KR-20260065320-A - APPARATUS FOR CHEMICAL MATERIAL ACCIDENT RISK ASSESSMENT BASED ON ARTIFICIAL INTELLIGENCE BASED ON ARTIFICIAL INTELLIGENCE PREDICTION AND METHOD THEREOF
Abstract
According to one embodiment of the present invention, an artificial intelligence prediction-based chemical accident risk assessment device comprises at least one processor, wherein the at least one processor learns a chemical accident occurrence prediction model and a chemical accident risk assessment model based on preset data, inputs data related to the current status of a business entity to assess chemical accident risk into the learned chemical accident occurrence prediction model to predict whether a chemical accident will occur, and if the learned chemical accident occurrence prediction model predicts that a chemical accident will occur at the business entity to assess chemical accident risk, determines input information based on the predicted chemical accident, inputs the determined input information into the learned chemical accident risk assessment model to assess the chemical accident risk for the business entity to assess chemical accident risk, and re-determines the chemical accident risk for the business entity to assess chemical accident risk based on the input information and Geographic Information System (GIS) data.
Inventors
- 김호현
Assignees
- 서경대학교 산학협력단
Dates
- Publication Date
- 20260508
- Application Date
- 20241101
Claims (6)
- In an artificial intelligence prediction-based chemical accident risk assessment device, It includes at least one processor, The above-mentioned at least one processor is, Based on pre-set data, train a chemical accident occurrence prediction model and a chemical accident risk assessment model, and Data related to the current status of businesses for assessing chemical accident risk is input into a trained chemical accident occurrence prediction model to predict whether a chemical accident will occur, and If the chemical accident occurrence prediction model, which has completed the above training, predicts that a chemical accident will occur at a business entity for evaluating the chemical accident risk, input information is determined based on the predicted chemical accident, and The above-determined input information is input into the above-determined chemical accident risk assessment model that has completed training to assess the chemical accident risk for the business entity for assessing the chemical accident risk, and An artificial intelligence prediction-based chemical accident risk assessment device for re-determining the chemical accident risk for a business entity to assess the chemical accident risk evaluated above, based on the above input information and Geographic Information System (GIS) data.
- In paragraph 1, The above-mentioned at least one processor is, Data related to the status of businesses is processed to generate the first training data, and Using the first training data generated above, the chemical accident occurrence prediction model is trained, and Process data related to chemical accidents to generate second training data, and An artificial intelligence prediction-based chemical accident risk assessment device that trains the chemical accident risk assessment model using the second training data generated above.
- In paragraph 1, The above at least one processor is, An artificial intelligence prediction-based chemical accident risk assessment device that determines the above chemical accident risk to one of the preset steps.
- A step of training a chemical accident occurrence prediction model and a chemical accident risk assessment model based on pre-set data; A step of predicting whether a chemical accident will occur by inputting data related to the current status of a business to assess the risk of chemical accidents into a trained chemical accident occurrence prediction model; If the chemical accident occurrence prediction model, having completed the above learning, predicts that a chemical accident will occur at a business entity for evaluating the chemical accident risk, a step of determining input information based on the predicted chemical accident; A step of evaluating the chemical accident risk for a business entity to evaluate the chemical accident risk by inputting the above-determined input information into the above-determined chemical accident risk assessment model that has completed learning; and A step of re-determining the chemical accident risk for a business entity to evaluate the chemical accident risk evaluated above, based on the above input information and Geographic Information System (GIS) data. A chemical accident risk assessment method based on artificial intelligence prediction including
- In paragraph 4, The step of training the above chemical accident occurrence prediction model and chemical accident risk assessment model is, A step of generating first training data by processing data related to the status of businesses; A step of training a chemical accident occurrence prediction model using the first training data generated above; A step of generating second training data by processing data related to chemical accidents; and Step of training the chemical accident risk assessment model using the second training data generated above A chemical accident risk assessment method based on artificial intelligence prediction including
- In paragraph 4, The step of evaluating the chemical accident risk for a business entity to evaluate the above chemical accident risk is, A step of determining the above chemical accident risk as one of the preset steps A chemical accident risk assessment method based on artificial intelligence prediction including
Description
Apparatus for Chemical Material Accident Risk Assessment Based on Artificial Intelligence Prediction and Method for Chemical Accident Risk Assessment Based on Artificial Intelligence Prediction The following embodiments relate to an artificial intelligence prediction-based chemical accident risk assessment device and an artificial intelligence prediction-based chemical accident risk assessment method. With the rapid development of industry, humanity has enjoyed growth and prosperity, and chemical substances have become indispensable to our daily lives and industrial activities, while the volume of chemical substances in circulation has also increased annually. However, on the other hand, the mismanagement and handling of the environment caused by the growth and prosperity of the chemical industry and related technology sectors have reached a point where they are affecting the development of humanity. Chemical accidents can have harmful effects not only on the workplace but also on the surrounding area, nearby residents, and ecosystems; since damage can manifest chronically over a long period, it is difficult to predict the post-accident impact. To mitigate damage, while it is important to predict the movement of chemical substances immediately after an accident and implement appropriate responses, it is equally important to assess the risks and prepare for such accidents. Machine learning is a field of artificial intelligence that has evolved from research in pattern recognition and computer learning theory, referring to the development of algorithms and technologies that enable computers to learn. The core of machine learning lies in representation and generalization. Representation refers to the evaluation of data, while generalization refers to the processing of data that is not yet known. It is also a field of computational learning theory. Deep learning is defined as a set of machine learning algorithms that attempt a high level of abstraction through a combination of various non-linear transformation techniques, and in a broad sense, it can be described as a field of machine learning that teaches computers human thinking methods. A Deep Neural Network (DNN) is an Artificial Neural Network (ANN) composed of multiple hidden layers between an input layer and an output layer. Like general artificial neural networks, Deep Neural Networks can model complex non-linear relationships. FIG. 1 is a diagram showing the configuration of an artificial intelligence prediction-based chemical accident risk assessment device according to one embodiment. Figure 2 is a diagram showing the configuration of a chemical accident risk assessment model according to one embodiment. FIG. 3 is a flowchart illustrating an artificial intelligence prediction-based chemical accident risk assessment method according to one embodiment. Specific structural or functional descriptions regarding embodiments according to the concept of the present invention disclosed herein are provided merely for the purpose of explaining embodiments according to the concept of the present invention, and embodiments according to the concept of the present invention may be implemented in various forms and are not limited to the embodiments described herein. Embodiments according to the concept of the present invention may be subject to various modifications and may take various forms; therefore, embodiments are illustrated in the drawings and described in detail in this specification. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes all modifications, equivalents, or substitutions that fall within the spirit and scope of the present invention. Terms such as "first" or "second" may be used to describe various components, but said components should not be limited by said terms. For the sole purpose of distinguishing one component from another, for example, without departing from the scope of rights according to the concept of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. Conversely, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions describing the relationship between components, such as "between" and "exactly between," or "adjacent to" and "directly adjacent to," should be interpreted in the same way. The terms used in this specification are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression un