Regional development plan formulation system

Abstract

The present invention relates to a regional development plan formulation system that establishes actual living zones on the basis of collected data and generates analysis information on the established actual living zones to formulate regional development plans for the actual living zones, wherein the regional development plan formulation system can improve the effectiveness of regional development plans by formulating the development plans based on actual living zones that have been established on the basis of big data including information such as topographies, traffic volumes, and movement volumes.

Description

Regional Development Plan Establishment System

[0001] The present invention relates to a regional development plan establishment system, and more specifically, to a regional development plan establishment system that establishes an actual living area based on big data including information such as terrain, traffic volume, and movement volume, and establishes a development plan based on the established actual living area.

[0002] In recent years, with rapid urban growth, sustainable city development has emerged as a new paradigm for urban growth management. Accordingly, local governments, public corporations, private construction companies, and developers are annually reviewing sustainable city development strategies. Mid-sized and smaller construction companies are also seeking ways to adapt to this paradigm through small-scale urban development projects.

[0003]

[0004] Traditionally, urban development plans have been established simply by administrative district. Consequently, in areas where interactions within a single administrative district are rare due to geographical constraints and other factors, resulting in effectively separate living areas, the effectiveness of a single regional development plan is limited.

[0005] Therefore, there is a need to establish regional development plans based on practical criteria rather than simply relying on administrative districts.

[0006]

[0007] One aspect of the present invention provides a regional development plan establishment system that establishes a real living area based on big data including information such as terrain, traffic volume, and movement volume, and establishes a development plan based on the established real living area.

[0008]

[0009] According to one aspect of the present invention, unlike conventional techniques that establish development plans for each simple administrative district, the effectiveness of regional development plans can be improved by establishing actual living areas based on big data that includes information such as terrain, traffic volume, and movement volume, and establishing development plans based on the established actual living areas.

[0010]

[0011] FIG. 1 is a diagram schematically illustrating the configuration of a regional development plan establishment system according to one embodiment of the present invention.

[0012]

[0013] The following detailed description of the present invention refers to the accompanying drawings, which illustrate specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention, while different from each other, are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the present invention. Furthermore, it should be understood that the positions or arrangements of individual components within each disclosed embodiment may be modified without departing from the spirit and scope of the present invention. Accordingly, the following detailed description is not intended to be limiting, and the scope of the present invention is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled, if properly described. Like reference numerals in the drawings designate the same or similar functionality throughout the several aspects.

[0014] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

[0015] FIG. 1 is a diagram schematically illustrating the configuration of a regional development plan establishment system according to one embodiment of the present invention.

[0016] The system for establishing a regional development plan according to the present invention, unlike conventional technologies that establish development plans for each simple administrative district, establishes an actual living area based on big data including information such as terrain, traffic volume, and movement volume, and establishes a development plan based on the established actual living area, thereby aiming to improve the effectiveness of the regional development plan.

[0017] For example, the southern Boryeong area in South Chungcheong Province, while administratively a single region, is surrounded by mountains running north-south, making interaction between villages in the east and west rare. In this case, defining the southern Boryeong area as a single area, as previously done, effectively combines two districts into a single area, necessitating the development plan to consider the unique characteristics of both regions.

[0018] In order to solve such problems, the present invention aims to improve the effectiveness of regional development plans by establishing actual living areas based on big data including information such as terrain, traffic volume, and movement volume as described above, and establishing development plans based on the established actual living areas.

[0019] To this end, a regional development plan establishment system according to one embodiment of the present invention includes a data collection unit (110), a living area establishment unit (120), an analysis unit (130), and a development plan establishment unit (140).

[0020] The data collection unit (110) collects data related to regional development. In one embodiment, the data collection unit (110) may collect data such as the geographical location, area, climate, industrial facilities, and building information of the area requiring development. However, this is not limited to this, and may also collect a wider variety of data related to regional development.

[0021] The living area setting unit (120) sets the actual living area based on the data collected by the data collection unit.

[0022] In one embodiment, the living area setting unit divides an area requiring a development plan into a plurality of unit areas, calculates a real neighborhood index for each divided unit area using the following mathematical formula, and sets a real living area for the area requiring a development plan based on the calculated real neighborhood index.

[0023]

[0024] [Mathematical formula]

[0025]

[0026] Here, L_n is the actual contiguity index for a specific unit area n, lati_n is the latitude of a specific unit area n, long_n is the longitude of a specific unit area n, r is the number of other unit areas adjacent to unit area n, p_i is a first weight value set in proportion to the number of population movements between another unit area i and unit area n located within a certain radius based on unit area n, t_i is a second weight value set in proportion to the number of vehicle traffic between another unit area i and unit area n, d_i is the distance between the center point of another unit area i and the center point of unit area n, w_n is the average housing area between unit areas n, and w_i is the average housing area of ​​another unit area i.

[0027] In this way, the living area setting department can calculate the real proximity index by considering the relationship (distance, traffic volume, population movement volume, etc.) between a specific unit area and other unit areas located around the specific unit area, and in particular, calculate the real proximity index by considering the difference between the average housing area of ​​the unit area and the average housing area of ​​other unit areas located around it.

[0028] In South Korea, housing not only serves as a place to live but also serves as a measure of wealth. People residing in a particular area tend to move to areas with similar housing types and sizes to their current residences, rather than simply to neighboring areas simply because they're nearby. Therefore, the Residential Area Development Department considers these factors to calculate a real neighborhood index for each unit area.

[0029] Accordingly, the reliability of the established real living area can be improved by setting geographically close unit areas with similar calculated real contiguousness indices as real living areas.

[0030] In some other embodiments, the living area setting unit basically sets the actual living area based on the actual proximity index using the above mathematical formula, but may also consider legal systems related to living area planning, such as the National Land Planning and Utilization Act and the Urban / County Basic Plan Establishment Guidelines, to set the actual living area. For example, the living area setting unit may set unit areas with similar actual proximity indices within the category of a legally defined planning area as a single actual living area.

[0031] The analysis unit generates analysis information on the actual living area set by the living area setting unit.

[0032] In one embodiment, the analysis unit generates analysis information including information predicting the diffusion path of pollutants by unit area based on data related to regional development, information on population movement between one unit area and another unit area, and information on cultural facilities, welfare facilities, green facilities, transportation facilities, and industrial facilities within an actual living area.

[0033] The Development Planning Department establishes a regional development plan for the actual living area based on the above analysis information.

[0034] In one embodiment, the development planning department estimates the pollution level for each unit area based on facility information in the actual living area.

[0035] Specifically, the development planning department implements terrain corresponding to the actual living area in virtual space, and simulates the diffusion path of pollutants generated from industrial facilities by implementing information on industrial facilities such as factories, wind direction information, precipitation information, and river levels in virtual space, and analyzes the pollution level by unit area based on the simulation results.

[0036] The Development Planning Department may establish a development plan that reflects plans for relocation of industrial facilities or expansion of purification facilities if the number of unit areas where the analyzed pollution level exceeds the standard exceeds the standard, and may establish a development plan that reflects plans for creating green zones in unit areas where the analyzed pollution level exceeds the standard if the number of unit areas where the analyzed pollution level exceeds the standard is below the standard.

[0037] In some other embodiments, the development planning department may calculate a development index for each unit area and, based on the calculated development index, establish a development plan that reflects whether to increase new facilities such as commercial facilities, neighborhood facilities, and residential facilities.

[0038]

[0039] [Equation 2]

[0040]

[0041] Here, DS is the development index, w is the first weight set in proportion to the average area of ​​buildings located within the actual living area, h is the second weight set in proportion to the average height of buildings located within the actual living area, L_a is the average value of the actual proximity index of the unit areas constituting the actual living area, s is the average sales of commercial areas located within the actual living area (ten thousand won), noise is the average noise level measured in the actual living area (db), and d is the average separation distance (km) between public transportation facilities within the actual living area.

[0042] In this way, the development plan establishment department can calculate the development index for the actual living area using the above mathematical formula 2, and establish a development plan that reflects the construction of new facilities based on the calculated development index. For example, if the development index is within the first threshold range (e.g., 0 to 5), the development plan establishment department can establish a development plan that reflects the construction of new commercial facilities, if the development index is within the second threshold range (e.g., 6 to 10), the development plan establishment department can establish a development plan that reflects the construction of new residential facilities, and if the development index is within the third threshold range (e.g., 11 or higher), the development plan establishment department can establish a development plan that reflects the construction of new green facilities. In this way, the development plan establishment department can improve the reliability of the development plan by establishing a development plan that reflects the construction of new facilities based on the development index calculated according to the above mathematical formula 2.

[0043] Meanwhile, the analysis unit can build a neural network that extracts contextual information about input data by learning training data using the Word2Vec algorithm to understand or estimate the meaning of text-based data.

[0044] The Word2Vec algorithm can incorporate a Neural Network Language Model (NNLM). A NNLM is essentially a neural network consisting of an input layer, a projection layer, a hidden layer, and an output layer. NNLM is used to vectorize words. Because NNLM is a well-known technology, a detailed description will be omitted.

[0045] The Word2vec algorithm, designed for text mining, determines proximity by examining the preceding and following relationships between words. The Word2vec algorithm is an unsupervised learning algorithm. As its name suggests, the Word2vec algorithm is a metric technique that represents the meaning of words in vector form. The Word2vec algorithm can represent each word as a vector in a space of approximately 200 dimensions. Using the Word2vec algorithm, a vector corresponding to each word can be derived.

[0046] The Word2vec algorithm can dramatically improve accuracy in natural language processing compared to other conventional algorithms. Word2vec can learn the meaning of words by leveraging the relationships between words and adjacent words in sentences within an input corpus. The Word2vec algorithm is based on artificial neural networks and assumes that words with similar contexts have similar meanings. The Word2vec algorithm trains on text documents, and it trains the artificial neural network on words that appear nearby (5 to 10 words before or after a word) as related words. Because words with related meanings are more likely to appear close together in a document, the two words can gradually have similar vectors through repeated training.

[0047] The Word2vec algorithm's learning methods include the Continuous Bag of Words (CBOW) method and the skip-gram method. The CBOW method predicts a target word using the context created by surrounding words. The skip-gram method predicts potential surrounding words based on a single word. The skip-gram method is known to be more accurate for large-scale datasets.

[0048] Therefore, in embodiments of the present invention, the Word2vec algorithm utilizing the skip-gram method is used. For example, if training is successfully completed using the Word2vec algorithm, similar words can be located nearby in a high-dimensional space. According to the Word2vec algorithm described above, the closer the distribution of surrounding words in a learning document is to a word, the more similar the resulting vector values ​​can be. In addition, words with similar resulting vector values ​​can be considered similar. Since the Word2vec algorithm is a well-known technology, a detailed description of the vector value calculation will be omitted.

[0049] The analysis unit can input the collected data into the neural network and extract the evaluation result vector value representing contextual information.

[0050] The analysis unit can calculate the similarity between the evaluation result vector value and each of the multiple reference vector values, and extract the reference vector value with the highest similarity to the evaluation result vector value among the multiple reference vector values. At this time, the similarity calculation method may be Euclidean distance, cosine similarity, Tanimoto coefficient, etc.

[0051] The analysis unit can extract the word corresponding to the reference vector value with the highest similarity to the evaluation result vector value as the word corresponding to the recognized text.

[0052] Additionally, the analysis unit can train an artificial neural network and utilize a trained artificial neural network. The processor can train or execute an artificial neural network stored in memory, and the memory can store a trained artificial neural network. The electronic device that trains the artificial neural network and the electronic device that uses the artificial neural network may be the same, but they may be separate. Artificial intelligence is a computer system that partially implements the functions of the human brain and can learn, guess, and make judgments on its own. As learning progresses, the probability of extracting an answer may increase. Artificial intelligence can be composed of learning and component technologies that utilize it. AI learning is an algorithmic technology that classifies and learns features based on input data, and the component technologies may be technologies that partially implement the functions of the human brain using learning algorithms.

[0053] AI is a technology that easily approaches problems with multiple probabilistic answers, enabling it to logically and probabilistically infer optimal cycles, methods, and plans based on input data. AI inference techniques can include evaluating input data, making optimal predictions, knowledge- and probability-based inferences, and preference-based planning.

[0054] An artificial neural network (ANN) is a learning algorithm in the field of machine learning. It implements the connections between neurons and synapses in the brain through a program. An ANN can be programmed to create a neural network structure and then train it to achieve a desired function. While errors may exist, it can learn from massive amounts of data, producing appropriate output data based on input data. Its advantages include the ability to obtain output data that yields statistically positive results and its resemblance to human reasoning.

[0055] The analysis unit can build a query / metric dataset required for learning using an artificial intelligence algorithm built on big data, and for this purpose, can include multiple pre-trained artificial neural networks.

[0056] The system according to the present invention may include multiple pre-trained artificial neural networks for performing machine learning algorithms. Machine learning allows the system to output data based on input data and utilize the results to learn independently, thereby enhancing its data processing capabilities. The artificial neural network extracts features from input data, infers patterns, and outputs result data. As these processes accumulate, the reliability of the result data increases.

[0057] In this embodiment, the artificial neural network may be an algorithm that outputs text data from at least one feature data item, including the shape, length, number, and height difference of objects recognized as text. The artificial neural network can infer optimal output data by using big data as input data directly or after processing it to remove unnecessary data.

[0058] Artificial intelligence machine learning models can be categorized into Supervised Learning, Unsupervised Learning, Semisupervised Learning, and Reinforcement Learning, depending on the learning type. Machine learning algorithms that can be used include Decision Trees, K-Nearest Neighbor, Artificial Neural Networks, Support Vector Machines, Ensemble Learning, Gradient Descent, Naive Bayes Classifiers, Hidden Markov Models, and K-Means Clustering.

[0059] An artificial neural network may be pretrained on various input values ​​that may be included in the input data. An artificial neural network may be trained using reinforcement learning, a learning method. Reinforcement learning is a method that gradually increases the probability of obtaining a correct result by setting rewards and constraints. An artificial neural network may also be modeled based on a convelutional neural network (CNN) or a recurrent neural network (RNN).

[0060] In this way, the analysis unit can estimate the meaning of document data using big data and artificial neural networks.

[0061] The technology according to the present invention may be implemented as an application or in the form of program commands that can be executed by various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, etc., either singly or in combination.

[0062] The program commands recorded on the above computer-readable recording medium are those specifically designed and configured for the present invention, and may also be known and available to those skilled in the art of computer software.

[0063] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, and flash memory.

[0064] Examples of program instructions include not only machine language codes, such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

[0065] Although the invention has been described above with reference to embodiments, it will be understood by those skilled in the art that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as set forth in the claims below.

Claims

1. A living area setting unit that sets the actual living area based on the data collected by the above data collection unit; An analysis unit that generates analysis information on the actual living area set by the above living area setting unit; and A regional development plan establishment system including a development plan establishment department that collects regional development plans for the actual living area based on the above analysis information.

2. In paragraph 1, The above living area setting section is, Divide the area requiring a development plan into multiple unit areas, calculate the actual adjacency index for each divided unit area using the following mathematical formula, and set the actual living area for the area requiring a development plan based on the calculated actual adjacency index. The above analysis section, A regional development plan establishment system that generates analysis information including information predicting the diffusion path of pollutants by region based on data related to regional development, information on population movement between one region and another, and information on cultural facilities, welfare facilities, green facilities, transportation facilities, and industrial facilities within actual living areas. [Mathematical formula] Here, L_n is the real contiguity index for a specific unit area n, lati_n is the latitude of a specific unit area n, long_n is the longitude of a specific unit area n, r is the number of other unit areas adjacent to unit area n, p_i is a first weight value set in proportion to the number of population movements between unit area n and another unit area i located within a certain radius based on unit area n, t_i is a second weight value set in proportion to the number of vehicle trips between unit area i and another unit area n, d_i is the distance between the center point of another unit area i and the center point of unit area n, w_n is the average housing area between unit areas n, and w_i is the average housing area of ​​another unit area i.

Images