging to
Property data value more deviates some fixed value betajIt is then better；Interval type attribute refers to attribute data values closer to fixed interval
It is then better；It refers to that attribute data values more deviate fixed interval to deviate interval type attributeIt is then better.In order to eliminate not same amount
Guiding principle can be influenced caused by evaluation result, can carry out standardization processing to attribute data as follows.
If attribute value is profit evaluation model, enable
In formula：
maxaj--- maximum attribute value in jth Column Properties data.
If attribute value is cost type, enable
In formula：
minaj--- minimum attribute value in jth Column Properties data.
If attribute value is fixed, enable
If attribute value is deviation type, enable
If attribute value is interval type, enable
If attribute value is to deviate interval type, enable
3.3, attribute data sorts.By evaluation goal viAttribute ar after standardizationij, according to descending suitable of attribute value
Ordered pair attribute value is ranked up, ranking results brij, the attribute of all evaluation goals is enumerated, new attribute square is formed
Battle array NA, the calculation formula of NA are as follows：
3.4, Calculation Estimation target synthesized attribute value.The weight of synthesized attribute value, that is, evaluation goal of evaluation goal (i.e. node)
It spends.The attribute value of evaluation goal is assembled using OWA operators, obtains the importance of each evaluation goalEvaluate mesh
Mark oiImportance value calculation formula it is as follows：
Wherein γ is the weighing vector of OWA operators, and m refers to the sum of the attribute data for assembly.Such as certain node
Possess 5 attributes, 3 A attributes, 2 B attributes.Therefore | η | it is 5, when calculating A attributes and assembling, M is 3, calculates M when B attributes are assembled
It is 2.
Present invention selection is determined the weighing vector γ of OWA operators using least variance method (MVM).Least variance method
Determine that the calculation formula of OWA operator weighing vectors γ is as follows：
Wherein Disp (γ) indicates that the degree that attribute is used on an equal basis, orness (γ) indicate that done mixing operation is grasped with or
The similarity degree of work.
Step 4: based on theory is emerged in large numbers, combine the weight of node to City Rail Transit System using Choquet integrals
Efficiency is emerged in large numbers state (i.e. efficiency characterization parameter) and is calculated.
4.1, in conjunction with efficiency network model, abstract graph such as Fig. 4 of the efficiency network model of City Rail Transit System is obtained
It is shown.
4.2, node energy valid value obtained by energy consumption data is calculated.
Using the calculation formula of efficiency, efficiency is equal to nominal power consumption than actual consumption, according to City Rail Transit System
Node energy valid value obtained by energy consumption data can be calculated in building energy consumption investigating data.
4.3, according to node weights, the Shapley values for obtaining node are calculated.
The section of Shapley values is 0-1, and in order to meet this definition, the present invention carries out the node weights being calculated
Normalized, the result after node weights are normalized is as the Shapley values of node.
4.4, the fuzzy mearue of the property set of City Rail Transit System is calculated
The mould of i.e. each set of node of each property set is calculated using Marchal entropys as the optimal model of object function by establishing
Paste is estimated.It is as follows as the optimal model of object function using Marchal entropys.
In formula
| S | it is the gesture of property set S.
4.5, the efficiency for calculating City Rail Transit System emerges in large numbers state (i.e. efficiency characterization parameter).
The efficiency state of emerging in large numbers of City Rail Transit System is calculated using Choquet integrals.Choquet integrals
It is defined as：If gλFor the λ fuzzy mearues being defined on P (X), f is defined in the non-negative real value measurable function on X, then f is about gλ
Discrete Choquet integrals (c) ∫ fdgλDefinition such as formula：
In formula：
i——f(xi) vector transformation so that 0≤f (x1)≤L≤f(xn)；Xi=(x1,x2,L,xn), and f (x0)=0.
f(xi) what is represented is that the efficiency of City Rail Transit System emerges in large numbers state；gλ(Ai) be each set of node fuzzy mearue；Node
Shapley values are used to calculate the fuzzy mearue of each set of node using Marichal entropys.
Step 5: the efficiency characterization parameter based on City Rail Transit System, to the efficiency of City Rail Transit System into
Row regulation and control.
5.1, node efficiency importance calculates.Subsystem is sought using reverse thinking using the Choquet properties integrated
Functional relation between efficiency characterization parameter and system energy efficiency characterization parameter determines the efficiency weight of each subsystem node according to coefficient
It spends.
According to the formula that Choquet is integrated, work as gλ(Ai) it is known in the case of, former formula can be changed into gushes about system energy efficiency
Present condition ∫ fdgλState f (x are emerged in large numbers with subsystem efficiencyi) between functional relation, as shown by the equation.
5.2, the efficiency control strategy of City Rail Transit System generates.According to the efficiency importance of each subsystem node,
Generate the control strategy based on subsystem efficiency threshold value.
According to the maxima and minima of investigation energy consumption data, the energy consumption (energy of subsystem obtained by energy consumption data is determined
Effect) threshold value, it is determining according to energy consumption (efficiency) threshold value of subsystem obtained by the efficiency characterization parameter of subsystem and energy consumption data
The efficiency characterization parameter threshold value of the unavailable subsystem of energy consumption data；Efficiency importance and energy consumption data according to each subsystem
The efficiency characterization parameter threshold value of unavailable subsystem determines the efficiency regulation and control method and strategy of City Rail Transit System.
Embodiment two
According to the above process, its specific implementation mode is introduced using real data as follows：
Step 1：Build nodal community matrix
Classified according to nodal community, builds the attribute matrix of node topology attributeWherein | ηA| it is node
The number of topological attribute.According to the information of urban rail system energy efficiency network model interior joint, it is known that | V | it is 25, | ηA| it is 3.It will be upper
The topological attribute for stating the Multi-Layered Network Model acquired is input in attribute matrix, then the attribute matrix A of node topology attribute is such as public
Shown in formula (a).
Classified according to nodal community, builds the attribute matrix of node data attributeWherein | ηB| it is node
The number of data attribute.According to the information of urban rail system energy efficiency network model interior joint, it is known that | V | it is 25, | ηB| it is 2.According to §
3.3.2 the acquisition methods of the data attribute of the Multi-Layered Network Model proposed in, by the volume of each node in urban rail efficiency network model
Surely consumption and node strength are input in attribute matrix, then shown in the attribute matrix B such as formula (b) of node data attribute.
Step 2：Attribute data standardizes and attribute data sequence
It can be found that the topological attribute or data attribute of either node, is all the attribute for belonging to profit evaluation model, i.e. attribute
Value is bigger, it is meant that the node is more important.Therefore, with the normalizing of wherein profit evaluation model attribute, to node topology attribute
The attribute matrix of the data attribute of attribute matrix and node standardizes.
Based on the attribute matrix after standardization, the attribute to belonging to same node is arranged according to attribute value size
Sequence is formed shown in new matrix N A such as formula (c) and shown in NB such as formula (d).
Step 3：The importance (i.e. weight) of calculate node
Due to the nonadditivity of urban rail system actual consumption, lead to there was only L in urban rail system energy efficiency network model4Layer
There is interaction relationships between node, i.e., only L4Node in layer possesses node strength this attribute.Therefore, different layers
Between node weights do not have comparativity, the weight of the node of different layers will be evaluated respectively herein.
Concrete numerical value in urban rail system energy efficiency network model is brought into the weighing vector calculation formula of OWA operators, is led to
It crosses MATLAB and solves the weighing vector γ that can obtain node topology attributeA=(0.584,0.333,0.083).Node data attribute
Weighing vector γB=(0.75,0.25).
Attribute value is assembled using OWA operators, obtains the weight of each nodeAs shown in table 2.
Table 2：The weight of network model node
Step 4：Urban rail system energy efficiency network model abstract graph
Urban rail system energy efficiency network model can be abstracted into shown in Fig. 4：
Step 5：Node energy valid value calculates
By the calculation formula of efficiency, in conjunction with the nominal power consumption data and actual consumption number of the subsystem node that investigation obtains
According to it is as shown in table 3 that subsystem node efficiency can be calculated in we.
Table 3：Urban rail system energy consumption data can survey the efficiency of subsystem
Step 6：Node Shapley values calculate
This paper interior joint Shapley values will be calculated by the weight of node.The Shapley values of node are as shown in table 4.
Table 4：The Shapley values of urban rail system energy efficiency model interior joint
Step 7：Node efficiency emerges in large numbers state computation
It is the Given information of be useful for computing system or subsystem efficiency above.It is maximum with Marichal entropys by calculating
The Optimized model of object function structure is turned to, the significance level that can obtain attribute and property set is as shown in table 5.
Table 5：The fuzzy mearue of urban rail system property collection
State computation process example is emerged in large numbers according to efficiency in mini system, is integrated from bottom to top to each node with Choquet
The efficiency state of emerging in large numbers integrated, can obtaining the efficiency of urban rail system and each subsystem, to emerge in large numbers state as shown in table 6.
Table 6：The efficiency of urban rail system emerges in large numbers state
According to above-mentioned urban rail system core efficiency node authentication method, the node in urban rail system energy efficiency network is distinguished
Know, it is as shown in table 7 to obtain each node layer efficiency importance.
Table 7：Urban rail system energy efficiency model interior joint efficiency importance
According to efficiency calculation formula, efficiency threshold value result such as 8 institute of table of subsystem obtained by energy consumption data is calculated
Show.
Table 8：Urban rail system energy consumption data can survey node efficiency threshold value
According to the computational methods of urban rail system energy efficiency characterization parameter, the unavailable system of energy consumption data in urban rail system is calculated
System, the efficiency threshold value of subsystem are as shown in table 9.
Table 9：Urban rail system energy efficiency threshold value
According to each node layer efficiency importance of urban rail system, obtain corresponding to urban rail system, subsystem efficiency characterization parameter
Calculation formula it is as shown in table 10.
Table 10：Urban rail system energy efficiency characterization parameter calculation formula
Urban rail system energy efficiency regulating strategy and each node changing value caused by urban rail system energy efficiency characterization parameter of regulation and control
As shown in table 11.In tableIndicate the changing value of urban rail system energy efficiency characterization parameter；Δ θ indicates subsystem efficiency characterization ginseng
Several changing values.
11 urban rail system energy efficiency characterization parameter of table changes table
In conclusion the embodiment of the present invention carries out modeling analysis using Multi-Layered Network Model to City Rail Transit System,
Regulate and control in conjunction with the theoretical efficiency for proposing the City Rail Transit System for emerging in large numbers state based on efficiency with fuzzy integral method is emerged in large numbers
Method and strategy.This method, which can be realized, calculates the efficiency of City Rail Transit System, and to urban track traffic system
The efficiency of system carries out Reasonable Regulation And Control.Specifically it can be applied to following aspect：
1. systematic research urban rail system is constituted, urban rail energy consumption system is constituted, urban rail system energy consumption feature.
2. constructing urban rail system energy efficiency network model based on Multi-Layered Network Model, and the aspect of model is analyzed,
Thinking is provided for urban rail system core energy consumption node authentication.
3. based on theoretical and urban rail system energy efficiency network model is emerged in large numbers, to being gushed by the microcosmic efficiency to macroscopic view in urban rail system
Existing process is studied, and realization regulates and controls urban rail system energy efficiency from system perspective.
One of ordinary skill in the art will appreciate that：Attached drawing is the schematic diagram of one embodiment, module in attached drawing or
Flow is not necessarily implemented necessary to the present invention.
One of ordinary skill in the art will appreciate that：The module in equipment in embodiment can describe to divide according to embodiment
It is distributed in the equipment of embodiment, respective change can also be carried out and be located in one or more equipment different from the present embodiment.On
The module for stating embodiment can be merged into a module, can also be further split into multiple submodule.
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a computer read/write memory medium
In, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Any one skilled in the art in the technical scope disclosed by the present invention, the change or replacement that can be readily occurred in,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims
Subject to.
Claims (8)
1. the energy consumption calculation and dispatching method of a kind of City Rail Transit System, which is characterized in that including：
Step 1: according to the constituent relation of City Rail Transit System, comprehensive energy consumption data build City Rail Transit System
Energy consumption network model；
Step 2: the operation energy consumption data by analyzing City Rail Transit System, in conjunction with the City Rail Transit System of structure
Efficiency network model, calculate City Rail Transit System interior joint topological attribute and data attribute；
Step 3: being merged to the topological attribute and data attribute of node using OWA operators, City Rail Transit System is obtained
The importance of interior joint；
Step 4: based on theory is emerged in large numbers, combine the importance of node to the energy of City Rail Transit System using Choquet integrals
Effect characterization parameter is calculated；
Step 5: the efficiency characterization parameter based on City Rail Transit System, adjusts the efficiency of City Rail Transit System
Control.
2. according to the method described in claim 1, it is characterized in that, described close according to the composition of City Rail Transit System
System, comprehensive energy consumption data build the energy consumption network model of City Rail Transit System, including：
1.1, the layer in the energy consumption network model of structure City Rail Transit System, the layer refer to same structure grade or
The subsystem of Performance Level and its between interaction relationship set, according to the division principle by upper layer to lower layer to city rail
All layers in pipeline transportation system are divided, and L is the set in the energy consumption network model middle level of City Rail Transit System；
1.2, will there is relatively independent function with what particular kind of relationship formed by a certain amount of part in City Rail Transit System
And the subsystem abstraction of energy consumption is node, V is the set of the energy consumption network model interior joint of City Rail Transit System；
1.3, existing interaction relationship between the subsystem in City Rail Transit System and subsystem energy consumption is abstracted as
Side is connected, using Pearson's coefficient between subsystem and subsystem energy consumption data as the weight on side, E is urban track traffic system
The set on side, the calculation formula of the weight w on side are in system energy consumption network model：
In formula：
N --- sample size；
ai,bi--- it is respectively the statistical value of two variables.
3. according to the method described in claim 2, it is characterized in that, the operation by analyzing City Rail Transit System
Energy consumption data calculates City Rail Transit System interior joint in conjunction with the efficiency network model of the City Rail Transit System of structure
Topological attribute and data attribute, including：
2.1, the central calculation formula of the degree of topological attribute interior joint i is：
In formula：
--- nodeDegree centrality；
--- with nodeThe quantity on the side being connected directly；
| V | --- the quantity of node set V interior joints；
2.2, the calculation formula of the betweenness of topological attribute interior joint i is：
In formula：
--- pass through node between arbitrary node pairShortest path number；
JS --- the summation of all shortest path numbers between node pair；
2.3, the calculation formula of the cluster coefficients of topological attribute interior joint i is：
In formula：
--- nodeThe existing number of edges between its neighbor node；
--- nodeTotal number of edges that may be present between its neighbor node；
2.4, the energy consumption of data attribute interior joint i is obtained by energy consumption data；
2.5, the calculation formula of the node strength of data attribute interior joint i is：
In formula：
--- with nodeFor the weight on the side of beginning or end.
4. according to the method described in claim 3, it is characterized in that, it is described using OWA operators to the topological attribute of node and
Data attribute is merged, and the importance of City Rail Transit System interior joint is obtained, including：
3.1, the attribute data of investigation and each node being calculated is built into node evaluation attribute matrix into row set；
3.2, specification is carried out to the attribute data in node evaluation attribute matrix according to attribute data normalizing；
3.3, attribute data is ranked up according to the size of the attribute data values of the node after standardization；
3.4, the attribute data values of node are assembled using OWA operators, obtains the importance value of each node.
5. according to the method described in claim 4, it is characterized in that, described comment node according to attribute data normalizing
Estimate the attribute data in attribute matrix and carries out specification, including：
The attribute type for the attribute data being arranged in node evaluation attribute matrix includes profit evaluation model, cost type, fixed, deviation
Type, interval type and deviation interval type, wherein profit evaluation model attribute refers to that the attribute data values the big then better；Cost type attribute refers to belonging to
Property data value is smaller then better；Fixed attribute refers to attribute data values closer to some fixed value αjIt is then better；Deviation type category
Property refers to that attribute data values more deviate some fixed value betajIt is then better；Interval type attribute refers to attribute data values closer to fixed area
BetweenIt is then better；It refers to that attribute data values more deviate fixed interval to deviate interval type attributeIt is then better；
Standardization processing is carried out to attribute data as follows：
If attribute value is profit evaluation model, enable
In formula：
max aj--- maximum attribute value in jth Column Properties data；
If attribute value is cost type, enable
In formula：
min aj--- minimum attribute value in jth Column Properties data；
If attribute value is fixed, enable
If attribute value is deviation type, enable
If attribute value is interval type, enable
If attribute value is to deviate interval type, enable
6. according to the method described in claim 5, it is characterized in that, it is described using OWA operators to the attribute data values of node
Assembled, obtains the importance value of each node, including：
The attribute data values of node are assembled using OWA operators, obtain the importance value of each nodeImportance value
Calculation formula it is as follows：
Wherein, brijFor the ranking results that the attribute after node standardizes is ranked up according to the descending sequence of attribute value,
brijIn i refer to which node, j refer to which attribute, m refer to the sum of the attribute data for assembly, γ
It is the weighing vector of OWA operators, the weighing vector of OWA operators is determined using least variance method, the calculation formula of γ is such as
Shown in lower：
Wherein Disp (γ) indicates that the degree that attribute is used on an equal basis, orness (γ) indicate done mixing operation and or operations
Similarity degree.
7. according to the method described in claim 4,5 or 6, which is characterized in that it is described based on theory is emerged in large numbers, utilize Choquet
Integral calculates the efficiency characterization parameter of City Rail Transit System in conjunction with the importance of node, including：
The importance value for the node being calculated is normalized, the result after the importance value of node is normalized is made
Section for the Shapley values of node, Shapley values is 0-1；
By establishing using Marchal entropys as the optimal model of object function, calculate the fuzzy mearue of each set of node, it is described with
Marchal entropys are that the optimal model of object function is as follows：
In formula
| S | it is the gesture of property set S；
The efficiency state of emerging in large numbers of node is calculated using Choquet integrals, the definition of Choquet integrals is：If gλFor definition
λ fuzzy mearues on P (X), f are defined in the non-negative real value measurable function on X, then f is about gλDiscrete Choquet integral
(c)∫fdgλIt is defined as follows shown in formula：
In formula：
i——f(xi) vector transformation so that 0≤f (x1)≤L≤f(xn)；
Xi=(x1,x2,L,xn), and f (x0)=0, f (xi) what is represented is that the efficiency of City Rail Transit System emerges in large numbers state, gλ
(Ai) be each set of node fuzzy mearue, the Shapley values of node are used to calculate the fuzzy of each set of node using Marichal entropys
Estimate.
8. the method according to the description of claim 7 is characterized in that the efficiency characterization based on City Rail Transit System
Parameter regulates and controls the efficiency of City Rail Transit System, including：
The property integrated using Choquet, seeks the efficiency characterization parameter of subsystem and the efficiency table of City Rail Transit System
The functional relation between parameter is levied, the efficiency importance of each subsystem is determined according to the functional relation；
According to the formula that Choquet is integrated, work as gλ(Ai) it is known in the case of, the efficiency characterization parameter ∫ of City Rail Transit System
fdgλWith the efficiency characterization parameter f (x of subsystemi) between functional relation be shown below：
According to the maxima and minima of investigation energy consumption data, the energy consumption threshold value of subsystem obtained by energy consumption data, root are determined
According to the energy consumption threshold value of the efficiency characterization parameter and subsystem obtained by energy consumption data of subsystem, determine that energy consumption data is unavailable
Subsystem efficiency characterization parameter threshold value；The unavailable subsystem of efficiency importance and energy consumption data according to each subsystem
Efficiency characterization parameter threshold value, determine the efficiency regulation and control method and strategy of City Rail Transit System.
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