Project reserve resource optimal configuration method and system based on multi-objective genetic algorithm

Abstract

The invention discloses a project reserve resource optimal configuration method and system based on a multi-objective genetic algorithm, and belongs to the technical field of resource optimal configuration, and the method comprises the steps: generating each project reserve resource configuration scheme through the construction of a project reserve resource multi-objective optimization model of a comprehensive plan and the setting of multiple objectives; and then based on a multi-target genetic algorithm, rough fitness and density information estimation values are calculated for each project reserve resource configuration scheme, and under the condition of comprehensively balancing each target, an optimal project reserve resource configuration scheme better meeting actual target requirements is generated, so that the target of multi-target collaborative optimization is realized, and the optimization efficiency is improved. The utilization rate of resource configuration is greatly improved; the project reserve resources are classified, and the core reserve resources and the elastic reserve resources are processed respectively, so that the lowest configuration requirement of the core reserve resources is ensured, the resource configuration cost optimization of the elastic reserve resources is also considered, and the accurate configuration of the resources is completed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of resource optimization allocation, and particularly relates to a project reserve resource optimization allocation method and system based on a multi-objective genetic algorithm. BACKGROUND

[0002] Under the current complex economic environment and fierce market competition, various organizations, especially large enterprise groups and public management institutions, generally face multiple challenges in resource allocation when implementing comprehensive planning projects. Comprehensive planning projects usually cover multiple business units and involve a large number of heterogeneous resources, and the allocation decision of reserve resources is essentially a high-dimensional combinatorial optimization problem seeking multi-objective balance under strong constraints (upper limit of total investment size). Existing resource allocation methods mostly rely on experience decision, linear programming or single-objective optimization model, which have great limitations in dealing with the complexity of modern projects; the existing resource allocation methods often simplify the resource allocation problem into multiple single-objective problems, and then convert each single-objective problem into the weighted sum of each single-objective, which not only cannot show the complex dependence between each objective, but also seriously depends on the preset of objective weight, and the adjustment of comprehensive planning and the fluctuation of resource allocation cannot be truly displayed, resulting in serious tilt of resource allocation or sacrifice of key objectives; and since the existing project allocation method is difficult to handle the diversity of resources and the strong coupling between projects, it is naturally impossible to achieve reasonable collaborative optimization of multiple types of objectives, resulting in low resource utilization efficiency of the generated resource allocation scheme and unsatisfactory overall project benefit. In addition, when allocating resources, the existing allocation method can only allocate resources according to the investment amount, but the types and importance of various resources are different, which may result in insufficient resource allocation to complete project requirements, leading to project failure.

[0003] As described above, how to provide a project reserve resource optimization allocation method and system based on a multi-objective genetic algorithm which can realize multi-objective collaborative optimization, improve resource allocation utilization, and complete accurate resource allocation has become a topic urgently to be studied in the field. SUMMARY

[0004] The purpose of the present application is to provide a project reserve resource optimization allocation method and system based on a multi-objective genetic algorithm to solve the above problems existing in the prior art.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: In a first aspect, the present application provides a project reserve resource optimization allocation method based on a multi-objective genetic algorithm, which comprises: acquire comprehensive plan project reserve data, extract each project unit and each project unit's reserve project from the comprehensive plan project reserve data, and construct a comprehensive plan project reserve resource multi-objective optimization model based on each project unit and each project unit's reserve project, so as to generate a plurality of project reserve resource allocation schemes through the comprehensive plan project reserve resource multi-objective optimization model, wherein the comprehensive plan project reserve resource multi-objective optimization model at least includes a comprehensive benefit maximization target, an investment balance optimization target, and a construction resource utilization rate maximization target; Based on the multi-objective genetic algorithm, each of the project reserve resource allocation schemes is taken as an individual, the individual intensity value of each individual is calculated, the individual intensity value of each individual is integrated to obtain the rough fitness of each individual, and the density information estimation value of each individual is calculated, so as to generate an optimal project reserve resource allocation scheme by using the rough fitness of each individual and the density information estimation value of each individual, wherein the optimal project reserve resource allocation scheme includes a plurality of optimal project units and each optimal reserve project corresponding to each optimal project unit; Based on each of the optimal reserve projects in the optimal project reserve resource allocation scheme, the project reserve resource is divided into core reserve resource and elastic reserve resource, and the core reserve resource allocation amount and the elastic reserve resource allocation amount are calculated for the core reserve resource and the elastic reserve resource respectively, so as to obtain the optimal resource allocation strategy of each of the optimal reserve projects, wherein the optimal resource allocation strategy includes the optimal core reserve resource allocation amount and the optimal elastic reserve resource allocation amount; Based on the optimal project reserve resource allocation scheme and the optimal resource allocation strategy of each of the optimal reserve projects, resource allocation is completed.

[0006] In a possible design, acquire comprehensive plan project reserve data, extract each project unit and each project unit's reserve project from the comprehensive plan project reserve data, and construct a comprehensive plan project reserve resource multi-objective optimization model based on each project unit and each project unit's reserve project, so as to generate a plurality of project reserve resource allocation schemes through the comprehensive plan project reserve resource multi-objective optimization model, including: Extract the comprehensive plan project reserve data from the comprehensive plan database, and perform entity recognition on the comprehensive plan project reserve data to identify each project unit and each project unit's reserve project; Integrate each project unit Into a project unit set Integrate each reserve project of each project unit Into a reserve project set of each project unit ; Based on the aforementioned project unit set and various project units The aforementioned reserve project set An optimization model for the reserve resources of comprehensive planning projects was established; Obtain the preset comprehensive planning management priority, and based on the comprehensive planning management priority, take the goal of maximizing comprehensive benefits as the first goal, the goal of optimizing investment balance as the second goal, and the goal of maximizing construction resource utilization as the third goal; For the first objective, the first objective function is established using the following formula (1). : (1) in, Represents the set of project units The total number of project units in the project. Indicates a project unit A reserve project The overall benefit score, This indicates that for a project unit A reserve project The investment decision variables, and the investment decision variables The value can be 0 or 1. This represents the summation operation; For the second objective, the second objective function is established using the following formula (2). : (2) in, Indicates a project unit Project investment amount, Represents the set of project units The average project investment amount for each project unit in the data; For the third objective, the third objective function is established using the following formula (3). : (3) in, Indicates a project unit Construction dynamic utilization rate, Indicates a project unit The resource importance weights, and the resource importance weights of each project unit. This is the default value; The first objective function Second objective function and the third objective function The overall planning and management priorities are integrated into the model optimization objective function. And optimize the objective function of the model. Input the comprehensive planning project reserve resource optimization model to form a multi-objective optimization model for comprehensive planning project reserve resources; Using the aforementioned multi-objective optimization model for project reserve resources in the comprehensive planning project, multiple project reserve resource allocation schemes are generated. Each of these schemes represents a set of project units. The various project units within the framework, and the reserve project collection for each project unit. The allocation plan for reserve resources of each reserve project.

[0007] In one possible design, before forming a multi-objective optimization model for the comprehensive project reserve resources, the following is also included: Obtain information from various project units Minimum investment requirements The first constraint condition is set by the following formula (4): (4) in, Indicates a project unit For the corresponding reserve project set One of the reserve projects The amount of investment; Obtain the upper limit of total investment The second constraint condition is set by the following formula (5): (5) in, Represents the set of project units The total project investment of all project units within the scope; Obtain information from various project units Upper limit of construction bearing capacity The third constraint condition is set by the following formula (6): (6) in, Indicates a project unit Reserve projects Quantity; The first constraint, the second constraint, and the third constraint are integrated into a model resource allocation constraint, and the model resource allocation constraint is input into the comprehensive planning project reserve resource optimization model.

[0008] In one possible design, based on a multi-objective genetic algorithm, each of the project reserve resource allocation schemes is taken as an individual, an individual intensity value of each individual is calculated, a rough fitness of each individual is obtained by integrating individual intensity values of all individuals, and a density information estimation value of each individual is calculated, so as to generate an optimal project reserve resource allocation scheme by using the rough fitness of each individual and the density information estimation value of each individual, including: Based on a multi-objective genetic algorithm, each of the project reserve resource allocation schemes is integrated into a resource allocation scheme set, and the resource allocation scheme set is taken as an evolution population, wherein each individual in the evolution population is each of the project reserve resource allocation schemes. In the evolution population, an optimization target value of each individual is calculated, and each individual is sorted according to a non-dominance degree of the optimization target value, so as to obtain an individual non-dominance sorting result, wherein the optimization target value includes a maximization target value of comprehensive benefits, an optimization target value of investment balance, and a maximization target value of construction resource utilization rate. According to the individual non-dominance sorting result, each individual with the highest sorting in the evolution population is selected to form a first front layer, and an advantage population is formed based on each individual in the first front layer. In the advantage population, each individual is not dominated by any other individual. In the advantage population, an individual intensity value of each individual is calculated by using the following formula (7): (7) Wherein, I represents an individual in the advantage population and an individual in the evolution population except the individual i, and f is a judgment function used to judge the dominance degree of the individual i to the individual j. If the individual i dominates the individual j, the value of f is 1, and if the individual i cannot dominate the individual j, the value of f is 0. According to the individual intensity value of each individual i, a rough fitness of each individual i is calculated by using the following formula (8): ​​​​​​​​​​​​​​​​​​​​​​ (8) in, This is a judgment function used to judge individuals. For individuals The degree of dominance; The density information is estimated by introducing a multi-objective genetic algorithm, and the following formula (9) is used to calculate the density of each individual. Density information estimate : (9) in: Represents an individual With evolutionary population The square of the nearest distance to other individuals in the group; Based on each individual rough fitness and each individual Density information estimate Each individual can be calculated using the following formula (10). final fitness : (10) According to each individual final fitness Select the final fitness The smallest individual is taken as the target individual, and the project reserve resource allocation scheme corresponding to the target individual is taken as the optimal project reserve resource allocation scheme.

[0009] In one possible design, based on each optimal reserve project in the optimal project reserve resource allocation scheme, the project reserve resources are divided into core reserve resources and flexible reserve resources. The allocation amounts of the core reserve resources and the flexible reserve resources are calculated respectively to obtain the optimal resource allocation strategy for each optimal reserve project, including: Based on the optimal project reserve resource allocation scheme, each of the optimal reserve projects is extracted, and the project requirements of each of the optimal reserve projects are obtained, wherein the project requirements of the optimal reserve projects include the project reserve resource allocation quantity requirements. Obtain a preset project reserve resource classification table, and classify the project reserve resource allocation requirements of each of the optimal reserve projects based on the project reserve resource classification table, so as to divide the project reserve resources in the project reserve resource allocation requirements of each of the optimal reserve projects into core reserve resources and flexible reserve resources; obtaining a core reserve resource configuration threshold of each of the optimal reserve projects, and calculating a core reserve resource configuration of each of the optimal reserve projects based on the core reserve resource configuration threshold of each of the optimal reserve projects for the core reserve resource required by each of the optimal reserve projects; obtaining a preset elastic reserve resource configuration strategy, and calculating an elastic reserve resource configuration of each of the optimal reserve projects based on the core reserve resource configuration of each of the optimal reserve projects by using the elastic reserve resource configuration strategy for the elastic reserve resource required by each of the optimal reserve projects; integrating the core reserve resource configuration and the elastic reserve resource configuration of each of the optimal reserve projects to form an optimal resource configuration strategy of each of the optimal reserve projects.

[0010] In a possible design, before forming the optimal resource configuration strategy of each of the optimal reserve projects, the method further includes: performing fuzzy processing on the core reserve resource configuration and the elastic reserve resource configuration of each of the optimal reserve projects by using a fuzzy chance constraint programming algorithm to obtain a fuzzy chance constraint, and performing deterministic adjustment on the core reserve resource configuration and the elastic reserve resource configuration by using the fuzzy chance constraint.

[0011] In a possible design, based on the optimal project reserve resource configuration scheme and the optimal resource configuration strategy of each of the optimal reserve projects, the method further includes: selecting each optimal project unit as a project carrying unit from the comprehensive plan project reserve data based on the optimal project reserve resource configuration scheme, and taking each optimal reserve project corresponding to each optimal project unit as a carrying project of each project carrying unit; performing corresponding resource configuration on the carrying project of each project carrying unit according to the optimal resource configuration strategy.

[0012] In a second aspect, the present application provides a project reserve resource optimization configuration system based on a multi-objective genetic algorithm, which includes: a multi-objective optimization model building unit, configured to obtain comprehensive plan project reserve data, extract each project unit and a reserve project of each project unit from the comprehensive plan project reserve data, and build a comprehensive plan project reserve resource multi-objective optimization model based on each project unit and the reserve project of each project unit, so as to generate a plurality of project reserve resource configuration schemes through the comprehensive plan project reserve resource multi-objective optimization model, wherein the comprehensive plan project reserve resource multi-objective optimization model at least includes a comprehensive benefit maximization target, an investment balance optimization target, and a construction resource utilization rate maximization target; The optimal resource configuration scheme generation unit is configured to generate an optimal project reserve resource configuration scheme based on the multi-objective genetic algorithm, taking each project reserve resource configuration scheme as an individual, calculating an individual intensity value for each individual, integrating the individual intensity values of each individual to obtain a rough fitness of each individual, and calculating a density information estimation value of each individual, so as to generate the optimal project reserve resource configuration scheme by using the rough fitness of each individual and the density information estimation value of each individual, wherein the optimal project reserve resource configuration scheme comprises a plurality of optimal project units and each optimal reserve project corresponding to each optimal project unit. The optimal resource configuration strategy generation unit is configured to divide the project reserve resource into core reserve resources and elastic reserve resources based on each optimal reserve project in the optimal project reserve resource configuration scheme, and calculate a core reserve resource configuration amount and an elastic reserve resource configuration amount for the core reserve resources and the elastic reserve resources respectively, so as to obtain an optimal resource configuration strategy for each optimal reserve project, wherein the optimal resource configuration strategy comprises a core reserve resource optimal configuration amount and an elastic reserve resource optimal configuration amount. The resource configuration execution unit is configured to complete resource configuration based on the optimal project reserve resource configuration scheme and the optimal resource configuration strategy of each optimal reserve project.

[0013] In a third aspect, the present application provides an electronic device comprising a memory, a processor and a transceiver connected in sequence and in communication, wherein the memory is configured to store a computer program, the transceiver is configured to receive and send messages, and the processor is configured to read the computer program and execute the project reserve resource optimization configuration method based on the multi-objective genetic algorithm according to the first aspect or any possible design of the first aspect.

[0014] In a fourth aspect, the present application provides a computer readable storage medium having instructions stored thereon, wherein when the instructions are run on a computer, the method for optimizing project reserve resource configuration based on the multi-objective genetic algorithm according to the first aspect or any possible design of the first aspect is executed.

[0015] In a fifth aspect, the present application provides a computer program product comprising instructions, wherein when the instructions are run on a computer, the computer is caused to execute the method for optimizing project reserve resource configuration based on the multi-objective genetic algorithm according to the first aspect or any possible design of the first aspect.

[0016] Beneficial effects: the application provides a project reserve resource optimization configuration method and system based on a multi-objective genetic algorithm, which comprises the following steps: first, obtaining comprehensive plan project reserve data, extracting each project unit and each project unit's reserve project from the comprehensive plan project reserve data, and constructing a comprehensive plan project reserve resource multi-objective optimization model based on each project unit and each project unit's reserve project, so as to generate multiple project reserve resource configuration schemes through the comprehensive plan project reserve resource multi-objective optimization model, wherein the comprehensive plan project reserve resource multi-objective optimization model at least comprises a comprehensive benefit maximization target, an investment balance optimization target and a construction resource utilization rate maximization target; second, based on the multi-objective genetic algorithm, taking each project reserve resource configuration scheme as an individual, calculating the individual intensity value of each individual, integrating the individual intensity value of each individual to obtain the rough fitness of each individual, and calculating the density information estimation value of each individual, so as to generate an optimal project reserve resource configuration scheme by using the rough fitness of each individual and the density information estimation value of each individual, wherein the optimal project reserve resource configuration scheme comprises multiple optimal project units and each optimal reserve project corresponding to each optimal project unit; then, based on each optimal reserve project in the optimal project reserve resource configuration scheme, dividing the project reserve resource into core reserve resource and elastic reserve resource, and calculating the core reserve resource configuration amount and the elastic reserve resource configuration amount of the core reserve resource and the elastic reserve resource respectively, so as to obtain the optimal resource configuration strategy of each optimal reserve project, wherein the optimal resource configuration strategy comprises a core reserve resource optimal configuration amount and an elastic reserve resource optimal configuration amount; finally, based on the optimal project reserve resource configuration scheme and the optimal resource configuration strategy of each optimal reserve project, completing resource configuration. Through the construction of the comprehensive plan project reserve resource multi-objective optimization model and the setting of multiple targets, each project reserve resource configuration scheme is generated, so as to fully consider the advantages and disadvantages of various configuration schemes and avoid scheme omission. Then, based on the multi-objective genetic algorithm, the rough fitness and the density information estimation value of each project reserve resource configuration scheme are calculated, and under the condition of balancing each target, an optimal project reserve resource configuration scheme more in line with actual target demand is generated, the multi-target collaborative optimization target is realized, and the utilization rate of resource configuration is greatly improved. Through the classification of the project reserve resource, the core reserve resource and the elastic reserve resource are processed respectively, which not only guarantees the minimum configuration demand of the core reserve resource, but also takes into account the optimization of the resource configuration cost of the elastic reserve resource, and completes the accurate configuration of the resource. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of the project reserve resource optimization configuration method based on the multi-objective genetic algorithm provided by the embodiments of the application is shown. Figure 2 This is a functional structure diagram of a project reserve resource optimization and allocation system based on a multi-objective genetic algorithm provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0019] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0020] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0021] Example: like Figure 1 As shown, the first aspect of this embodiment provides a method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm, which may include, but is not limited to, the following steps: S1. Obtain comprehensive planning project reserve data, extract each project unit and the reserve projects of each project unit from the comprehensive planning project reserve data, and construct a multi-objective optimization model for comprehensive planning project reserve resources based on each project unit and the reserve projects of each project unit, so as to generate multiple project reserve resource allocation schemes through the multi-objective optimization model for comprehensive planning project reserve resources, wherein the multi-objective optimization model for comprehensive planning project reserve resources includes at least the comprehensive benefit maximization objective, the investment balance optimization objective, and the construction resource utilization maximization objective; In one possible implementation, step S1 involves acquiring comprehensive planning project reserve data, extracting each project unit and its reserve projects from the comprehensive planning project reserve data, and constructing a multi-objective optimization model for comprehensive planning project reserve resources based on each project unit and its reserve projects. This model is used to generate multiple project reserve resource allocation schemes. This can be broken down into, but is not limited to, the following steps S11-S19, specifically including: S11. Extract comprehensive planning project reserve data from the comprehensive planning database, and perform entity identification on the comprehensive planning project reserve data to identify each project unit and each project unit's reserve projects; S12. All project units Integrate into project unit collection Each project unit Various reserve projects They were respectively integrated into various project units. Reserve Project Collection ; S13. Based on the aforementioned project unit set and various project units The aforementioned reserve project set An optimization model for the reserve resources of comprehensive planning projects was established; S14. Obtain the preset comprehensive planning management priority, and based on the comprehensive planning management priority, take the goal of maximizing comprehensive benefits as the first goal, the goal of optimizing investment balance as the second goal, and the goal of maximizing construction resource utilization as the third goal; S15. For the first objective, the first objective function is established using the following formula (1). : (1) in, Represents the set of project units The total number of project units in the project. Indicates a project unit A reserve project The overall benefit score, This indicates that for a project unit A reserve project The investment decision variables, and the investment decision variables The value can be 0 or 1. This represents the summation operation; S16. For the second objective, the second objective function is established using the following formula (2). : (2) in, Indicates a project unit Project investment amount, Represents the set of project units The average project investment amount for each project unit in the data; S17. For the third objective, the third objective function is established using the following formula (3). : (3) in, Indicates a project unit Construction dynamic utilization rate, Indicates a project unit The resource importance weights, and the resource importance weights of each project unit. This is the default value; S18. The first objective function... Second objective function and the third objective function The overall planning and management priorities are integrated into the model optimization objective function. And optimize the objective function of the model. Input the comprehensive planning project reserve resource optimization model to form a multi-objective optimization model for comprehensive planning project reserve resources; S19. Using the multi-objective optimization model for the comprehensive planning project reserve resources, generate multiple project reserve resource allocation schemes, wherein each of the project reserve resource allocation schemes represents a set of project units. The various project units within the framework, and the reserve project collection for each project unit. The allocation plan for reserve resources of each reserve project.

[0022] It should be noted that the first target in the comprehensive plan project reserve resource multi-objective optimization model constructed in the embodiment is a comprehensive benefit maximization target, and the first target is also the target with the highest priority. In subsequent calculation, the first target value of each project reserve resource allocation scheme is given priority; the investment balance optimization target is taken as the second target, and the construction resource utilization rate maximization target is taken as the third target. Therefore, while meeting the comprehensive benefit maximization, the investment balance and the construction resource utilization rate are also ensured, and the investment balance maximization and the resource utilization rate maximization are realized without sacrificing the comprehensive benefit. Specifically, the first objective function represents the total sum of the comprehensive scores of the selected project units in each project reserve resource allocation scheme, the function value of the first objective function is greater, the greater the comprehensive benefit is, the function value of the second objective function represents the relative difference of the project investment amount of each selected project unit in each project reserve resource allocation scheme, the function value of the second objective function is smaller, the more balanced the investment is, and the function value of the third objective function represents the construction resource utilization rate (that is, the utilization rate of construction carrying capacity) of each selected project unit in each project reserve resource allocation scheme, the function value of the third objective function is greater, the higher the utilization rate of construction resources is.

[0023] The model optimization objective function can calculate the corresponding optimization objective value for each project reserve resource allocation scheme. In possible applications, the optimization objective values of each project reserve resource allocation scheme can be marked in each project reserve resource allocation scheme, so as to facilitate subsequent non-dominance sorting of each project reserve resource allocation scheme according to the optimization objective values of each project reserve resource allocation scheme. Taking the optimization objective value of a project reserve resource allocation scheme as an example, the optimization objective value of a project reserve resource allocation scheme .

[0024] In one possible implementation, before the comprehensive plan project reserve resource multi-objective optimization model is formed in step S18, the following steps S181-S184 can be included, but are not limited to: S181. Obtain the minimum investment demand of each project unit , and set the first constraint condition by the following formula (4): (4) wherein, represents the minimum investment demand of a project unit for the corresponding reserve project set One of the reserve projects The amount of investment; S182.1 Obtain the upper limit of total investment scale The second constraint condition is set by the following formula (5): (5) in, Represents the set of project units The total project investment of all project units within the scope; S183. Obtain the units for each project Upper limit of construction bearing capacity The third constraint condition is set by the following formula (6): (6) in, Indicates a project unit Reserve projects Quantity; S184. Integrate the first constraint, the second constraint, and the third constraint into a model resource allocation constraint, and input the model resource allocation constraint into the comprehensive planning project reserve resource optimization model.

[0025] It should be noted that, in the multi-objective optimization model for comprehensive project reserve resources provided in this embodiment, three constraints are preferably set. The first constraint is an investment demand constraint, which is used to ensure that each project unit in the generated project reserve resource allocation schemes is within the specified limits. The amount of project investment obtained (that is, for a project unit) The various reserve projects it supports The sum of the investments shall not be less than the minimum investment required to support each of the reserve projects. In one possible setup, investment demand constraints could be allowed to fluctuate within a flexible range, for example, requiring... and This approach aims to balance the rigid requirements for project investment with the flexibility of resource allocation, thereby enabling more flexible solution configuration.

[0026] Correspondingly, the second constraint is a total investment constraint, used to ensure that the sum of the total investment of all selected reserve projects is within the limit. It shall not exceed the given upper limit of total investment. , and due to the maximization of the comprehensive benefit, a certain elasticity of overspending (the preferred overspending amount in this embodiment is 5%, which can be adjusted adaptively according to the specific comprehensive plan) can be generally allowed; the third constraint condition is a construction carrying capacity constraint, which is used to ensure that the number of each project unit carrying project reserves cannot exceed the upper limit of the construction carrying capacity . .

[0027] By introducing the constraint, it can be ensured that the project reserve resource allocation scheme generated by the comprehensive plan project reserve resource optimization model meets the actual requirements of project resource allocation, avoids the situation that the optimal project reserve resource allocation scheme obtained in step S2 is not implementable, and improves the adaptability of the comprehensive plan project reserve resource optimization model.

[0028] S2. Based on the multi-objective genetic algorithm, each project reserve resource allocation scheme is taken as an individual, the individual strength value of each individual is calculated, the individual strength values of each individual are integrated to obtain the rough fitness of each individual, and the density information estimate value of each individual is calculated, so as to generate an optimal project reserve resource allocation scheme by using the rough fitness of each individual and the density information estimate value of each individual, wherein the optimal project reserve resource allocation scheme includes multiple optimal project units and each optimal reserve project corresponding to each optimal project unit; In a possible implementation, in step S2, based on the multi-objective genetic algorithm, each project reserve resource allocation scheme is taken as an individual, the individual strength value of each individual is calculated, the individual strength values of each individual are integrated to obtain the rough fitness of each individual, and the density information estimate value of each individual is calculated, so as to generate an optimal project reserve resource allocation scheme by using the rough fitness of each individual and the density information estimate value of each individual, which can be but not limited to decomposed into steps S21-S28, and specifically includes: S21. Based on the multi-objective genetic algorithm, each project reserve resource allocation scheme is integrated into a resource allocation scheme set, and the resource allocation scheme set is taken as an evolution population, wherein each individual in the evolution population is each project reserve resource allocation scheme; S22. In the evolution population, the optimization target value of each individual is calculated, and each individual is sorted according to the non-dominated degree according to the optimization target value of each individual, to obtain an individual non-dominated degree sorting result, wherein the optimization target value includes a comprehensive benefit maximization target value, an investment balance optimization target value and a construction resource utilization rate maximization target value; S23. According to the individual non-dominated degree sorting result, the evolution population The individuals with the highest ranks constitute a first front layer, and a dominant population is formed based on the individuals in the first front layer wherein the individuals in the dominant population do not dominate each other; S24. In the dominant population , the individual strength value of each individual is calculated using the following formula (7): (7) wherein represents an individual other than the individual in the dominant population and the evolutionary population , and is a judgment function for judging the dominance of the individual over the individual , wherein if the individual dominates the individual , the value of is 1, and if the individual cannot dominate the individual , the value of is 0; S25. The rough fitness of each individual is calculated using the following formula (8) based on the individual strength value of each individual : (8) wherein is a judgment function for judging the dominance of the individual over the individual ; S26. The density information estimate value of each individual is calculated using the following formula (9) by introducing a density information estimate value calculation by a multi-objective genetic algorithm: (9) wherein: represents the square value of the nearest distance of the individual from other individuals in the evolutionary population ; S27. The fitness of each individual is calculated using the following formula (10) based on the rough fitness of each individual and the density information estimate value final fitness : (10) S28. According to each individual final fitness Select the final fitness The smallest individual is taken as the target individual, and the project reserve resource allocation scheme corresponding to the target individual is taken as the optimal project reserve resource allocation scheme.

[0029] It should be noted that the configuration method provided in this embodiment uses a multi-objective genetic algorithm for scheme optimization calculation. The first-generation evolutionary population is actually a set of resource allocation schemes, where each individual is formed by encoding various project reserve resource allocation schemes. Ranking these schemes by their non-dominance degree is actually based on the optimization objective value of each project reserve resource allocation scheme calculated in step S1. In the comparison and ranking, the project reserve resource allocation plan with higher overall benefits, more balanced investment, and higher construction resource utilization rate is considered the superior plan and is ranked higher.

[0030] Specifically, when ranking two individuals, the optimization objective values ​​of the two individuals are compared. If and only if the objective value of maximizing comprehensive benefits, the objective value of optimizing investment balance, and the objective value of maximizing construction resource utilization of one individual are all no worse than that of the other individual, and at least one objective value is better than that of the other individual, then this individual is considered to be ranked higher than the other individual.

[0031] In this embodiment, the first frontier layer is actually a non-dominated solution set. Furthermore, it can also be derived from the evolutionary population. The individuals with the second highest ranking are selected as the second frontier layer. Each individual in the second frontier layer is dominated by at least one individual in the first frontier layer; the third and fourth frontier layers follow the same pattern.

[0032] And for each individual Individual strength value In fact, it represents the individual. The number of other individuals under its control, The larger the value, the better it indicates the individual's... The higher the quality, the more other individuals it can dominate, thus benefiting the dominant population. and evolutionary population Each individual in Calculate the rough fitness Rough adaptability Represents all individuals that can be controlled. The sum of the individual strength values ​​of the individuals, individual The more individuals a person dominates, or the higher the individual strength value they are dominated by, the higher their rough fitness. The larger the value, the more it indicates that this individual... The worse the quality.

[0033] Furthermore, for the same non-dominated frontier layer ( Individuals within this range, based solely on rough fitness Since it is impossible to distinguish the merits of different schemes, it is necessary to introduce density information estimates. This promotes population diversity and prevents the final optimization result from converging to a local region. It should be noted that the individuals described in this embodiment... With evolutionary population The distance to other individuals refers to the distance for each individual. In the objective space (that is, by optimizing the objective function through the model) The three-dimensional structure In space, with the evolutionary population The Euclidean distances to other individuals in the dataset are selected, along with the individual. The nearest neighbor is the individual whose distance is considered the distance between them. The smaller the nearest distance, the better. The more crowded the surrounding area (smaller the convergence region, and the more individuals with similar final solution quality), the greater the nearest distance. This represents an individual. The sparser the surrounding area (the larger the convergence region, the fewer individuals with similar final scheme quality), the higher the density information estimate. The smaller the value, the better it helps maintain population diversity, leading to a more balanced and optimal solution.

[0034] In the multi-objective genetic algorithm, when performing tournament selection or roulette wheel selection, the final fitness is selected. Smaller individuals, acting as parents, have a higher probability of participating in reproduction (crossover or mutation) to generate the next generation of evolutionary population. Through such iterations, the optimal resource allocation scheme for the project reserve can be gradually approached.

[0035] The final fitness calculation method proposed in this embodiment comprehensively considers the multi-objective characteristics of resources in the comprehensive planning project reserve allocation, effectively avoids the problem of coarse fitness overlap, and achieves accurate differentiation of individuals by introducing density information estimation values. In the multi-objective genetic algorithm, it provides a precise convergence direction for scheme optimization, thereby improving the accuracy and rationality of the obtained optimal project reserve resource allocation scheme.

[0036] S3. Based on each of the optimal reserve projects in the optimal project reserve resource allocation scheme, the project reserve resources are divided into core reserve resources and elastic reserve resources, and the core reserve resource allocation amount and the elastic reserve resource allocation amount are calculated respectively for the core reserve resources and the elastic reserve resources, to obtain an optimal resource allocation strategy for each of the optimal reserve projects, wherein the optimal resource allocation strategy comprises a core reserve resource optimal allocation amount and an elastic reserve resource optimal allocation amount. In a possible implementation, in step S3, based on each of the optimal reserve projects in the optimal project reserve resource allocation scheme, the project reserve resources are divided into core reserve resources and elastic reserve resources, and the core reserve resource allocation amount and the elastic reserve resource allocation amount are calculated respectively for the core reserve resources and the elastic reserve resources, to obtain an optimal resource allocation strategy for each of the optimal reserve projects, which can but is not limited to be decomposed into steps S31-S35, and specifically comprises: S31. Based on the optimal project reserve resource allocation scheme, each of the optimal reserve projects is extracted, and the project demand of each of the optimal reserve projects is obtained, wherein the project demand of the optimal reserve project comprises a project reserve resource allocation amount demand. S32. A preset project reserve resource classification table is obtained, and based on the project reserve resource classification table, the project reserve resource allocation amount demand of each of the optimal reserve projects is classified, so as to divide the project reserve resources in the project reserve resource allocation amount demand of each of the optimal reserve projects into core reserve resources and elastic reserve resources. S33. The core reserve resource allocation amount threshold of each of the optimal reserve projects is obtained, and for the core reserve resources required by each of the optimal reserve projects, the core reserve resource allocation amount of each of the optimal reserve projects is calculated based on the core reserve resource allocation amount threshold of each of the optimal reserve projects respectively. S34. A preset elastic reserve resource allocation strategy is obtained, and for the elastic reserve resources required by each of the optimal reserve projects, the elastic reserve resource allocation amount of each of the optimal reserve projects is calculated based on the core reserve resource allocation amount of each of the optimal reserve projects by using the elastic reserve resource allocation strategy. S35. For each of the optimal reserve projects, the corresponding core reserve resource allocation amount and elastic reserve resource allocation amount are integrated to form an optimal resource allocation strategy for each of the optimal reserve projects.

[0037] It should be noted that in actual application, the calculation of the core reserve resource allocation amount can be realized by the following formula (11): (11) Wherein: Indicates the allocation of core reserve resources. For the resource reserve cycle, For resource utilization, This indicates the threshold for the allocation of core reserve resources.

[0038] The calculation of the flexible reserve resource allocation amount can be achieved by the following formula (12): (12) in: Indicates the amount of flexible reserve resources allocated. This is a flexibility coefficient, set according to the characteristics of the industry to which the comprehensive plan belongs. This refers to the deviation in investment demand for the previous phase of reserve projects extracted from the comprehensive planning database. This is the attenuation factor.

[0039] In one possible implementation, step S35, before forming the optimal resource allocation strategy for each of the optimal reserve projects, may also include, but is not limited to, the following steps: The fuzzy opportunity constraint programming algorithm is used to fuzzify the core reserve resource allocation and the flexible reserve resource allocation of each optimal reserve project to obtain fuzzy opportunity constraints, and then the fuzzy opportunity constraints are used to make deterministic adjustments to the core reserve resource allocation and the flexible reserve resource allocation.

[0040] It should be noted that in practical applications, the fuzzy chance-constrained programming algorithm is used to optimize (fuzzify) the calculation process of core reserve resource allocation and flexible reserve resource allocation. Triangular fuzzy numbers or trapezoidal fuzzy numbers can be used to describe the uncertain parameters in the calculation process (such as minimum investment requirements). Upper limit of construction bearing capacity Resource utilization rate Elasticity coefficient and attenuation factor These parameter values, which are based on the integrated plan and whose accuracy cannot be guaranteed (these uncertain parameters are actually predicted values ​​estimated from various reserve projects based on the integrated plan), can be used to set an investment demand confidence level according to the risk level of the integrated plan. (Used to indicate that the probability of the optimal resource allocation strategy meeting investment needs is no less than) ), and utilize investment demand confidence level The original uncertainty constraint is transformed into an opportunity constraint, and fuzzy theory is used to transform this opportunity constraint into a given confidence level of investment demand. The equivalent deterministic constraints under the conditions allow for deterministic adjustments to the calculated core reserve resource allocation and the flexible reserve resource allocation, avoiding the deterioration of resource allocation strategies caused by incorrect parameter settings or real-time fluctuations. This enables the final output of the optimal project reserve resource allocation scheme and the optimal resource allocation strategy to cope with fluctuations in the comprehensive plan within a certain range, making it more practical and in line with the requirements of actual implementation.

[0041] S4. Based on the optimal project reserve resource allocation scheme and the optimal resource allocation strategy for each of the optimal reserve projects, complete the resource allocation.

[0042] In one possible implementation, step S4, based on the optimal project reserve resource allocation scheme and the optimal resource allocation strategy for each of the optimal reserve projects, completes resource allocation. This can be decomposed into, but is not limited to, the following steps S41-S42, specifically including: S41. Based on the optimal project reserve resource allocation scheme, select each optimal project unit from the comprehensive plan project reserve data as the project carrying unit, and take each optimal reserve project corresponding to each optimal project unit as the carrying project of each project carrying unit; S42. According to the optimal resource allocation strategy, allocate resources accordingly for the projects carried by each project carrying unit.

[0043] like Figure 2 As shown, the second aspect of this embodiment provides a hardware system for implementing the project reserve resource optimization and allocation method based on a multi-objective genetic algorithm described in the first aspect of the embodiment, including: A multi-objective optimization model building unit is used to acquire comprehensive planning project reserve data, extract each project unit and its reserve projects from the comprehensive planning project reserve data, and construct a comprehensive planning project reserve resource multi-objective optimization model based on each project unit and its reserve projects. This model is used to generate multiple project reserve resource allocation schemes. The comprehensive planning project reserve resource multi-objective optimization model includes at least the objectives of maximizing comprehensive benefits, optimizing investment balance, and maximizing construction resource utilization. The optimal resource configuration scheme generation unit is configured to take each project reserve resource configuration scheme as an individual, calculate an individual intensity value of each individual, integrate the individual intensity values of each individual to obtain a rough fitness of each individual, and calculate a density information estimation value of each individual, so as to generate an optimal project reserve resource configuration scheme by using the rough fitness of each individual and the density information estimation value of each individual, wherein the optimal project reserve resource configuration scheme includes a plurality of optimal project units and each optimal reserve project corresponding to each optimal project unit. The optimal resource configuration strategy generation unit is configured to divide project reserve resources into core reserve resources and elastic reserve resources based on each optimal reserve project in the optimal project reserve resource configuration scheme, and calculate a core reserve resource configuration amount and an elastic reserve resource configuration amount for the core reserve resources and the elastic reserve resources respectively, so as to obtain an optimal resource configuration strategy of each optimal reserve project, wherein the optimal resource configuration strategy includes a core reserve resource optimal configuration amount and an elastic reserve resource optimal configuration amount. The resource configuration execution unit is configured to complete resource configuration based on the optimal project reserve resource configuration scheme and the optimal resource configuration strategy of each optimal reserve project.

[0044] The working process, working details and technical effects of the system provided by the embodiment can be referred to the first aspect of the embodiment, and will not be described here.

[0045] As shown in Figure 3 The third aspect of the embodiment provides an electronic device, which includes a memory, a processor and a transceiver connected in sequence, wherein the memory is configured to store a computer program, the transceiver is configured to transceive messages, and the processor is configured to read the computer program and execute the project reserve resource optimization configuration method based on a multi-objective genetic algorithm as described in the first aspect of the embodiment.

[0046] For example, the memory can include, but is not limited to, random access memory (RAM), read only memory (ROM), flash memory, first input first output (FIFO) memory, first in last out (FILO) memory, and the like; specifically, the processor can include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array), and the processor can also include a main processor and a co-processor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the co-processor is a low-power processor for processing data in a standby state.

[0047] In some embodiments, the processor can be integrated with a GPU (Graphics Processing Unit) for rendering and drawing the content required to be displayed by the display screen. For example, the processor can be, but is not limited to, a microprocessor of the STM32F105 series, a RISC (reduced instruction set computer) microprocessor, an X86 architecture processor, or a processor integrated with an embedded neural network processing unit (NPU); the transceiver can be, but is not limited to, a WIFI wireless transceiver, a Bluetooth wireless transceiver, a GPRS (General Packet Radio Service) wireless transceiver, a ZigBee wireless transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver, and the like. In addition, the device can also include, but is not limited to, a power module, a display screen, and other necessary components.

[0048] The working process, working details and technical effects of the electronic device provided in the embodiment can be referred to the first aspect of the embodiment, and will not be repeated here.

[0049] The fourth aspect of the embodiment provides a storage medium storing instructions of the project reserve resource optimal configuration method based on the multi-objective genetic algorithm, i.e., the storage medium stores the instructions, and when the instructions run on a computer, the project reserve resource optimal configuration method based on the multi-objective genetic algorithm is executed.

[0050] The storage medium refers to a carrier for storing data, which can include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash disk, a memory stick and the like, and the computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0051] The working process, working details and technical effects of the storage medium provided by the embodiment can be referred to the first aspect of the embodiment, and will not be described here.

[0052] The fifth aspect of the embodiment provides a computer program product containing instructions, which, when running on a computer, causes the computer to execute the project reserve resource optimal configuration method based on the multi-objective genetic algorithm, and the computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0053] Finally, it should be noted that: the above only describes the preferred embodiments of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm, characterized in that, include: Acquire comprehensive project reserve data, extract each project unit and its reserve projects from the comprehensive project reserve data, and construct a multi-objective optimization model for comprehensive project reserve resources based on each project unit and its reserve projects. This model is used to generate multiple project reserve resource allocation schemes. The multi-objective optimization model for comprehensive project reserve resources includes at least the objectives of maximizing comprehensive benefits, optimizing investment balance, and maximizing construction resource utilization. Based on a multi-objective genetic algorithm, each of the project reserve resource allocation schemes is treated as an individual. The individual strength value is calculated for each individual, and the individual strength values ​​of each individual are integrated to obtain the coarse fitness of each individual. The density information estimate of each individual is also calculated. Using the coarse fitness and the density information estimate of each individual, an optimal project reserve resource allocation scheme is generated. The optimal project reserve resource allocation scheme includes multiple optimal project units and each optimal reserve project corresponding to each optimal project unit. Based on each of the optimal reserve projects in the optimal project reserve resource allocation scheme, the project reserve resources are divided into core reserve resources and flexible reserve resources. The allocation amount of core reserve resources and the allocation amount of flexible reserve resources are calculated for the core reserve resources and the flexible reserve resources respectively, so as to obtain the optimal resource allocation strategy for each optimal reserve project. The optimal resource allocation strategy includes the optimal allocation amount of core reserve resources and the optimal allocation amount of flexible reserve resources. Based on the optimal project reserve resource allocation scheme and the optimal resource allocation strategy for each of the optimal reserve projects, resource allocation is completed.

2. The method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm according to claim 1, characterized in that, Acquire comprehensive project reserve data, extract each project unit and its reserve projects from the comprehensive project reserve data, and construct a multi-objective optimization model for comprehensive project reserve resources based on each project unit and its reserve projects. This model is used to generate multiple project reserve resource allocation schemes, including: Extract comprehensive planning project reserve data from the comprehensive planning database, and perform entity identification on the comprehensive planning project reserve data to identify each project unit and each project unit's reserve projects; Each project unit Integrate into project unit collection Each project unit Various reserve projects They were respectively integrated into various project units. Reserve Project Collection ; Based on the aforementioned project unit set and various project units The aforementioned reserve project set An optimization model for the reserve resources of comprehensive planning projects was established; Obtain the preset comprehensive planning management priority, and based on the comprehensive planning management priority, take the goal of maximizing comprehensive benefits as the first goal, the goal of optimizing investment balance as the second goal, and the goal of maximizing construction resource utilization as the third goal; For the first objective, the first objective function is established using the following formula (1). : （1） in, Represents the set of project units The total number of project units in the project. Indicates a project unit A reserve project The overall benefit score, This indicates that for a project unit A reserve project The investment decision variables, and the investment decision variables The value can be 0 or 1. This represents the summation operation; For the second objective, the second objective function is established using the following formula (2). : （2） in, Indicates a project unit Project investment amount, Represents the set of project units The average project investment amount for each project unit in the data; For the third objective, the third objective function is established using the following formula (3). : （3） in, Indicates a project unit Construction dynamic utilization rate, Indicates a project unit The resource importance weights, and the resource importance weights of each project unit. This is the default value; The first objective function Second objective function and the third objective function The overall planning and management priorities are integrated into the model optimization objective function. And optimize the objective function of the model. Input the comprehensive planning project reserve resource optimization model to form a multi-objective optimization model for comprehensive planning project reserve resources; Using the aforementioned multi-objective optimization model for project reserve resources in the comprehensive planning project, multiple project reserve resource allocation schemes are generated. Each of these schemes represents a set of project units. The various project units within the framework, and the reserve project collection for each project unit. The allocation plan for reserve resources of each reserve project.

3. The method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm according to claim 2, characterized in that, Before developing a multi-objective optimization model for the reserve resources of the comprehensive planning project, the following steps are also included: Obtain information from various project units Minimum investment requirements The first constraint condition is set by the following formula (4): （4） in, Indicates a project unit For the corresponding reserve project set One of the reserve projects The amount of investment; Obtain the upper limit of total investment The second constraint condition is set by the following formula (5): （5） in, Represents the set of project units The total project investment of all project units within the scope; Obtain information from various project units Upper limit of construction bearing capacity The third constraint condition is set by the following formula (6): （6） in, Indicates a project unit Reserve projects Quantity; The first constraint, the second constraint, and the third constraint are integrated into a model resource allocation constraint, and the model resource allocation constraint is input into the comprehensive planning project reserve resource optimization model.

4. The method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm according to claim 1, characterized in that, Based on a multi-objective genetic algorithm, each of the project reserve resource allocation schemes is treated as an individual. An individual strength value is calculated for each individual, and the individual strength values ​​of each individual are integrated to obtain a coarse fitness. A density information estimate for each individual is also calculated. Using the coarse fitness and density information estimate of each individual, an optimal project reserve resource allocation scheme is generated, including: Based on a multi-objective genetic algorithm, the resource allocation schemes of each project are integrated into a resource allocation scheme set, and the resource allocation scheme set is used as an evolutionary population, wherein each individual in the evolutionary population is a resource allocation scheme of each project. In the evolutionary population, the optimization objective value of each individual is calculated, and the individuals are ranked according to their non-dominance degree based on their optimization objective value to obtain the individual non-dominance degree ranking result. The optimization objective value includes the comprehensive benefit maximization objective value, the investment balance optimization objective value, and the construction resource utilization maximization objective value. The evolutionary population was selected based on the individual non-dominance ranking results. The individuals with the highest ranking in the middle form the first frontier layer, and based on the individuals in the first frontier layer, a dominant population is formed. Among them, the dominant species The individuals within do not control each other; The dominant species In the middle, the following formula (7) is used to calculate the value of each individual. Individual strength value : （7） in, Indicates the dominant species and evolutionary population Excluding individuals Other than individuals, This is a judgment function used to judge individuals. For individuals The degree of dominance of the individual Dominant Individual ,but The value of is 1, if the individual Unable to control individuals ,but The value of is 0; According to each individual Individual strength value Using the following formula (8), the value of each individual can be calculated. rough fitness : （8） in, This is a judgment function used to judge individuals. For individuals The degree of dominance; The density information is estimated by introducing a multi-objective genetic algorithm, and the following formula (9) is used to calculate the density of each individual. Density information estimate : （9） in: Represents an individual With evolutionary population The square of the nearest distance to other individuals in the group; Based on each individual rough fitness and each individual Density information estimate Each individual can be calculated using the following formula (10). final fitness : （10） According to each individual final fitness Select the final fitness The smallest individual is taken as the target individual, and the project reserve resource allocation scheme corresponding to the target individual is taken as the optimal project reserve resource allocation scheme.

5. The method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm according to claim 1, characterized in that, Based on each optimal reserve project in the optimal project reserve resource allocation scheme, the project reserve resources are divided into core reserve resources and flexible reserve resources. The allocation amounts of the core reserve resources and the flexible reserve resources are calculated respectively to obtain the optimal resource allocation strategy for each optimal reserve project, including: Based on the optimal project reserve resource allocation scheme, each of the optimal reserve projects is extracted, and the project requirements of each of the optimal reserve projects are obtained, wherein the project requirements of the optimal reserve projects include the project reserve resource allocation quantity requirements. Obtain a preset project reserve resource classification table, and classify the project reserve resource allocation requirements of each of the optimal reserve projects based on the project reserve resource classification table, so as to divide the project reserve resources in the project reserve resource allocation requirements of each of the optimal reserve projects into core reserve resources and flexible reserve resources; Obtain the core reserve resource allocation threshold for each of the optimal reserve projects. For the core reserve resources required by each of the optimal reserve projects, calculate the core reserve resource allocation amount for each of the optimal reserve projects based on the core reserve resource allocation threshold for each of the optimal reserve projects. Obtain a preset elastic reserve resource allocation strategy. For the elastic reserve resources required by each of the optimal reserve projects, calculate the elastic reserve resource allocation amount of each of the optimal reserve projects based on the core reserve resource allocation amount of each of the optimal reserve projects using the elastic reserve resource allocation strategy. For each of the optimal reserve projects, the corresponding core reserve resource allocation and the flexible reserve resource allocation are integrated to form the optimal resource allocation strategy for each optimal reserve project.

6. The method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm according to claim 5, characterized in that, Before formulating the optimal resource allocation strategy for each of the aforementioned optimal reserve projects, the following is also included: The fuzzy opportunity constraint programming algorithm is used to fuzzify the core reserve resource allocation and the flexible reserve resource allocation of each optimal reserve project to obtain fuzzy opportunity constraints, and then the fuzzy opportunity constraints are used to make deterministic adjustments to the core reserve resource allocation and the flexible reserve resource allocation.

7. The method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm according to claim 1, characterized in that, Based on the optimal project reserve resource allocation scheme and the optimal resource allocation strategy for each of the optimal reserve projects, resource allocation is completed, including: Based on the optimal project reserve resource allocation scheme, each optimal project unit is selected from the comprehensive plan project reserve data as a project undertaking unit, and each optimal reserve project corresponding to each optimal project unit is used as the undertaking project of each project undertaking unit. According to the optimal resource allocation strategy, corresponding resource allocation is performed for the projects undertaken by each project undertaking unit.

8. A project reserve resource optimization and allocation system based on a multi-objective genetic algorithm, characterized in that, The method for optimizing the allocation of project reserve resources based on a multi-objective genetic algorithm, as described in any one of claims 1 to 7, includes: A multi-objective optimization model building unit is used to acquire comprehensive planning project reserve data, extract each project unit and its reserve projects from the comprehensive planning project reserve data, and construct a comprehensive planning project reserve resource multi-objective optimization model based on each project unit and its reserve projects. This model is used to generate multiple project reserve resource allocation schemes. The comprehensive planning project reserve resource multi-objective optimization model includes at least the objectives of maximizing comprehensive benefits, optimizing investment balance, and maximizing construction resource utilization. The optimal resource allocation scheme generation unit is used to generate an optimal project reserve resource allocation scheme based on a multi-objective genetic algorithm. Each project reserve resource allocation scheme is treated as an individual, and the individual strength value of each individual is calculated. The individual strength values ​​of each individual are integrated to obtain the coarse fitness of each individual, and the density information estimate of each individual is calculated. The optimal project reserve resource allocation scheme is generated using the coarse fitness and density information estimate of each individual. The optimal project reserve resource allocation scheme includes multiple optimal project units and corresponding optimal reserve projects. The optimal resource allocation strategy generation unit is used to divide the project reserve resources into core reserve resources and elastic reserve resources based on each of the optimal reserve projects in the optimal project reserve resource allocation scheme, and to calculate the core reserve resource allocation amount and the elastic reserve resource allocation amount for the core reserve resources and the elastic reserve resources respectively, so as to obtain the optimal resource allocation strategy for each of the optimal reserve projects, wherein the optimal resource allocation strategy includes the optimal allocation amount of core reserve resources and the optimal allocation amount of elastic reserve resources; The resource allocation execution unit is used to complete resource allocation based on the optimal project reserve resource allocation scheme and the optimal resource allocation strategy for each of the optimal reserve projects.

9. An electronic device, characterized in that, The device includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the project reserve resource optimization allocation method based on a multi-objective genetic algorithm as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the project reserve resource optimization allocation method based on any one of claims 1 to 7.

Images