Many-to-many resource intelligent allocation method and device, medium and product

Abstract

The invention discloses a many-to-many resource intelligent allocation method and device, a medium and a product, and relates to the field of resource allocation, and the method comprises the steps: determining the matching degree of a resource to a target; determining the dynamic matching degree of many-to-many resources from the perspective of statistics based on the matching degree; performing priority ranking on the resource target allocation based on the dynamic matching degree to obtain a dynamic ranking strategy; constructing a target function based on the matching degree of the resources to the target and the dynamic matching degree; migration allocation is carried out on the resources based on a biogeography optimization algorithm to obtain an allocation result, and the allocation result is regarded as a habitat; and in each migration distribution process, determining a suitability index for generating the habitat by adopting an objective function, and taking the habitat corresponding to the maximum suitability index as an optimal distribution result until an iteration termination condition is reached, so as to complete intelligent distribution of many-to-many resources. According to the method, the multi-type resources can be dynamically, quickly and intelligently allocated to the multi-type targets, and the global optimization of the allocation effect is realized.

Description

Technical Field

[0001] The present application relates to the field of resource allocation, and in particular to a many-to-many resource intelligent allocation method, device, medium and product. Background Art

[0002] With the development of technologies such as bee swarms, many-to-many game technology has received more and more attention. Resource allocation is the key link in many-to-many games. Efficient and accurate resource allocation can help achieve the best results. In the resource allocation process, it is necessary to consider the multi-type resources of the party and the multi-type goals of the opponent, accurately describe the matching degree of different resource goal combinations, and realize the global optimal allocation strategy search. Especially under strong time constraints, it is crucial to achieve rapid allocation of many-to-many resources.

[0003] Currently, there is no relevant research on the dynamic matching degree and many-to-many fast allocation strategy of many-to-many resource allocation. There is also no corresponding research on how to dynamically, quickly and intelligently allocate multi-type resources to multi-type targets and achieve the global optimal allocation effect. Summary of the invention

[0004] The purpose of this application is to provide a many-to-many resource intelligent allocation method, device, medium and product, which can dynamically, quickly and intelligently allocate multiple types of resources to multiple types of targets and achieve global optimization of the allocation effect.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a many-to-many resource intelligent allocation method, comprising:

[0007] Determine the match of resources to objectives;

[0008] Based on the matching degree of resources to targets, the matching degree of different resources to targets is determined from a statistical perspective, and the dynamic matching degree of many-to-many resources is obtained;

[0009] Prioritizing resource target allocation based on the dynamic matching degree to obtain a dynamic sorting strategy;

[0010] Constructing an objective function based on the matching degree of resources to the target and the dynamic matching degree;

[0011] The resource migration and allocation is carried out based on the biogeographic optimization algorithm to obtain the allocation results, and each allocation result is regarded as a habitat;

[0012] In each migration allocation process, the objective function is used to determine the suitability index of the habitat until the iteration termination condition is reached, and the habitat corresponding to the maximum suitability index is used as the optimal allocation result to complete the intelligent allocation of many-to-many resources.

[0013] Optionally, based on the matching degree of resources to targets, the matching degree of different resources to targets that dynamically changes is determined from a statistical perspective to obtain the dynamic matching degree of many-to-many resources, specifically including:

[0014] Select the target with the least number of optional resources;

[0015] When the number of optional resources and the number of required resources of the target are the same and are 1, determining the dynamic matching degree to be 1;

[0016] When the number of optional resources of the target is greater than 1, the optional resource corresponding to the maximum resource matching degree is selected, and the dynamic matching degree of the optional resource corresponding to the maximum resource matching degree is determined to be r(i) is the number of optional resources for the current target i;

[0017] When the number of optional resources of the target is 0, determining the dynamic matching degree to be 0;

[0018] Delete the optional resources and the target corresponding to the maximum resource matching degree, and return to "select the target with the least number of optional resources" until all resource target combinations are traversed to obtain the dynamic matching degree of many-to-many resources.

[0019] Optionally, the resource matching degree of each resource target combination is expressed as:

[0020]

[0021] In the formula, p i,j is the resource matching degree, when p i,j ∈(0,1), the corresponding resources are optional resources; the degree of relevance in the formula is determined based on the set relevance threshold.

[0022] Optionally, the dynamic sorting strategy is expressed as:

[0023] P(temp,i)=(i,j);

[0024] Where P(temp,i) is the dynamic sorting strategy, temp is the temporary quantity, i is the target number, and j is the resource number.

[0025] Optionally, the objective function constructed based on the matching degree of the resource to the target and the dynamic matching degree is expressed as:

[0026]

[0027] In the formula, f represents the total benefit value under the current resource allocation strategy; x ij is a decision variable, representing resource r j Is it assigned to the target t?i , if not, it is 0, if yes, it is 1; ρ j For resources j Dynamic matching degree; p ij For resources j For target t i The matching degree is , n represents the number of targets, and m represents the number of resources.

[0028] Optionally, a biogeographic optimization algorithm is used to migrate and allocate resources to obtain allocation results, and each allocation result is regarded as a habitat, specifically including:

[0029] Take resources as species, assign species to targets in a numerically corresponding manner to obtain allocation results, and regard each allocation result as a habitat;

[0030] Combined with dynamic sorting strategies, the migration direction of species is guided, and mutations are generated to promote species migration between different habitats until convergence is reached, completing species migration and obtaining the final habitat.

[0031] Optionally, the mutation probability of generating a mutation is expressed as:

[0032]

[0033] Where P s,i is the probability that the number of species in habitat i is s, P s,i (t) is the probability that the number of species in habitat i is s in the t-1th migration allocation, P s,i (t-1) is the probability that the number of species in habitat i is s in the t-1th migration allocation, The probability P s,i The derivative of M max () is the maximum value, P max is the probability that the maximum number of species exists in the current habitat.

[0034] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the many-to-many resource intelligent allocation methods described above.

[0035] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the many-to-many resource intelligent allocation methods described above.

[0036] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any one of the many-to-many resource intelligent allocation methods described above.

[0037] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0038] The present application provides a method, device, medium and product for intelligent allocation of many-to-many resources. According to the different interference capabilities of different interference resources on different targets, the dynamic matching degree of many-to-many resources is determined to obtain a dynamic sorting strategy. In the process of finding the optimal allocation solution by migrating and allocating resources based on the biogeographic optimization algorithm to obtain the allocation result, the dynamic sorting strategy is combined to guide the allocation direction of resources, which can reduce unnecessary search processes and improve the convergence speed of the algorithm. In addition, by introducing dynamic matching degree and dynamic sorting strategy, it is avoided that the biogeographic optimization algorithm falls into the local optimum too early. The maximum suitability index of the habitat is determined by constructing an objective function to obtain the optimal allocation result. While achieving the global optimal allocation effect, it reduces the loss of advantageous information in the search process, effectively improves the convergence speed of the algorithm, and can dynamically, quickly and intelligently allocate multiple types of resources to multiple types of targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 This is an application environment diagram of a many-to-many resource intelligent allocation method in one embodiment of the present application;

[0041] Figure 2 A flowchart of a many-to-many resource intelligent allocation method provided in one embodiment of the present application;

[0042] Figure 3 An implementation architecture diagram of a many-to-many resource intelligent allocation method provided in one embodiment of the present application;

[0043] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0046] The many-to-many resource intelligent allocation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the resources and targets to be allocated to the server 104. After the server 104 receives the resources and targets to be allocated, the server 104 determines the matching degree of the resources to the targets for the resources and targets to be allocated. Based on this matching degree, the dynamic matching degree of many-to-many resources is determined from a statistical perspective. The resource target allocation is prioritized based on the dynamic matching degree to obtain a dynamic sorting strategy. The objective function is constructed based on the matching degree of resources to the target and the dynamic matching degree. The allocation result is obtained by migrating and allocating resources based on the biogeographic optimization algorithm, and the allocation result is regarded as a habitat. In each migration and allocation process, the objective function is used to determine the fitness index of the habitat, until the iteration termination condition is reached, and the habitat corresponding to the maximum fitness index is used as the optimal allocation result to complete the intelligent allocation of many-to-many resources. The server 104 may feed back the obtained many-to-many resource intelligent allocation result to the terminal 102. In addition, in some embodiments, the many-to-many resource intelligent allocation method may also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 may directly perform intelligent allocation on the resources and targets to be allocated, or the server 104 may obtain the resources and targets to be allocated from the data storage system and perform intelligent allocation on the resources and targets to be allocated.

[0047] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0048] In an exemplary embodiment, Figure 2 As shown, a method for intelligent allocation of many-to-many resources is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 206. Among them:

[0049] Step 201: Determine the matching degree of resources to targets.

[0050] Step 202: Based on the matching degree of resources to targets, the matching degree of different resources to dynamically change targets is determined from a statistical perspective to obtain the dynamic matching degree of many-to-many resources.

[0051] Step 203: Prioritize resource target allocation based on the dynamic matching degree to obtain a dynamic sorting strategy.

[0052] Step 204: construct an objective function based on the matching degree and dynamic matching degree of resources to the target.

[0053] Step 205: Use a biogeographic optimization algorithm to migrate and allocate resources to obtain allocation results, and regard each allocation result as a habitat.

[0054] Step 206: In each migration allocation process, the objective function is used to determine the suitability index of the generated habitat until the iteration termination condition is reached, and the habitat corresponding to the maximum suitability index is used as the optimal allocation result to complete the intelligent allocation of many-to-many resources.

[0055] By implementing the above-mentioned steps 201 to 206, the present application determines the dynamic matching degree of many-to-many resources in view of the different interference capabilities of different interference resources on different targets, so as to obtain a dynamic sorting strategy. And in the process of finding the optimal allocation solution by migrating and allocating resources based on the biogeographic optimization algorithm to obtain the allocation result, the dynamic sorting strategy is combined to guide the allocation direction of resources, which can reduce unnecessary search processes and improve the convergence speed of the algorithm. In addition, by introducing dynamic matching degree and dynamic sorting strategy, it is avoided that the biogeographic optimization algorithm falls into the local optimum too early, and the maximum suitability index of the habitat generated is determined by constructing an objective function to obtain the optimal allocation result. While achieving the global optimal allocation effect, it reduces the loss of advantageous information in the search process, effectively improves the convergence speed of the algorithm, and can dynamically, quickly and intelligently allocate multiple types of resources to multiple types of targets.

[0056] In another exemplary embodiment of the present application, since there are a large number of different types of resources and targets, the matching degree calculation methods are different. The present application focuses on the dynamic matching degree calculation and global optimal result search under the condition of known resource matching degree, so only the resource matching degree is standardized. Based on this, in step 201, the resource r j For target t i The matching degree p i,j It can be defined as:

[0057]

[0058] In the formula, the numerical intervals are all closed on the left and open on the right. i,j is the resource matching degree, when p i,j ∈(0,1), the corresponding resource is used as an optional resource. The correlation degree in the formula is determined based on the set correlation threshold.

[0059] In another exemplary embodiment of the present application, in step 202, the dynamically changing matching degree between different resources and targets can be measured from a statistical perspective. Based on this, the dynamic matching degree of many-to-many resources ρ is defined as j for:

[0060]

[0061] Where r(i) is the number of optional resources for the current target i.

[0062] Based on the above formula, in actual application, the process of determining the dynamic matching degree of many-to-many resources can be described as:

[0063] Step 1: Initialize the temporary variable temp, temp=1.

[0064] Step 2: Select the target t with the least number of optional resources i If there are multiple targets with the same matching degree and the least number of optional resources, select one of them.

[0065] Step 3: When the number of optional resources is the same as the number of required resources and is 1, then ρ j =1.

[0066] Step 4: When there are multiple optional resources, select the one with the largest matching degree. j , and its dynamic matching

[0067] Step 5: Delete the resource j and target t i , delete the selected combination, set temp'=temp+1 and return to step 1 to calculate the remaining combinations.

[0068] This cycle continues until all resources are deleted. j or target t i , then a local optimal combination is found at this time, and the current local optimal dynamic matching degree is obtained.

[0069] In another exemplary embodiment of the present application, by calculating the dynamic matching degree, the resource target allocation can be prioritized accordingly. The present application refers to this priority ranking result as a dynamic ranking strategy. In the dynamic matching degree calculation process, the resource r of the kth cycle j If the dynamic matching degree is Then in the current loop, resource r j The dynamic sorting strategy is: P(temp,i)=(i,j), where P(temp,i) is the dynamic sorting strategy, temp≤i, i is the target number, and j is the resource number.

[0070] In another exemplary embodiment of the present application, the objective is to achieve the maximum total allocation benefit through the optimal resource allocation strategy. The objective function constructed in step 204 can be expressed as:

[0071]

[0072] In the formula, f represents the total benefit value under the current resource allocation strategy. ij is a decision variable, representing resource r j Is it assigned to the target t? i , if not, it is 0, if yes, it is 1. j For resources j Dynamic matching degree. ij For resources j For target t i The matching degree is , n represents the number of targets, and m represents the number of resources.

[0073] In another exemplary embodiment of the present application, a heuristic intelligent algorithm can be used to solve the above optimization problem. Based on this, the present application proposes a biogeography-based optimization algorithm (BBO) in step 205. The algorithm designs a step-by-step optimization algorithm to achieve rapid allocation of resource targets. Based on this, the implementation process of using the biogeography-based optimization algorithm to migrate and allocate resources to obtain the allocation result in step 205 can be:

[0074] 1) Take resources as species, assign species to targets in accordance with numerical correspondence to obtain allocation results, and regard each allocation result as a habitat.

[0075] 2) Combine dynamic sorting strategies to guide the migration direction of species and promote species migration between different habitats by generating mutations until convergence is reached, completing species migration and obtaining the final habitat.

[0076] In actual application, the main process of using biogeographic optimization algorithm to migrate resources and obtain allocation results includes the following three parts:

[0077] A) Initialization: Under the premise of m resources and n targets, several n-dimensional allocation results are randomly generated, and each dimension represents the allocation of the resource represented by its value to the target of the current dimension number. Each allocation result is regarded as an independent habitat, and each resource is regarded as a species. The Habitat Suitability Index (HSI) is determined based on the established objective function, which indicates the degree of excellence of the allocation result. Among them, the Habitat Suitability Index is mainly determined based on the total benefit value under the current resource allocation strategy. The Habitat Suitability Index variable (SuitableIndexVector, SIV) is randomly generated. The Habitat Suitability Index variable represents a set of external factors that can affect HSI, such as weather, vegetation, etc. Sort the habitats according to HSI, and design the number of species S that each habitat can contain.

[0078] B) Species migration: Species can migrate between habitats to achieve information exchange. Based on this, the dynamic sorting strategy is combined to guide the migration direction of species. Among them, the calculation method of the immigration rate λ, the emigration rate μ and the migration operation is:

[0079]

[0080] x i,j ′=α·x i,j +(1-α)x i,j

[0081] Among them, I and E are the maximum values ​​of immigration rate and emigration rate, respectively, S is the number of species that each habitat can contain, S max is the maximum number of species that can coexist in a habitat, x i,j For habitat x i The j-th dimension fitness index variable, x i,j ′ is the updated habitat x i The j-th dimension fitness index variable.

[0082] C) Species variation: Promoting species migration between different habitats by generating mutations. The probability of such mutations is M s,i The calculation method of the mutation operation is as follows:

[0083]

[0084] x i,j ′=x i,j +d i,j

[0085] Where P s,i is the probability that the number of species in habitat i is s, t represents the tth iteration, P s,i The corresponding derivative, M max () is the maximum value, P max is the probability that the maximum number of species exists in the current habitat, d i,j For x i,j The current mutation level, λ i is the immigration rate of habitat i, μ i is the emigration rate of habitat i, μ i+1 is the emigration rate of habitat i+1, P s,i+1 is the probability that the number of species in habitat i+1 is s, λ i-1 is the immigration rate of habitat i-1, P s,i-1 is the probability that the number of species in habitat i-1 is s, S i is the number of species in habitat i. s,i (t) is the probability that the number of species in habitat i is s in the t-1th migration allocation, P s,i (t-1) is the probability that the number of species in habitat i is s in the t-1th migration allocation.

[0086] Highlighting randomness through the above-mentioned mutation method can improve the global search capability of the algorithm.

[0087] Through the above-mentioned three steps A)-C), the proposed heuristic intelligent algorithm can be used to efficiently and accurately solve the resource allocation problem proposed by the present invention. In the process of algorithm evolution, if the iteration starts with a random population, there will be a large number of tentative searches in the early stage, and the convergence speed is slow. Therefore, in the early stage of algorithm evolution, combining a dynamic sorting strategy to guide its search direction can reduce unnecessary search processes and increase the convergence speed of the algorithm. The introduction of dynamic matching and dynamic sorting strategies avoids the algorithm from falling into "local optimality" too early. The use of an elite retention strategy to screen the elite population (i.e., the optimal allocation result) in the evolution process can reduce the loss of advantageous information in the search process and effectively improve the convergence speed of the algorithm. Based on this, the entire implementation process of the many-to-many resource intelligent allocation method provided in this application can be found in Figure 3,This method proposes a dynamic matching degree of multiple interference resources according to the different interference capabilities of different interference resources on different targets.,The matching degree between different resource-target pairs can be calculated according to the resource and target types.,Combined with the optimal allocation solution process, a many-to-many fast allocation strategy is proposed, which can efficiently and accurately realize the fast allocation of resources under many-to-many conditions, and then facilitate the fast and intelligent allocation of resources in the many-to-many game process. Figure 3 In the above equation, V(tr,n)=P(temp)=x i,j , represents the j-th dimension SIV of habitat xi in the tr-th generation population. temp' represents the updated temporary variable.

[0088] The present application also provides an application scenario, which applies the many-to-many intelligent resource allocation method mentioned above. Specifically: the many-to-many intelligent resource allocation method provided in this embodiment can be applied in the content distribution scenario. The internal distribution scenario includes the content production link, the content processing link and the content distribution link. Resources and targets enter the content processing link from the content production link, obtain the corresponding allocation results through human-computer collaboration, and enter the downstream content distribution link. The many-to-many intelligent resource allocation method provided in this embodiment belongs to the intelligent classification link in the content processing link. Specifically, in the content processing link process for resources, resources can be allocated based on the collaborative method of machine allocation and manual allocation, that is, resources are allocated to corresponding targets.

[0089] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store video tag processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a many-to-many resource intelligent allocation method is implemented.

[0090] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0091] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0092] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0093] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0094] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0095] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0096] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0097] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A many-to-many resource intelligent allocation method, characterized in that: The many-to-many resource intelligent allocation method comprises: Determine the match of resources to objectives; Based on the matching degree of resources to targets, the matching degree of different resources to targets is determined from a statistical perspective, and the dynamic matching degree of many-to-many resources is obtained; Prioritizing resource target allocation based on the dynamic matching degree to obtain a dynamic sorting strategy; Constructing an objective function based on the matching degree of resources to the target and the dynamic matching degree; The resource migration and allocation is carried out based on the biogeographic optimization algorithm to obtain the allocation results, and each allocation result is regarded as a habitat; In each migration allocation process, the objective function is used to determine the suitability index of the habitat until the iteration termination condition is reached, and the habitat corresponding to the maximum suitability index is used as the optimal allocation result to complete the intelligent allocation of many-to-many resources.

2. The many-to-many resource intelligent allocation method according to claim 1, characterized in that: Based on the matching degree of resources to targets, the matching degree of different resources to targets is determined from a statistical perspective, and the dynamic matching degree of many-to-many resources is obtained, including: Select the target with the least number of optional resources; When the number of optional resources and the number of required resources of the target are the same and are 1, determining the dynamic matching degree to be 1; When the number of optional resources of the target is greater than 1, the optional resource corresponding to the maximum resource matching degree is selected, and the dynamic matching degree of the optional resource corresponding to the maximum resource matching degree is determined to be r(i) is the number of optional resources for the current target i; When the number of optional resources of the target is 0, determining the dynamic matching degree to be 0; Delete the optional resources and the target corresponding to the maximum resource matching degree, and return to "select the target with the least number of optional resources" until all resource target combinations are traversed to obtain the dynamic matching degree of many-to-many resources.

3. The many-to-many resource intelligent allocation method according to claim 1, characterized in that: The resource matching degree of each resource target combination is expressed as: In the formula, p i,j is the resource matching degree, when p i,j ∈(0,1), the corresponding resources are optional resources; the degree of relevance in the formula is determined based on the set relevance threshold.

4. The many-to-many resource intelligent allocation method according to claim 1, characterized in that: The dynamic sorting strategy is expressed as: P(temp,i)=(i,j); Where P(temp,i) is the dynamic sorting strategy, temp is the temporary quantity, i is the target number, and j is the resource number.

5. The many-to-many resource intelligent allocation method according to claim 1, characterized in that: The objective function constructed based on the matching degree of resources to targets and the dynamic matching degree is expressed as: In the formula, f represents the total benefit value under the current resource allocation strategy; x ij is a decision variable, representing resource r j Is it assigned to the target t? i , if not, it is 0, if yes, it is 1; ρ j For resources j Dynamic matching degree; p ij For resources j For target t i The matching degree is , n represents the number of targets, and m represents the number of resources.

6. The many-to-many resource intelligent allocation method according to claim 1, characterized in that: The resource migration and allocation results are obtained by using a biogeographic optimization algorithm. Each allocation result is regarded as a habitat, including: Take resources as species, assign species to targets in a numerically corresponding manner to obtain allocation results, and regard each allocation result as a habitat; Combined with dynamic sorting strategies, the migration direction of species is guided, and mutations are generated to promote species migration between different habitats until convergence is reached, completing species migration and obtaining the final habitat.

7. The many-to-many resource intelligent allocation method according to claim 6, characterized in that: The mutation probability of generating a mutation is expressed as: Where P s,i is the probability that the number of species in habitat i is s, P s,i (t) is the probability that the number of species in habitat i is s in the t-1th migration allocation, P s,i (t-1) is the probability that the number of species in habitat i is s in the t-1th migration allocation, The probability P s,i The derivative of M max () is the maximum value, P max is the probability that the maximum number of species exists in the current habitat.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the many-to-many resource intelligent allocation method described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the many-to-many resource intelligent allocation method described in any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the many-to-many resource intelligent allocation method described in any one of claims 1 to 7 are implemented.

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