Smart Infrastructure Design System with Hybrid Optimization Method and Topographic Data Integration

Abstract

The invention enables the loading of 3D topographic map data into the system and the identification of infrastructure nodes. determination, establishment of the initial population, SMA-based biofitness calculation, creation of adaptive pheromone weight coefficient, candidate routes with Dijkstra algorithm calculation, electrical load and loss analysis, and final network result from multi-criteria optimization. The process of creating structure 10 and updating pheromone parameters through the learning mechanism. with its steps, the most efficient electricity and similar network line is achieved in the fastest, shortest and optimum way. It includes the method of implementation.

Description

1 Specifications Smart Infrastructure Design with Hybrid Optimization Method and Topographic Data Integration The system Technical Area 5 This invention relates to computer-aided infrastructure design, electricity distribution network planning, and metaheuristics. It is related to optimization algorithms and topographic data processing systems. State of the Art 10 Today, when electricity and similar infrastructure distribution networks are being created, urban planning is taken into account. These distribution channels are being developed entirely based on the existing positioning. Because of this implementation, network channels are getting longer, which both increases energy consumption and... This causes delays in infrastructure works. 15 Although studies have been conducted on distributed energy optimization, route planning and No studies have been conducted on topographic data collection. The route algorithm and topographic data... This lack of visibility into the system prevents it from detecting potential risks in the background and limits energy optimization. This causes it to do so. 20 As a result of our research, we have come across invention number CN115392581A. The invention, the technical field of electricity distribution network power flow optimization, especially in the context of electric vehicles and Park power distribution grid power flow optimization taking into account energy storage access. It is related to the method. The method includes the following steps: S1, estimate the charging load of the electric vehicle 25 To do this, we need to create a charge load model and estimate the output power of the distributed energy. to create a distributed energy output power model; S2, which includes electric vehicles and distributed energy To create a physical model of a power distribution network; S3, a mathematical model of a power distribution network, power objective function of the distribution network mathematical model and constraint of power flow optimization to create the conditions; and S4, an optimization according to the objective function and constraint condition 30 Solving the mathematical model of the power distribution network through an algorithm and intelligent parking power distribution. To obtain a power flow control strategy for the network. According to this invention, electric vehicles, distributed energy, and energy storage devices have good access. can be considered in some way and power flow under the access of electric vehicles and distributed energy 35 Optimization can be performed. 2 Another invention, numbered CN111065143A, is a wireless sensor network for monitoring power field equipment. This describes the routing optimization algorithm, and the method includes the following steps: 100, grouping network nodes into clusters and designating a head node in each cluster; 200, to ensure homogeneity in the energy of all network nodes; 300, 5 for energy homogenization. Once clustering is complete, the network nodes will be responsible for collecting monitoring data. to provide the characteristics of network nodes and monitoring data through cluster head nodes to combine and then send the combined data to recipient nodes; 400, cluster head To determine a communication threshold value based on the distribution density of the nodes and the network nodes. According to the comparison between distribution density and communication threshold value, the cluster head nodes are 10 Dynamically adjusting the communication radius. According to the invention, safety monitoring of energy equipment. The routing protocol algorithm of the wireless sensor network in the system aims to extend the lifespan of the network, each To reduce the energy consumption of a node and the entire network, and to balance the network's energy consumption load. It is used for this purpose. Invention CN118052248A relates to the collaborative optimization of ecological network function and structure. It relates to the multi-type ant colony algorithm; this algorithm uses fuzzy C-means clustering. a function optimization operator and a structure for ecological networks based on the method The optimization operator designs a space function operator and a space structure operator. integrates a type of ant colony optimization algorithm into a geographical parallel computing architecture 20 It presents and optimizes the structure of a multi-type ant colony optimization algorithm. Ecological network A high-performance land use optimization model has been created for its optimization. and from a quantitative and dynamic simulation perspective, the functions of the ecological network at the plate level and Collaborative optimization of their structures has been carried out. According to this method, social, economic, ecological and suitability targets can be improved, such as ecological resource land quality and plate linkage. Functions can be improved and the fragmentation of the local ecological network can be reduced; furthermore, a GPU-based parallel system can be implemented. The calculation framework is optimized for high-precision land use simulations. It resolves the contradiction between large-scale ecological network creation and computational efficiency in terms of calculations, and more. It provides a reference for refined ecological restoration and land-use planning decisions. Invention number US11522947B1 describes the development of hybrid cloud computing engines using infrastructure optimization. Systems, computer program products, and methods for its implementation are described. The invention creates an infrastructure that can fulfill a computing request from among a group of existing infrastructures. It can be configured to determine; where the existing infrastructure group constitutes an enterprise infrastructure and a or includes the infrastructure of more cloud providers. This invention relates to the defined infrastructure and a blockchain 35 through a distributed ledger, provide the calculation request and the input data for the calculation request. It can be further configured in this way; where the computation request is in the blockchain's distributed ledger. 3 The desire to generate output data using a recurrent neural network, based on input data. This invention includes output data from the defined infrastructure and via a blockchain distributed ledger. It can be further configured to receive. Invention number US9697575B2 relates to parameters offered by buyers and sellers of electrical energy. According to this, the dynamic real-time transmission of electrical energy is achieved using a feedback control scheme. A method and system for directing something in a certain way. A control node, wide area network. to obtain parameters and based on these parameters, as well as the current supply and demand in the network It is configured to create a route plan. The control node is also responsible for executing the route plan. Transmission and 10 for dynamically routing electrical energy between matching buyers and sellers. It also depends on the distribution systems. Invention US20150285651A1 describes the function of locating two positions on a graph with multi-sided constraints. It offers systems and methods that find the fastest route in an efficient way in terms of time and space. In some applications, the Dijkstra algorithm is used when a) a multi-edge constraint is reached and b) many 15 The bounded constraint is divided into separate universes along each edge. In some applications, the division operation... Finding the fastest (i.e., lowest weighted) route to the intersection points at the ends of the constraints. This is done for this purpose. In some applications, these universes intersect at the end of the constraint. When found, they are combined or discarded. Using these systems and methods, in some applications, The shortest path between two locations in a multi-sided constrained road network is found efficiently in 20 It can be determined. The distribution is mentioned in the link: https: / / www.sciencedirect.com / science / article / pii / S1877050923019373? planning grid assessment strategies in a reasonable manner and power system planning In order to increase its efficiency and accuracy, this study focuses on distribution network planning and 25 The study aims to investigate the application of intelligent optimization algorithms in its evaluation. The principle, method, and model structure of the intelligent optimization algorithm are included, and it is related to GIS (Geographic Information Systems). A distribution using Ant Colony Optimization Algorithms combined with an Information System. A network planning and evaluation model is being designed. This article discusses the distribution of a specific region. It is applying this as an example of network planning, and the results are 30% better than traditional algorithms. In comparison, the highest voltage value of the intelligent optimization algorithm is 230.2V, and the lowest voltage is... its voltage is 220.2V; the highest load value is 2.75 Megawatts, and the lowest load value is... and that its power output is 1.19 Megawatts; its longest operating time is 8913 ms; and in every aspect This algorithm demonstrates that it surpasses traditional algorithms. This algorithm, in the distribution network... It improves voltage stability and load balance and reduces operating time. This study, energy system 35 Its planning has a certain promotional and practical value for research and application; 4 It can improve the efficiency and accuracy of energy system planning and the reliability of energy supply. and it is mentioned that a scientific basis can be provided for its effectiveness. This invention, numbered CN103971184A, describes grid processing in a GIS geographic information system and Dijkstra By combining its algorithms, it completes route generation in power transmission line design and automatically 5 Route planning, route length, corner count statistics, complex terrain assessment It has functions such as these. This invention, numbered CN109063992B, takes into account the optimum operation of a regional energy system. This describes a method for planning the expansion of a distribution network. First, a regionally comprehensive 10 An energy system model is created and the internal energy flow relationship is analyzed. Secondly, a multi-agent system is used. Based on the system, it is possible to control the interaction of different entities at a spatial scale. A three-layered interactive structure is created, including a tier, a regional layer, and an equipment layer. Later, for the distribution network which includes residential, commercial and industrial regional integrated energy systems. A two-layered optimization model is created. The upper layer is the expansion of the distribution network. 15 while carrying out its planning, the optimum operation of the underlying regional integrated energy system It performs and uses genetic membrane algorithms and ordered quadratic algorithms to solve upper and lower models. The programming method uses nested elements. Finally, a simulation example, regional integrated energy. Distribution network planning that takes into account the optimum operation of the system, the total cost of planning that it can reduce, improve energy use efficiency and new energy consumption rate 20 It shows. In conclusion, in current systems, the Dijkstra algorithm operates in a two-dimensional plane, SMA Convergence is slow, ACO algorithms can get stuck in local minima, hybrid systems are generally single. It includes directional integration. 25 The current technique also takes terrain slope into account to a limited extent, and does not allow for energy loss modeling. It doesn't integrate, it doesn't improve performance through learning. Purpose of the Invention 30 The aim of this invention is to be able to process three-dimensional topographic data into the system. Another objective is to take into account the parameters of the electrical infrastructure. 35 Another objective is to perform SMA-based biological optimization and Dijkstra-based shortest path calculation. to provide. Another objective is to increase yield in each iteration by integrating adaptive pheromone weighting. The aim is to provide a hybrid optimization method that provides this. Detailed description of the invention. This invention combines three-dimensional topographic data with electrical distribution infrastructure parameters. computer-based methods for determining the optimum route and network topology of a distribution network It relates to applied hybrid optimization methods. The system involves topographic data processing, network modeling, Electrical infrastructure through the combined operation of metaheuristic optimization and energy loss analysis modules. It produces the most suitable solution in its design. 10 The invention's operating principle consists of the following stages: Topographic Data Collection and Upload to the System In the first stage of the method, three-dimensional topographic data of the planned infrastructure area are fed into the system. This data is transferred. This data can be obtained from the following sources: - Digital Elevation Models (DEM) - geographic information system (GIS) data - satellite images 20 - lidar or photogrammetry data - A topographic dataset may include the following parameters: - terrain elevation - slope - terrain roughness 25 - obstacle areas (mountains, lakes, settlements, etc.) This data is processed within the system to create a digital terrain model and for infrastructure planning. It is converted into a data format that algorithms can use. Defining Infrastructure Node Points After the topographic model is created, the node points required for the electricity distribution network are determined. These nodes are determined. These nodes may include the following elements: - energy production points 35 - transformer substations - distribution centers 6 - consumption areas For each node, the following electrical parameters are defined in the system: - load request 5 - voltage level - line capacity - power flow parameters Using these nodes, the infrastructure network is modeled as a graph structure. 10 Creating the Graph Structure Potential infrastructure routes are identified on topographic data, and a graph model is created. This In the graph model: 15 - nodes are infrastructure points, - The edges represent possible line paths. The following cost parameters are calculated for each edge: 20 - line length - cost of topographic crossing - installation cost - energy loss 25 These costs will then form the cost function that the optimization algorithm will evaluate. It creates. Establishing the Initial Population 30 A randomly generated starter is used by the system to initiate the optimization process. A solution population is generated. Each individual in the population represents a network structure containing the following information: 35 - selected route paths 7 - connections between nodes - network topology At this stage, each individual represents a potential infrastructure design. SMA-Based Biofitness Assessment Each solution in the initial population was biologically based on the Slime Mould Algorithm (SMA). It is evaluated using an optimization approach. At this stage, the following criteria are calculated for each solution: 10  total line length  total installation cost - energy transmission loss - cost of topographic crossing A fitness value is calculated using these criteria. The SMA algorithm uses natural slime mold. By mimicking the foraging behavior of organisms, the best solutions carry more weight. It enables him to win. Adaptive Pheromone Weighting Mechanism 20 In subsequent iterations of the optimization process, depending on the performance of the previous solutions A pheromone weight coefficient is established. This coefficient works as follows: 25 - Routes used by successful solutions acquire higher pheromone values. - The paths of low-performance solutions are weakened. Thanks to this mechanism, the system learns from past iterations and moves towards better solutions. 30 Calculating Candidate Routes Using the Dijkstra Algorithm The Dijkstra algorithm uses candidate network structures to find the best connections between nodes in each iteration. It is calculated using 35. Dijkstra's algorithm considers the following cost function: 8 - line length - topographic slope - installation cost - pheromone weight coefficient 5 This process determines the least costly routes between the nodes. Electrical Load and Energy Loss Analysis An electrical performance analysis is performed on the determined network structure. This analysis includes the following: Includes calculations: - power flow analysis - line resistance calculation - energy transmission loss 15 These calculations verify whether the created network is technically feasible. Multi-Criteria Optimization The obtained solutions are optimized using a multi-criteria optimization function according to the following criteria: is evaluated: - total cost - energy loss 25 - infrastructure length - topographic suitability Based on this evaluation, the solution with the highest performance is selected. Establishment of the Final Infrastructure Network When the optimization process reaches a certain number of iterations, the system selects the best solution for the final infrastructure network. It defines it as a topology. 35 This output contains the following information: 9 - railway routes - connections between nodes - installation cost - energy loss values This network structure provides engineering output that can be used in real infrastructure planning. This method provides the following technical advantages: - The total length of electricity distribution lines decreases. 10 - energy transmission losses decrease - infrastructure installation costs are optimized. - more suitable railway line routes are created based on topographical conditions. - The optimization process continuously improves through iterative learning. Therefore, the invention enables more efficient and economical network planning in electrical infrastructure design. provides. SMA biological optimization + adaptive pheromone system 20 + energy loss model + hybrid algorithm architecture This method reduces line length, lowers energy loss, reduces installation costs, and improves convergence. The duration is shortened, and the system becomes more efficient with each run. Thus, it has physical and measurable results. The technical effect comes into play. 35

Claims

REQUESTS 1. The invention uses three-dimensional topographic data and electrical infrastructure parameters to create a... optimizing the physical line route and network topology of the electricity distribution network It is a computer-applied method and its characteristic is; 5  Uploading three-dimensional topographic data of the planned infrastructure area to the system,  infrastructure nodes including energy generation points, transformer substations and load centers Determining the points, 10  a graph structure that represents potential connections between the nodes in question creation,  Line length, topographic traverse cost and electrical 15 for connections on the graph Calculation of cost values ​​including parameters,  Creation of an initial solution population representing candidate infrastructure network structures,  The solution population in question is biologically analyzed using the Slime Mould Algorithm (SMA) 20 Evaluation using optimization methods,  The optimal connection paths between nodes are determined using the Dijkstra algorithm. calculation,  In the iterative optimization process, an adaptive approach is used depending on the performance of the candidate solutions. Updating the pheromone weight coefficient,  Performing electrical power flow and energy loss analysis for candidate network structures,  Multi-criteria optimization including total cost, energy loss, and line length criteria. It includes the steps to determine the most suitable network topology using the function. It relates to the method being characterized.

2. The method according to Claim 1 is that the topographic data includes the following parameters: 35 It is characterized by:  terrain elevation 11  terrain slope  land crossing cost  obstacle areas.

3. The method according to claim 1 is that the cost function is 5 for each link in the graph structure. It is characterized by including the following parameters:  line length  conductor cost  Installation cost 10  energy transmission loss.

4. The method is according to Claim 1, and the adaptive pheromone weight coefficient in previous iterations It is characterized by being updated based on the performance of the solution obtained.

5. The method according to Claim 1 is power flow simulation of electrical performance analysis. It is characterized by being carried out using [method / technique].

6. According to claim 1, the method involves a specific number of iterations in the optimization process. When reached, the network structure with the highest fitness value is selected as the final solution. 20 It is characterized by...

7. The method according to Claim 1 is to design the following infrastructure networks of the system. It is characterized by its applicability:  electricity distribution networks  water transmission lines  natural gas infrastructure networks  Fiber optic communication networks.

8. Optimizing the route of an infrastructure network using three-dimensional topographic data. It is a computer system characterized by containing the following modules:  Topographic data processing module  Infrastructure node identification module 35  Graph-based network modeling module  hybrid optimization module 12  energy loss analysis module  Module for generating the resulting network topology. 10 20 30 35

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