Power transmission and distribution engineering construction stage multi-objective optimization method and device based on particle swarm optimization

Abstract

The invention provides a power transmission and distribution engineering construction stage carbon reduction-cost-construction period multi-objective optimization method based on a particle swarm algorithm, and the method focuses on the crossing field of engineering construction management and low-carbon technology, constructs a carbon emission analysis model in a power transmission and distribution engineering construction stage, and recognizes a main carbon emission source. Establishing a multi-objective optimization model which simultaneously considers carbon emission minimization, total cost minimization and total construction period minimization; solving the model by adopting a multi-target particle swarm algorithm to obtain an optimal solution set; and an optimal scheme selection is provided for a project manager through a decision support system. According to the method, the carbon reduction target, the cost target and the construction period target are in the same important position for the first time, multi-target collaborative optimization of the power transmission and distribution project construction stage is achieved, and the carbon reduction intelligent optimization method capable of being popularized is provided for the field of project management.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of engineering construction management and low-carbon technology, and in particular to a multi-objective optimization method and device for the construction phase of power transmission and distribution projects based on particle swarm optimization algorithm. Background Technology

[0002] In recent years, due to rapid urbanization and the continuous improvement of manufacturing and service industries in my country, electricity demand across the country has grown rapidly, leading to a stronger demand for power infrastructure construction. To meet this sustained and rapid growth in electricity demand, investment in power transmission and distribution projects in my country has remained at a high level, with a large number of projects constructed annually. However, the construction phase of power transmission and distribution projects involves significant consumption of energy and carbon-intensive products, generating substantial carbon emissions from material production, transportation, construction machinery energy consumption, and auxiliary facility construction. Existing research largely focuses on cost and schedule optimization, with insufficient consideration given to the impact on carbon emissions, and lacks a systematic multi-objective optimization method that simultaneously considers carbon reduction, cost, and schedule. Therefore, researching a multi-objective intelligent optimization algorithm for carbon emissions during the construction phase of power transmission and distribution projects is crucial for promoting the optimization of carbon reduction technologies across society and reducing their costs. Summary of the Invention

[0003] To address the challenges of high carbon emission intensity, uneven distribution of construction resources, and the mutual constraints between construction period and cost objectives in the construction phase of power transmission and distribution projects, this application proposes a multi-objective optimization method and apparatus based on particle swarm optimization (PSO) for the construction phase of power transmission and distribution projects. This method is a quantitative analysis of carbon emissions and a multi-objective collaborative optimization approach for the construction process.

[0004] The technical solution adopted in this application is: a multi-objective optimization method for the construction phase of power transmission and distribution projects based on particle swarm optimization, comprising the following steps:

[0005] First, based on the energy consumption characteristics and construction process logic of the power transmission and distribution project construction phase, a phased carbon emission model is established, including multiple links such as materials, transportation, machinery and on-site energy consumption, and correction parameters are introduced to achieve dynamic adaptation to different construction scenarios.

[0006] Secondly, a multi-objective optimization model with "minimizing carbon emissions, minimizing costs, and minimizing construction period" as its core is constructed, and the coordination and unity of carbon reduction, economic and schedule objectives are achieved through dynamic weights and collaborative adjustment mechanisms.

[0007] Finally, a multi-objective intelligent optimization method based on an improved particle swarm optimization algorithm is proposed, which introduces dynamic inertial weights at the algorithm level to achieve intelligent global optimization for low-carbon goals.

[0008] Furthermore, the phased carbon emission models include: material carbon emission models, mechanical carbon emission models, and artificial carbon emission models.

[0009] Furthermore, the phased carbon emission model is constructed using the carbon emission factor method, assuming that the project includes emissions during the construction phase. Main materials Various mechanical equipment and Human-like activities, then the total carbon emissions It can be represented as:

[0010] ;

[0011] in, For the first Consumption of this material For the first The carbon emission factor of this material For the first The daily consumption of this type of machinery. For the first The carbon emission factor of a machine's operating hours. This refers to the number of man-days consumed. Carbon emission factor per workday.

[0012] Furthermore, the expression for the multi-objective optimization model is as follows:

[0013] ;

[0014] in, Total carbon emissions This represents the total cost during the construction phase. This is the unit price of the material. For machine shift costs, For daily wages, Other indirect costs related to construction organization, The total project duration, For the first The construction activities are in time The first Class of resources For this type of resource at any time Maximum available quantity, For budget costs, For the planned period, This represents the maximum carbon emissions.

[0015] Furthermore, the three objectives in the multi-objective optimization model are normalized and weighted to form the following comprehensive optimization function:

[0016] ;

[0017] in, For the target normalized value, Let be the weighting coefficient, satisfying =1, and the weight can be dynamically adjusted according to the construction stage or management needs.

[0018] Furthermore, the weights in the dynamic inertia weights are updated as follows:

[0019] ;

[0020] in, For the normalized first The weight of each objective, For adjustment coefficients, The target mean, For the first There are several objective function values. When a certain objective deviates significantly from the average level, its weight is automatically increased.

[0021] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0022] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the method.

[0023] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the method.

[0024] The beneficial effects of this application compared to existing technologies are as follows: Based on the energy consumption characteristics and construction process logic of the power transmission and distribution engineering construction phase, this application establishes a phased carbon emission model including materials, transportation, machinery, and on-site energy consumption, and introduces correction parameters such as regional energy structure, equipment performance, and transportation distance to achieve dynamic adaptation to different construction scenarios. A multi-objective optimization model centered on "minimizing carbon emissions, minimizing costs, and shortening the construction period" is constructed, achieving a coordinated unity of carbon reduction, economic, and schedule objectives through dynamic weights and collaborative adjustment mechanisms. Furthermore, a multi-objective intelligent optimization method based on an improved particle swarm optimization algorithm is proposed, introducing dynamic inertial weights at the algorithm level to achieve intelligent global optimization oriented towards low-carbon goals. This method is applicable to various types of power transmission and distribution projects, including transmission lines, substations, and distribution engineering, providing a theoretical basis and decision support for low-carbon and intelligent construction during the construction phase. Attached Figure Description

[0025] The following description, in conjunction with the accompanying drawings, further illustrates this application:

[0026] Figure 1 A breakdown diagram of carbon emissions during the construction phase provided in this application embodiment;

[0027] Figure 2 This is a flowchart illustrating the method provided in an embodiment of this application.

[0028] Figure 3 This is a schematic diagram of the computer device structure provided in an embodiment of this application. Detailed Implementation

[0029] like Figure 1-3 As shown, this application provides a multi-objective optimization method for carbon reduction, cost, and schedule during the construction phase of power transmission and distribution projects based on particle swarm optimization (PSO). This method primarily addresses the collaborative optimization problem of carbon reduction, cost, and schedule during the construction phase of power transmission and distribution projects. The method establishes a carbon emission model for the construction phase of power transmission and distribution projects, constructs a multi-objective optimization model, and solves the model based on a multi-objective particle swarm optimization algorithm, thereby providing intelligent decision support for low-carbon construction management.

[0030] The method described in this application mainly includes the following steps:

[0031] First, based on the energy consumption characteristics and construction process logic of the power transmission and distribution project construction phase, a phased carbon emission model was established, including materials, transportation, machinery and on-site energy consumption, etc. Correction parameters such as regional energy structure, equipment performance and transportation distance were introduced to achieve dynamic adaptation to different construction scenarios.

[0032] Specifically, carbon emissions are calculated for various categories based on the relevant content and characteristics of the power transmission and distribution project construction phase. For example... Figure 1 As shown, carbon emissions from building materials are primarily indirect. To ensure the feasibility of the calculations, the material balance method is mainly used to determine the corresponding emission factors and carbon source consumption, and to calculate the total carbon emissions of various materials. During the construction phase, according to the method of dividing the project into sub-items, the emission factors of various machine shifts are determined based on the fuel emission factors, and the corresponding carbon emissions of mechanical equipment are calculated. Through this series of decomposition processes, all direct and indirect carbon emission sources in the power transmission and distribution project construction phase are identified, and appropriate methods are used to calculate and summarize them to determine the total carbon emissions during the power transmission and distribution project construction phase, making the carbon emission calculations during the power transmission and distribution project construction phase more scientific and reasonable.

[0033] The carbon emission factor method quantifies the carbon emission levels of different processes by combining the activity quantities of various construction activities with corresponding emission factors. In the construction phase of power transmission and distribution projects, this method focuses on the construction process, first identifying the main emission links, including material use, machinery operation, and manual labor. Then, based on construction progress records, project statistical reports, and quota data, key data related to each link are extracted, such as material consumption, machinery shifts, and man-days. By combining the characteristics of different activities, appropriate carbon emission factors are matched and multiplied by the actual activity quantities to obtain the carbon emission value corresponding to each link. By summing the emissions from different sources, the total carbon emissions during the construction phase of the power transmission and distribution project can be obtained.

[0034] Based on this, the carbon emission model for the construction phase of power transmission and distribution projects includes the following parts:

[0035] 1) Calculation of carbon emissions from materials

[0036] For the building materials production stage, a carbon emission factor method is used to construct a calculation model for carbon dioxide emissions during the construction stage of power transmission and distribution projects.

[0037] The basic formula for calculating the carbon emissions of materials is:

[0038] (1);

[0039] in, For the first Consumption of various materials (building material unit). For the first Carbon emission factor of a building material (kg CO2e / building material unit).

[0040] 2) Calculation of carbon emissions from machinery

[0041] To calculate carbon emissions from machinery, the first step is to determine the types and specifications of the machinery used on the construction site. The second step is to obtain data on the number of machine shifts used for each type of machinery. Finally, the appropriate machinery carbon emission factor is selected to calculate the total machinery carbon emissions.

[0042] (2);

[0043] in, For the first The daily consumption of a type of machinery (per shift). For the first Carbon emission factor (kg CO2e / shift) of a type of machinery per shift.

[0044] 3) Calculation of man-made carbon emissions

[0045] Human-caused carbon emissions not only originate from workers' breathing, but more importantly from the carbon emissions generated by workers' consumption of energy resources such as lighting, HVAC, and hot water during construction and daily life on construction sites. A carbon emission factor per man-day can be used to correlate these carbon emissions with the number of man-days, thereby more accurately assessing the amount of human-caused carbon emissions in construction projects.

[0046] (3);

[0047] in, This refers to the number of man-days consumed. Carbon emission factor per man-day (kgCO2e / man-day)

[0048] The total carbon emissions during the construction phase of power transmission and distribution projects can be expressed as:

[0049] (4).

[0050] In summary, the carbon emission model established in this application for the construction phase of power transmission and distribution projects covers a comprehensive carbon emission accounting system encompassing materials, transportation, machinery, and on-site energy consumption. The model not only provides reliable data support and constraints for subsequent multi-objective optimization of carbon reduction, cost, and schedule, but also offers a scientific theoretical framework for carbon emission monitoring, analysis, and control during the construction phase of power transmission and distribution projects. The construction of this carbon emission model marks a significant step forward in the research of low-carbon transformation of power transmission and distribution projects, moving from empirical estimation to quantitative and systematic scientific modeling, laying the foundation for carbon emission optimization and intelligent decision-making in the field of engineering construction.

[0051] Secondly, a multi-objective optimization model with "minimizing carbon emissions, minimizing costs, and shortening the construction period" as its core was constructed, and the coordination and unity of carbon reduction, economic and schedule objectives were achieved through dynamic weights and collaborative adjustment mechanisms.

[0052] In the field of engineering management, the Time-Cost-Quality (TCQ) triangle is the core focus of traditional optimization. This application introduces carbon emissions into this framework, constructing it as a three-objective optimization problem. The reason for choosing carbon reduction, cost, and schedule as the three objectives is that carbon emissions are both an environmental indicator and an externality of construction activities, while cost and schedule remain the primary economic indicators of concern to both contractors and owners. Elevating carbon emissions to an objective, giving it equal weight to cost and schedule, not only responds to policy requirements but also establishes achievable emission reduction paths during project execution.

[0053] From a mathematical perspective, the problem is a constrained multi-objective constrained optimization problem, formalized as:

[0054] (5);

[0055] Wherein, the target vector is , its origin The objective function consists of several variables; the decision variables are... There are a total of 1; decision variables The decision space constitutes Target vector The target space constitutes for The objective function that needs to be optimized simultaneously; the first Inequality constraints are used Indicates; the Equality constraints are used express.

[0056] During the construction phase of power transmission and distribution projects, there is a complex coupling relationship between carbon emissions, costs, and schedule. Carbon emissions mainly originate from the production and transportation of construction materials, fuel consumption of construction machinery, and indirect energy consumption from human activities. Costs consist of material costs, machinery operating costs, and labor costs, and are influenced by energy prices and project organization methods. The schedule depends on external conditions such as construction planning, resource allocation, and climate. To achieve systematic coordination, this application incorporates all three objective functions into the optimization model simultaneously, comprehensively balancing the needs of low carbon emissions, economic efficiency, and schedule.

[0057] In constructing carbon emission targets, the carbon emission factor method is used to calculate emissions at each stage. Based on the carbon emission model constructed in the previous step for the construction phase of power transmission and distribution projects, it is assumed that the project includes [emissions during the construction phase]. Main materials Various mechanical equipment and For human-related activities, the total carbon emissions can be expressed as:

[0058] (6).

[0059] In establishing cost targets, total costs are divided into direct costs and indirect costs. Direct costs include material costs, machinery usage fees, and labor costs, while indirect costs cover construction management fees, on-site energy consumption, and equipment maintenance costs. Let the unit price of materials be... The machine shift cost is The daily wage for workers is The total cost of the construction phase can then be expressed as:

[0060] (7);

[0061] in, This indicates other indirect costs related to construction organization.

[0062] The objective is to achieve the optimal distribution of overall costs while reasonably controlling input and resource allocation. Unlike carbon emission targets, cost targets are not only affected by fluctuations in material and equipment prices but are also closely related to construction organization strategies. Therefore, in the solution process, the cost function needs to be parametrically correlated with the carbon emission target to reflect the inherent balance between low carbon emissions and economic efficiency.

[0063] Construction period is a crucial indicator for measuring the efficiency of construction organization and the rationality of resource allocation. A reasonable construction period not only improves resource utilization and reduces indirect costs, but also significantly impacts the frequency of machinery use and energy consumption. The establishment of construction period targets is based on the logical relationships between construction activities, breaking down the project construction process into several activity units. Each activity has a clearly defined start time. and duration The end time is = When the activity Dependent on activity Construction logic constraints must be met before construction can begin, provided that all other requirements are met. .

[0064] Under this constraint, the total project duration can be expressed as the maximum value of the end times of all construction activities, i.e.:

[0065] (8);

[0066] Therefore, the project duration optimization objective can be defined as:

[0067] (9).

[0068] The goal of this study is to minimize the overall project duration while satisfying construction logic and resource constraints. By rationally adjusting the sequence of activities and resource allocation, construction efficiency can be improved, thereby ensuring an optimal balance between reducing carbon emissions and controlling costs, and maintaining project progress.

[0069] The goal of this study is to minimize the overall project duration while satisfying construction logic and resource constraints. By rationally adjusting the sequence of activities and resource allocation, construction efficiency can be improved, thereby ensuring an optimal balance between reducing carbon emissions and controlling costs, and maintaining project progress.

[0070] (10);

[0071] in, For the first The construction activities are in time The first Class of resources For this type of resource at any time The maximum available quantity.

[0072] In addition, the model must also meet the requirements that the total cost does not exceed the budget, the total construction period does not exceed the planned period, and the total carbon emissions do not exceed the limit, which can be expressed as:

[0073] (11);

[0074] in, For total cost, For budget costs, For the total construction period, For the planned period, Total carbon emissions This represents the maximum carbon emissions.

[0075] These constraints collectively define the feasible region of the model, making the optimization results meaningful for practical engineering applications.

[0076] Based on the above objectives and constraints, a multi-objective optimization model for the construction phase of power transmission and distribution projects can be constructed, which takes the following form:

[0077] (12).

[0078] To facilitate algorithm solution, the three objectives can be combined into a comprehensive optimization function through normalization and weighted summation:

[0079] (13);

[0080] in, For the target normalized value, Let be the weighting coefficient, satisfying =1. The weight can be dynamically adjusted according to the construction stage or management needs, thereby achieving a phased balance between carbon reduction, cost and construction period.

[0081] In summary, this model treats the goal of minimizing carbon emissions alongside cost and schedule objectives, fundamentally changing the traditional optimization structure dominated by economic indicators and making environmental constraints the core decision variable. Secondly, the model introduces a dynamic weighting mechanism, which adaptively adjusts the focus of objectives according to changes in the construction stage, ensuring a balance between low carbon emissions and economic efficiency. Finally, the constraint system incorporates a dual-layer constraint of carbon emission limits and resource utilization ceilings, making the optimization process closer to the actual construction scenario of power transmission and distribution projects. This model provides a clear mathematical structure and computational foundation for subsequent particle swarm optimization, offering a decision-making basis for low-carbon and intelligent construction in the power transmission and distribution project construction phase.

[0082] Finally, a multi-objective intelligent optimization method based on an improved particle swarm optimization algorithm is proposed. This method introduces dynamic inertia weights, adaptive learning factors, and a carbon emission penalty mechanism at the algorithm level to achieve intelligent global optimization oriented towards low-carbon goals. It is applicable to various types of power transmission and distribution projects, including transmission lines, substations, and distribution engineering, providing a theoretical basis and decision support for low-carbon and intelligent construction during the construction phase.

[0083] In the optimization decision-making problem during the construction phase of power transmission and distribution projects, carbon reduction, cost, and schedule often present significant conflicts. It is necessary to reduce carbon emissions during construction, control project investment, and ensure construction progress. These problems typically fall under the category of multi-objective, nonlinear, and multi-constraint optimization, with a complexity far exceeding that of single-objective optimization. Traditional analytical methods and linear programming approaches, limited by assumptions of model continuity and convexity, struggle to address the uncertainties and multi-constraint coupling inherent in real-world engineering problems. Therefore, it is necessary to introduce intelligent optimization algorithms with global search capabilities and adaptive learning features to solve these complex problems.

[0084] Among them, Particle Swarm Optimization (PSO) is widely used in fields such as building construction optimization, energy system planning and carbon emission control due to its simple structure, few parameters and fast convergence speed.

[0085] The Particle Swarm Optimization (PSO) algorithm was first proposed by Kennedy and Eberhart in 1995. Its basic idea is derived from the cooperative behavior of flocks of birds or schools of fish searching for food in nature. The algorithm gradually approaches the optimal solution of the objective function through information sharing and global cooperation among individual particles. In the algorithm, each particle represents a candidate solution; its position indicates the value of the current solution, and its velocity represents the search direction and step size. Particles continuously update their positions in the search space, and its mathematical expression is as follows:

[0086] (14);

[0087] (15);

[0088] in, For the first The particle in the first The current position of the generation Its speed; This represents the historical best position of an individual particle. The globally optimal position; For inertial weights, , For individual and group learning factors, , The value is a random number in the range [0,1]. By continuously updating the velocity and position of the particle swarm, the algorithm searches for the Pareto optimal solution set globally.

[0089] The core algorithm flow includes initialization, fitness calculation, non-dominated sorting, particle position update, and elite retention. First, parameters such as the number of particles, maximum number of iterations, and inertia weight are set according to the problem size, and an initial solution set is randomly generated. Second, the fitness values ​​of the particles are calculated based on each objective function, and particles superior to others are selected as leaders through non-dominated sorting. Then, crowding distance is used to maintain the diversity of the solution set distribution, preventing the algorithm from getting trapped in local optima. To further improve search performance, inertia weight... Adopt a linear decreasing strategy:

[0090] (16);

[0091] in This represents the current iteration number. This represents the maximum number of iterations. Through this dynamic adjustment, the algorithm can maintain strong global exploration capabilities in the early stages and enhance local search accuracy in the later stages.

[0092] In multi-objective optimization problems, a single optimal solution often cannot simultaneously satisfy the extremum requirements of different objectives. Therefore, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is introduced. This algorithm incorporates non-dominated sorting and crowding distance mechanisms into the traditional PSO framework to identify and maintain the uniformity of the Pareto front solution distribution. In MOPSO, the particle swarm no longer uses a single global optimum as a reference, but rather a set of non-dominated solutions as guidance, ensuring balanced optimization of different objectives.

[0093] In multi-objective optimization, the importance of different objectives changes with the construction stage. To reflect this dynamic characteristic, this application employs a "Dynamic Weight Adjustment" mechanism to achieve adaptive balancing of staged objectives. The weight update formula is as follows:

[0094] (17);

[0095] in, For the normalized first The weight of each objective, For adjustment coefficients, The target mean, For the first There are several objective function values. When an objective deviates significantly from the average level, its weight is automatically increased, thereby strengthening the optimization of that objective in subsequent iterations. This mechanism can automatically balance multiple objectives without relying on manual weight settings.

[0096] To adapt to the characteristics of the power transmission and distribution engineering construction phase, this application adopts the objective normalization method in the fitness design, converting carbon emissions, costs, and construction period into the same dimension. The comprehensive fitness function is defined as an expression consistent with formula (13):

[0097] (18);

[0098] , , These are dynamic weighting coefficients that reflect changes in the emphasis on objectives at different construction stages. For example, in the early stages of a project, the cost weight can be increased to control investment scale, while in later stages, the carbon emission target weight should be increased to strengthen the orientation towards low-carbon construction. This dynamic weighting mechanism enables phased adaptive control of the optimization process and is of great significance for multi-objective optimization during the construction phase of power transmission and distribution projects.

[0099] In summary, this application systematically elucidates the multi-objective optimization problem and its intelligent solution method from a theoretical perspective. First, it reviews the mathematical principles of multi-objective optimization and Pareto optimality theory, clarifying that the essence of multi-objective problems lies in the balance between objectives and the search for non-dominated solutions. Second, it details the operation mechanism of the particle swarm optimization algorithm, its velocity and position update formulas, and parameter adjustment logic. Based on this, a linearly decreasing inertial weight strategy is introduced to coordinate the dynamic balance between global search and local convergence. Furthermore, this application incorporates improvements to the multi-objective particle swarm optimization (MOPSO) algorithm, introducing non-dominated sorting, crowding distance calculation, and elite file retention mechanisms, enabling the algorithm to achieve diversified and stable distribution of multi-objective solutions under complex engineering conditions. In addition, considering the strong coupling characteristics and significant stage differences in the construction phase of transportation projects, this application proposes a dynamic weighting mechanism and a comprehensive fitness function design method to achieve adaptive adjustments of the algorithm to carbon emission, cost, and schedule objectives at different construction stages.

[0100] Figure 3 A structural block diagram of a computer device according to a specific embodiment of this application is shown. Figure 3As shown, the computer device includes a memory and a processor, the memory storing instructions executable on the processor. When the processor executes the instructions, it implements the methods described in the above embodiments. The number of memories and processors can be one or more. This computer device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computer device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0101] The computer device may also include a communication interface for communicating with external devices and exchanging data. The devices are interconnected using different buses and can be mounted on a common motherboard or otherwise as needed. The processor can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0102] Optionally, in a specific implementation, if the memory, processor, and communication interface are integrated on a single chip, then the memory, processor, and communication interface can communicate with each other through an internal interface.

[0103] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting advanced RISC machines (ARM) architecture.

[0104] This application provides a computer-readable storage medium (such as the memory described above) that stores computer instructions, which, when executed by a processor, implement the method provided in this application.

[0105] Optionally, the memory may include a stored program area and a stored data area, wherein the stored program area may store the operating system and application programs required for at least one function; the stored data area may store data created based on the use of the computer device for mapping. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the computer device for mapping via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-objective optimization method for power transmission and distribution project construction phase based on particle swarm algorithm, characterized in that: Comprise the following steps: Firstly, based on the energy consumption characteristics and construction process logic of power transmission and distribution engineering construction stage, a multi-link carbon emission model including materials, transportation, machinery and site energy consumption in different stages is established, and a correction parameter is introduced to realize dynamic adaptation to different construction scenes; Secondly, a multi-objective optimization model with "carbon emission minimization-cost minimization-construction period minimization" as the core is constructed, and the coordination and unification of carbon reduction, economy and progress targets are realized through dynamic weight and collaborative adjustment mechanism; Finally, a multi-objective intelligent optimization method based on improved particle swarm algorithm is proposed, and dynamic inertia weight is introduced at the algorithm level to realize intelligent global optimization for low-carbon target. 2.The power transmission and distribution project construction phase multi-objective optimization method based on a particle swarm algorithm according to claim 1, characterized in that: The multi-stage carbon emission model includes: material carbon emission model, mechanical carbon emission model, artificial carbon emission model.

3. The multi-objective optimization method for power transmission and distribution project construction phase based on particle swarm algorithm according to claim 2, characterized in that: The phased carbon emission model is constructed by using the carbon emission factor method, assuming that the project in the construction phase contains a main material, a mechanical device and a kind of artificial activity, then the total carbon emission can be expressed as: ； wherein, is the consumption amount of the nth material, is the carbon emission factor of the nth material, is the consumption amount of the nth material, is the carbon emission factor of the nth material, is the consumption amount of the nth machine, is the carbon emission factor of the nth machine, is the consumption amount of the nth machine, is the carbon emission factor of the nth machine, is the consumption amount of the nth machine, is the carbon emission factor of the nth machine.

4. The power transmission and distribution project construction phase multi-objective optimization method based on a particle swarm algorithm according to claim 3, characterized in that: The expression of the multi-objective optimization model is as follows: ； wherein, is the total amount of carbon emissions, is the total cost of the construction phase, is the unit price of the material, is the cost of the mechanical plant, is the daily wage of the labor, is the other indirect cost related to the construction organization, is the total duration of the project, is the amount of the first type of resource used by the construction activity at time , is the amount of the first type of resource used by the construction activity at time , is the budget cost, is the planned duration, is the maximum amount of carbon emissions.

5. The multi-objective optimization method for power transmission and distribution project construction phase based on particle swarm optimization algorithm according to claim 4, characterized in that: The three objectives in the multi-objective optimization model are normalized and weighted to form the following comprehensive optimization function: ； wherein, is a target normalized value, is a weight coefficient, satisfying = 1, and the weight can be dynamically adjusted according to the construction stage or management requirements.

6. The power transmission and distribution project construction phase multi-objective optimization method based on a particle swarm algorithm according to claim 1, characterized in that: The weight updating of the dynamic inertia weight is as follows: ； wherein, is the normalized value of the i-th target, is the weight of the i-th target, is the adjustment coefficient, is the average value of the targets, is the i-th target function value, is automatically increased when the i-th target deviates from the average value.

7. A computer apparatus comprising a memory, a processor, and a computer program stored on the memory, wherein: The processor executes the computer program to realize the steps of the method of claim 1.

8. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that: The computer program / instruction is executed by the processor to realize the steps of the method of claim 1.

9. A computer program product comprising computer programs / instructions, characterized in that: The computer program / instruction is executed by the processor to realize the steps of the method of claim 1.

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