CN115390946A - Mine accident rescue task unloading method based on mobile edge calculation - Google Patents
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Abstract
The invention discloses a mine accident rescue task unloading method based on mobile edge calculation, which completes rescue tasks based on dynamic unloading strategies of evolutionary game theory in mobile edge calculation and belongs to the field of mine accidents. The method mainly comprises the following four steps: firstly, constructing a 'cloud + edge' mixed mine accident scene formed by 'a central mine accident + a plurality of chain mine accidents', and calculating an optimization target formula; secondly, designing the form of the evolutionary game by using a dynamic evolutionary game rule, wherein the form comprises participants, groups and strategies, and customizing an evolutionary stable strategy; then, designing a copy dynamic equation, and introducing an excitation coefficient; finally, driving an evolutionary game model through probability distribution to finally generate a multi-evolutionary stable strategy for service unloading; the invention considers that the chain mine accidents in the real environment are unstable and fluctuate along with time, designs and copies a dynamic equation, introduces an excitation coefficient and improves the efficiency strategy of strategy stability.
Description
Technical Field
The invention discloses a mine accident rescue task unloading method based on mobile edge calculation, belongs to the field of mine accidents and edge calculation, and particularly relates to a mine accident rescue task unloading problem.
Background
In recent years, mine accidents frequently occur in China, the economy and the society are seriously influenced, and the problem of social wide attention is how to improve the emergency management level and effectively deal with various mine accidents. Emergency rescue teams are used as the core of the whole life cycle of emergency management, and emergency rescue task deployment is very necessary.
The traditional game theory is Nash equilibrium, and needs a single rescue team to maximize the self income, so that a balanced state which can be accepted by each rescue team is obtained. Meanwhile, because the research on the rescue system is started later in China and the complexity of accidents in mine areas is added, the research on the aspect of mine rescue has the problems that: because most of the existing researches only consider the ideal conditions, the problem of complex mine accidents cannot be solved. And the site of the mine accident is not fixed and changes with time. If the rescue scheme is designed according to ideal conditions, great resource waste can be caused. In the environment of the present invention, rescue teams are not fully rational. Under the incomplete rationality state, the invention considers the maximum fitness of pursuit groups, and further obtains an evolution stable strategy.
Aiming at the scene of task unloading of multiple rescue teams in the mixed cloud + edge environment, the invention designs a mine accident rescue task unloading method based on mobile edge computing; the problem is solved by using the evolutionary game, because the traditional game theory pursues nash equilibrium and needs a single rescue team to maximize the self income under the condition of complete rationality, thereby achieving a balanced state which can be accepted by each participant. In a mixed cloud + edge environment, multiple rescue teams distributed in geographic positions do not have completely transparent information and global logic deduction capability, so that the completeness cannot be achieved. Under the incomplete rationality state, the method considers the maximum fitness of pursuit population, and then obtains an evolution stable strategy; the strategy can not cause the overall recalculation because the decision of one participant is changed, thereby saving a large amount of decision calculation cost.
Disclosure of Invention
The invention provides a mine accident rescue task unloading method based on mobile edge calculation, which is used for solving the problems that mine accidents occur and how a work rescue task can be efficiently unloaded.
The technical scheme of the invention is as follows: the mine accident rescue task unloading method based on the copied dynamic evolution game strategy comprises the following specific steps:
step1, constructing a 'cloud + edge' mixed mine accident scene formed by 'center mine accidents + a plurality of chain mine accidents', and calculating an optimization target formula.
And Step2, designing the form of the evolutionary game by utilizing the dynamic evolutionary game rule, wherein the evolutionary game form comprises participants, groups and strategies, and customizing an evolutionary stable strategy.
And Step3, designing and copying a dynamic equation, introducing an excitation coefficient, and solving the model.
As a further aspect of the present invention, step1 comprises:
for rescue team S i Firstly, determining the distance between the mine accident and each mine accident; if r (i, k) is satisfied<R k Then, it indicates rescue team S i May be an emergency EM j Providing a rescue; with A i Denotes S i The set of alternative mine accidents of
Where J is the set of mine accidents { EM 0 ,EM 1 ,...,EM j }; rescue team S i Will be from A i Selecting one mine accident to rescue, and rescue time
The system consists of four parts, namely task dispatching time, distance-to time, distance-returning time and accident handling time; wherein the task dispatch time T i as Is very short and can be ignored,
T i re and T i ac Calculating according to the mine accident distance and the difficulty; thus rescue team S i The rescue time of (d) may be expressed as:
the monetary cost of the rescue team comprises three parts, namely transportation cost, material cost and labor cost;
and
respectively represent EM j The price per unit transportation cost and the cost per unit material cost; grade G if mine accident j Is higher and have moreThe more rescue teams rescue, the lower the rescue labor cost allocated by each rescue team; thus rescue team S i The cost of the rescue currency is as follows:
in the formula: m i I rescue team rescue currency cost; m j tr The transportation cost of the number j mine accident selected by the number i rescue team; m j ma The I rescue team selects the material cost of the J mine accident; m is a group of j ar The labor cost of selecting the mine accident of No. j by the rescue team of No. i;
-a price representing the transportation cost per unit of mine accident number j; r (i, j) -the distance from the rescue team to the mine accident of number j;
the cost of the material cost of the number j mine accident unit; g j -grade of mine accident # j; m is a group of j -total labor cost for number j mine accidents; num j And (5) the total number of the number j mine accident rescue teams.
The invention uses FS i To represent S i The desired achievement of the rescue team, which can be expressed as the probability that the unload time and monetary cost constraints are both satisfied:
FS i ＝Pr(T i ≤t i )·Pr(M i ≤m i ) (13)
in the formula: FS (file system) i The expected achievement degree of the number i rescue team; t is i The actual rescue time of the i number rescue team; t is t i I rescue team rescue time constraint; m is a group of i I rescue team rescue currency cost; m is a unit of i And I, rescue team rescue currency cost constraint.
in the formula: q k A number k rescue team group;
-expected achievement of rescue team group number k; FS (file system) i The expected achievement degree of the number i rescue team; num k And the number of rescue teams in the number k rescue team group.
FS is the desired achievement of the whole area and is taken as the final optimization target, and the optimization target formula is as follows:
in the formula: FS — desired achievement of the entire area;
-expected achievement of rescue team group number k; k is the total number of rescue team groups in the area.
As a further scheme of the invention, step2 designs an evolutionary game form as follows:
the form of the evolutionary game is designed as follows:
the participants: for the 'cloud + edge' mixed multi-rescue team environment shown in fig. 1, each terminal rescue team in the area is a participant in the evolutionary game; under the assumption of rationality, the aim of participating in the game is to maximize the benefit of the game.
Group: as shown in figure 1, rescue teams can be divided into K different groups according to positions, and the number of the rescue teams in each group is num k Represents; for the invention { Q 0 ,Q 1 ,...,Q K-1 Represents the rescue team of K groupsAnd (4) aggregating, wherein the participants in each group are located in the same geographical area and the sum of the rescue teams of all the groups is the total rescue team number.
Strategy: the strategy of each rescue team refers to the mine accident that can be selected by the rescue team, 1+ J mine accidents are selected by participants in the game environment, 1 is a central mine accident, and J is a chain mine accident; using x j To indicate whether the rescue team selects the mine accident EM j The actual situation of task offloading is as follows:
wherein Q is k Optional set of mine accidents S k Is a subset of J.
Group share:
is shown at Q k Selection of EM in the population j The total number of rescue teams for rescuing the mine accidents,
representation of the entire population selection EM j Share of population to rescue;
group status: by vectors
To represent a population P k Selects a state and satisfies
The total population state space containing J population states is represented by a matrix P as follows:
the benefits are as follows: the benefit of the participant depends on the net utility function of the participant, and is determined by the maximum bearable rescue time and cost and the actual rescue time and cost; then Q is k Rescue team S of a group i Selection of EM j The gains obtained in a mine incident may be expressed as follows:
and Step3, designing and copying a dynamic equation, introducing an excitation coefficient, and solving the model.
In the traditional game, all participants can reach a stable state, namely, no participant can further obtain additional benefits by changing the strategy in a single direction, and the state is called NE; the selection and resource allocation of the servers under the hybrid architecture are regarded as a game gamma, the participant set is N, and the strategy set is U = [ U1, U2, U3.. U.N ]]Representing that the strategy of each participant is counted; by u -i ＝[u 1 ,...,u i ,u i+1 ...,u N ]Indicating the removal of a participant S i Policy combinations selected by others; in the revenue set W = [ γ 1, γ 2, γ 3., γ N]Wherein the participant S i The profit of (d) is related to the self-selection strategy and the other-person selection strategy and is expressed as gamma (u) i ,u -i )。
In a limited game player group, strategies with results superior to the average level are adopted by more game players gradually, and the proportion of the strategies of the game players is changed; the method comprises the following specific steps:
in the formula: lambda-controlling the convergence speed of the participant strategy adaptation in the same group;
-a function derived by first-order derivation of time;
-participant selection server EM in a group j Current revenue obtained from the hour;
average profit of the population at time _ t;
wherein δ is used to control the convergence speed of participant strategy adaptation in the same group; rate of growth
Representing a function derived first-order from time,
is a participant selection server EM in a group j The current gain to be achieved when the current gain,
the average profit for the population at time t can be calculated by the following formula:
based on the presence of group Q k The replicator dynamic equation of the medium strategy selection shows that when the profit of the selected strategy u is higher than the average profit of the same group, the number of the terminal rescue teams selected by the medium strategy selection tends to be increased on the whole. By setting up
The invention can obtain the dynamic immobile point of the replicator, and at the immobile point, because all participants in the same group have the same profit, the group state can not be changed, and no participant is willing to change the strategy.
The invention has the beneficial effects that:
the invention researches the environment of a plurality of mine accidents, namely the rescue problem in the mixed environment formed by one central mine accident and a plurality of mine accidents; the method is different from a plurality of traditional methods and strategies, and the fluctuation and the change of the real-time situation of the mine accident along with the time are considered; the rescue strategy of the rescue team is obtained dynamically by proposing a strategy of an evolutionary game in a targeted manner, so that the rescue efficiency of the rescue team is improved.
Drawings
FIG. 1 is a flow chart of the invention.
Fig. 2 is a detailed scene diagram according to an embodiment.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the scope of the present invention is not limited to the examples.
In order to solve the problem that the rescue task is completed more efficiently, the embodiment of the invention provides a mine accident rescue task unloading method based on mobile edge calculation, and a mine accident rescue task unloading model based on mobile edge calculation is constructed to solve the problem that the mine accident occurs and how the work rescue task can be unloaded efficiently.
Example 1
The method is particularly applicable to environments including but not limited to scenes in which a plurality of mine accidents are concurrent. First, in the accident occurrence aspect, when a central mine accident occurs, a plurality of mine accidents occur together, and a linkage effect is achieved. Secondly, in areas where a plurality of mine accidents occur, a plurality of rescue teams are distributed at different positions, and the rescue teams can be divided into a plurality of groups. Then, there are various schemes for the rescue team to select the mine accident for rescue. In this embodiment, in this environment, with the rescue cost and time limitation as targets, the dynamic evolution game is used for analysis to obtain the best rescue strategy of the rescue team, so as to improve the rescue efficiency, and specifically includes the following steps:
step1, constructing a 'cloud + edge' mixed mine accident scene formed by 'a central mine accident + a plurality of chain mine accidents', and calculating an optimization target formula.
The invention assumes that the central mine accident and the chain mine accident prevention devices have respective rescue ranges, and rescue teams in the ranges can rescue the central mine accident and the chain mine accident prevention devices; chain mine accidents can be rescued nearby by rescue teams. Meanwhile, a rescue environment consisting of single central mine accidents and J interlocked mine accidents is considered. The rescue range of the central mine accident is large; and J chain mine accidents are closer to the rescue team. And (5) the rescue cost of the mine accidents. The area of the mixed environment can be generally divided into K small service areas, the rescue team in each area is regarded as a group, and the division principle is to ensure that the number of mine accidents in chain of each area is almost equal.
For rescue team S i Firstly, determining the distance between the mine accident and each mine accident; if r (i, k) is satisfied<R k Then, it indicates rescue team S i May be an emergency EM j Providing a rescue; with A i Denotes S i The set of alternative mine accidents of
Where J is the set of mine accidents { EM 0 ,EM 1 ,...,EM j }; rescue team S i Will be from A i Selecting one mine accident to rescue, and rescue time
The system comprises four parts, namely task dispatching time, distance-to-go time, distance-returning time and accident handling time; wherein the task dispatch time T i as Is very short and can be ignored,
T i re and T i ac Calculating according to the mine accident distance and the difficulty; thus rescue team S i The rescue time of (c) can be expressed as:
money for rescue teamThe cost comprises three parts, namely transportation cost, material cost and labor cost;
andrespectively represent EM j The price per unit transportation cost and the cost per unit material cost; grade G if mine accident j The higher the rescue team is, the lower the rescue cost allocated by each rescue team is; thus rescue team S i The cost of the rescue currency is as follows:
the invention uses FS i To represent S i The desired achievement of the rescue team, which can be expressed as the probability that the unload time and monetary cost constraints are both satisfied:
FS i ＝Pr(T i ≤t i )·Pr(M i ≤m i ) (23)。
FS is the desired achievement for the entire area and is taken as the final optimization objective:
and Step2, designing the form of the evolutionary game by utilizing the dynamic evolutionary game rule, wherein the evolutionary game form comprises participants, groups and strategies, and customizing an evolutionary stable strategy.
The classic game standard includes three factors: the participants of the game, the set of strategies that each participant can select and the combination of strategies that can be selected for all participants, and the revenue function of each participant in selecting the strategies; in the context of evolutionary gaming, a population may be used to represent a group of participants having the same properties.
The form of the evolutionary game is designed as follows:
the participants: for the "cloud + edge" mixed multi-rescue team environment shown in fig. 1, each terminal rescue team in the area is a participant in the evolutionary game; under the assumption of rationality, the aim of participating in the game is to maximize the benefit of the game.
Group: as shown in figure 1, rescue teams can be divided into K different groups according to positions, and the number of the rescue teams in each group is num k Represents; for the invention { Q 0 ,Q 1 ,...,Q K-1 And represents a set of rescue teams of K groups, wherein participants in each group are located in the same geographical area and the sum of the numbers of the rescue teams of all the groups is the total number of the rescue teams.
Strategy: the strategy of each rescue team refers to the mine accident that can be selected by the rescue team, 1+ J mine accidents are selected by participants in the game environment, 1 is a central mine accident, and J is a chain mine accident; using x j To indicate whether the rescue team selects the mine accident EM j The actual situation of task offloading is as follows:
wherein Q is k Optional set of mine accidents S k Is a subset of J.
Group share:
is shown at Q k Selection of EM from the population j MineThe total number of rescue teams for rescuing in accidents,
representation of the entire population selection EM j The share of the population for rescue;
group state: using vectors
To represent a population P k Selects a state and satisfies
The total population state space containing J population states is represented by a matrix P as follows:
the benefits are as follows: the benefit of the participant depends on the net utility function of the participant, and is determined by the maximum bearable rescue time and cost and the actual rescue time and cost; then Q is k Rescue team S of a group i Selection of EM j The gains obtained over a mine incident may be expressed as follows:
and Step3, designing and copying a dynamic equation, introducing an excitation coefficient, and solving the model.
In the invention, terminal rescue teams under the cloud-edge hybrid architecture adjust their strategies in a limited set of strategies to obtain better return. At each moment, the terminal rescue team can obtain a strategy set of the terminal rescue team and average income information of the group; therefore, as time goes on, the own server selection strategy is continuously evolved; through sufficient repetition and deduction, the number of mutation participants using other strategies is continuously reduced due to low income, and finally mutation players are killed to reach the evolution stable state of the group. This dynamic process the present invention can be solved by copying the dynamic equations.
The evolutionary game model is mainly based on a mutation mechanism and a selection mechanism, wherein mutation means that in a group, a small part of participants select strategies different from a large part of participants in a random mode, the selected strategies can obtain higher income and lower income, but the excellent strategies cannot be eliminated by history and are reserved; the selection means that the strategy can obtain higher income and can be continuously adopted by other participants in the future; this selection mechanism is the key to the replicator dynamic model, which provides a way to obtain information about other people's groups and converges towards the equilibrium point until the strategy adapts to reach Evolutionary Equilibrium (EE), i.e. the group does not change its selection. The basic idea is that in a limited game player group, a strategy with a result superior to the average level is adopted by more game players gradually, and the proportion of the strategies of the game players is changed; the method comprises the following specific steps:
wherein δ is used to control the convergence speed of participant strategy adaptation in the same group; rate of growth
Representing a function derived first-order from time,
is a participant selection server EM in a group j The current gain to be achieved when the current gain,
the average profit for the population at time t can be calculated by the following formula:
based on the presence of group Q k The replicator dynamic equation of the medium strategy selection shows that when the profit of the selected strategy u is higher than the average profit of the same group, the number of the terminal rescue teams selected by the medium strategy selection tends to be increased on the whole. By setting up
The invention can obtain the dynamic immobile point of the replicator, and on the immobile point, because all participants in the same group have the same income, the group state can not be changed, and no participant is willing to change the strategy.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (4)
1. A mine accident rescue task unloading method based on mobile edge calculation is characterized by comprising the following specific steps:
step1, constructing a 'cloud + edge' mixed mine accident scene formed by 'a central mine accident + a plurality of chain mine accidents', and calculating an optimization target formula;
step2, designing the form of the evolutionary game by utilizing the dynamic evolutionary game rule, wherein the evolvable game form comprises participants, groups and strategies, and customizing an evolutionary stable strategy;
and Step3, designing and copying a dynamic equation, introducing an excitation coefficient, and solving the model.
2. The mine accident rescue task unloading method based on mobile edge computing according to claim 1, characterized in that: the Step1 comprises the following steps:
the monetary cost of a rescue team consists of three parts, namely transportationCost of capital, materials and labor;
and
respectively representing the price of the unit transportation cost of the number j mine accident and the cost of the unit material cost; grade G if mine accident j Higher, and more rescue teams rescue, the lower the extra rescue cost allocated by each rescue team; therefore, rescue team S No. i i The cost of the rescue currency is as follows:
in the formula:
M i i rescue team rescue currency cost;
r (i, j) -the distance from the rescue team No. i to the mine accident No. j;
G j -grade of number j mine accident;
M j -total labor cost for number j mine accidents;
num j -the total number of mine accident rescue teams;
using FS i To represent the rescue team's desired achievement, which can be expressed as the probability that both rescue time and monetary cost constraints are satisfied:
FS i ＝Pr(T i ≤t i )·Pr(M i ≤m i ) (2)
in the formula:
FS i the expected achievement degree of the number i rescue team;
T i the actual rescue time of the rescue team No. i;
t i rescue time constraint of the number i rescue team;
M i i rescue team rescue currency cost;
m i i, rescue team rescue currency cost constraint;
in the formula:
Q k a number k rescue team group;
FS i i number of rescue teams expect achievement degree;
num k the number of rescue teams in the number k rescue team group is-K;
FS is the desired achievement of the whole area and is taken as the final optimization objective, and the optimization objective formula is as follows:
in the formula:
FS — desired achievement of the entire area;
k is the total number of rescue team groups in the area.
3. The mine accident rescue task unloading method based on mobile edge computing according to claim 1, characterized in that: step2 designs the form of the evolutionary game as follows:
the form of the evolutionary game is designed as follows:
the participants: in a 'cloud + edge' mixed multi-rescue team environment, each terminal rescue team in a region is a participant in an evolutionary game; under the assumption of rationality, the aim of participating in the game is to realize the maximization of self benefit by participating in the game;
group: rescue teams can be divided into K different groups according to positions, and the number of rescue teams in each group is num k Representing; by { Q 0 ,Q 1 ,...,Q K-1 The method comprises the steps of (1) representing a set of rescue teams of K groups, wherein participants in each group are located in the same geographical area and the sum of the rescue team numbers of all the groups is the total rescue team number;
the strategy is as follows: the strategy of each rescue team is that mine which can be selected by the rescue team1+ J mine accidents are totally available for participants to select in the game environment, wherein 1 is a central mine accident, and J is a chain mine accident; using x j To indicate whether the rescue team selects the mine accident EM j The actual situation of task offloading is as follows:
wherein Q is k Optional set of mine accidents S k Is a subset of J;
group share:
is shown at Q k Selection of EM in the population j The total number of rescue teams for rescuing the mine accidents,
representation of the entire population selection EM j Share of population to rescue;
group status: using vectors
To represent a population P k Selects a state and satisfies
The total population state space containing J population states is represented by a matrix P as follows:
and (4) yield: the participant's profit depends on its net utility function, from its maximum affordable rescue time and costAnd actual generated rescue time and cost decision; then Q is k Rescue team S of a group i Selection of EM j The gains obtained in a mine incident may be expressed as follows:
4. the mine accident rescue task unloading method based on mobile edge computing according to claim 1, characterized in that: step3 introduces a replicator dynamic equation into the convergence speed of strategy adaptation of participants in the same group, wherein delta is designed as follows:
in a limited game player group, the strategy with the result superior to the average level is adopted by more game players gradually, and the proportion of the strategies of the game players is changed; the method comprises the following specific steps:
in the formula:
lambda-controlling the convergence rate of participant strategy adaptation in the same group;
based on the presence of group Q k When the profit of the selected strategy u is higher than the average profit of the same group, the number of the selected terminal rescue teams is in a positive growth trend on the whole; by setting up
A dynamic immobility point for replicators is available at which the group status does not change, nor is a participant willing to change the strategy, since all participants in the same group have the same benefit.
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| CN116055490B (en) * | 2023-01-31 | 2023-09-26 | 烟台大学 | Control method, system and device for edge cloud service stability |
| US12039372B1 (en) | 2023-01-31 | 2024-07-16 | Yantai University | Control method, system and device with edge cloud service stability |
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