A method, apparatus, equipment, medium, and product for determining a combination of service resources.

Abstract

This application discloses a method, apparatus, device, medium, and product for determining service resource combinations. The method includes: acquiring service resource demand information for a first task published by a service demander; decomposing the first task using a preset task decomposition algorithm to obtain subtasks and their sequential structure; searching for a set of candidate service resources matching the subtasks using a preset search algorithm; solving a service resource optimization model with an optimization objective as a constraint; and determining the service resource combination for the first task based on the solution of the service resource optimization model. The method proposed in this application can organically combine various service resources to meet the service demands of the service demander.

Description

Technical Field

[0001] This application belongs to the field of information processing, and in particular relates to a method, apparatus, equipment, medium and product for determining a combination of service resources. Background Technology

[0002] With the development of technology, service providers are offering an increasing number of service resources. Some service requesters need to find high-quality service resources, thus, service platforms have emerged.

[0003] The basic idea of ​​the service platform is "centralized use of decentralized resources and decentralized service of centralized resources," achieving supply and demand matching by integrating information on service providers and service demanders. However, the dispersion of service resources and the diversity of service types result in numerous combinations of service resources offered by a one-stop service platform. How to organically combine these various service resources to meet the service needs of demanders is a problem that urgently needs to be solved. Summary of the Invention

[0004] This application provides a method, apparatus, device, storage medium, and computer program product for determining a combination of service resources to improve the accuracy of evaluating the overall quality of an enterprise.

[0005] Firstly, a method for determining a combination of service resources is provided, including:

[0006] Obtain service resource demand information for the first task published by the service requester;

[0007] According to the preset task decomposition algorithm, the first task is decomposed to obtain the subtasks and serial structure of the first task;

[0008] Based on the preset search algorithm, search for a set of candidate service resources that match the subtask;

[0009] Solve the service resource optimization model by taking the optimization objective as the constraint.

[0010] Based on the solution of the service resource optimization model, determine the service resource combination for the first task.

[0011] Secondly, a device for determining a combination of service resources is provided, comprising:

[0012] The acquisition module retrieves service resource requirement information for the first task published by the service requester.

[0013] The generation module is used to decompose the first task according to a preset task decomposition algorithm to obtain the subtasks and serial structure of the first task.

[0014] The search module is used to search for a set of candidate service resources that match the subtasks according to a preset search algorithm.

[0015] The solver module is used to solve the service resource optimization model with the optimization objective as the constraint.

[0016] The determination module is used to determine the service resource combination for the first task based on the solution of the service resource optimization model.

[0017] Thirdly, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, the computer program performing the method provided by any possible implementation of the first aspect above.

[0018] Fourthly, a computer storage medium is provided, characterized in that the computer storage medium is subjected to a method provided by any possible implementation of the first aspect by a processor.

[0019] Fifthly, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, it implements a method provided by any possible implementation of the first aspect described above.

[0020] Compared with existing technologies, the service resource combination determination method, apparatus, medium, and product provided in this application embodiment are based on obtaining service resource demand information for a first task published by a service demander. The first task is decomposed according to a preset task decomposition algorithm to obtain subtasks and their sequential structure. A preset search algorithm is then used to search for a set of candidate service resources matching the subtasks. Since each subtask is decomposed from the first task, the two are strongly correlated. Then, using the optimization objective as a constraint, the solution to the service resource optimization model is obtained to determine the service resource combination for the first task. Thus, the service resource combination determined through subtasks can meet the service resources required by the first task, allowing for the organic combination of various service resources to satisfy the service demands of the service demander. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a method for determining a combination of service resources according to an embodiment of this application;

[0023] Figure 2 This is a simulation experiment service decomposition diagram provided in one embodiment of this application;

[0024] Figure 3 This is the average fitness change curve of the T index in a simulation experiment provided in one embodiment of this application;

[0025] Figure 4 This is the average fitness change curve of the C index in a simulation experiment provided in one embodiment of this application.

[0026] Figure 5 It is the Pareto solution set obtained in a simulation experiment provided in one embodiment of this application.

[0027] Figure 6 This is a schematic diagram of a device for determining a combination of service resources according to an embodiment of this application.

[0028] Figure 7 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0029] The features and exemplary embodiments of various aspects of this application will now be described in detail. Numerous specific details are set forth in the following detailed description in order to provide a comprehensive understanding of this application. However, it will be apparent to those skilled in the art that this application can be implemented without some of these specific details. The following description of embodiments is merely intended to provide a better understanding of this application by illustrating examples thereof.

[0030] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0031] With the development of technology, service providers are offering an increasing number of service resources. Some service requesters need to find high-quality service resources, thus, service platforms have emerged.

[0032] The basic idea of ​​the service platform is "centralized use of decentralized resources and decentralized service of centralized resources," achieving supply and demand matching by integrating information on service providers and service demanders. However, the dispersion of service resources and the diversity of service types result in numerous combinations of service resources offered by a one-stop service platform. How to organically combine these various service resources to meet the service needs of demanders is a problem that urgently needs to be solved.

[0033] This application provides a method, apparatus, device, storage medium, and product for determining a combination of service resources. The method for determining a combination of service resources provided in this application is described below.

[0034] Figure 1 This is a flowchart illustrating a method for determining a combination of service resources provided in an embodiment of this application. For example... Figure 1 As shown, the method includes:

[0035] S110: Obtain the service resource requirement information for the first task published by the service requester.

[0036] S120, according to the preset task decomposition algorithm, decompose the first task to obtain the subtasks and serial structure of the first task.

[0037] S130, according to the preset search algorithm, search for a set of candidate service resources that match the subtask.

[0038] S140, with the optimization objective as the constraint, solve the service resource optimization model.

[0039] S150, Based on the solution of the service resource optimization model, determine the service resource combination for the first task.

[0040] Therefore, by obtaining the service resource requirements of the first task published by the service requester, the first task is decomposed according to a preset task decomposition algorithm, resulting in subtasks and their sequential structure. A preset search algorithm then searches for a set of candidate service resources that match the subtasks. Since each subtask is derived from the first task, the two are strongly correlated. Next, using the optimization objective as a constraint, the solution to the service resource optimization model is obtained, determining the service resource combination for the first task. This combination of service resources determined through subtasks meets the service resources required by the first task, allowing for the organic combination of various service resources to satisfy the service requester's needs.

[0041] The specific implementation methods for each of the above steps are described below.

[0042] In some embodiments, in S110, the service requester can be any object that needs service resources, such as an enterprise or an individual. The first task can be the service resources needed by the service requester. Obtaining the service resource requirement information for the first task published by the service requester can be done by obtaining the information published by the service requester from the service platform where the service requester is located.

[0043] In some embodiments, in S120, the preset task decomposition algorithm can be a task decomposition algorithm selected according to the actual situation. Subtasks can be multiple tasks obtained by decomposing the first task, specifically a portion of the service resources required by the service requester. The concatenation structure can be information used to represent the structure between the subtasks. By decomposing the first task according to the preset task decomposition algorithm, the subtasks and concatenation structure of the first task can be obtained.

[0044] In some embodiments, in S130, the preset search algorithm can be a search algorithm selected according to the actual situation, and the candidate service resource set can be a set of multiple service resources that can provide services to the subtask, which is not limited here.

[0045] As an example, a complex task ST published by a service requester can be broken down into multiple subtasks ST. i Various STs i There is a corresponding set of candidate service resources, from which one service resource is selected to provide the service during execution.

[0046] In some embodiments, in S140, the optimization objective refers to the objective that maximizes the benefit to the service demander's primary task. This could be a Quality of Service (QoS) indicator such as the time and cost required to complete the service, or one or more optimization objectives such as improving the efficiency of resource allocation or reducing resource allocation. Cost constraints can be conditions such as minimizing the service demander's time or cost, and are not limited here. The service resource optimization model can be a computational model selected based on actual conditions; specifically, it can involve inputting constraints into the service resource model to obtain a solution to the service resource optimization model.

[0047] As an example, suppose a service requester publishes the requirement information for a certain task on the service platform. After receiving the task information, the service platform decomposes the complex task into sub-tasks and task structure according to the internal system settings of the service platform, and then selects a suitable set of candidate service resources under the premise of considering optimization goals and constraints.

[0048] In some embodiments, in S150, the service resource combination of the first task is determined based on the solution of the service resource optimization model. Specifically, the service resources corresponding to the solution of the service resource optimization model can be determined as the service resource combination of the first task.

[0049] In some embodiments, in order to more accurately solve the service resource optimization model with the optimization objective as a constraint for different optimization objectives, the optimization objective is to minimize the time and cost for service demanders. The service resource optimization model can be:

[0050] minF=min QoS=min{T,C} (1)

[0051]

[0052] in,

[0053]

[0054] Where T represents the total time of the service resource, T p T represents the total effective service time of the service resource. l This indicates the total waiting time for the service resource. It is resource S ij To resources The sum of the docking and delivery time and the waiting time.

[0055] in,

[0056]

[0057] Where C represents the total service cost, C p C represents the cumulative cost of providing effective services using service resources. l This represents the sum of the service resource integration and delivery costs and the time costs. It is service resource S ij The actual cost of providing effective service for a certain task i It is resource S ij To resources The sum of the docking and delivery costs and the time costs;

[0058] T max This indicates that the selected service resources for the subtask satisfy the maximum time constraint, C. max This indicates that the selected service resources for the subtask satisfy the maximum cost constraint.

[0059] In some embodiments, C p This represents the cumulative cost of providing effective services using a service resource S, i.e., the cost of providing effective services using a certain resource S. ij The cumulative cost of actually providing effective service for a certain task i (including a certain resource S) ij (Additional rework costs incurred when the service provided for task i fails to meet standards), C lThis represents the sum of the service resource integration and delivery costs and the time costs, i.e., resource S. ij To resources The cumulative value of the sum of docking and delivery costs and time costs; It is service resource S ij The actual cost of providing effective service for a certain task i (including a certain resource S) ij Additional rework costs incurred when the service provided for task i fails to meet standards.

[0060] As an example, the service platform can process complex tasks into STs based on customer needs and rules. i Decompose into multiple subtasks ST i The structure between the subtasks is a serial structure, and the service platform obtains the set of candidate service resources by searching and matching according to the internal system settings.

[0061] The symbols used in the above service resource optimization model are shown in Table 1:

[0062] Table 1. Symbols and their meanings

[0063]

[0064] This technical example takes the perspective of the demand side and establishes a mathematical model with the optimization objective of achieving optimal service quality (shortest time, lowest cost). The decision variables are as follows:

[0065]

[0066] Where, x ij It is a 0-1 variable.

[0067]

[0068] The above formula represents the constraint of matching candidate service resources for the i-th subtask, that is, any subtask ST i Only one candidate service resource S can be selected. ij To provide services to them.

[0069] Optimization goals: The QoS optimization goals of the demand side include time and cost. The smaller the QoS value, the higher the service quality obtained by the demand side.

[0070] Time optimization goal:

[0071] min T=min (T p +T l (18)

[0072] Thus, by determining the service resource optimization model with the optimization objective of minimizing the time and cost for the service demand side, and since the resource combination required for different optimization objectives may differ, the solution of the service resource optimization model can be solved more accurately with the optimization objective as the constraint for different optimization objectives.

[0073] In some embodiments, in order to more accurately solve the service resource optimization model with the optimization objective as a constraint for different optimization objectives, the optimization objective includes an upper-level optimization objective and a lower-level optimization objective;

[0074] The upper-level optimization objective is to minimize the weighted sum of time and cost for the service requester; the lower-level optimization objective is to maximize the performance of the service operator, with performance determined based on service resource availability, service satisfaction, and service resource profitability. The service resource optimization model can be:

[0075] U:F1=minQoS=min{ω T ·T+ω C ·C}(6)

[0076]

[0077] L: F2=maxPeR=max{R,SA,FN} (8)

[0078]

[0079] Where F1 represents the optimization objective of the service demander, consisting of the total time and total cost of the service combination, where ω T and ω C F2 represents the demand side's preference for each sub-optimization objective, and T represents the service operator's optimization objective. max This indicates that the selected service resources for the subtask satisfy the maximum time constraint, C. max This indicates that the selected service resources for the subtask satisfy the maximum cost constraint, R. min This represents the minimum percentage of manufacturing services that are in a usable state during use, and represents the percentage of resource S that task i is in relation to resource S. ij The degree to which the services provided satisfy the user is represented by resource S. ij The financial profit margin that completing task i brings to the enterprise.

[0080] In some embodiments, there are total time constraints, total cost constraints, availability constraints, service satisfaction constraints, and resource profitability constraints; and each subtask selects only one service resource to provide services to it.

[0081] As an example, the meanings of some symbols are shown in Table 2.

[0082] Table 2 Symbols and Meanings

[0083]

[0084] The optimization goals for the service demand side are as follows:

[0085] F1 = min QoS = min{ω T ·T+ω C ·C} (19)

[0086] The performance of service operators can be mainly divided into resource availability, service satisfaction, and service resource utilization.

[0087] Resource availability refers to the proportion of candidate service resources that are readily available for use while providing services to various subtasks. Experience shows that when a machine is overloaded, its efficiency often decreases to varying degrees. Therefore, this application stipulates that the efficiency of resources in completing services decreases as the resource load increases. Thus, service composition should prioritize service resources with higher availability to ensure optimal resource allocation and efficiency across the entire system. The calculation formula is as follows:

[0088]

[0089] Satisfaction refers to the emotional experience gained from the time a service user submits a task request to the time the service provider completes the task. Higher satisfaction indicates better service received by the customer. Therefore, satisfaction can be used as a performance indicator for service operators, calculated using the following formula:

[0090]

[0091] For the same sub-task, different candidate resources will have different financial profit margins. This is because the service resources differ in location, production process, and level of specialization. The service portfolio, as a whole, needs to be managed throughout its entire lifecycle, eliminating outdated service resources. Therefore, the financial performance optimization objectives are as follows:

[0092]

[0093] Thus, with the optimization objectives including upper-level and lower-level optimization objectives; the upper-level optimization objective is to minimize the weighted sum of the time and cost for the service requester; and the lower-level optimization objective is to maximize the performance of the service operator. Performance is determined based on service resource availability, service satisfaction, and service resource profitability. Therefore, a service resource optimization model is established. Since the resource combination required for different optimization objectives may vary, this approach allows for a more accurate solution to the service resource optimization model, using the optimization objectives as constraints.

[0094] In some embodiments, to more accurately solve the service resource optimization model using the optimization objectives as constraints for different optimization goals, the optimization objectives include upper-level and lower-level optimization objectives; the upper-level optimization objective is to minimize the time and cost for the service requester; the lower-level optimization objective is to maximize the performance of the service operator, with performance determined based on service resource availability, service satisfaction, and service resource profitability; the service resource optimization model can be:

[0095] F1 = min QoS = min{T, C} (10)

[0096] F2=maxPeR=max{R,SA,FN} (11)

[0097]

[0098] Where F1 represents the optimization objective of the service demander, consisting of the total time and total cost of the service combination, where ω T and ω C F2 represents the demand side's preference for each sub-optimization objective, and T represents the service operator's optimization objective. max This indicates that the selected service resources for the subtask satisfy the maximum time constraint, C. max This indicates that the selected service resources for the subtask satisfy the maximum cost constraint, R. min This represents the minimum percentage of manufacturing services that are in a usable state during use, and represents the percentage of resource S that task i is in relation to resource S. ij The degree to which the services provided satisfy the user is represented by resource S. ij The financial profit margin that completing task i brings to the enterprise.

[0099] Thus, with the optimization objectives including upper-level and lower-level optimization objectives; the upper-level optimization objective is to minimize the time and cost for the service requester; and the lower-level optimization objective is to maximize the performance of the service operator. The performance is determined based on the availability of service resources, service satisfaction, and service resource profit margin. Therefore, a service resource optimization model is determined. Since the resource combination required for different optimization objectives may vary, the solution of the service resource optimization model can be solved more accurately with the optimization objectives as constraints for different optimization objectives.

[0100] In some embodiments, to more accurately solve the service resource optimization model using the optimization objectives as constraints for different optimization goals, the optimization objectives include upper-level optimization objectives and lower-level optimization objectives. The upper-level optimization objectives are short-term optimization objectives, and the lower-level optimization objectives are long-term optimization objectives. The short-term optimization objectives are based on the total short-term service time T. S and total cost of short-term services C S PeR short Including short-term resource availability R SShort-term service satisfaction (SA) S and short-term service resource profit margin FN S Sure.

[0101] Long-term optimization goals include long-term Quality of Service (QoS). long and long-term performance PeR long QoS long Including total long-term service time T L and total cost of long-term service C L PeR long Including long-term resource availability R L Long-term service satisfaction (SA) L and long-term service resource profit margin FN L ;

[0102] Service resource optimization models can be:

[0103] U: minF1=min{ω Qos QoS short -ω PeR PeR short} 13)

[0104] =min{ω Qos (ω T ·T S +ω C ·C S )-ω PeR ·(ω R ·R S +ω sA ·SA S +ω FN ·FN S )}

[0105]

[0106] L: minF2=min{T L C L -R L -SA L -FN L} 15)

[0107]

[0108] As an example, the meanings of some symbols are shown in Table 3.

[0109] Table 3 Symbols and Meanings

[0110]

[0111]

[0112] Optimization goal:

[0113] I. Short-term optimization goals

[0114] Short-term optimization goals include short-term Quality of Service (QoS) short ) and short-term performance (PeR) short ), QoS short Including total short-term service time (T) S ) and total cost of short-term services (C S PeR short Including short-term resource availability (R S Short-term service satisfaction (SA) S ) and short-term service resource profit margin (FN) S Each sub-optimization objective is assigned a weight coefficient, representing the decision-maker's preference for each sub-optimization objective, ω. QoS It is to optimize the target QoS short The weighting coefficient represents the degree of importance that the decision-making body (service demander and service operator) attaches to the optimization objective. ω PeR Similarly; ω T It is to optimize sub-objective T S The weighting coefficient represents the degree of importance that the decision-making entity (service demander) attaches to this optimization sub-objective. ω C Similarly; ω R It is to optimize the sub-objective R S The weighting coefficient represents the degree of importance that the decision-making entity (service operator) attaches to this optimization sub-objective. ω SA and ω FN Similarly, in summary, the expression for the short-run optimization objective is as follows:

[0115] F1=ω QoS QoS short -ω PeR PeR short (twenty three)

[0116] =ω QoS (ω T ·T S +ω C ·C S )-ω PeR ·(ω R ·R S +ω SA ·SA S +ω FN ·FN S ) (twenty four)

[0117] II. Long-term optimization goals

[0118] Long-term optimization goals include long-term Quality of Service (QoS) long ) and long-term performance (PeR) long ), QoS long Including total long-term service time (T) L ) and total cost of long-term service (C L PeR long Including long-term resource availability (R L ), long-term service satisfaction (SA) L ) and long-term service resource profit margin (FN) L In summary, the expression for the long-term optimization objective is as follows:

[0119] F2 = {QoS long PeR long}={T L C L ,-R L ,-SA L ,-FN L} (25)

[0120] Thus, the optimization objectives include upper-level optimization objectives and lower-level optimization objectives. The upper-level optimization objectives are short-term optimization objectives, and the lower-level optimization objectives are long-term optimization objectives. The short-term optimization objectives are based on the total short-term service time T. S and total cost of short-term services C S PeR short Including short-term resource availability R S Short-term service satisfaction (SA) S and short-term service resource profit margin FN S It is determined that long-term optimization goals include long-term Quality of Service (QoS). long and long-term performance PeR long QoS long Including total long-term service time T L and total cost of long-term service C L PeR long Including long-term resource availability R L Long-term service satisfaction (SA) L and long-term service resource profit margin FN L In this case, a service resource optimization model is determined. Since the resource combination required for different optimization objectives may vary, the solution of the service resource optimization model can be solved more accurately with the optimization objective as the constraint for different optimization objectives.

[0121] To more accurately determine the service resource combination for the first task, in some embodiments, the service resource combination for the first task is determined by solving a service resource optimization model with the optimization objective as a constraint. The determination of the service resource combination for the first task based on the solution of the service resource optimization model may include:

[0122] Repeat steps A1 to A6 until the iteration stopping condition is met, and obtain the Pareto solution set that satisfies the lower-level optimization objective;

[0123] Step A1: Encode the candidate service resources using integer encoding to generate the chromosome genes corresponding to the individuals in the population. For example, if the service combination of the population individuals is {S} 41 S 42 S 33 S 24 S 15 If the chromosome code is [4,4,3,2,1], then an initial parent population of size N is randomly generated.

[0124] Step A2: The parent population is crossovered and mutated to obtain a new generation of offspring populations, each with a population size of N.

[0125] Step A3: Repair the individuals that have crossed the boundary;

[0126] Step A4: Merge the populations into a new population of size 2N;

[0127] Step A5: Calculate the fitness of the new population, calculate the non-dominated ordination value of the new population, and sort them by crowding.

[0128] Step A6: Use elite retention strategies to preserve superior individuals to form the next generation of the population;

[0129] Based on the solution set, determine the service resource combination for the first task.

[0130] Step A7: Sort the Pareto solution set according to the upper-level optimization objective and select the service quality combination that meets the optimization objective.

[0131] Thus, by introducing the Pareto algorithm and iteratively executing steps A1 to A6, a Pareto solution set that satisfies the lower-level optimization objective is obtained. Then, the Pareto solution set is sorted according to the upper-level optimization objective, and the service quality combination that meets the optimization objective is selected, which can more accurately determine the service resource combination of the first task.

[0132] To more accurately determine the service resource combination for the first task, the service resource optimization model is solved with the optimization objective as a constraint. Based on the solution of the service resource optimization model, the service resource combination for the first task is determined, including:

[0133] Step B1: Repeat steps A1 to A6 of claim 6 until the iteration stopping condition is met, obtaining the Pareto solution set that satisfies the lower-level optimization objective; the Pareto solution set constitutes the initialization decision matrix X = (x ij ) m×n ;

[0134] Step B2: Standardize the decision matrix to construct a normalized decision matrix Y = (y ij ) m×n ,in,

[0135] Step B3: Construct a weighted normalized decision matrix Z = (z ij ) m×n , z ij =ω j ·y ij ;

[0136] Step B4: Select the ideal solution A + With negative ideal solution A - , where A + =(z1) + z2 + ,…,z n + ), A - =(z1) - z2 - ,…,z n - ), z i + =max{z i1 ,z i2 ,…z in}, z i - =min{z i1 ,z i2 ,…z in};

[0137] Step B5: Calculate the Eulerian distance between each solution set and the positive and negative ideal solutions. Among them, z i =(z i1 ,z i2 ,…,z in );

[0138] Step B6: Calculate the satisfaction level C for each solution. i : If z i =A + Then C i =1; z i =A- Then C i =0; C i The higher the value, the higher the satisfaction level of the solution;

[0139] Based on satisfaction levels, determine the service resource combination corresponding to the first task.

[0140] Thus, by processing the Pareto solution set and standardizing the original matrix X, the influence of dimensions on the results can be eliminated, thereby more accurately determining the service resource combination for the first task.

[0141] In some embodiments of this application, in order to more clearly understand the technical solution of this application, the technical solution of this application will be introduced below using specific scenarios of simulation experiments.

[0142] First, the service platform's decomposition of the required tasks was simulated. The decomposed tasks require a total of 5 steps, such as... Figure 2 As shown, the decomposed subtasks can be represented as: ST = {ST1, ST2, ST3, ST4, ST5}. After service search and matching by the service platform, each subtask yields several candidate service resource sets. Finally, based on the optimization objective and constraints, one service resource is selected from each candidate service resource set and combined in sequence to form the optimal service combination scheme.

[0143] The NSGA-II algorithm is used to solve a specific example of service composition optimization based on maximizing demand-side benefits. The initial population size of the algorithm is N = 20, and the crossover probability P is... c =0.6, mutation probability P m =0.03, maximum number of generations G max =200, calculate the average fitness of each generation of the population under different fitness functions in the MATLAB 2016a computing environment, such as Figure 3 , 4 As shown. During the 200 generations of evolution, the average fitness tends to stabilize after 120 generations. Therefore, after 120 generations of evolution, the Pareto optimal solution set in the following case is obtained. The Pareto front of the Pareto optimal solution set consisting of 6 solutions is calculated, as shown below. Figure 5 As shown.

[0144] Table 4. Service composition ranking in the simulation experiment of the service resource optimization model.

[0145]

[0146] As shown in Table 4, the service combination with serial number 3 (S) 14 →S 23 →S 33 →S 41 →S53 The highest overall satisfaction score is 0.8673. Therefore, in this case, the optimal service combination is (S... 14 →S 23 →S 33 →S 41 →S 53 The time T is 540 minutes, and the cost C is 20,700 yuan.

[0147] Based on the same inventive concept, this application also provides a device for determining a combination of service resources. (Specifically combined with...) Figure 6 Please provide a detailed explanation.

[0148] Figure 6 This is a schematic diagram of the structure of a device for determining a combination of service resources according to an embodiment of this application. Figure 6 As shown, the service resource combination determining device 600 may include:

[0149] Module 601 retrieves service resource requirement information for the first task published by the service requester.

[0150] The generation module 602 is used to decompose the first task according to a preset task decomposition algorithm to obtain the subtasks and serial structure of the first task.

[0151] Search module 603 is used to search for a set of candidate service resources that match the subtask according to a preset search algorithm;

[0152] Solver module 604 is used to solve the service resource optimization model with the optimization objective as the constraint.

[0153] The determination module 605 is used to determine the service resource combination for the first task based on the solution of the service resource optimization model.

[0154] Therefore, by obtaining the service resource requirements of the first task published by the service requester, the first task is decomposed according to a preset task decomposition algorithm, resulting in subtasks and their sequential structure. A preset search algorithm then searches for a set of candidate service resources that match the subtasks. Since each subtask is derived from the first task, the two are strongly correlated. Next, using the optimization objective as a constraint, the solution to the service resource optimization model is obtained, determining the service resource combination for the first task. This combination of service resources determined through subtasks meets the service resources required by the first task, allowing for the organic combination of various service resources to satisfy the service requester's needs.

[0155] Figure 7A schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application is shown. The electronic device may include a processor 701 and a memory 702 storing computer program instructions.

[0156] Specifically, the processor 701 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0157] Memory 702 may include mass storage for data or instructions. For example, and not limitingly, memory 702 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, memory 702 may include removable or non-removable (or fixed) media. Where suitable, memory 702 may be internal or external to an electronic device. In a particular embodiment, memory 702 is a non-volatile solid-state memory.

[0158] Memory 702 may include read-only memory (ROM), flash memory device, random access memory (RAM), disk storage medium device, optical storage medium device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory 702 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods described above according to the foregoing aspects of this disclosure.

[0159] The processor 701 implements any of the service resource combination determination methods in the above embodiments by reading and executing computer program instructions stored in the memory 702.

[0160] In one example, the electronic device may also include a communication interface 703 and a bus 710. For example, Figure 7 As shown, the processor 701, memory 702, and communication interface 703 are connected through bus 710 and complete communication with each other.

[0161] The communication interface 703 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0162] Bus 710 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 710 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0163] This electronic device can achieve integration based on a method for determining the combination of service resources. Figures 1 to 6 The method and apparatus for determining the combination of service resources described herein.

[0164] Furthermore, in conjunction with the service resource combination determination method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the service resource combination determination methods in the above embodiments.

[0165] In addition, this application also provides a computer program product, including a computer program, which, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0166] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0167] It should be understood that in the embodiments of this application, "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean that B is determined solely based on A; B can also be determined based on A and / or other information.

[0168] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining a combination of service resources, characterized in that, include: Obtain service resource demand information for the first task published by the service requester; According to the preset task decomposition algorithm, the first task is decomposed to obtain the subtasks and the chain structure of the first task; According to the preset search algorithm, a set of candidate service resources that match the subtask is searched; Solve the service resource optimization model by taking the optimization objective as the constraint. Based on the solution of the service resource optimization model, determine the service resource combination for the first task; The optimization objectives include upper-level optimization objectives and lower-level optimization objectives. The upper-level optimization objectives are short-term optimization objectives, and the lower-level optimization objectives are long-term optimization objectives. The short-term optimization objectives include short-term service quality. and short-term performance , Including total short-term service time and total cost of short-term services , Including short-term resource availability Short-term service satisfaction and short-term service resource profit margin ; The long-term optimization goals include long-term service quality. and long-term performance , Including total service time Total cost of long-term service , Including long-term resource availability Long-term service satisfaction and long-term service resource profit margin ; The service resource optimization model is as follows: （13） （14） （15） in, The weighting coefficient representing the short-term service quality. This represents the weighting coefficient for the short-term performance. This represents the weighting coefficient of the total short-term service time. This represents the weighting coefficient of the total cost of the short-term service. The weighting coefficients representing the short-term resource availability. The weighting coefficient represents the short-term service satisfaction level. This represents the weighting coefficient for the profit margin of the short-term service resources. This represents the amount of resources used to provide services for the i-th subtask, where n represents the number of subtasks. This represents the processing time of service resources within the total short-term service time. This represents the transportation time of service resources within the total short-term service time. This represents the processing cost of service resources within the total cost of the short-term service. This represents the transportation cost of service resources within the total cost of the short-term service. This indicates the service resources within the total short-term service time. Processing time, This represents the service resources in the total cost of the short-term service. Processing costs, This indicates the service resources used during the manufacturing service usage process in the short-term resource availability. For the task The available ratio, This indicates the service resources in the short-term service satisfaction. For the task The degree of satisfaction This indicates that the service resources account for the short-term resource profit margin. For the task Financial profit margin This represents the total time for other periods. This indicates the total cost of services during other periods. Indicates resource availability at other times. This indicates service satisfaction at other times. This indicates the profit margin of service resources in other periods.

2. The method according to claim 1, characterized in that, Using the optimization objective as a constraint, solve the service resource optimization model; based on the solution of the service resource optimization model, determine the service resource combination for the first task, including: Repeat steps A1 to A6 until the iteration stopping condition is met, and obtain the Pareto solution set that satisfies the lower-level optimization objective; Step A1: Encode the candidate service resources using integer encoding to generate the chromosome genes corresponding to the individuals in the population, and randomly generate a population size of... The initial parent population; Step A2: The initial parent population is crossovered and mutated to obtain a new generation of offspring populations, with each generation having a population size of [missing information]. ; Step A3: Repair the individuals that have crossed the boundary; Step A4: Merge the populations into groups of size [size missing]. A new population; Step A5: Calculate the fitness of the new population, calculate the non-dominated ordination value of the new population, and sort them by crowding. Step A6: Use elite retention strategies to preserve superior individuals to form the next generation of the population; The service resource combination for the first task is determined based on the solution set. Step A7: Sort the Pareto solution set according to the upper-level optimization objective and select the service quality combination that meets the optimization objective.

3. The method according to claim 2, characterized in that, Using the optimization objective as a constraint, solve the service resource optimization model; based on the solution of the service resource optimization model, determine the service resource combination for the first task, including: Step B1: Repeat steps A1 to A6 of claim 2 until the iteration stopping condition is met, obtaining the Pareto solution set that satisfies the lower-level optimization objective; the Pareto solution set constitutes the initialization decision matrix. ; Step B2: Standardize the decision matrix to construct a normalized decision matrix. ,in, ; Step B3: Construct a weighted normalized decision matrix based on the weights of each objective. , ; Step B4: Select the ideal solution Negative ideal solution ,in, , , , ; Step B5: Calculate the Eulerian distance between each solution set and the positive and negative ideal solutions. , ,in, ; Step B6: Calculate the satisfaction level of each solution. : ,if ,but ; ,but ; The higher the value, the higher the satisfaction level of the solution; Based on the satisfaction level, determine the service resource combination corresponding to the first task.

4. A device for determining a combination of service resources, characterized in that, The apparatus is used in the method for determining the combination of service resources as described in claims 1-3, comprising: The acquisition module retrieves service resource requirement information for the first task published by the service requester. The generation module is used to decompose the first task according to a preset task decomposition algorithm to obtain the subtasks and serial structure of the first task. The search module is used to search for a set of candidate service resources that match the subtask according to a preset search algorithm. The solver module is used to solve the service resource optimization model with the optimization objective as the constraint. The determination module is used to determine the service resource combination of the first task based on the solution of the service resource optimization model.

5. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method for determining the combination of service resources as described in any one of claims 1-3.

6. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the method for determining the combination of service resources as described in any one of claims 1-3.

7. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method for determining a combination of service resources as described in any one of claims 1-3.

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