Iot-based alternative credit rating method and iot-based alternative credit rating system

Abstract

An IoT-based alternative credit rating method is disclosed. The IoT-based alternative credit rating method includes the steps of collecting data from multiple factory facilities through IoT sensors attached to multiple factory facilities equipped in a factory, calculating operating rate data based on the collected data from multiple factory facilities, and determining a corporate credit rating based on the operating rate data. Accordingly, the accuracy of the rating can be improved by utilizing physical data related to factory facilities in addition to the company's financial information to determine the corporate credit rating.

Description

Technology Field

[0001] The present invention relates to an IoT-based alternative credit rating method and an IoT-based alternative credit rating system, and more specifically, to an IoT-based alternative credit rating method and an IoT-based alternative credit rating system that determines a credit rating based on utilization rate data. Background Technology

[0002] Credit rating information refers to information that evaluates the creditworthiness of a counterparty in commercial transactions, such as financial transactions. Financial institutions utilize a borrower's credit rating information to assess the potential for damage caused by corporate insolvency in the process of providing credit-based lending services (e.g., loans, credit card issuance, etc.), and can use this information to determine loan approval, limits, interest rates, etc.

[0003] Existing models for generating credit ratings primarily utilize financial data; however, in the case of companies where financial data is absent or insignificant, there may be a problem with the reliability of the credit rating information.

[0004] Accordingly, there has been an increasing need to improve the reliability of credit rating information by predicting credit risk using data other than corporate financial data. The problem to be solved

[0005] The objective of the present invention is to provide an IoT-based alternative credit rating method and an IoT-based alternative credit rating system that determine a credit rating based on operating rate data. means of solving the problem

[0006] An IoT-based alternative credit rating method according to one embodiment of the present invention for achieving such an objective includes the steps of: collecting data from a plurality of factory facilities through IoT sensors attached to a plurality of factory facilities equipped in a factory; calculating operating rate data based on the collected data from the plurality of factory facilities; and determining a corporate credit rating based on the operating rate data.

[0007] Here, the step of collecting the plurality of factory equipment data can acquire vibration data of the plurality of factory equipment through the IoT sensor.

[0008] In addition, the step of calculating the operating rate data may calculate an operating rate data graph by performing a time series analysis of the transmission cycle, measurement cycle, operating rate, and operating rate count included in the vibration data.

[0009] In addition, the operating rate data graph above can continuously display the operating rate figures of the plurality of factory facilities by time and date.

[0010] In addition, the step of determining the corporate credit rating can determine the corporate credit rating based on the operating rate data graph for a preset period.

[0011] In addition, the step of determining the corporate credit rating can update the corporate credit rating by machine learning the shape of the operating rate data graph and comparing it with a newly calculated operating rate data graph.

[0012] Meanwhile, an IoT-based alternative credit rating system according to one embodiment of the present invention includes an IoT sensor device attached to a plurality of factory equipment equipped in a factory to collect a plurality of factory equipment data, and a server that calculates operating rate data based on the collected plurality of factory equipment data and determines a corporate credit rating based on the operating rate data.

[0013] In addition, in a computer-readable recording medium storing a program for providing an IoT-based alternative credit rating service according to one embodiment of the present invention, the program comprises the steps of: collecting data from a plurality of factory facilities through IoT sensors attached to a plurality of factory facilities provided in a factory; calculating operating rate data based on the collected data from the plurality of factory facilities; and determining a corporate credit rating based on the operating rate data. Effects of the invention

[0014] According to various embodiments of the present invention as described above, a corporate credit rating can be determined by utilizing physical data related to factory facilities in addition to the company's financial information, thereby increasing the accuracy of the rating. Brief explanation of the drawing

[0015] FIG. 1 is a flowchart illustrating an IoT-based alternative credit evaluation method according to one embodiment of the present invention. FIG. 2 is a drawing illustrating a graph of operating rate data according to one embodiment of the present invention. FIG. 3 is a diagram illustrating the configuration of an IoT-based alternative credit rating system according to one embodiment of the present invention. FIG. 4 is a block diagram showing the specific configuration of a server according to one embodiment of the present invention. FIG. 5 is a drawing relating to a software module stored in a storage unit according to an embodiment of the present invention. Specific details for implementing the invention

[0016] The present invention will be described in more detail below with reference to the drawings. Furthermore, in describing the present invention, detailed descriptions of related known functions or configurations are omitted if it is determined that such detailed descriptions would unnecessarily obscure the essence of the invention. Additionally, the terms described below are defined considering their functions in the present invention, and these may vary depending on the intentions or relationships of the user or operator. Therefore, their definitions should be based on the content throughout this specification.

[0017] FIG. 1 is a flowchart illustrating an IoT-based alternative credit evaluation method according to one embodiment of the present invention.

[0018] Referring to FIG. 1, an IoT-based alternative credit rating method according to one embodiment of the present invention includes the step of collecting multiple factory equipment data through IoT sensors attached to multiple factory equipment equipped in a factory (S110), the step of calculating operating rate data based on the collected multiple factory equipment data (S120), and the step of determining a corporate credit rating based on the operating rate data (S130).

[0019] Here, alternative credit assessment refers to evaluating credit based on non-financial information such as payment history for telecommunications, electricity, and gas bills, and social media data; recently, the types of non-financial information are expanding to include athletic ability data, purchase history data, credit card usage data, and psychological data.

[0020] An alternative credit evaluation method according to one embodiment of the present invention performs credit evaluation based on physical data collected using IoT devices, wherein the IoT sensors may be attached to each of several factory facilities equipped in the factory.

[0021] And, each IoT sensor collects factory equipment data from each factory equipment. Specifically, the step of collecting multiple factory equipment data (S110) can acquire vibration data of multiple factory equipment through the IoT sensor.

[0022] Specifically, the IoT sensor includes a vibration sensor, which can detect shaking vibrations of the factory equipment when the factory equipment is in operation, thereby enabling the acquisition of data regarding whether the factory equipment is currently in operation and for how long it has been in operation.

[0023] In addition, the step of calculating operating rate data (S120) can calculate an operating rate data graph by performing a time series analysis of the transmission cycle, measurement cycle, operating rate, and operating rate count included in the vibration data.

[0024] Here, the transmission period refers to the period during which the IoT sensor transmits vibration data, the measurement period refers to the vibration sensing period of the IoT sensor, the operating rate refers to the rate of increase or decrease in the numerical value of the vibration data, and the operating rate count refers to the sequence information of the operating rate data according to chronological order.

[0025] Accordingly, the step of calculating operating rate data (S120) may calculate the operating rate data graph in various forms by changing the transmission period, measurement period, operating rate, and operating rate count included in the vibration data.

[0026] FIG. 2 is a drawing illustrating a graph of operating rate data according to one embodiment of the present invention.

[0027] Referring to Figure 2, a graph of operating rate data regarding factory equipment is shown, where the horizontal axis represents time and date, and the vertical axis represents the operating rate value.

[0028] That is, the operating rate data graph continuously displays the operating rate figures of multiple factory facilities by time and date, and specifically includes the Monday operating rate (210) of the factory facilities, the Tuesday operating rate (220) of the factory facilities, the Wednesday operating rate (230) of the factory facilities, the Thursday operating rate (240) of the factory facilities, the Friday operating rate (250) of the factory facilities, the Saturday operating rate (260) of the factory facilities, and the Sunday operating rate (270) of the factory facilities.

[0029] Referring to Figure 2, the operating rate figures and the shape of the operating rate graphs differ for each day and hour, because the vibration of the factory equipment in actual operation varies by hour and day.

[0030] Meanwhile, the step of determining the corporate credit rating (S130) can determine the corporate credit rating based on a graph of operating rate data over a preset period.

[0031] For example, the step of determining the corporate credit rating (S130) can determine the corporate credit rating based on a graph of operating rate data for the corresponding 24 hours, calculated by analyzing vibration information of the factory equipment for 24 hours through an IoT sensor attached to the factory equipment.

[0032] Specifically, if the operating rate data obtained through IoT sensors attached to factory equipment shows a high operating rate, the corporate credit rating increases, and if the operating rate shows a low operating rate, the corporate credit rating is determined to be low.

[0033] Here, a high corporate credit rating means that credit is improving, while a low corporate credit rating means that credit is deteriorating.

[0034] In addition, the step of determining the corporate credit rating (S130) can update the corporate credit rating by machine learning the shape of the utilization rate data graph and comparing it with the newly calculated utilization rate data graph.

[0035] Specifically, the step (S130) for determining the corporate credit rating can learn the shape of the capacity utilization data graph, that is, the image itself, and by comparing the image with the image of the newly calculated capacity utilization data graph, if the image of the newly calculated capacity utilization data graph becomes smaller, the corporate credit rating can be lowered, and if the image becomes larger, the corporate credit rating can be raised.

[0036] In addition, the step (S130) for determining the corporate credit rating can obtain information regarding whether there is a problem with the operation of the factory equipment or whether performance has deteriorated at a certain time or date, based not only on the size of the image of the operating rate data graph but also on the shape deformation part of the image, and can reflect this in the corporate credit rating.

[0037] Additionally, the step of determining the corporate credit rating (S130) may be corrected by reflecting the credit rating results of other institutions provided as open source. In this case, the corporate credit rating may be corrected by calculating different weights for the credit rating results of other institutions provided as open source and the utilization rate data graph.

[0038] FIG. 3 is a diagram illustrating the configuration of an IoT-based alternative credit rating system according to one embodiment of the present invention.

[0039] Referring to FIG. 3, an IoT-based alternative credit rating system (10) according to one embodiment of the present invention includes a server (100) and an IoT sensor device (200), and the IoT sensor device (200) can be attached to a plurality of factory equipment provided in a factory to collect data from a plurality of factory equipment.

[0040] Here, the IoT sensor device (200) may include a vibration sensor internally, and may include various sensors such as a temperature / humidity detection sensor, a harmful gas detection sensor, in addition to the vibration sensor.

[0041] Additionally, the IoT sensor device (200) includes a communication unit (not shown) and can transmit collected data to the server (100) through it.

[0042] Additionally, the server (100) can calculate operating rate data based on collected multiple factory equipment data and determine a corporate credit rating based on the operating rate data.

[0043] In addition, it is obvious that all processes performed in the aforementioned IoT-based alternative credit evaluation method can be performed on the server (100).

[0044] FIG. 4 is a block diagram showing the specific configuration of a server according to one embodiment of the present invention.

[0045] Referring to FIG. 4, the server (100) includes a processor (110) and a storage unit (120).

[0046] The processor (110) controls the overall operation of the server (100).

[0047] Specifically, the processor (110) includes RAM (111), ROM (112), main CPU (113), graphics processing unit (114), first to n interfaces (115-1 to 115-n), and a bus (116).

[0048] RAM (111), ROM (112), main CPU (113), graphics processing unit (114), first to n interfaces (115-1 to 115-n), etc. can be connected to each other via a bus (116).

[0049] The first to n interfaces (115-1 to 115-n) are connected to the various components described above. One of the interfaces may be a network interface connected to an external device through a network.

[0050] The main CPU (113) accesses the storage unit (120) and performs booting using the O / S stored in the storage unit (120). Then, it performs various operations using various programs, content, data, etc. stored in the storage unit (120).

[0051] In particular, the main CPU (113) can collect data on multiple factory equipment through IoT sensors attached to multiple factory equipment equipped in the factory, calculate operating rate data based on the collected data on multiple factory equipment, and determine a corporate credit rating based on the operating rate data.

[0052] A set of instructions for booting the system is stored in the ROM (112). When a turn-on command is input and power is supplied, the main CPU (113) copies the O / S stored in the storage unit (120) to the RAM (111) according to the instructions stored in the ROM (112), and executes the O / S to boot the system. When booting is complete, the main CPU (113) copies various application programs stored in the storage unit (120) to the RAM (111), and executes the application programs copied to the RAM (111) to perform various operations.

[0053] The graphics processing unit (114) generates a screen containing various objects such as icons, images, and text using a calculation unit (not shown) and a rendering unit (not shown). The calculation unit (not shown) calculates attribute values ​​such as coordinate values, shape, size, and color for each object to be displayed according to the layout of the screen based on a received control command. The rendering unit (not shown) generates a screen with various layouts containing objects based on the attribute values ​​calculated by the calculation unit (not shown).

[0054] In particular, the graphics processing unit (114) can implement objects generated by the main CPU (113) into a GUI (Graphic User Interface), icon, user interface screen, etc.

[0055] Meanwhile, the operation of the above-described processor (110) can be performed by a program stored in the storage unit (120).

[0056] The storage unit (120) stores various data, such as an O / S (Operating System) software module for operating the server (100) and various multimedia content.

[0057] In particular, the storage unit (120) may include a software module for collecting multiple factory equipment data through IoT sensors attached to multiple factory equipment equipped in the factory, calculating operating rate data based on the collected multiple factory equipment data, and determining a corporate credit rating based on the operating rate data.

[0058] FIG. 5 is a drawing relating to a software module stored in a storage unit according to an embodiment of the present invention.

[0059] Referring to FIG. 5, the storage unit (120) may store programs such as a factory equipment data collection module (121), an operating rate data calculation module (122), and a corporate credit rating determination module (123).

[0060] Meanwhile, the operation of the processor (110) described above can be performed by a program stored in the storage unit (120). Below, the detailed operation of the processor (110) using the program stored in the storage unit (120) will be explained in detail.

[0061] Specifically, the factory equipment data collection module (121) can collect multiple factory equipment data through IoT sensors attached to multiple factory equipment equipped in the factory.

[0062] In addition, the operating rate data calculation module (122) can calculate operating rate data based on collected multiple factory equipment data.

[0063] In addition, the corporate credit rating determination module (123) can determine the corporate credit rating based on operating rate data.

[0064] Meanwhile, in a computer-readable recording medium storing a program for providing an IoT-based alternative credit rating service according to one embodiment of the present invention, the program may include the steps of collecting a plurality of factory equipment data through IoT sensors attached to a plurality of factory equipment equipped in a factory, calculating operating rate data based on the collected plurality of factory equipment data, and determining a corporate credit rating based on the operating rate data.

[0065] Meanwhile, a non-transitory computer-readable medium storing a program that sequentially performs the control method according to the present invention may be provided.

[0066] A non-transient readable medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, the various applications or programs described above may be stored and provided on non-transient readable media such as CDs, DVDs, hard disks, Blu-ray discs, USBs, memory cards, and ROMs.

[0067] In addition, although the bus is not depicted in the block diagram described above for the server, communication between each component of the server may be performed via the bus. Furthermore, each device may additionally include a processor, such as a CPU or a microprocessor, that performs the various steps described above.

[0068] Furthermore, although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above. It is understood that various modifications can be made by those skilled in the art without departing from the essence of the invention as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present invention. Explanation of the symbols

[0069] 10: IoT-based alternative trial evaluation system 100: Server 200: IoT sensor device

Claims

Claim 1 An IoT-based alternative credit evaluation method in an IoT-based alternative credit evaluation system comprising IoT sensors and a server, wherein the server collects factory equipment data including vibration data of each factory equipment from IoT sensors attached to a plurality of factory equipment provided in a factory; wherein the server generates a data graph of the factory equipment's operating rate by performing time-series analysis of transmission cycle, measurement cycle, operating rate, and operating rate count information included in the vibration data; and wherein the server determines a corporate credit evaluation grade based on a data graph of the operating rate over a preset period; wherein the step of determining the corporate credit evaluation grade is characterized by learning the shape of the data graph of the operating rate using a machine learning model, and updating the corporate credit evaluation grade by comparing the image of the data graph of the operating rate calculated through the machine learning model with the image of the newly calculated data graph of the operating rate. Claim 2 delete Claim 3 delete Claim 4 An IoT-based alternative credit evaluation method according to claim 1, wherein the operating rate data graph continuously displays the operating rate figures of the plurality of factory facilities by time and date. Claim 5 delete Claim 6 delete Claim 7 An IoT-based alternative credit rating system comprises: an IoT sensor device attached to a plurality of factory equipment provided in a factory to collect a plurality of factory equipment data; and a server that calculates operating rate data based on the collected plurality of factory equipment data and determines a corporate credit rating based on the operating rate data. The server collects factory equipment data including vibration data of each factory equipment from IoT sensors attached to a plurality of factory equipment provided in a factory, generates a factory equipment operating rate data graph by time-series analysis of transmission cycle, measurement cycle, operating rate, and operating rate count information included in the vibration data, and determines a corporate credit rating based on the operating rate data graph for a preset period, wherein the shape of the operating rate data graph is learned using a machine learning model, and the corporate credit rating is determined by updating the corporate credit rating by comparing the operating rate data graph calculated through the machine learning model with the image of the newly calculated operating rate data graph. Claim 8 A computer-readable recording medium storing a program for providing an IoT-based alternative credit rating service, wherein the program comprises: a step of collecting factory equipment data including vibration data of each factory equipment from IoT sensors attached to a plurality of factory equipment provided in a factory; a step of generating a data graph of the operating rate of the factory equipment by time-series analysis of transmission cycle, measurement cycle, operating rate, and operating rate count information included in the vibration data; and a step of determining a corporate credit rating based on the operating rate data graph for a preset period; wherein the step of determining the corporate credit rating is characterized by learning the shape of the operating rate data graph using a machine learning model and updating the corporate credit rating by comparing the image of the operating rate data graph calculated through the machine learning model with the image of the newly calculated operating rate data graph.

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