Task allocation mechanism based on excess resources

Abstract

The invention discloses a task allocation mechanism based on excess resources, which restrains a feasible alliance set from creditworthiness and redundant resources, can comprehensively evaluate the creditworthiness and resource redundancy of each alliance in a task allocation process, not only effectively identifies and utilizes the advantage of resource excess, but also improves the task allocation efficiency. Meanwhile, the accuracy and efficiency of task allocation are ensured, a configuration function and a cost compensation rule with excitation compatibility and individual rationality properties are designed, the alliance real report cost can be effectively excited, the excitation and authenticity of competitive bidding are ensured, edge information can be fully utilized in the environment of excessive resources, the decision accuracy is improved, and the method is suitable for popularization and application. According to the method, the calculation efficiency in a resource surplus scene is improved, the quality of understanding is balanced, and the limitation of a traditional integer programming method in similar scenes is overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of task allocation and relates to a task allocation mechanism based on surplus resources. Background Art

[0002] In the scenario of unmanned aerial vehicle (UAV) operations, there is currently a lack of model research on task allocation and incentive mechanisms for individuals and organizations in the dual-line mode of "establishment" and "formation". At the same time, compared with existing research results, in the process of multi-party participation on demand, not only more consideration needs to be given to real-time performance to meet dynamic adjustment requirements and quickly form efficient cooperation, but also the "belief differences" among participants and the impacts caused by the uncertainties of benefits and cooperation results during the game process need to be considered.

[0003] Traditional task allocation mechanisms are insufficiently adaptable to scenarios of resource surplus. Many task allocation mechanisms are usually designed for resource-scarce scenarios, giving priority to minimizing resource consumption or optimizing resource utilization efficiency. However, in scenarios of resource surplus, traditional methods may no longer be effective because the core problem in these scenarios is how to use resource redundancy to optimize task allocation rather than the management of resource scarcity. In the case of resource surplus, task publishers may lack sufficient incentives to invest in task management and resource coordination. If the expected losses of task publishers cannot be effectively reduced, their willingness to invest may decrease, thus affecting system efficiency and task completion effects. The set of feasible coalitions in complex environments is too large, lacking effective refinement and screening methods, which affects the efficiency and accuracy of task allocation. Traditional integer programming methods have an exponential growth in computational complexity when dealing with scenarios of multiple participation units and multiple resource types, resulting in a significant decrease in the solution speed. Especially in scenarios of resource surplus, there may be a large number of tasks and coalitions, lacking efficient approximation algorithms to balance computational efficiency and solution quality. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems of insufficient evaluation of feasible coalitions, incompatible bidding incentives, and low computational efficiency in the task allocation mechanism under the condition of resource surplus in the prior art, and to provide a task allocation mechanism based on surplus resources.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A task allocation mechanism based on surplus resources, comprising the following steps:

[0007] Construct a set of feasible coalitions for the task according to the resource requirements of the task to be executed, and constrain the set of feasible coalitions from the aspects of credibility and redundant resources;

[0008] Set the upper and lower bounds of the cost quotes for the set of feasible coalitions, estimate the density function of the cost quotes based on the upper and lower bounds of the cost quotes, and construct the utility function of the feasible coalitions based on the density function;

[0009] Based on the utility function of the feasible coalitions, design an allocation function and a cost compensation rule with the properties of incentive compatibility and individual rationality, and allocate the tasks to be executed and compensate the cost of the feasible coalitions participating in the bidding according to the allocation function and the cost compensation rule.

[0010] A further improvement of the present invention lies in:

[0011] The construction of the set of feasible coalitions for the tasks to be executed, and the constraints on the set of feasible coalitions from the reputation and redundant resources include:

[0012] The set of feasible coalitions is constrained from the cumulative reputation, average reputation, redundant resource value, and relative redundant resources. The set of feasible coalitions is represented by the following formula:

[0013]

[0014] Among them, N represents the set of all participating units; A represents the feasible coalition selected from N to execute the task based on a certain bidding mechanism; the basic resource requirement of the task is R = (R 1 , R 2 , …, R m ); r i represents the resource vector owned by participating unit i, specifically r i = (r i1 , r i2 , …, r im ); δ A represents the cumulative reputation; represents the average reputation; c A represents the redundant resource value; represents the relative redundant resources.

[0015] The cumulative reputation:

[0016] δ A = Π i∈A δ i

[0017] The average reputation:

[0018]

[0019] The redundant resource value:

[0020]

[0021] Among them, ck Denote the reserve price corresponding to the k-th resource;

[0022] The relative redundant resources.

[0023]

[0024] where r k is the k-th component of r.

[0025] When constructing the utility function of a feasible coalition, it also includes:

[0026] During the bidding process, the task initiator needs to estimate the cost quotes for all possible feasible coalitions. For a feasible coalition A i ∈F, the task initiator will evaluate the upper and lower bounds of its quote and its probability density function:

[0027] The lower bound of the cost quote is the total value of the effective resources when the feasible coalition executes the task:

[0028]

[0029] The upper bound of the cost quote for the set of feasible coalitions is:

[0030]

[0031] where c k denotes the reserve price corresponding to the k-th resource; r jk denotes the quantity of the k-th resource owned by the j-th unit; v′ Aj denotes the quote of coalition A j ; r ik denotes the quantity of the k-th resource owned by the i-th unit;

[0032] The upper bound of the cost quote is the product of the maximum premium ratio and the value of the resources owned by the feasible coalition:

[0033]

[0034] The cost quote density function is the uniform distribution density function within the cost quote interval:

[0035]

[0036] The utility function of the feasible coalition is expressed by the following formula:

[0037]

[0038] where denotes the cost quote of coalition A i ; is for coalition A iThe true cost price; the allocation function Indicates the feasible coalition A i Whether it wins the bid at the given offer; Indicates the expected compensation value; Indicates the expected consumption cost.

[0039] Based on the above-mentioned utility function of the feasible coalition, design an allocation function and a cost compensation rule with the properties of incentive compatibility and individual rationality, including:

[0040] By the constraints of individual rationality and incentive compatibility conditions, minimize the expected loss of the task publisher to obtain the optimization problems of the allocation function and the compensation payment function, that is:

[0041]

[0042] Among them, Indicates the feasible coalition A i Whether it wins the bid at the given offer; Indicates the lower bound of the cost offer of the set of feasible coalitions; Indicates the upper bound of the cost offer of the set of feasible coalitions; Indicates the quasilinear utility of the feasible coalition;

[0043] Based on the above formula, convert the cost offer of the original coalition into the revised offer

[0044]

[0045] Among them, Indicates the cost offer of the original coalition;

[0046] Let the second-highest revised offer be:

[0047]

[0048] Allocate the task to be executed to the coalition with the lowest revised offer to obtain the allocation function:

[0049]

[0050] Among them, Indicates the cost offer of the original coalition; Is the revised second-highest offer; Indicates the offers of other coalitions excluding coalition A i ;

[0051] The cost compensation is:

[0052]

[0053] The above-mentioned:

[0054] The individual rationality condition is as follows:

[0055]

[0056] where, represents the expected compensation value; represents the expected consumption cost; represents when coalition A i quotes a price of v Ai , and the other coalitions quote a price of v -Ai , the corresponding cost compensation; represents whether coalition A, a feasible coalition i wins the bid at the given quote;

[0057] Set the incentive compatibility condition as:

[0058]

[0059] where, represents the cost quote of the original coalition; represents the true quotes of the other coalitions excluding coalition A i .

[0060] A task allocation system based on surplus resources, comprising:

[0061] A feasible coalition set acquisition module, configured to construct a feasible coalition set of a task according to the resource requirements of the task to be executed, and perform constraints on the feasible coalition set from the perspectives of credibility and redundant resources;

[0062] A utility function acquisition module, configured to set an upper bound and a lower bound of the cost quotes of the feasible coalition set, estimate the density function of the cost quotes based on the upper bound and the lower bound of the cost quotes, and construct the utility function of the feasible coalition based on the density function;

[0063] An allocation result acquisition module, configured to design a configuration function and a cost compensation rule with the properties of incentive compatibility and individual rationality based on the utility function of the feasible coalition, and allocate the task to be executed and perform cost compensation on the feasible coalitions participating in the bid according to the configuration function and the cost compensation rule.

[0064] A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of any method of the present invention are implemented.

[0065] A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of any method of the present invention are implemented.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention discloses a task allocation mechanism based on surplus resources, which restricts the feasible coalition set from the aspects of credibility and redundant resources. It can comprehensively evaluate the credibility and resource redundancy of each coalition during the task allocation process, not only effectively identifying and utilizing the advantages of resource surplus, but also ensuring the accuracy and efficiency of task allocation. By designing an allocation function and a cost compensation rule with the properties of incentive compatibility and individual rationality, it can effectively motivate the coalition to truthfully report the cost, ensuring the incentive and authenticity of the bidding. In an environment of resource surplus, it can make full use of marginal information to improve the accuracy of decision-making. This method improves the computational efficiency in the scenario of resource surplus, balances the quality of the solution, and overcomes the limitations of traditional integer programming methods in similar scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0069] Figure 1 is the system flow chart of the present invention;

[0070] Figure 2 is the algorithm flow chart of the task allocation and cost compensation mechanism in the resource surplus scenario of the present invention;

[0071] Figure 3 is the algorithm efficiency comparison chart of 20 participating units and 50 types of resources in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations.

[0073] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0074] It should be noted that like reference numerals and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it is not necessary to further define and explain it in subsequent figures.

[0075] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the figures, or the orientation or positional relationship in which the product of the present invention is usually placed during use. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0076] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0077] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if terms such as "set", "install", "connect", "couple" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0078] The present invention will be further described in detail below with reference to the figures:

[0079] See Figures 1 to 2 , the embodiments of the present invention disclose a task allocation mechanism based on surplus resources. This mechanism realizes efficient task allocation and fair cost compensation under the condition of resource surplus by evaluating indicators such as the credibility of the coalition and redundant resources, and optimizing the calculation efficiency using the Lagrangian dual relaxation method.

[0080] Specifically, it includes the following steps:

[0081] Step 1: Describe the resource requirements and types of tasks and participating units, and construct a system data model;

[0082] Step 2: Generate a set of feasible coalitions through depth-first search based on task requirements and participating unit resources;

[0083] Furthermore, the feasible coalition evaluation method

[0084] There are n participating units applying in the system, and the set of all participating units is denoted as N = {1, 2,..., n}, and each participating unit has its own credibility δ i . There are m types of resources in the battlefield. Resources can be regarded as specific combat resources (equipment, ammunition quantity), information (whether it can provide key intelligence), permissions (whether it can dispatch specific unmanned devices), etc. Each participating unit has the resources it owns or is willing to provide to the task initiator. The resources of participating unit i are represented as an m-dimensional vector r i = (r i1 , r i2 , …, r im ).

[0085] The task initiator needs to select a self-organizing coalition to execute the task from N based on a certain bidding mechanism The basic resource requirement of the task is R = (R 1 , R 2 , …, R m ). Only when the sum of the resources of all participants in the self-organizing coalition A reaches this basic resource requirement can this task be executed.

[0086] Meanwhile, task T j has a battlefield value of w(T j ), which represents the utility of completing this task for the overall battle situation. Due to the complex and changeable battlefield environment, the value of resources is difficult to be effectively estimated. It is necessary for participating units to master local battlefield information before they can estimate the resources required for the task. When a participating unit applies for a task, it will, based on its understanding of local battlefield information, evaluate the value v i of the resources it consumes by itself, or a distribution function Φ i (v i ), and the corresponding probability density function of the resource value is φ i (v i ). The resources themselves have a base price, denoted as (c 1 , c 2 , …, c m ). The base price means that using this resource will generate corresponding costs in any case, such as the value of consuming equipment and ammunition, the energy consumption of unmanned aircraft, etc. Through the task allocation mechanism and cost compensation rules, the winning participating units will be compensated for the cost t i .

[0087] In the scenario of resource surplus, the system does not need to strictly require the minimum resource consumption. Based on the goal of minimizing the expected loss of the task publisher, this invention conducts task allocation and cost compensation. By minimizing the expected loss of the task publisher, it can encourage the task publisher to further invest in the detection and strike of emergency tasks, thus achieving the improvement of self-organization efficiency.

[0088] In the evaluation stage, first, through the algorithm, search for all participant sets that can cover the basic resource requirements of the task to form the feasible coalition set of the task:

[0089]

[0090] For the feasible coalition, it can be evaluated from multiple perspectives such as credibility and redundant resources:

[0091] Cumulative credibility:

[0092] δ A =Π i∈A δ i

[0093] Average credibility:

[0094]

[0095] Value of redundant resources:

[0096]

[0097] Relative redundant resources.

[0098]

[0099] By further setting the constraint conditions of the evaluation parameters, the feasible coalition set can be further refined to cope with the complex battlefield environment, that is:

[0100]

[0101] Furthermore, in the bidding process, the task initiator needs to estimate the cost quotes for all possible feasible coalitions. For the feasible coalition A i ∈F, the task initiator will evaluate the upper and lower bounds of its quote and its probability density function. The feasible estimation method is:

[0102] Set the lower bound of the cost quote as the total value of effective resources when this feasible coalition executes the task:

[0103]

[0104] Set the upper bound of the cost quote It is the product of the maximum premium ratio and the value of the resources owned by the feasible coalition:

[0105]

[0106] Cost quotation density function It is a uniform distribution density function within the cost quotation range:

[0107]

[0108] Step 3: Design the bidding mechanism

[0109] The design of the bidding mechanism needs to take into account its incentive and authenticity. To analyze the above properties, the present invention first assumes that the participating feasible coalitions all have quasilinear utility with neutral risk That is, the feasible coalition believes that the benefit earned when participating in the task and obtaining cost compensation is the expected compensation value And the expected consumption cost The difference, that is:

[0110]

[0111] Where Is the cost quotation of the original coalition, Is the true cost price of the original coalition, and the allocation function Indicates whether the feasible coalition A i Wins the bid at the given quotation.

[0112] In the bidding stage, it is first necessary to ensure that the feasible coalition participating in the bidding can generate non-negative utility when reporting the cost truthfully, that is, the individual rationality condition:

[0113]

[0114] Secondly, it is necessary to force the feasible coalition participating in the bidding to report the truly estimated cost through a suitable compensation payment scheme, so as to effectively utilize the information at the edge of the battlefield. The Bayesian incentive compatibility property is taken as the constraint condition as follows:

[0115]

[0116] Furthermore, under the constraint of the above properties, by minimizing the expected loss of the task publisher, the mechanism design problem can be formalized as an optimization problem of the allocation function and the compensation payment function, that is:

[0117]

[0118] Based on mathematical derivation, the allocation scheme of the mechanism first needs to convert the cost quotation Of the original coalition into a revised quotation As follows:

[0119]

[0120] Let the second-highest revised bid be:

[0121]

[0122] The mechanism assigns tasks to the coalition with the smallest revised bid, that is, the configuration function is designed as:

[0123]

[0124] The cost compensation given to the self-organizing coalition A i is:

[0125]

[0126] Specifically, for the algorithm implementation of the embodiments of the present invention, see Table 1:

[0127]

[0128] In this embodiment, it is mainly divided into three functional modules.

[0129] Module 1 System Data Module mainly realizes the characterization of system units and tasks within the system, constructs a system unit type group based on data such as resources, reputation, and bids, and establishes a task type group based on required resources, task value, etc.

[0130] Module 2 Feasible Coalition Algorithm Module mainly realizes the search, analysis, and evaluation of task feasible coalitions based on given scenario data, retrieves coalitions that meet the task resource requirements based on the depth-first search algorithm, and realizes the analysis and calculation of indicators such as the coalition characteristic function, cumulative reputation, average reputation, redundant resources, and relative redundant resources.

[0131] Module 3 Task Matching and Payment Rule Calculation Module mainly realizes the calculation of task configuration coalitions and cost compensation payments within the coalition based on given scenario data according to the algorithm in the research report.

[0132] Step 4: Design a fast approximation algorithm module using the Lagrangian dual relaxation method;

[0133] Furthermore, this embodiment also discloses how to realize the proper process of feasible coalitions in the specific implementation process:

[0134] Fast approximation algorithm for cooperative coalition structure generation and utility estimation

[0135] The selection of self-organizing coalitions and the analysis of cooperation utility both depend on the integer programming problem (P) and (P -S ), and the form of problem (P) is as follows.

[0136]

[0137] where \(x=(x 1 ,x 2 ,\cdots,x n ), x i is the indicator function of participating unit \(i\) in coalition \(x\). Since problem \((P -S ) can be regarded as a programming problem \((P)\) that restricts the participating units to be elements in \(N\setminus S\), the following approximation algorithms are all analyzed in problem \((P)\), and the algorithms can be applied to problem \((P -S ).

[0138] Let \(\lambda j \geq0\) be a set of multipliers. Then the Lagrangian relaxation problem \((PR)\) of problem \((P)\) can be written as:

[0139]

[0140] The optimal value of any set of relaxation problems \((PR)\) with respect to the decision variable \(x\) is a lower bound on the optimal value of problem \((P)\). By maximizing the optimal value of the relaxation problem \((PR)\) with respect to the variable \(\lambda\), the dual problem \((PR λ ) can be obtained, and the objective function of problem \((PR λ ) can be explicitly transformed:

[0141]

[0142] Thus, problem \((PR λ ) can be transformed into a continuous optimization problem with linear constraints, that is, problem \((PR λ ) can be written as:

[0143]

[0144] Furthermore, by setting the slack variable \(l = l i , i = 1, 2, \cdots, n\), problem \((PR λ ) can be transformed into a linear programming problem \((LP λ,l ), that is:

[0145]

[0146] Using the trust region method to solve the transformed problem \((PR λ ), or using the interior point method or simplex method to solve the linear programming problem \((LP λ,l ), and substituting the corresponding optimal value for the optimal value of the original problem \((P)\) for the corresponding calculation can greatly improve the computational efficiency.

[0147] Using the implicit enumeration method (branch and bound method) to solve the original problem \((P)\), byFigure 3 As can be seen from the comparison, the traditional method is restricted by the participating units, and the calculation time increases exponentially with the size of the participating unit set N. For problem (PR λ ), and problem (LP λ,l ), the solutions have extremely high efficiency and are hardly affected by the participating unit set N, with a calculation error of only 5%-10%.

[0148] This embodiment also discloses a task allocation system based on surplus resources, including:

[0149] A feasible coalition set acquisition module, configured to construct a feasible coalition set of tasks according to the resource requirements of the tasks to be executed, and constrain the feasible coalition set from the perspectives of credibility and redundant resources;

[0150] A utility function acquisition module, configured to set an upper bound and a lower bound for the cost quotation of the feasible coalition set, estimate the density function of the cost quotation based on the upper bound and the lower bound of the cost quotation, and construct a utility function of the feasible coalition based on the density function;

[0151] An allocation result acquisition module, configured to design a configuration function and a cost compensation rule with the properties of incentive compatibility and individual rationality based on the utility function of the feasible coalition, and allocate the tasks to be executed and compensate the feasible coalitions participating in the bidding according to the configuration function and the cost compensation rule.

[0152] By introducing multi-dimensional indicators such as cumulative credibility, average credibility, and redundant resources, the accuracy and allocation efficiency of coalition evaluation are significantly improved; the modified quotation model and the second-highest modified quotation compensation rule are adopted to ensure the incentive and authenticity of the bidding mechanism, encourage coalitions to truthfully report costs and make full use of marginal information; the proposed fast approximation algorithm (such as the Lagrangian dual relaxation method) effectively solves the calculation bottleneck of traditional integer programming in large-scale task allocation scenarios, achieving a balance between calculation efficiency and accuracy; the mechanism design is optimized for the resource surplus scenario, making full use of redundant resources and reducing the expected loss of the task publisher; in addition, the modular design has clear logic, is easy to apply and expand in practice, adapts to different task types and resource constraint conditions, and provides an efficient solution for complex task allocation problems.

[0153] This study established an optimization model for task allocation and cost compensation mechanisms in a resource surplus scenario, and adopted a method based on coalition evaluation and fast approximation algorithms to solve the efficiency problem of task allocation under resource surplus conditions, and improved the fairness and cost optimization ability of task allocation.

[0154] Schematic diagram of a terminal device provided by an embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in each of the above method embodiments. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in each of the above device embodiments.

[0155] The computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention.

[0156] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0157] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0158] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0159] If the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0160] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A task allocation mechanism based on excess resources, characterized in that: The following steps are involved: Construct a feasible alliance set for the task based on the resource requirements of the task to be executed, and constrain the feasible alliance set based on reputation and redundant resources; Set the upper bound and lower bound of the cost offer of the feasible alliance set, estimate the density function of the cost offer based on the upper bound and lower bound of the cost offer, and construct the utility function of the feasible alliance based on the density function; Based on the utility function of the feasible alliance, a configuration function and cost compensation rules with incentive compatibility and individual rationality are designed. According to the configuration function and cost compensation rules, the tasks to be executed are allocated and the feasible alliances participating in the bidding are compensated for their costs.

2. A task allocation mechanism based on excess resources according to claim 1, characterized in that: The feasible alliance set of tasks is constructed according to the resource requirements of the tasks to be executed, and the feasible alliance set is constrained from the perspective of reputation and redundant resources, including: The feasible alliance set is constrained by cumulative reputation, average reputation, redundant resource value and relative redundant resources. The feasible alliance set is expressed by the following formula: Where N represents the set of all participating units; A represents the feasible alliance selected from N to perform the task based on a certain bidding mechanism; the basic resource requirement of the task is R = (R1, R2, ..., R m );r i Represents the resource vector owned by participating unit i, specifically r i =(r i1 ,r i2 ,…,r im );δ A Indicates cumulative reputation; represents the average credibility; c A Indicates the value of redundant resources; Indicates relatively redundant resources.

3. A task allocation mechanism based on excess resources according to claim 2, characterized in that: The cumulative reputation: d A =P i∈A d i The average reputation: The redundant resource value: Among them, c k Indicates the reserve price corresponding to the k-th resource; The relatively redundant resources. Among them, r k is the kth component of r.

4. The task allocation mechanism based on excess resources according to claim 1, characterized in that: The utility function of a feasible coalition is also constructed by: During the bidding process, the task initiator needs to estimate the cost of all possible feasible alliances. i ∈F, the task initiator will evaluate the upper and lower bounds of its bid and its probability density function: The lower bound of the cost quotation is the sum of the effective resource values ​​of the feasible alliance when performing the task: The upper bound of the cost quotation of the feasible alliance set is: Among them, c k represents the reserve price corresponding to the kth resource; r jk represents the number of k-th resource owned by the j-th unit; v′ Aj Alliance A j The quotation; ik It indicates the quantity of the kth resource owned by the i-th unit; The upper bound of the cost quotation is the product of the maximum premium ratio and the value of the resources owned by the feasible alliance: The cost quotation density function is a uniform distribution density function within the cost quotation interval:

5. A task allocation mechanism based on excess resources according to claim 4, characterized in that: The utility function of the feasible alliance is expressed as follows: in, Alliance A i Cost quotation; For Alliance A i The true cost price; configuration function Indicates feasible alliance A i Whether the bid was successful given the quotation; represents the expected compensation value; Represents the expected consumption cost.

6. A task allocation mechanism based on excess resources according to claim 5, characterized in that: The utility function based on the feasible alliance is used to design a configuration function and cost compensation rule with incentive compatibility and individual rationality, including: By constraining individual rationality and incentive compatibility conditions, the expected loss of the task publisher is minimized, and the optimization problem of the configuration function and the compensation payment function is obtained, namely: in, Indicates feasible alliance A i Whether the bid was successful given the quotation; represents the lower bound of the cost bids of the feasible coalition set; represents the upper bound of the cost bids of the feasible coalition set; represents the quasi-linear utility of a feasible coalition; Based on the above formula, the original alliance's cost quotation Convert to corrected quote in, Indicates the cost quote of the original alliance; The next highest revised quote is: Assign the tasks to be executed to the alliance with the smallest revised bid, and obtain the configuration function: in, Indicates the cost quote of the original alliance; The second highest bid price after revision; Indicates that Alliance A is removed i Offers from other alliances; The cost compensation is:

7. A task allocation mechanism based on excess resources according to claim 6, characterized in that: Said: The individual rationality conditions are: in, represents the expected compensation value; represents the expected consumption cost; Indicates that when Alliance A i The price is v Ai , other alliances offer v -Ai Compensation for the corresponding costs; Indicates feasible alliance A i Whether the bid was successful given the quotation; The incentive compatibility condition is set as: in, Indicates the cost quote of the original alliance; Indicates that Alliance A is removed i Real quotes from other alliances.

8. A task allocation system based on excess resources, characterized in that: include: A feasible alliance set acquisition module is used to construct a feasible alliance set for a task according to the resource requirements of the task to be executed, and to constrain the feasible alliance set from the perspective of reputation and redundant resources; A utility function acquisition module is used to set the upper bound and lower bound of the cost quotation of the feasible alliance set, estimate the density function of the cost quotation based on the upper bound and lower bound of the cost quotation, and construct the utility function of the feasible alliance based on the density function; The allocation result acquisition module is used to design a configuration function and cost compensation rule with incentive compatibility and individual rationality based on the utility function of the feasible alliance, allocate the tasks to be executed according to the configuration function and cost compensation rule, and compensate the feasible alliance participating in the bidding for costs.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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