CN110569543B - Complex system self-adaption method and system supporting mapping dimension lifting - Google Patents

Complex system self-adaption method and system supporting mapping dimension lifting Download PDF

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CN110569543B
CN110569543B CN201910713219.9A CN201910713219A CN110569543B CN 110569543 B CN110569543 B CN 110569543B CN 201910713219 A CN201910713219 A CN 201910713219A CN 110569543 B CN110569543 B CN 110569543B
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张宏军
谭玮
刘广
罗永亮
邱伯华
李海旭
张珺
秦远辉
黄百乔
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Abstract

The invention relates to a complex system self-adaptive method and system supporting mapping dimension increase, wherein the method decomposes a complex system layer by layer according to capability to element level to form a complex system element set; extracting association relations among elements from a complex system element set layer by layer, respectively establishing association networks among elements of the same level and elements of different levels, and forming a multi-main element multi-dimensional association network; establishing a multi-scale model of elements of different levels according to the multi-main element multi-dimensional association network; finally, according to the multi-main-body element multi-dimensional association network and the multi-scale model of the complex system, carrying out self-adaptive scheduling of the complex system to form a required system scheduling scheme; the invention realizes the self-adaptive scheduling of complex systems including aviation support systems, combat weapon systems and the like, solves the adaptability problem of the complex systems, greatly improves the operation speed, improves the scheduling efficiency and improves the scheduling optimization capacity of the complex systems under the multi-target and multi-constraint conditions.

Description

Complex system self-adaption method and system supporting mapping dimension lifting
Technical Field
The invention relates to a complex system self-adaption method and system supporting mapping dimension lifting, and belongs to the technical field of information.
Background
The traditional complex system design and development mainly adopts the idea of a reduction theory, namely a design method of dimension reduction analysis, as shown in fig. 1, the complex system is decomposed into a plurality of simple systems, and the method is applicable to the determination of the structural fixing and causal relationship of the system based on the method that the whole is equal to the sum of all parts, and has repeatable and predictable results and stable state. When the system scale is small, the interactivity and the integrity loss caused by the dimension reduction analysis process can be well corrected in the system integration verification process, so that the final result is not greatly influenced, but when the system scale is large, the interactivity and the integrity loss caused by the dimension reduction analysis can only be compensated by continuous iteration of a prototype model machine in a gradual approximation mode, so that the development period of a complex large system is long, the cost is high, and the efficiency is low.
Therefore, the method of dimension reduction analysis is taken as a method for avoiding the complexity of the system through dimension reduction, and is a negative coping strategy for the complexity of the system. For a 'man-machine-ring' complex large system similar to an aviation support system, a complex coupling mechanism among elements is difficult to fully find by adopting a traditional dimension reduction analysis method, and multi-element coupling is a quantitative influence on the comprehensive efficiency of the aviation support system, so that real and comprehensive depiction and analysis on the complex system cannot be realized.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a complex system self-adaptive method supporting mapping dimension increase, which firstly excavates the association relation among system elements based on continuously accumulated internal field simulation data and external field test data, then adopts a complex network method to construct a complex system multi-main element multi-dimensional association network, and carries out element multi-agent model construction based on the data, thereby realizing complex system self-adaptive scheduling facing capability requirements and finally solving the adaptability problem of the complex system.
It is a further object of the invention to provide an adaptive system that supports a complex system that maps up dimensions.
The above object of the present invention is mainly achieved by the following technical solutions:
a complex system adaptation method supporting mapping dimension-lifting, comprising:
decomposing the complex system layer by layer according to the capability to an element level to form a complex system element set;
extracting association relations among elements from the complex system element set layer by layer, respectively establishing association networks among elements of the same level and elements of different levels, and forming a complex system multi-main element multi-dimensional association network;
Establishing a complex system resource multi-scale model of different levels of elements according to the complex system multi-main element multi-dimensional association network;
and carrying out self-adaptive scheduling on the complex system according to the complex system multi-main-element multi-dimensional association network and the complex system resource multi-scale model to form a required system scheduling scheme.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, the complex system is decomposed layer by layer according to the capability, at least two layers are decomposed to element level.
In the above-mentioned complex system self-adaptive method supporting mapping dimension-increasing, the association relation between elements is extracted layer by layer from the complex system element set, and association networks between elements of the same level and elements of different levels are respectively established, and the specific method for forming the complex system element network is as follows:
(1) Dividing elements in the complex system element set into a system main element and an environment element;
(2) Classifying the system main body elements and the environment elements to establish a hierarchical structure diagram, wherein the hierarchical structure diagram is a set of each hierarchical element;
(3) Extracting the association relations among the elements in different levels and the association relations among the elements in the same level, classifying the association relations, and constructing an association relation judgment matrix among the elements;
(4) And constructing a multi-main-body element multi-dimensional association network according to the association relation judgment matrix among the elements, wherein the multi-main-body element multi-dimensional association network comprises association networks among elements in different levels and association networks among elements in the same level.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, the main system elements in the step (1) include a human element, an airplane element and a ship element, and the ship element is divided into a guarantee equipment element and a guarantee array element; the environmental elements include weather, hydrology, and oceans.
In the complex system self-adaptive method supporting mapping dimension increase, the human elements are commanders and security personnel; the elements of the aircraft are different types of aircraft.
In the above-mentioned complex system adaptive method supporting mapping dimension-up, the step (2) classifies system main elements or environment elements according to functions, classifies the same class of elements into the same hierarchy, that is, the same hierarchy contains at least one element set composed of the same class of elements, and the built hierarchy structure is composed of a plurality of element sets.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, the association relationship between two elements in different levels in the step (3) and the association relationship between two elements in the same level include a control constraint relationship or a data association relationship, where the control constraint relationship is classified into a guarantee device constraint relationship, a guarantee flow constraint relationship or a guarantee position constraint relationship, and an association relationship judgment matrix is established according to the guarantee device constraint relationship, the guarantee flow constraint relationship, the guarantee position constraint relationship or the data association relationship, and each value in the association relationship judgment matrix represents an association relationship type between two elements.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, in the step (4), a multi-main element multi-dimensional association network is constructed according to the association relation judgment matrix between the elements, the association relation of the same type is divided into the same network, and all the association relation networks of different types are overlapped to form the multi-main element multi-dimensional association network.
In the complex system self-adaptive method supporting mapping dimension increase, the established multi-scale models of different levels of elements are a human resource model, a work flow model or a guarantee resource model according to the complex system multi-main element multi-dimensional association network.
In the above complex system adaptive method supporting mapping dimension increase, the method for constructing the human resource model is as follows:
(1) Constructing a personnel individual model according to the personnel work type, wherein the personnel individual model comprises a cognitive fatigue model or a physical fatigue model;
(2) And constructing a personnel team model according to the personnel individual model, wherein the personnel team model comprises the personnel individual model and team characteristic elements.
In the complex system self-adaptive method supporting mapping dimension increase, a cognitive fatigue model is built according to command personnel, and a physical fatigue model is built according to guarantee personnel; the team feature elements include a team work target and a work environment.
In the complex system self-adaptive method supporting mapping dimension increase, the operation flow model comprises defining primitive types and data structures thereof; the primitive types include an active node, a sequence node, a gateway node, or an event node.
In the complex system self-adaptive method supporting mapping dimension increase, the guarantee resource model comprises static properties and dynamic behaviors; the static attribute comprises the position of the guarantee resources, the quantity of the guarantee resources and the guarantee objects, and the dynamic behavior comprises a calculation formula of the use time of the guarantee resources.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, the complex system includes an aviation support system or a combat system.
In the above-mentioned complex system adaptive method supporting mapping dimension-lifting, according to the complex system multi-main element multi-dimensional association network and the complex system resource multi-scale model, the complex system adaptive scheduling is performed, and the specific method for forming the required system scheduling scheme is as follows:
(1) Injecting a fault model of the complex system guarantee resource into a complex system multi-main-element multidimensional association network to form a multi-main-element fault association network;
(2) Constructing a complex system resource optimization multi-objective planning model according to the multi-main-body element fault correlation network and the complex system resource multi-scale model;
(3) And receiving task demands of the complex system, and optimizing a multi-objective planning model according to the complex system resources to obtain an optimal complex system resource scheduling solution, thereby obtaining a required system scheduling scheme.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, the fault model of the complex system guarantee resource in the step (1) includes fault types, different types of fault occurrence probabilities and different types of fault occurrence times.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, in the step (2), a complex system resource optimization multi-objective planning model is formed according to the association constraint relation among elements in the multi-main element fault association network and the complex system resource multi-scale model, wherein the multi-objective planning model is a set of a guarantee operation time model, a guarantee position selection model, a guarantee position conflict occupation model and a guarantee equipment conflict occupation model.
In the above complex system adaptive method supporting mapping dimension increase, the guarantee operation time model is a guarantee operation working mode and a guarantee operation time, wherein the guarantee operation working mode is as follows: after each guarantee operation starts, the guarantee operation cannot be stopped until the guarantee operation is finished, and the working time of the guarantee operation is the sum of the preparation time and the execution time.
In the above complex system adaptive method supporting mapping dimension increase, the guarantee position selection model is: corresponding guarantee equipment is arranged at a guarantee position where the guarantee operation is located, and the guarantee position is in an idle available state currently.
In the above complex system adaptive method supporting mapping dimension increase, the guarantee part conflict occupation model is: when more than one guarantee object needs to carry out guarantee operation on the same guarantee position, the guarantee object with the highest priority is selected for carrying out the guarantee operation.
In the complex system self-adaptive method supporting mapping dimension increase, the conflict occupation model of the guarantee device is as follows: when more than one guarantee object needs to use the same guarantee device to carry out the guarantee operation, selecting the guarantee object with the highest priority to use the guarantee device.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, the specific method for receiving the task demand of the complex system in the step (3) and calculating to obtain the optimal complex system resource scheduling solution according to the complex system resource optimization multi-objective planning model is as follows:
(3.1) receiving task demands of a complex system, and generating an initial rough solution set by using a random sampling method, wherein the initial rough solution set comprises a guarantee object element, a guarantee part element, a guarantee equipment element, a guarantee operation element, a guarantee time element and an association relation among the elements, and the association relation meets element association constraint relation in a multi-main-body element fault association network;
(3.2) according to a complex system resource optimization multi-objective planning model, sequencing the solutions in the initial rough solution set, and selecting the first N solutions as an optimal solution set;
(3.3) obtaining an optimal scheduling solution by using an improved genetic algorithm according to the optimal solution set.
In the above complex system adaptive method supporting mapping dimension increase, in the step (3.2), the solutions in the initial coarse solution set are ordered, and an ordering rule is as follows: the method comprises the steps of comprehensively calculating the score of a solution according to set weights by taking the shortest guarantee time and the least guarantee object movement times as targets, wherein the smaller the score is, the better the solution is, and the earlier the sequencing is; and (3.2) the value of N in the step (3.2) is 30-100.
In the above complex system adaptive method supporting mapping dimension increase, the score calculation formula of the solution is as follows:
wherein: score i Score of ith solution, W ST To ensure the weight of time, ST i For the guard time of the ith solution, max (ST N ) In N solutionsGuaranteeing the maximum value of time, W SM SM for weight of number of movements i For the number of movements of the ith solution, max (ST M ) Maximum number of shifts in N solutions, where W ST +W SM =1, each solution value interval is [0,1]。
In the above-mentioned complex system adaptive method supporting mapping dimension increase, the task requirements of the complex system in the step (3.1) are as follows: providing a guarantee resource and a guarantee position, carrying out Y guarantee operations for N different types of guarantee objects, wherein the required guarantee operation can only adopt the required guarantee resource to guarantee at the required guarantee position; the guarantee resource comprises M guarantee devices, Z guarantee positions and T guarantee operation time Y Wherein N, M, Y, Z is a positive integer.
In the above-mentioned complex system adaptive method supporting mapping dimension-lifting, the specific method for obtaining an optimal scheduling solution by using the improved genetic algorithm in the step (3.3) according to the optimal solution set is as follows:
(3.3.1) performing cross operation on N initial solutions in the optimal solution set to obtain N crossed solutions;
(3.3.2) performing mutation operation on the N solutions formed after the crossing to obtain N mutated solutions;
(3.3.3), calculating the scheduling time of each solution for the original N solutions, the N solutions after crossing and the N solutions after mutation, selecting N solutions with the shortest scheduling time as new initial solutions, and entering the step (3.3.4);
(3.3.4), if the set iteration number is reached, entering a step (3.3.5), and if the set iteration number is not reached, returning to the step (3.3.1);
and (3.3.5) selecting a solution with the shortest scheduling time from the N new initial solutions as an optimal scheduling scheme.
In the above-mentioned complex system adaptive method supporting mapping dimension-lifting, the step (3.3.1) performs the cross operation on N initial solutions in the optimal solution set by adopting the following four methods:
Randomly selecting a guarantee object j, and intersecting the guarantee object j in the i initial solution and the i+1 initial solution;
randomly selecting one guarantee object j, and respectively and correspondingly crossing the 1 st to the j-th guarantee objects in the i-th initial solution and the i+1-th initial solution;
randomly selecting a guarantee object j, randomly selecting a position node t with a guarantee position changed in the ith and the (i+1) th initial solutions of the guarantee object j, and intersecting the guarantee operation after the position node t in the ith and the (i+1) th initial solutions;
and (c) randomly selecting a guarantee object j, finding all the same positions of the guarantee object j in the ith and the (i+1) th initial solutions, randomly selecting the same position m, and intersecting the guarantee equipment used by the ith and the (i+1) th initial solutions in the position m.
In the above-mentioned complex system adaptive method supporting mapping dimension increase, in the step (3.3.2), the following three methods are adopted to perform mutation operation on the N solutions formed after the intersection:
randomly selecting a guarantee object j for an ith solution, finding out all position nodes of the guarantee object j, which are subjected to position change, randomly selecting a position node t from the position nodes, and carrying out mutation on the position node t, wherein the mutation operation is to randomly select one guarantee position from all the guarantee positions meeting the operation requirement of the position node t and ensure equipment meeting the operation requirement in the position to replace the position node t and ensure equipment of the position node t;
Randomly selecting a guarantee object j for the ith solution, finding out position nodes with position change of the guarantee object j, randomly selecting a position node t from the position nodes t, mutating all position nodes after the position node t, wherein mutation operation is to traverse the position node t to a last node, randomly selecting one guarantee position from all guarantee positions meeting operation requirements of the node t and guarantee devices meeting operation requirements in the position to replace the position node t and the guarantee devices of the node t, randomly selecting one guarantee position from all guarantee positions meeting operation requirements of the node t+1 and the guarantee devices meeting operation requirements in the position to replace the position node t+1 and the guarantee devices of the node t+1, … …, and so on until one guarantee position is randomly selected from all guarantee positions meeting operation requirements of the last node and the guarantee devices meeting operation requirements in the position to replace the last position node and the guarantee devices of the last position node;
and (3) randomly selecting a guarantee object j for the ith solution, randomly selecting a guarantee operation, and mutating the guarantee position and the guarantee equipment used by the guarantee operation.
A complex system self-adaptive system supporting mapping dimension increase comprises an element set construction module, a multi-dimensional association network construction module, a multi-scale model construction module and a system self-adaptive scheduling scheme construction module, wherein:
the element set construction module: decomposing the complex system layer by layer according to the capability to an element level to form a complex system element set, and sending the complex system element set to a multidimensional associated network construction module;
the multidimensional association network construction module: extracting association relations among elements from the complex system element set layer by layer, respectively establishing association networks among elements of the same level and elements of different levels, forming a multi-main element multi-dimensional association network of the complex system, and transmitting the multi-main element multi-dimensional association network to a multi-scale model building module and a system self-adaptive scheduling scheme building module;
the multi-scale model building module: according to the multi-main-body element multi-dimensional association network of the complex system, complex system resource multi-scale models of elements of different levels are established and sent to a system self-adaptive scheduling scheme establishment module;
and a system self-adaptive scheduling scheme establishing module: and carrying out self-adaptive scheduling on the complex system according to the complex system multi-main-element multi-dimensional association network and the complex system resource multi-scale model to form a required system scheduling scheme.
In the above-mentioned complex system adaptive system supporting mapping dimension increase, the multi-dimensional association network construction module extracts association relations among elements layer by layer from the complex system element set, and respectively establishes association networks among elements of the same level and elements of different levels, and the specific method for forming the complex system multi-main element multi-dimensional association network is as follows:
(1) Dividing elements in the complex system element set into a system main element and an environment element;
(2) Classifying the system main body elements and the environment elements to establish a hierarchical structure diagram, wherein the hierarchical structure diagram is a set of each hierarchical element;
(3) Extracting the association relations among the elements in different levels and the association relations among the elements in the same level, classifying the association relations, and constructing an association relation judgment matrix among the elements;
(4) And constructing a multi-main-body element multi-dimensional association network according to the association relation judgment matrix among the elements, wherein the multi-main-body element multi-dimensional association network comprises association networks among elements in different levels and association networks among elements in the same level.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention excavates the association relation among system elements based on continuously accumulated internal field simulation data and external field test data, then builds a multi-main-element multi-dimensional association network of the complex system by adopting a complex network method, builds an element multi-agent model based on the data, and further realizes the self-adaptive scheduling of the complex system including an aviation guarantee system, a combat weapon system and the like facing the capability requirement, and finally solves the adaptability problem of the complex system.
(2) The invention realizes comprehensive and systematic characterization of the internal mechanism of the complex system by fully excavating the multi-constraint association relation among node elements, thereby supporting the dimension-increasing mapping of the complex system;
(3) According to the invention, through modeling the multi-element hierarchy of the complex system and combining heuristic rules and improved genetic algorithm, the efficient solution of the complex problem target is realized, and the target scheme optimization under the multi-constraint condition is realized;
(4) In the invention, in the process of carrying out self-adaptive scheduling of a complex system according to a multi-dimensional association network and a multi-scale model, firstly, a fault model of a complex system guarantee resource is injected into the multi-main-element multi-dimensional association network of the complex system to form a multi-main-element fault association network; then constructing a complex system resource optimization multi-objective planning model according to the multi-main-body element fault correlation network and the complex system resource multi-scale model; and finally, receiving task demands of the complex system, optimizing a multi-objective planning model according to complex system resources to obtain an optimal complex system resource scheduling solution.
(5) The invention provides a complex system large-scale limited resource scheduling model with space constraint for the first time, considers various complex constraints of space, personnel, station position assurance and equipment assurance, introduces and improves an intelligent algorithm, and obtains good optimizing effect and higher operation efficiency;
(6) The invention builds a flexible and variable complex system multi-target planning model for the first time, and the model is expandable, reconfigurable, diversified and modularized, so that the resource allocation of almost all types and resource-variable complex systems can be supported under the condition of complete data, and the application range is wide;
(7) The invention provides a complete complex system optimal scheduling solving method for the first time, and a method for quickly obtaining a feasible solution by using an intelligent algorithm, a construction algorithm and the like, thereby breaking through the limitation and one-sided property in the traditional complex system modeling scheduling;
(8) The method provided by the invention is applied to a complex system, for example, an aviation security system, solves the problem of multi-objective optimization of aviation security resource elements under a large-scale constraint condition, and can calculate a better scheduling scheme aiming at a specific security task, so that the system can finish various security operations of all security objects by utilizing the existing security resources in the shortest time, the balance rate of the use of the aviation security resources is improved, the security flow is more reasonable, the comprehensive efficiency of the aviation security system is improved, and the comprehensive capacity of the aviation security system is improved to the greatest extent.
(9) The method and the system can be applied to various complex systems including aviation guarantee systems, combat weapon systems and the like, have strong universality and practicability, and are suitable for the self-adaptive scheduling control of 'man-machine-ring' complex systems.
Drawings
FIG. 1 is a diagram of a conventional dimension-reduction analysis method;
FIG. 2 is a schematic diagram of a complex system adaptive approach to support mapping upscales in accordance with the present invention;
FIG. 3 is a flow chart of a complex system adaptation method supporting mapping dimension increase in accordance with the present invention;
FIG. 4 is a schematic diagram of an element set of an aerospace security system according to an embodiment of the invention;
FIG. 5 is a diagram of a human resources model architecture of the present invention;
FIG. 6 is a flow chart of a complex system resource optimization adaptive scheduling method of the present invention;
FIG. 7 is a flowchart of an optimization scheduling method of an aviation support system in an embodiment of the invention;
FIG. 8 is a graph showing the convergence of the improved genetic algorithm in an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and specific examples:
FIG. 2 is a schematic diagram of a complex system adaptive method supporting mapping dimension increase according to the present invention; FIG. 3 is a flowchart of a method for supporting mapping up-scaling in a complex system according to the present invention, where the method for supporting mapping up-scaling in a complex system according to the present invention includes the following steps:
1. And decomposing the complex system layer by layer according to the capability to an element level to form a complex system element set. According to the invention, the complex system is decomposed layer by layer according to the capability, and at least two layers are decomposed to element level. As shown in fig. 2, in one embodiment, the complex system may be divided into subsystem 1, subsystem 2, … …, subsystem N, and subsystem N, which in turn is broken down into a set of elements.
Fig. 4 is a schematic diagram of an element set of an aviation support system in an embodiment of the present invention, and takes the aviation support system as an example, various entity elements related to the aviation support system are analyzed, and the entity elements are mainly divided into a system main element and an environment element. The method mainly comprises the following steps:
main elements of complex system for aviation security
The elements related to the aviation security system are mainly the elements of the aviation security system, as shown in fig. 4, and the elements of the aviation security system mainly comprise the elements of a ship, the elements of an airplane and the elements of a person, and the elements of the ship are divided into security equipment elements and security array elements.
1. The guarantee equipment elements mainly comprise various equipment resource elements related to an aviation guarantee system, and specifically comprise oiling equipment elements, power supply equipment elements, oxygen supply equipment elements and the like.
2. The guarantee array element mainly comprises space resource elements related to an aviation guarantee system and is used for stopping an airplane and executing related operations, such as a take-off part, a guarantee part and the like;
3. The elements of the aircraft mainly refer to the objects of aviation guarantee operation, and can be divided into fixed wing aircraft, helicopters and the like according to types;
4. the factors of personnel mainly refer to various personnel involved in the aviation security operation process, and according to different work types, the personnel can be classified into command personnel, security personnel and the like, and the security personnel can be classified into machine service security personnel and service security personnel.
(II) aviation security complex system environment element
The method is mainly related environmental influence factors including weather, hydrology and ocean in the operation process of the aviation support system.
2. The association relation among elements of the complex system element set is extracted layer by layer, association networks among elements of the same level and elements of different levels are respectively established, and a multi-main-body element multi-dimensional association network of the complex system is formed, and the specific method is as follows:
first, the elements in the complex system element set are divided into a system main element and an environment element. For example, taking an aviation security system as an example, the main system elements are human elements, airplane elements and ship elements, and the ship elements are divided into security equipment elements and security array elements; environmental elements are weather, hydrology and sea. The human elements are commanders and security personnel; the elements of an aircraft are different types of aircraft.
And secondly, classifying the system main body elements and the environment elements, classifying the same class elements into the same hierarchy, namely, the same hierarchy comprises at least one element set formed by the same class elements, and the established hierarchy structure diagram consists of a plurality of element sets. The hierarchy structure is a collection of elements of each hierarchy.
For example: the system main body element and the environment element belong to the same hierarchy, and the guarantee equipment element and the guarantee array element in the personnel element, the airplane element and the ship element belong to the same hierarchy, so that the element set formed by the guarantee equipment and the element set formed by the guarantee array element belong to the same hierarchy, and the hierarchy comprises the element sets formed by various airplanes.
And thirdly, extracting the association relations among the elements in different levels and the association relations among the elements in the same level, classifying the association relations, and constructing an association relation judgment matrix among the elements. The association relation between two elements in different levels and the association relation between two elements in the same level in the step comprise control constraint relation or data association relation, wherein the control constraint relation is divided into a guarantee equipment constraint relation, a guarantee flow constraint relation or a guarantee position constraint relation, an association relation judgment matrix is established according to the guarantee equipment constraint relation, the guarantee flow constraint relation, the guarantee position constraint relation or the data association relation, and each value in the association relation judgment matrix represents the association relation type between the two elements. The data association relationship includes a data output association and a data input association.
And fourthly, constructing a multi-main-body element multi-dimensional association network according to the association relation judgment matrix among the elements, wherein the multi-main-body element multi-dimensional association network comprises association networks among the elements in different levels and association networks among the elements in the same level. Dividing the same type of association relationship into the same network, and superposing all the association relationship networks of different types to form a multi-main-body element multi-dimensional association network. In an alternative embodiment of the invention, a complex network modeling method may be used to construct a multi-principal element multidimensional association network, for example, a scaleless network model construction method may be used.
For the overall analysis of a complex system, the complex network provides a very effective method, which can effectively reflect the overall properties of the system. The relation of each element in the system is represented by a network-like structure, and elements in a network layer can mutually influence and mutually dominate, and the network-like structure is not a simple hierarchical structure; the method is a multi-objective decision method suitable for processing dependence and feedback relationship, can effectively and scientifically describe the coupling association relationship among multiple elements, is suitable for describing a complex system of internal dependence and feedback effect, and realizes complex mapping dimension increase.
Taking an aviation security system as an example, the multi-main-body element multi-dimensional association network generation of the aviation security complex system comprises the following points:
1. extraction of association relation among multiple main body elements of aviation support system
The method comprises the steps of classifying association relations among multiple main body elements, and classifying control constraint relations and data association relations according to the association relations among two elements among different levels of an aviation support system and the association relations among two elements in the same level.
(1) The control constraint relation comprises a guarantee flow constraint relation, a guarantee equipment constraint relation and a guarantee position constraint relation, and the concrete description of the guarantee equipment constraint relation, the guarantee flow constraint relation and the guarantee position constraint relation is as follows:
(1.1) ensuring Process constraint relationship
The serial-parallel connection relation of aviation guarantee operation events is expressed in a grouping form, the operation events among groups are serial operation events, the next group operation event can be started after all the previous group operation events are finished, the operation events in the groups are parallel operation events, the operation events in the same group can be started at the same time, and the grouping situation is as shown in table 1:
TABLE 1 operation series-parallel Condition schematic
S i,j ≥C i',j
Wherein S is i,j Starting time of ith operation for jth frame machine, C i',j The end time of the ith operation is carried out for the jth machine, the ith operation event is the successor operation event of the ith operation event, and the successor constraint indicates that the successor operation event can not start to be executed until all the successor operation events are completed.
S i,j ＝S i',j Or S i,j >S i',j Or S i,j <S i',j
Wherein: s is S i,j Start time of ith operation for jth frame machine, S i',j And (3) starting time of the ith operation for the jth machine, wherein the ith operation event is a parallelizable operation event of the ith operation event, and the parallelization constraint shows that two parallelizable works can be executed simultaneously without influencing each other.
(1.2) ensuring Equipment constraint relationship
In the aviation security operation process, a specific operation event requires a specific type of security equipment. In the aviation security system, each type of security equipment can only be used when one operation event is guaranteed, and at most one type of security equipment can be used for each operation event, and if the operation event i does not need to use the security equipment, all the aircrafts j and M are subjected to the operation event i i,j =0, each guarantee device can only be used by one aircraft at the same time:
i.e. for any two planes j, j', if M i,j ＝M i,j' Not equal to 0), then: s is S i,j ≥C i,j' Or S i,j' ≥C i,j The method comprises the steps of carrying out a first treatment on the surface of the Wherein S is i,j Starting time of ith operation for jth frame machine, C i',j Ending of the ith' job for the jth frameTime, S i,j' Starting time of ith operation for jth frame machine, C i,j And (5) finishing time of the ith operation for the jth frame. The list of required security devices for a job event is shown in table 2 below.
TABLE 2 required assurance device for an operation event
Job event Type of equipment to be secured
Dispatching and transporting Without any means for
Mooring of Without any means for
Cabin air conditioner ventilation Air-conditioning gas-I-cabin
Equipment cabin air conditioner ventilation Air-conditioning gas-I-equipment cabin
Hydraulic pressure guarantee Hydraulic-I
Nitrogen gas guarantee Nitrogen-I
Oxygen safeguard oxygen-I
In the process of aviation security, due to the limitation of electric power, command personnel and the like, each operation event of aviation security has a fixed number of simultaneously available aircrafts, for example, due to the limitation of electric power supply, the aircraft power security can only allow N aircrafts to be simultaneously secured:
(1.3) ensuring the array position constraint relation
In the ship surface guarantee process, each operation event of each aircraft can be completed only at one guarantee position, and the operation event can be started to be executed after the aircraft reaches a specified guarantee array position:
namely, for any aircraft j and any operation event i thereof, the following steps are provided:
S i,j ≥SP i,j
wherein: s is S i,j Start time of ith job for jth shelf machine, SP i,j The start time for aircraft j to reach the location where job i is performed.
Each guarantee position can be used for a plurality of aircrafts to finish a plurality of operation events in the whole aviation guarantee process, but each guarantee position can be used by only one aircraft at the same time:
i.e. for any j, j 'two aircraft and any operational event i, i' if B i,j ＝B i',j' Then:
SP i,j ≥CP i',j' or SP i',j' ≥CP i,j
Wherein: b (B) i,j The position where the operation i is carried out for the airplane j is B i',j' The SP is the position where the operation i' is performed for the aircraft j i,j For the start time of aircraft j to the position where job i is performed, CP i',j' For the ending time of the j 'th aircraft at the position where the i' th operation is performed, SP i',j' For the start time of arrival of the aircraft j 'at the location where the job i' is performed, CP i,j The ending time of the j-th aircraft at the position where the i-th operation is performed.
If two continuous operation events of a certain aircraft are not completed at the same guarantee position, the subsequent tasks can start to use the guarantee position appointed by the operation event after the previous tasks are ended and the subsequent tasks are moved to the appointed guarantee position:
in the course of the flight safeguarding, each type of safeguarding equipment is limited, and the safeguarding equipment is uniformly distributed on the deck surface. Each type of security equipment has a fixed security range, such as jet fuel equipment, and because of the length limitation of the refueling hose, each refueling equipment can only be used by the aircraft parked on the security site within the required range, so that the coupling constraint of the security array site and the security equipment is formed, that is, the aircraft on each security array site can only use the security equipment associated with the security array site.
(1.4) ensuring personnel constraint relationship
In the aviation security operation process, besides the description, related security personnel participate, namely, security personnel constraint relations are also reserved. If the oiling operation needs oiling personnel, the transferring operation needs transferring personnel and the like, and meanwhile, the quantity of the guaranteeing personnel is limited, and the guaranteeing operation which can be simultaneously supported is limited, so that the fatigue, quantity and other limiting constraints of the personnel are comprehensively considered, and constraint conditions exist for personnel allocation of each guaranteeing operation.
Further, the data association relationship includes a data output association and a data input association.
2. Construction of association relation judgment matrix of aviation support system
Based on the association relation of the carding, the following element judgment matrix is constructed. Wherein, various constraint relations exist among each element, such as fueling operation and fueling equipment, namely, a guarantee equipment constraint relation exists, which indicates whether the fueling equipment can perform fueling operation or not in the current state; meanwhile, there is a data association relationship, namely, how long the oiling equipment can support the oiling operation, and the following table 3 shows an aviation support system association relationship judgment matrix:
table 3 aviation support system association relation judgment matrix
3. Multi-main-element multi-dimensional association network generation of aviation support complex system
The aviation guarantee element association networks with different levels and different dimensions are generated based on the association relation judgment matrix, the networks with different dimensions can be generated according to the association relation types, meanwhile, superposition of multi-dimensional networks can be realized, and further more complex association networks are generated. The connection between two nodes in the network represents the association type and coupling strength between two main bodies.
3. And establishing a complex system resource multi-scale model of different levels of elements according to the complex system multi-main element multi-dimensional association network, wherein the model is a human resource model, a workflow model or a guarantee resource model.
The construction method of the human resource model comprises the following steps:
1. constructing a personnel individual model according to the personnel work type, wherein the personnel individual model comprises a cognitive fatigue model or a physical fatigue model; and constructing a cognitive fatigue model according to command personnel and constructing a physical fatigue model according to guarantee personnel.
2. And constructing a personnel team model according to the personnel individual model, wherein the personnel team model comprises the personnel individual model and team characteristic elements. The team feature elements include team work objectives and work environments.
The workflow model comprises defining primitive types and data structures thereof; primitive types include active nodes, sequence nodes, gateway nodes, or event nodes.
The guarantee resource model comprises static properties and dynamic behaviors; the static attribute comprises the position of the guarantee resources, the quantity of the guarantee resources and the guarantee objects, and the dynamic behavior comprises a calculation formula of the use time of the guarantee resources. And the calculation formula of the guaranteed resource use time is calculated by adopting a mathematical statistics method according to the collected guaranteed resource operation use data.
Taking an aviation support system as an example, building multi-scale element models of different levels according to a complex system element network. The oiling guarantee resource model and the operation flow model are mainly as follows:
1. oiling guarantee resource model construction
In order to meet the self-use scheduling requirement of a complex system, key parameters influencing an optimal scheduling model, such as the position and the number of the guaranteed resources, the type of the guaranteed objects, the guarantee time of the operation event corresponding to the guaranteed objects and the like, need to be examined. The position, the number and the type of the guaranteed resources are determined according to the actual operation business of the complex system. The influence of the guarantee resource on the type of the guarantee object and the proceeding time of the operation event is needed to be performed by using a mode of combining dynamic calculation and statistical analysis.
Firstly, according to the mechanism analysis of the operation process of the gas station device, the following theoretical fueling process model can be obtained:
according to the input and output variables of the existing real ship measurement data book, the formula can be simplified appropriately, and a basic fueling time prediction model is obtained. The other input variables (such as cross-sectional area, height difference, etc.) in the above formula are mainly replaced by coefficients, so that the basic fueling time prediction model can be obtained as follows:
wherein: m is the accumulated total fueling amount; f is the fuel pressure in the metal pipe; q is the flow rate of the fuel in the metal tube, alpha 1 、α 2 The coefficient is corrected for the kinetic energy of the relative section.
2. Workflow model construction
The construction of the operation flow model is mainly based on BPMN specification, the node type of the design primitive and the semantic description thereof are mainly composed of the following nodes: active node, sequence node, gateway node, event node. The logical semantics of each node are expressed as follows:
definition 1 Activity node mainly describes various attribute information of a certain Activity, and the main expression is as follows:
Activity＝(BasInfo,IOPE)
BasInfo＝(ID,Name.Resources,User,Type,…)；
IOPE＝(InputPara,OutputPara,StartCondition,EndCondition)
in the definition, basInfo represents basic attribute information of the active node, and mainly includes information such as active codes, names, resource configurations, personnel configurations, types and the like. The IOPE represents the input parameters (InputPara), output parameters (OutputPara), start activation conditions (StartCondition) and end activation conditions (EndCondition) 4-aspect attributes that define an activity using service oriented technology (SOA). The concrete representation is as follows:
InputPara＝(IPGroupType,IPOrigin,IPName,IPID,IPDataType,IPValue)
OutputPara＝(OPOrigin,OPName,OPID,OPDataType,OPMethod)
StartCondition＝(ScType,OriginNodeID,OriginNodeOutEvent)
EndCondition＝(EcType,OriginNodeID,OriginNodeOutEvent,OriginRel)
In the above definition:
InputPara refers to various parameters required by the activity and its simulator to execute, including parameter group name, parameter source type (custom parameter, upper layer reference parameter, same layer reference parameter), parameter name, parameter code, parameter data type and parameter initial value;
OutputPara refers to data generated during active runs. Includes parameter output source type (user-defined parameter, upper layer reference parameter and same layer reference parameter), output parameter name, coding, data type and parameter output mode (settlement output divided into activity and simulator output)
StartCondition refers to a precondition for activity execution, and may be plural. The main information comprises an activation type (active node activation, event node activation, sequence node activation, logic node activation), an activation source node code, an output event of a source node (if the source node is an active node, the source node is divided into an active start, an active end, a settlement start and a settlement end) and the like;
EndCondition refers to a precondition for the end of an activity, and may be plural. The main information includes activation type, source node code, source node output event, the data information is similar to the related parameter information in StartCondition, and OrigineRel represents the logical relationship between end conditions, mainly the AND, OR relationship.
A gateway node (Gate) is defined 2, which mainly realizes the expression of various control relations among the activities in the process and mainly comprises nodes of class 5 of connection, branch, or branch judgment. Formalized representation of the above nodes is as follows:
Gate＝(GType,GName,GId,GSCondition)
GSCondition＝(ScType,OriginNodeID,OriginNodeOutEvent)
in the above definition, the attributes of Gate mainly include gateway type, name, code, and activation condition, where the activation condition GSCondition is similar to the start activation condition StartCondition of Activity. In addition, for the determination gateway, P0 is an input parameter, P1, P2, and P3 are output branch determination parameters, and if p1=p0, the flow selects the P1 branch, and similarly p2=p0, the flow selects the P2 branch.
Define 3 Event nodes (Event), mainly including start Event (StartEvent), intermediate Event (MiddleEvent), end Event (EndEvent). And satisfies the following conditions:
a: a sequence only allows 1 start event node, multiple end event nodes and multiple intermediate event nodes;
b: the starting event node has no activation condition, and the sequence starting defaults to the starting event node activation; the intermediate event node and the end event node are provided with activating conditions;
c: when the sequence flow is executed, the start node is used as the flow start state, the end event node is used as the flow end state, and the activation conditions of a plurality of end event nodes are satisfied.
The above events are represented as follows:
Event＝(EType,EName,EId,ESCondition)
where EType is the event type, EName is the event name, EId is the event code, ESCondition is the StartCondition of the Start activation condition with event activation conditions similar to Activity.
A4-sequence (Flow) node is defined, which is a basic unit for packaging and scheduling the operation Flow, and is formed by combining the above nodes according to a certain rule, wherein the operation Flow with a certain function is constructed according to a certain specific service. The tuples are represented as follows:
Flow＝(FBasInfo,FComInfo,FCondition,Nodes)
FBasInfo＝(FID,FName.FType,FStatus,FCreatTim)
wherein: FBasInfo mainly refers to basic information of a sequence, and includes a sequence code (the code is a unique identifier of the sequence in a sequence library), a sequence name, a service type to which the sequence belongs, and a current release state of the sequence (including newly created, edited, released and the like). FComInfo mainly refers to a common information part of a sequence, is a parameter interface and an event interface which are externally provided by the sequence as a whole, and mainly comprises two parts (shown below) of a sequence parameter (FlowPara) and a sequence event (FlowEvent). Fconditions refer to the activation conditions of a sequence when the sequence is an encapsulation unit, also similar to the start activation condition StartCondition description of Activity. Nodes is a collection of various Nodes within the sequence.
FComInfo＝(FlowPara,FlowEvent)
The above is a definition of common information of a sequence, wherein the flowPara includes a sequence operation parameter, a sequence resource parameter, and a sequence personnel parameter, and the parameter information can be referred to for each node in the sequence. The tuples are represented as follows:
FlowPara＝(RunPara,RsPara,UserPara)
RunPara＝(ParaTyoe,ParaName,ParaID,ParaDataType,ParaOriginID,ParaValue)
RsPara＝(RsType,RsCode,RsID,RsVsersion,OriginRsCode)
UserPara＝(RoleName,UserCode,UserID,OriginUserCode)
the sequence event FlowEvent mainly comprises a sequence input event and a sequence output event, and can be expressed as follows by using multiple groups:
FlowEvent＝(InputEvent,OutputEvent)
InputEvent＝(EventType,EventID,OriginEventID)
OutputEvent＝(EventID,OrigintID,OriginIDOutEvent)。
3. human resource model
Fig. 5 shows a human resource model architecture diagram of the present invention, and a personnel individual model is constructed according to personnel types related to an aviation security system. The personnel individual model comprises a cognitive fatigue model and a physical fatigue model; the cognitive fatigue model is mainly used for modeling for commanders mainly engaged in mental labor, such as flight commanders, carrier landing commanders and the like; the physical fatigue model is mainly used for modeling for operators who are mainly engaged in physical labor, such as oiling guaranteeing personnel, bullet hanging guaranteeing personnel and the like.
And constructing a personnel team model according to the personnel individual model, wherein the personnel team model comprises the personnel individual model and team characteristic elements. For example, a team in fueling mainly comprises fueling security personnel, and the team model in fueling mainly comprises fueling objects, fueling time of different objects, fueling environment and other condition requirements.
4. According to the complex system multi-main element multidimensional association network and the complex system resource multi-scale model, the complex system self-adaptive scheduling is carried out to form a required system scheduling scheme, and as shown in fig. 6, the flow chart of the complex system resource optimization scheduling method of the invention specifically comprises the following steps:
injecting a fault model of the complex system guarantee resource into a multi-main-element multi-dimensional association network of the complex system to form the multi-main-element fault association network.
The multi-main-body element multi-dimensional association network is a network set formed by overlapping association relation networks of different types, wherein elements of the same type of association relation are divided into the same association relation network. As described above, the above-mentioned association relationship includes a control constraint relationship or a data association relationship, and the control constraint relationship is classified into a security device constraint relationship, a security flow constraint relationship or a security part constraint relationship; the data association includes a data output association or a data input association.
The equipment constraint relation is ensured, for example, the oiling equipment of an aviation support system is taken as an example, and mainly the equipment can only carry out oiling operation on one aircraft at a specified stand at a certain moment, and when a plurality of aircraft simultaneously need to carry out oiling on the equipment, the equipment is sequentially queued for use according to the priority.
The guarantee flow constraint relation, for example, takes an aviation guarantee system as an example, mainly refers to the logic sequence of related guarantee operations, such as that the refueling operation and the supply operation of a certain aircraft cannot be carried out in parallel, and the related guarantee operations can be carried out sequentially.
Ensuring a position constraint relationship, for example, taking an aviation ensuring system as an example, and in the ensuring process, each operation event of each aircraft can be completed only at one ensuring position; each guarantee position can be used for a plurality of aircrafts to finish a plurality of operation events, but each guarantee position can be used for only one aircraft at the same time.
The fault model of the complex system guarantee resource comprises fault types, different types of fault occurrence probabilities and different types of fault occurrence times. The fault types include faults that can be repaired on site, faults that require repair, and faults that cannot be repaired.
Taking the fault injection of an aviation support resource model as an example, aiming at aviation support system resources such as oiling equipment, the probability of faults occurring in the operation process of the aviation support system needs to be considered, so that the faults are described by adopting a homogeneous poisson process { N (t), t is more than or equal to 0}, and three types of faults are considered here:
(1) Small faults that can be repaired on the spot;
(2) The fault to be repaired of the equipment factory is returned;
(3) Failure to repair.
The occurrence probability of the three types of faults is p respectively 1 ,p 2 ,1-p 1 -p 2 At time intervals (0, t]In which the occurrence times of the three faults are N 1 (t)、N 2 (t)、N 3 (t)。
And, the occurrence of the three fault events is independent, and the occurrence interval time compliance parameter of each fault i (i=1, 2, 3) is p i λ (i=1, 2, 3).
Secondly, constructing a complex system resource optimization multi-objective planning model according to the multi-main-body element fault correlation network and the complex system resource multi-scale model, namely forming the complex system resource optimization multi-objective planning model according to the association constraint relation among elements in the multi-main-body element fault correlation network and the complex system resource multi-scale model, wherein the multi-objective planning model is a set of a guarantee operation time model, a guarantee position selection model, a guarantee position conflict occupation model and a guarantee equipment conflict occupation model, and in addition, the complex system resource optimization multi-objective planning model also comprises a conflict occupation model of a tractor for an aviation guarantee system. The association constraint relation comprises a control constraint relation or a data association relation, wherein the control constraint relation is divided into a guarantee equipment constraint relation, a guarantee flow constraint relation or a guarantee part constraint relation; the data association includes a data output association or a data input association.
And (3) guaranteeing a working time model: in order to ensure the working mode of the operation and the working time of the operation, the working mode of the operation is as follows: after each guarantee operation starts, the guarantee operation cannot be stopped until the guarantee operation is finished, and the working time of the guarantee operation is the sum of the preparation time and the execution time.
Ensuring a part selection model: corresponding guarantee equipment is arranged at a guarantee position where the guarantee operation is located, and the guarantee position is in an idle available state currently.
Ensuring a part conflict occupation model: when more than one guarantee object needs to carry out guarantee operation on the same guarantee position, the guarantee object with the highest priority is selected for carrying out the guarantee operation. The priority rule may be set by itself as needed.
Conflict occupation model of guarantee equipment: when more than one guarantee object needs to use the same guarantee device to carry out the guarantee operation, the guarantee device is used by selecting the guarantee object with the highest priority. The priority rule may be set by itself as needed.
The complex system resource multi-scale model comprises a manpower model or a guarantee resource model.
The construction method of the manpower model comprises the following steps:
(1) And constructing a personnel individual model according to the personnel working type, wherein the personnel individual model comprises a cognitive fatigue model and a physical fatigue model, for example, constructing the cognitive fatigue model according to command personnel, and constructing the physical fatigue model according to guarantee personnel (the crew and the service guarantee personnel).
(2) And constructing a personnel team model according to the personnel individual model, wherein the personnel team model comprises the personnel individual model and team characteristic elements, and the team characteristic elements comprise team work targets, work environments and the like.
The guarantee resource model comprises static attributes and dynamic behaviors, wherein the static attributes comprise positions and quantity of guarantee equipment, guarantee positions and guarantee objects, and the dynamic behaviors comprise calculation of guarantee resource use time. And calculating the guaranteed resource use time according to the collected guaranteed resource operation use data by adopting a mathematical statistics method.
Taking an aviation guarantee system resource optimization scheduling multi-target planning model as an example, based on the constraint, the scheduling of aviation guarantee operation needs to solve the following problems: for any aircraft, job event i:
(1) The aircraft completes the operation event i at what guaranteed position;
(2) What the aircraft uses to ensure that the equipment completes the operation event i;
(3) When the aircraft begins a working event i, when the working event i is completed;
(4) When the aircraft enters the warranty location required for it to complete the operational event i, and when it leaves the warranty location used for it to complete the operational event i.
Thus, according to the above model, the following solution method is generated:
(1) Regular model of processing time
After each job event is defined to start, the working process of the job event cannot be stopped, and the completion time of the job event is the start time plus (+) the execution time of the job event.
For any aircraft j, any operational event i, there is:
C i,j ＝S i,j +p ij
wherein: s is S i,j A start time for the job; p is p ij For the execution time of the job event, C i,j Is the completion time of the job.
(2) Selection rule model for guarantee part
If the operation event i requires a safety device, for example a refuelling operation event requires jet fuel, the safety location can only be selected to be carried out at a safety location in the vicinity of which this type of safety device is present.
If the operation event i does not require a security device, such operation event may be completed by parking at any security location. If such a job event i belongs to a parallel set par i If par is i All the work in the group is completed in parallel on a guarantee part without guaranteeing equipment; if par is i If the operation event needing to ensure the equipment exists, the operation event i is executed in parallel with the operation event which is executed earliest and uses the same ensuring part in the group and needs to ensure the equipment:
i∈par i If for all job events ii εpar i ，nM ii For all job events ii, =0
S i,j ＝S ii,j
B i,j ＝B ii,j
SP i,j ＝SP ii,j
i∈par i If there is a job event ii ε par i ，B i,j ＝B ii,j ，nM ii Not equal to 0, then
S i,j ＝min(S ii,j )(ii∈par i ，nM ii ≠0)
SP i,j ＝SP ii,j (S i,j ＝min(S ii,j ))
Wherein: nM (nM) ii The number of equipment required for the operation time ii is ensured, S i,j For the start time of operation i of aircraft j, S ii,j Start time for operation ii of aircraft j, B i,j For plane jThe position where the operation i is performed, B ii,j The position where the operation ii is performed for the aircraft j, SP i,j For the start time of aircraft j to the position where job i is performed, SP ii,j The start time for aircraft j to reach the location where job ii is performed.
(3) Conflict occupation rule model for guarantee part
Because the guaranteeing parts and the guaranteeing equipment are limited, the situation that a plurality of aircrafts use the same guaranteeing part for guaranteeing can occur in the ship surface guaranteeing process, and under the situation, the aircrafts with high take-off priority use the conflict guaranteeing parts first:
namely: if rank is j >rank j' ,B i,j ＝B i',j' Then
SP i',j' ≥CP i,j
Wherein: rank (rank) j 、rank j’ Priority levels of the jth and jth aircraft, B i,j 、B i',j' The operation positions required by the ith operation and the ith operation of the jth aircraft and the jth aircraft are respectively SP i',j' For the start time of the j 'th aircraft to arrive at the position where the i' th operation is performed, CP i,j The ending time of the j-th aircraft at the position where the i-th operation is performed.
Under the condition that the take-off priorities of a plurality of aircrafts are the same, the aircraft with the station processing operation time being shorter uses the guarantee part preferentially. When two aircraft use guarantee positions conflict, the aircraft with shorter processing operation time should be selected to preferentially use the guarantee positions, so that the effect of optimizing and finishing the total ship surface guarantee time can be achieved.
If it isThen
SP i',j' ≥CP i,j
Wherein: i is the processing operation time; SP (service provider) i',j' For the time of the start of the ith operation of the jth aircraft, CP i,j The time of ending the ith operation of the jth aircraft; b (B) i,j 、B i,j' The guarantee positions required by the ith operation and the ith operation of the jth aircraft and the jth aircraft are respectively B ii,j 、B ii,j' The guarantee positions of the jth aircraft and the jth aircraft required in the ii operation are respectively provided, and p is the guarantee position.
(4) Conflict occupation rule model of guarantee equipment
Because the guarantee equipment is limited, the situation that the same guarantee equipment is used for guaranteeing the same operation event of a plurality of aircrafts can occur in the ship surface guarantee, and in the situation, the aircrafts with high take-off priority firstly use conflict guarantee equipment:
namely: if rank is j >rank j' ,M i,j ＝M i,j' Then
S i,j' ≥C i,j
Wherein: m is M i,j 、M i,j' The guarantee equipment is used for the ith operation of the jth aircraft and the jth' aircraft respectively;
rank j 、rank j’ Priority levels of the jth and jth aircraft, S i,j' Starting time of ith operation for jth frame machine, C i,j And (5) finishing time of the ith operation for the jth frame.
If the priorities of the aircraft take off are the same, the aircraft with stronger processing capacity at the guarantee position is firstly enabled to use the guarantee equipment, so that the guarantee position with stronger processing capacity is vacated more quickly to finish the guarantee tasks of the rest aircraft:
namely: if it isThen
S i,j' ≥C i,j
Wherein: m is M i,j 、M i,j' The guarantee equipment is used for the ith operation of the jth aircraft and the jth' aircraft respectively; s is S i,j' Start time of ith operation for jth aircraft, C i,j The end time of the ith operation for the jth aircraft.
(5) Conflict occupation rule model of tractor
In the ship surface guarantee process of the aviation guarantee system, the situation that a plurality of planes move by using the same tractor can occur due to the limited number of the tractors, and the planes are preferably selected to finish moving work by using the tractors at the earliest time.
j 1 ,j 2 ,...,j c Aircraft, i, using tractors 1,2, …, c respectively 1 ,i 2 ,...,i c Respectively, for the operation events completed by the airplanes going to the next guarantee position, then:
for the aircraft and the operation event i about to change the guarantee position to finish, there are
Wherein: CP (control program) i-1,j The time when the ith-1 operation of the jth aircraft is finished; SP (service provider) i',j' Start time, SP, of ith operation for jth aircraft i”,j” For the start time of the ith "operation for the jth" aircraft, carga is the time interval between two uses of the tractor, car i,j Tractor id, car for the j-th work of the j-th machine i”,j” Tractor id used when performing the ith job for the jth shelf machine.
And thirdly, receiving task demands of the complex system, and calculating to obtain an optimal complex system resource scheduling solution according to the complex system resource optimization multi-objective planning model. The specific method comprises the following steps:
1. receiving task demands of a complex system, and generating an initial rough solution set by utilizing a random sampling method, wherein the initial rough solution set comprises a guarantee object, a guarantee part, a guarantee device, a guarantee operation, a guarantee time element and an association relation between the elements, and the association relation meets element association constraint relation in a multi-main-body element fault association network. Taking an aviation guarantee system as an example, taking a guarantee object as an airplane, taking a guarantee position as a stand, wherein the guarantee equipment comprises oiling equipment, power supply equipment, supply equipment and the like, and the guarantee operation comprises oiling operation, power supply operation, dispatching operation and the like, and the guarantee time refers to the time required by a certain airplane to develop a certain guarantee operation at a certain stand, for example, the oiling operation time is 20 minutes.
The task requirements of the complex system are as follows: providing a guarantee resource and a guarantee position, carrying out Y guarantee operations for N guarantee objects, wherein the required guarantee operations can only be guaranteed by adopting the required guarantee resource at the required guarantee position; the guarantee resource comprises M guarantee devices, Z guarantee positions and T guarantee operation time Y Wherein N, M, Y, Z is a positive integer, T in the embodiment of the invention Y The value range of (2) is 1 min-30 min, and the positive integer is taken.
For example, in an alternative embodiment of the present invention, the values are n=5, y=6, m= 7,Z =11, t 1 ＝3，T 2 ＝3，T 3 ＝9，T 4 ＝4，T 5 ＝4，T 6 ＝10。
2. And according to the complex system resource optimization multi-objective planning model, sequencing the solutions in the initial rough solution set according to the shortest time and the least moving times, and selecting the first N solutions as the optimal solution set. The ordering rule is: the method aims at the shortest guarantee time and the least guarantee object moving times, and comprehensively calculates the score of the solution according to the set weight, wherein the smaller the score is, the better the solution is, the earlier the ranking is, and the value of N is preferably 30-100.
The score calculation formula of the solution is as follows:
wherein: score i Score of ith solution, W ST To ensure the weight of time, ST i For the guard time of the ith solution, max (ST N ) For the maximum value of the guard time in N solutions, W SM SM for weight of number of movements i For the ith solutionNumber of movements, max (ST M ) Maximum number of shifts in N solutions, where W ST +W SM =1, the combined score interval for each solution is [0,1]。
For example, assuming that N has a value of 30, the guarantee time of solution a with the longest guarantee time among the 30 solutions is 60min, the number of movements is 2, the guarantee time of solution b with the largest number of movements is 50min, the number of movements is 3, and the guarantee time of solution c is 40min and the number of movements is 1. Taking weights of 0.7 and 0.3 according to expert experience, obtaining a score of 0.9 of a solution a, a score of 0.8833 of a solution b, a score of 0.5667 of a solution c and so on according to the above formula, calculating the score of 30 solutions, wherein the smallest score represents that the solution is optimal, and selecting the solution with the score arranged in the first 30 bits as an optimal solution set.
3. According to the optimal solution set, an optimal scheduling solution is obtained by utilizing an improved genetic algorithm, and the specific method is as follows:
(1) And performing cross operation on N initial solutions in the optimal solution set to obtain N crossed solutions, wherein the cross operation can be performed by adopting the following four methods:
The method (1.1) randomly selects one guarantee object j, and the i-th initial solution and the guarantee object j in the i+1-th initial solution are crossed, namely only one guarantee object j is crossed.
The method (1.2) randomly selects one guarantee object j, and respectively and correspondingly crosses the 1 st to the j (i.e. the first j) guarantee objects in the i initial solution and the i+1 initial solution; namely, the positions, the devices and the jobs of the first j guarantee objects are respectively crossed, for example, in the ith solution, which operation is carried out at which position of the 1 st to the jth guarantee objects, which devices are adopted, in the (i+1) th solution, which operation is carried out at which position of the 1 st to the jth guarantee objects, which devices are adopted, are also arranged, and the arrangement of the first j guarantee objects of the ith solution and the (i+1) th solution is exchanged, namely, in the two solutions, the 1 st and the 1 st exchanges, the 2 nd exchanges … …, and the j exchanges with the j.
The method (1.3) randomly selects one guarantee object j, randomly selects a position node t with a guarantee position change in the ith and the (i+1) th initial solutions of the guarantee object j, and respectively and correspondingly crosses, namely exchanges, the guarantee operations after the position node t in the ith and the (i+1) th initial solutions.
The method (1.4) randomly selects a guarantee object j, finds all the same positions of the guarantee object j in the ith and the (i+1) th initial solutions, randomly selects the same position m, and crosses the guarantee equipment used by the ith and the (i+1) th initial solutions at the position m.
(2) Performing mutation operation on the N solutions formed after the crossing by adopting the following three methods to obtain N mutated solutions;
the method (2.1) randomly selects one guarantee object j for the ith solution, finds out all position nodes of the guarantee object j, randomly selects one position node t from the position nodes, and mutates the position node t, wherein the mutation operation is to randomly select one guarantee position from all the guarantee positions meeting the operation requirement of the position node t and ensure equipment meeting the operation requirement in the position to replace the position node t and ensure equipment of the position node t.
The method (2.2) comprises the steps of randomly selecting a guarantee object j for an ith solution, finding out position nodes with position change of the guarantee object j, randomly selecting a position node t from the position nodes t, mutating all position nodes at the position node t and later, traversing the position node t to a last node, randomly selecting one guarantee position from all the guarantee positions meeting the operation requirement of the node t and the guarantee devices meeting the operation requirement in the position to replace the guarantee devices of the position node t and the node t, randomly selecting one guarantee position from all the guarantee positions meeting the operation requirement of the node t+1 and the guarantee devices meeting the operation requirement in the position to replace the guarantee devices of the position node t+1 and the node t+1, … …, and so on until one guarantee position from all the guarantee positions meeting the operation requirement of the last node and the guarantee devices meeting the operation requirement in the position to replace the guarantee devices of the last position node and the last position node.
And (2.3) randomly selecting a guarantee object j for the ith solution, randomly selecting a guarantee operation, and mutating a guarantee position and a guarantee device used for the guarantee operation.
(3) The original N solutions, the N solutions after crossing and the N solutions after mutation are summed into 3N solutions, the scheduling time of each solution is calculated according to various constraint rule conditions in the complex system resource optimization multi-objective planning model, N solutions with the shortest scheduling time are selected as new initial solutions, and the step (4) is entered;
(4) If the set iteration times are reached, the step (5) is carried out, and if the set iteration times are not reached, the step (1) is returned;
(5) And selecting a solution with the shortest scheduling time from N new initial solutions with the shortest time obtained finally as an optimal scheduling scheme.
Taking an aviation support system as an example:
according to constraint conditions and multi-objective optimization rules in aviation security operation, security positions and security equipment of each operation event of a group of aircraft with common ship security are determined, then operation scheduling conditions are calculated according to constraint relations among related elements in a multi-dimensional association network, and then the size of an objective function is calculated. Therefore, the complex ship surface guarantee process can be disassembled into the determination process of the guarantee position and the associated guarantee equipment.
How to give a reasonable aircraft guarantee sequence on each guarantee position, prevent the situation that the aircraft has no guarantee, and reasonably arrange corresponding personnel and equipment at the same time, thereby efficiently completing all guarantee operations of the aircraft contained in the whole flight plan.
Because the searching range is too large and exceeds the effective working range of a general intelligent algorithm, the genetic algorithm added with the searching rule can be considered for solving, and finally, in order to prevent the jump-in local optimal solution from being unable to jump out, a proper variation rule is added for solving, and the specific solving process is as follows:
(1) Generating an initial rough solution set by utilizing a random sampling method, wherein the selection of the initial rough solution is in accordance with the availability constraint of a guarantee object, a guarantee position, a guarantee device, a guarantee operation and a guarantee time element, the constraint is obtained according to an element association constraint relation in a main element fault association network, and the representation mode of the initial rough solution is shown in a table 4:
table 4 representation of the coarse solution
(2) According to the initial rough solution set, according to various rule sets in the multi-target planning model and various operation time of an aviation guarantee operation flow, solutions in the initial rough solution set are targeted according to the shortest guarantee time and the minimum guarantee object moving times, the scores of the solutions are calculated according to the set weights, the solutions are ordered, and the first 30 solutions are selected as optimal solution sets.
(3) According to the initial solution set, the first 30 better solutions in the initial solution set are reserved, and an optimal scheduling result is generated and output according to the improved genetic algorithm. The main steps of the improved genetic algorithm are as follows:
(3.1) crossing. 30 solutions are traversed, each time the interval is 2, and the odd solution i and the even solution i+1 are taken. Initial de-interleaving, randomly selecting one of the following 4 interleaving modes:
(3.1.1) one workpiece crossing: and randomly selecting an airplane j, and intersecting the guarantee position and the guarantee equipment of the jth airplane in the ith initial solution and the (i+1) th initial solution.
(3.1.2) multiple workpiece intersections: and randomly selecting an airplane j, and respectively and correspondingly crossing the 1 st to the j th airplanes in the i initial solution and the i+1 initial solution.
(3.1.3) crossing the position of an aircraft in the event of a change in position and corresponding machine: and randomly selecting an airplane j, randomly selecting a node t of the airplane, which is subjected to guarantee position change in the ith and the (i+1) th initial solutions, and respectively carrying out corresponding intersection on guarantee operations after the node t in the ith and the (i+1) th initial solutions.
(3.1.4) machine crossing places with the same position: an aircraft j is randomly selected, all identical positions of the aircraft at the ith and the (i+1) th initial solutions are found, an identical position m is randomly selected, and the machines used for the ith and the (i+1) th initial solutions at the position m are crossed.
After crossing 30 solutions will result.
(3.2) variation. The crossover offspring may be mutated, and mutation operation is performed on 30 solutions formed after the crossover, and one of the following 3 mutation modes is randomly selected:
(3.2.1) random position of a random aircraft and corresponding machine variations: and randomly selecting an airplane i, randomly selecting a node t with the position changed of the airplane, and randomly selecting a guarantee position from all guarantee positions meeting the operation requirement of the position node t and the guarantee equipment capable of carrying out the operation requirement in the position to replace the guarantee equipment of the position node t and the node t.
(3.2.2) a series of positions following the random position of a random aircraft with a change in position and corresponding machine variations: randomly selecting an airplane j, randomly selecting a node t with position change of the airplane, wherein the mutation operation is to traverse the position node t to the last node, randomly selecting one guarantee position and guarantee equipment in the position from all guarantee positions meeting the operation requirement of the node t to replace the guarantee equipment of the position node t and the node t, randomly selecting one guarantee position and the guarantee equipment in the position from all the guarantee positions meeting the operation requirement of the node t+1 to replace the guarantee equipment of the position node t+1 and the node t+1, … …, and so on until randomly selecting one guarantee position and the guarantee equipment in the position from all the guarantee positions meeting the operation requirement of the last node to replace the last position node and the guarantee equipment of the node.
(3.2.3) machine variation for a random position: an aircraft i is randomly selected, an operation j is randomly selected, and the position and machine used for the operation are mutated.
After mutation 30 solutions were generated.
(3) And (5) evaluating. The 30 solutions of the new generation and the old generation (the original 30 solutions, the 30 solutions after crossing and the 30 solutions after mutation) are evaluated in total, and 30 solutions with the shortest time are selected as new initial solutions according to the total scheduling time of each solution.
(4) And (5) iterating. If the set iteration times are not reached, based on the 30 new initial solutions, executing the step 1) again until the iteration is finished, and taking out the optimal solution with the shortest scheduling time from the last 30 optimal solutions as an optimal scheduling scheme.
Specific examples are as follows:
for example, the first solution A is in the form of
For example, the second solution B is in the form of
If a cross-mode is performed on one workpiece,
randomly selecting an aircraft, for example, a first aircraft, and exchanging all the positions of the first aircraft in the solutions A and B and all the machines used at the positions, wherein the changes are as follows:
the form after the first solution A is crossed is
The second solution B is crossed into the form of
The evaluation is based on the time taken for each solution. Assuming the fueling time is t 1 The oxygen supply time is t 2 The power supply time is t 3 The motion speed of the plane is v 1 The distance between the position 1 and the position 2 is d 1,2 Other distances and so on.
Time T taken for the first solution A A The method comprises the following steps:
max((t 1 +d 1,3 /v 1 +t 2 ),(t 1 +d 2,4 /v 1 +t 3 ))
time T taken for the first solution B B The method comprises the following steps:
max((t 2 +d 3,5 /v 1 +t 3 ),(t 1 +d 4,6 /v 1 +t 3 ))
comparison T A And T B Is a better solution with short time.
Fig. 7 is a flowchart of an optimization scheduling method of an aviation security system in an embodiment of the invention. FIG. 8 shows a convergence curve of the improved genetic algorithm in an embodiment of the invention.
Through the giving of the optimal scheduling scheme, the maximum guarantee capability of the aviation guarantee system under the specific task requirement can be found, so that the guarantee time is shortened, the multi-objective optimization decision problem under the condition of large-scale limited resources is solved, the efficiency and the quality are improved, the balance rate of aviation guarantee resource use is improved, the guarantee flow is more reasonable, the comprehensive efficiency of the aviation guarantee system is improved, and the comprehensive capability of the aviation guarantee system is improved to the greatest extent.
The invention also provides a complex system self-adaptive system supporting mapping dimension increase, which comprises an element set construction module, a multidimensional association network construction module, a multi-scale model construction module and a system self-adaptive scheduling scheme construction module, wherein:
And the element set construction module is used for decomposing the complex system layer by layer according to the capability to the element level to form a complex system element set and transmitting the complex system element set to the multidimensional association network construction module.
And the multidimensional association network construction module extracts association relations among elements layer by layer from the complex system element set, respectively establishes association networks among elements of the same level and elements of different levels, forms a complex system multi-main element multidimensional association network, and sends the complex system multi-main element multidimensional association network to the multi-scale model construction module and the system self-adaptive scheduling scheme establishment module.
And the multi-scale model construction module is used for constructing complex system resource multi-scale models of different levels of elements according to the complex system multi-main element multi-dimensional association network and transmitting the complex system resource multi-scale models to the system self-adaptive scheduling scheme construction module.
And the system self-adaptive scheduling scheme establishing module is used for carrying out self-adaptive scheduling on the complex system according to the complex system multi-main-body element multi-dimensional association network and the complex system resource multi-scale model to form a required system scheduling scheme.
The specific functions of the respective modules are similar to those described above for the method, and are not described here again.
In the application of the aviation security system, the optimal scheduling scheme can be given in a shorter time based on the scheduling model and algorithm of the invention aiming at typical security tasks, so that the security resource is more uniformly used, the security flow is more reasonable, and the comprehensive capacity of the aviation security system is improved to the greatest extent.
The foregoing is merely illustrative of the best embodiments of the present invention, and the present invention is not limited thereto, but any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be construed as falling within the scope of the present invention.
What is not described in detail in the present specification belongs to the known technology of those skilled in the art.

Claims (29)

1. A complex system self-adapting method supporting mapping dimension increase is characterized in that: comprising the following steps:
decomposing the complex system layer by layer according to the capability to an element level to form a complex system element set;
extracting association relations among elements from the complex system element set layer by layer, respectively establishing association networks among elements of the same level and elements of different levels, and forming a complex system multi-main element multi-dimensional association network;
establishing a complex system resource multi-scale model of different levels of elements according to the complex system multi-main element multi-dimensional association network;
according to the complex system multi-main element multi-dimensional association network and the complex system resource multi-scale model, carrying out complex system self-adaptive scheduling to form a required system scheduling scheme;
The method for forming a required system scheduling scheme by performing complex system self-adaptive scheduling according to the complex system multi-main element multi-dimensional association network and the complex system resource multi-scale model comprises the following steps:
injecting a fault model of the complex system guarantee resource into a complex system multi-main-element multidimensional association network to form a multi-main-element fault association network;
constructing a complex system resource optimization multi-objective planning model according to the multi-main-body element fault correlation network and the complex system resource multi-scale model;
receiving task demands of a complex system, and optimizing a multi-objective planning model according to the complex system resources to obtain an optimal complex system resource scheduling solution, thereby obtaining a required system scheduling scheme;
the complex system element set includes system body elements including human elements, aircraft elements, and ship elements.
2. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 1, wherein: and decomposing the complex system layer by layer according to the capability, and decomposing at least two layers to element level.
3. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 1, wherein: the association relation among the elements of the complex system element set is extracted layer by layer, association networks among the elements of the same level and the elements of different levels are respectively established, and the specific method for forming the complex system element network is as follows:
(1) Dividing elements in the complex system element set into a system main element and an environment element;
(2) Classifying the system main body elements and the environment elements to establish a hierarchical structure diagram, wherein the hierarchical structure diagram is a set of each hierarchical element;
(3) Extracting the association relations among the elements in different levels and the association relations among the elements in the same level, classifying the association relations, and constructing an association relation judgment matrix among the elements;
(4) And constructing a multi-main-body element multi-dimensional association network according to the association relation judgment matrix among the elements, wherein the multi-main-body element multi-dimensional association network comprises association networks among elements in different levels and association networks among elements in the same level.
4. A complex system adaptation method supporting mapping upscales according to claim 3, characterized by: the elements of the ship are divided into a guarantee equipment element and a guarantee array element; the environmental elements include weather, hydrology, and oceans.
5. The complex system adaptation method supporting mapping dimension lifting as recited in claim 4, wherein: the human elements are commanders and security personnel; the elements of the aircraft are different types of aircraft.
6. A complex system adaptation method supporting mapping upscales according to claim 3, characterized by: and (2) classifying system main elements or environment elements according to functions, classifying the same class of elements into the same hierarchy, namely, the same hierarchy comprises at least one element set formed by the same class of elements, and the established hierarchy structure diagram consists of a plurality of element sets.
7. A complex system adaptation method supporting mapping upscales according to claim 3, characterized by: the association relation between two elements in different levels and the association relation between two elements in the same level in the step (3) comprise control constraint relation or data association relation, wherein the control constraint relation is divided into a guarantee equipment constraint relation, a guarantee flow constraint relation or a guarantee position constraint relation, an association relation judgment matrix is established according to the guarantee equipment constraint relation, the guarantee flow constraint relation, the guarantee position constraint relation or the data association relation, and each value in the association relation judgment matrix represents the association relation type between the two elements.
8. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 7, wherein: in the step (4), a multi-main-body element multi-dimensional association network is constructed according to the association relation judgment matrix among the elements, the association relation of the same type is divided into the same network, and all the association relation networks of different types are overlapped to form the multi-main-body element multi-dimensional association network.
9. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 1, wherein: according to the complex system multi-main-body element multi-dimensional association network, the established multi-scale models of different levels of elements are a human resource model, a workflow model or a guarantee resource model.
10. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 9, wherein: the construction method of the human resource model comprises the following steps:
(1) Constructing a personnel individual model according to the personnel work type, wherein the personnel individual model comprises a cognitive fatigue model or a physical fatigue model;
(2) And constructing a personnel team model according to the personnel individual model, wherein the personnel team model comprises the personnel individual model and team characteristic elements.
11. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 10, wherein: constructing a cognitive fatigue model according to command personnel and constructing a physical fatigue model according to guarantee personnel; the team feature elements include a team work target and a work environment.
12. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 9, wherein: the workflow model comprises defining primitive types and data structures thereof; the primitive types include an active node, a sequence node, a gateway node, or an event node.
13. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 9, wherein: the guarantee resource model comprises static properties and dynamic behaviors; the static attribute comprises the position of the guarantee resources, the quantity of the guarantee resources and the guarantee objects, and the dynamic behavior comprises a calculation formula of the use time of the guarantee resources.
14. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 1, wherein: the complex system comprises an aviation support system or a combat system.
15. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 1, wherein: the method is characterized in that: the fault model of the complex system guarantee resource in the step (1) comprises fault types, different types of fault occurrence probabilities and different types of fault occurrence times.
16. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 1, wherein: the method is characterized in that: in the step (2), a complex system resource optimization multi-objective planning model is formed according to the association constraint relation among elements in the multi-main-body element fault association network and the complex system resource multi-scale model, wherein the multi-objective planning model is a set of a guarantee operation time model, a guarantee position selection model, a guarantee position conflict occupation model and a guarantee equipment conflict occupation model.
17. The complex system adaptation method supporting mapping dimension lifting as recited in claim 16, wherein: the guarantee operation time model is a work mode of guarantee operation and guarantee operation time, wherein the work mode of the guarantee operation is as follows: after each guarantee operation starts, the guarantee operation cannot be stopped until the guarantee operation is finished, and the working time of the guarantee operation is the sum of the preparation time and the execution time.
18. The complex system adaptation method supporting mapping dimension lifting as recited in claim 16, wherein: the guarantee position selection model is as follows: corresponding guarantee equipment is arranged at a guarantee position where the guarantee operation is located, and the guarantee position is in an idle available state currently.
19. The complex system adaptation method supporting mapping dimension lifting as recited in claim 16, wherein: the guarantee part conflict occupation model is as follows: when more than one guarantee object needs to carry out guarantee operation on the same guarantee position, the guarantee object with the highest priority is selected for carrying out the guarantee operation.
20. The complex system adaptation method supporting mapping dimension lifting as recited in claim 16, wherein: the conflict occupation model of the guarantee equipment is as follows: when more than one guarantee object needs to use the same guarantee device to carry out the guarantee operation, selecting the guarantee object with the highest priority to use the guarantee device.
21. The complex system adaptation method supporting mapping dimension lifting as claimed in claim 1, wherein: the specific method for receiving the task demands of the complex system in the step (3) and calculating to obtain the optimal complex system resource scheduling solution according to the complex system resource optimization multi-objective planning model is as follows:
(3.1) receiving task demands of a complex system, and generating an initial rough solution set by using a random sampling method, wherein the initial rough solution set comprises a guarantee object element, a guarantee part element, a guarantee equipment element, a guarantee operation element, a guarantee time element and an association relation among the elements, and the association relation meets element association constraint relation in a multi-main-body element fault association network;
(3.2) according to a complex system resource optimization multi-objective planning model, sequencing the solutions in the initial rough solution set, and selecting the first N solutions as an optimal solution set;
(3.3) obtaining an optimal scheduling solution by using an improved genetic algorithm according to the optimal solution set.
22. The complex system adaptation method supporting mapping dimension lifting as recited in claim 21, wherein: in the step (3.2), the solutions in the initial coarse solution set are ordered, and the ordering rule is as follows: the method comprises the steps of comprehensively calculating the score of a solution according to set weights by taking the shortest guarantee time and the least guarantee object movement times as targets, wherein the smaller the score is, the better the solution is, and the earlier the sequencing is; and (3.2) the value of N in the step (3.2) is 30-100.
23. The complex system adaptation method supporting mapping dimension lifting as recited in claim 22, wherein: the score calculation formula of the solution is as follows:
wherein: score i Score of ith solution, W ST To ensure the weight of time, ST i For the guard time of the ith solution, max (ST N ) For the maximum value of the guard time in N solutions, W SM SM for weight of number of movements i For the number of movements of the ith solution, max (ST M ) Maximum number of shifts in N solutions, where W ST +W SM =1, each solution value interval is [0,1]。
24. The complex system adaptation method supporting mapping dimension lifting as recited in claim 21, wherein: the task requirements of the complex system in the step (3.1) are as follows: providing a guarantee resource and a guarantee position, carrying out Y guarantee operations for N different types of guarantee objects, wherein the required guarantee operation can only adopt the required guarantee resource to guarantee at the required guarantee position; the guarantee resource comprises M guarantee devices, Z guarantee positions and T guarantee operation time Y Wherein N, M, Y, Z is a positive integer.
25. The complex system adaptation method supporting mapping dimension lifting as recited in claim 21, wherein: the specific method for obtaining an optimal scheduling solution by using the improved genetic algorithm according to the optimal solution set in the step (3.3) is as follows:
(3.3.1) performing cross operation on N initial solutions in the optimal solution set to obtain N crossed solutions;
(3.3.2) performing mutation operation on the N solutions formed after the crossing to obtain N mutated solutions;
(3.3.3), calculating the scheduling time of each solution for the original N solutions, the N solutions after crossing and the N solutions after mutation, selecting N solutions with the shortest scheduling time as new initial solutions, and entering the step (3.3.4);
(3.3.4), if the set iteration number is reached, entering a step (3.3.5), and if the set iteration number is not reached, returning to the step (3.3.1);
and (3.3.5) selecting a solution with the shortest scheduling time from the N new initial solutions as an optimal scheduling scheme.
26. The complex system adaptation method supporting mapping upscale of claim 25, wherein: and (3.3.1) performing cross operation on N initial solutions in the optimal solution set by adopting one of the following four methods:
randomly selecting a guarantee object j, and intersecting the guarantee object j in the i initial solution and the i+1 initial solution;
randomly selecting one guarantee object j, and respectively and correspondingly crossing the 1 st to the j-th guarantee objects in the i-th initial solution and the i+1-th initial solution;
Randomly selecting a guarantee object j, randomly selecting a position node t with a guarantee position changed in the ith and the (i+1) th initial solutions of the guarantee object j, and intersecting the guarantee operation after the position node t in the ith and the (i+1) th initial solutions;
and (c) randomly selecting a guarantee object j, finding all the same positions of the guarantee object j in the ith and the (i+1) th initial solutions, randomly selecting the same position m, and intersecting the guarantee equipment used by the ith and the (i+1) th initial solutions in the position m.
27. The complex system adaptation method supporting mapping upscale of claim 25, wherein: in the step (3.3.2), mutation operation is performed on the N solutions formed after the crossing by one of the following three methods:
randomly selecting a guarantee object j for an ith solution, finding out all position nodes of the guarantee object j, which are subjected to position change, randomly selecting a position node t from the position nodes, and carrying out mutation on the position node t, wherein the mutation operation is to randomly select one guarantee position from all the guarantee positions meeting the operation requirement of the position node t and ensure equipment meeting the operation requirement in the position to replace the position node t and ensure equipment of the position node t;
Randomly selecting a guarantee object j for the ith solution, finding out position nodes with position change of the guarantee object j, randomly selecting a position node t from the position nodes t, mutating all position nodes after the position node t, wherein mutation operation is to traverse the position node t to a last node, randomly selecting one guarantee position from all guarantee positions meeting operation requirements of the node t and guarantee devices meeting operation requirements in the position to replace the position node t and the guarantee devices of the node t, randomly selecting one guarantee position from all guarantee positions meeting operation requirements of the node t+1 and the guarantee devices meeting operation requirements in the position to replace the position node t+1 and the guarantee devices of the node t+1, … …, and so on until one guarantee position is randomly selected from all guarantee positions meeting operation requirements of the last node and the guarantee devices meeting operation requirements in the position to replace the last position node and the guarantee devices of the last position node;
and (3) randomly selecting a guarantee object j for the ith solution, randomly selecting a guarantee operation, and mutating the guarantee position and the guarantee equipment used by the guarantee operation.
28. A complex system adaptive system supporting mapping dimension increase, characterized in that: the system comprises an element set construction module, a multidimensional associated network construction module, a multi-scale model construction module and a system self-adaptive scheduling scheme construction module, wherein:
the element set construction module: decomposing the complex system layer by layer according to the capability to an element level to form a complex system element set, and sending the complex system element set to a multidimensional associated network construction module;
the multidimensional association network construction module: extracting association relations among elements from the complex system element set layer by layer, respectively establishing association networks among elements of the same level and elements of different levels, forming a multi-main element multi-dimensional association network of the complex system, and transmitting the multi-main element multi-dimensional association network to a multi-scale model building module and a system self-adaptive scheduling scheme building module;
the multi-scale model building module: according to the multi-main-body element multi-dimensional association network of the complex system, complex system resource multi-scale models of elements of different levels are established and sent to a system self-adaptive scheduling scheme establishment module;
and a system self-adaptive scheduling scheme establishing module: according to the complex system multi-main element multi-dimensional association network and the complex system resource multi-scale model, carrying out complex system self-adaptive scheduling to form a required system scheduling scheme;
The system self-adaptive scheduling scheme establishing module carries out complex system self-adaptive scheduling according to the complex system multi-main element multi-dimensional association network and the complex system resource multi-scale model, and the method for forming the required system scheduling scheme comprises the following steps:
injecting a fault model of the complex system guarantee resource into a complex system multi-main-element multidimensional association network to form a multi-main-element fault association network;
constructing a complex system resource optimization multi-objective planning model according to the multi-main-body element fault correlation network and the complex system resource multi-scale model;
and receiving task demands of the complex system, and optimizing a multi-objective planning model according to the complex system resources to obtain an optimal complex system resource scheduling solution, thereby obtaining a required system scheduling scheme.
29. The complex system adaptation system supporting mapping upscale of claim 28, wherein: the multi-dimensional association network construction module extracts association relations among elements layer by layer from the complex system element set, and respectively establishes association networks among elements of the same level and elements of different levels, and the specific method for forming the complex system multi-main element multi-dimensional association network is as follows:
(1) Dividing elements in the complex system element set into a system main element and an environment element;
(2) Classifying the system main body elements and the environment elements to establish a hierarchical structure diagram, wherein the hierarchical structure diagram is a set of each hierarchical element;
(3) Extracting the association relations among the elements in different levels and the association relations among the elements in the same level, classifying the association relations, and constructing an association relation judgment matrix among the elements;
(4) And constructing a multi-main-body element multi-dimensional association network according to the association relation judgment matrix among the elements, wherein the multi-main-body element multi-dimensional association network comprises association networks among elements in different levels and association networks among elements in the same level.
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