KR20200075143A - Asset trading and traceability system based on block chain - Google Patents

Abstract

The present invention relates to an asset transaction and history tracking system based on a blockchain, which is implemented to enhance security by encrypting a distribution process of goods using the blockchain. In the asset transaction and history tracking system based on the blockchain which converts a transaction process of intangible or tangible assets into data using a blockchain, the present invention comprises: an intangible asset brokerage system which mediates intangible asset transactions between an owner or a creator of intangible assets and consumers; and a tangible asset brokerage system which mediates a transaction of tangible assets between a producer and a consumer of tangible assets.

Description

Asset trading and traceability system based on block chain

The present invention relates to an asset transaction and history tracking system based on a blockchain, and more specifically, based on a blockchain implemented to enhance security by encrypting the distribution process of goods using a blockchain. It relates to an asset trading and history tracking system.

The Quick Response code is a two-dimensional barcode in a matrix format that displays information in a black and white grid pattern. It was developed by Denso Wave, a subsidiary of Toyota, Japan in 1994 for logistics management, and its name is from Quick Response, a registered trademark of Denso Wave. In contrast, the existing vertical stripes barcodes can store only numbers, which limits the amount of information that can be stored. On the other hand, Qalcode can store text infinitely, increasing the amount of information infinitely and connecting to the Internet for detailed product information and images. It is an advanced technology that shows videos, etc., and is a matrix type 2D code in which small square dots are paralleled by the same number of widths and lengths, and there are various versions ranging from 21 to 177 (version 40) listed on one side. Unlike barcodes, a finder pattern is arranged at the corner of the corner, so it can be recognized in any direction of 360 degrees, and the version 40 can record up to 23,648 bits of information. Such QR codes are displayed on advertisements or signboards, and are used for information transmission.

Blockchain can be applied to various technical fields, but has been developed as a technology to realize cryptocurrency (or virtual currency) as fundraising becomes easier by ICO (Initial Coin Offering). For example, Bitcoin is also using blockchain technology to achieve the same value as a currency even if a third party such as a central bank does not secure the value, unlike ordinary currencies. Before the invention of the blockchain, in order to create an electronic currency to replace the currency, a'middle man', which guarantees its value or manages the transaction records of the currency, was necessary. However, with the following structure, a certain cryptocurrency can function as a real currency without a third party institution with the following structure.

(1) Blocks are sorted by time, and it is difficult to forge and falsify because the block contains transaction records and connection information for the previous block.

(2) By adopting a distributed ledger structure, transaction transparency is high and double payment is difficult.

For the above reasons, cryptocurrency can secure the function as a currency. Bitcoin, Litecoin, Ripple, Etherium, Dash, Monero, and other cryptocurrencies with these properties are publicly issued. In particular, large-scale fundraising is being conducted for specific tokens through ICO (Initial Coin Offering). Although various cryptocurrencies have been issued in this way, they are divided into those that are mainly used and those that are not.

In addition, various applications using blockchain are being tried. For example, international remittance services such as Sentbe, Align Commerce, Moin, credit transaction services verified by Bank of America and HSBC Holdings, various financing, electricity sales services, content services such as Steemit, Streamium, and notary services such as Nem Copyright management services such as, Blockai, goods transaction tracking services such as Everledger, and contract management and execution services of Visa and Docusign are widely attempted.

On the other hand, the above-mentioned background technology is the technical information acquired by the inventor for the derivation of the present invention or acquired in the derivation process of the present invention, and is not necessarily a known technology disclosed to the general public before filing the present invention. .

Korean Registered Patent No. 10-1628009 Korean Registered Patent No. 10-1689815

Another aspect of the present invention, to solve the problems of the optimal production and consumption environment of producers and consumers, block data to ensure continuous monitoring and trust on transparent data and solve problems through rapid history investigation when problems occur In order to be able to continuously manage using QR code, it is possible to induce transparent transactions by blockchaining the history, delivery, and provision of products, increase reliability with safe supply, and use QR code to improve overall product It provides an asset transaction and history tracking system based on a blockchain that can manage data.

And, production big data collection through IOT Internet of Things, big data collection of products, quantity, supply time through O2O joint purchasing platform, purchase and real-time delivery through data artificial intelligence analysis, from production to delivery with automatic generation QR code traceability system AI-based control and information provision, evaluation of purchases through O2B survey system, consumer rewards, and big data collection, production decision and purchase decision information, and blockchain-based assets that can block hacking with blockchain technology It provides a transaction and history tracking system.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

An asset transaction and history tracking system based on a blockchain according to an embodiment of the present invention is an asset transaction and history tracking system based on a blockchain that in turn transforms an intangible or tangible asset transaction process using a blockchain. A system comprising: an intangible asset brokerage system for brokering an intangible asset transaction between an owner of an intangible asset or a creator and a consumer; And a tangible asset brokering system that brokers transactions of tangible assets between producers and consumers of tangible assets.

In one embodiment, the physical property brokerage system stores information of each institution or individual, receives the scanned contents from the management terminal, issues a unique QR code corresponding to the product, manages it, and manages the institution, purchase product And an intangible asset brokerage server that constructs a new transaction using status information or consumer, delivery, type, and status information, blocks it through a data verification step, and queries the blockchained information. It collects at least one production element among temperature, humidity, water supply and water quality to build big data related to production environment information, and uses the application, QR code and GPS to provide information related to logistics and delivery environment information. A big data construction unit that builds data; It collects demand from consumers and analyzes the tendency of each consumer and previous orders and clusters/samples them through the k-means algorithm, and affirms the words used in the collected evaluations using sentiment analysis technology and machine learning algorithm. An AI unit that predicts future demands of consumers by analyzing the degree of corruption and injustice, and infers production by region by using deep learning technology through collected data; And installs a history management application, is paid to each institution or individual, transmits his/her information, QR code information and status value to the intangible asset brokerage server according to the type of scanned QR code, and transmits the blockchained information. It may include a management terminal to query.

According to one aspect of the present invention described above, by scanning the attached QR code, it is possible to recognize the producers and distributors of each consumer, and to identify the distribution process, production date, producers and distributors of the product in real time, and all of these processes are blockchained. Because it exists as data that cannot be modified, it is possible to create a transparent distribution structure by making the distribution history data seamless and by revealing the distribution history to all.

1 is a diagram illustrating a schematic configuration of an asset transaction and history tracking system based on a blockchain according to an embodiment of the present invention.
FIG. 2 is a view showing the physical property brokering system of FIG. 1.
3 and 4 are diagrams illustrating history tracking management according to the present invention.

For a detailed description of the present invention, which will be described later, reference is made to the accompanying drawings that illustrate, by way of example, specific embodiments in which the present invention may be practiced. These examples are described in detail enough to enable those skilled in the art to practice the present invention. It should be understood that the various embodiments of the invention are different, but need not be mutually exclusive. For example, certain shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with one embodiment. In addition, it should be understood that the location or placement of individual components within each disclosed embodiment can be changed without departing from the spirit and scope of the invention. Therefore, the following detailed description is not intended to be taken in a limiting sense, and the scope of the present invention, if appropriately described, is limited only by the appended claims, along with all ranges equivalent to those claimed. In the drawings, similar reference numerals refer to the same or similar functions across various aspects.

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

1 is a diagram illustrating a schematic configuration of an asset transaction and history tracking system based on a blockchain according to an embodiment of the present invention.

Referring to FIG. 1, the asset trading and history tracking system 10 based on a blockchain according to an embodiment of the present invention is a method of intangible or tangible asset trading process using a blockchain, and intangible Asset brokerage system 100 and tangible asset brokerage system 200.

The intangible asset brokerage system 100 mediates intangible asset transactions between the owner or creator of the intangible asset and the consumer.

The tangible asset brokering system 200 brokers the transaction of tangible assets between producers and consumers of tangible assets.

The specific configuration of the intangible asset brokerage system 100 or the tangible asset brokerage system 200 will be described below in FIG. 2.

The asset transaction and history tracking system 10 based on a blockchain having the above-described configuration may be implemented on a development environment management system (not shown in the drawing for convenience of description).

In the development environment, multiple developers use multiple development systems for software development, and each development system can develop and manage software components and software under the control of the developer. Each development system uses Trusted Platform Module (TPM) standard technology, so that software components can be used only in licensed development systems. The Trust Platform Module (TPM) is a kind of security device, and can generate and manage a security key for data encryption.

The development environment management system can restrict the developer's authority to use the software component and perform security authentication for the development system used by the developer.

When the development environment management system receives a request for permission to create or modify a software component from the development system, the development environment management system may process permission message for creation or modification of the software component by checking authorization information of the corresponding development system.

Here, the software component may be configured to include at least one of a source code, a binary containing debugging information, a pure binary containing no debugging information, a document for detailed description of the code, and a process formula model for understanding the code. Can.

The permission information may include at least one of a read permission to read the software component, a storage permission to create and modify the software component, and a permission adjustment permission to adjust the permission information.

When a software component is generated or modified by any one development system that satisfies authority information, the development environment management system can store it and control it to be shared by other development systems. This is because a module built by one development system may need to be used in another development system.

The development environment management system can build a database storing the history of creation or modification of these software components. This is because it is possible to track step-by-step how the value of a specific variable has changed through history information, and also to understand how the specific variable has changed the value of any other variable.

Specifically, the development environment management system is the type of software component, whether the software component is created/modified, the date of creation/modification of the software component, the frequency of creation/modification of the software component, and the development that creates/modifies the software component. It is possible to generate history information including the system's authority information.

The development environment management system may assign an index to each generation of history information and store it in the history information database.

At this time, the development environment management system may build a history information database for each type of software component. That is, the development environment management system can build a history information database according to the importance of the software component, and the history information database of the software component corresponding to the highest importance type is a database update target for storage space management as described below. It may be excluded.

Alternatively, the development environment management system may build a history information database for each creation/modification date of the software component. As an example, the development environment management system may build a history information database on a specific date, and exclude the history information database from the database update target.

Alternatively, the development environment management system may establish a history information database for each authority information. For example, the development environment management system constructs a database of the history information of the software component by the development system corresponding to the authority to adjust the authority information regarded as the highest authority information, and excludes the history information database from the database update target. I can do it.

The development environment management system may update the history information database for efficient storage space management as described above.

Specifically, the development environment management system may update the history information database when the index of the history information database reaches a preset index.

For example, the development environment management system may divide the entire index of the history information database into three sections in ascending order.

The development environment management system can delete the history information corresponding to the section including the lowest index among the three sections without conditions. The history information corresponding to the section can be regarded as a long time after the creation/modification date. Therefore, it is unlikely that the history information will be referenced again, so it can be deleted without conditions.

The development environment management system may update the history information corresponding to the middle section among the three sections by determining whether to delete or maintain it according to the type of software component. That is, the development environment management system can classify the importance according to the type of the software component, and maintains only the history information corresponding to the type of the software component having the highest importance among the history information corresponding to the middle section among the three sections and the remaining history The history information database can be updated by deleting all information.

Alternatively, the development environment management system may update the history information corresponding to the middle section among the three sections by determining whether to delete or maintain it according to the frequency of creation/modification of the software component. That is, the history information database may be updated by deleting all of the history information corresponding to the middle section among the three sections whose generation/modification frequency is higher than a preset reference frequency and maintaining the remaining history information.

Alternatively, the development environment management system may update the history information corresponding to the middle section among the three sections by determining whether to delete or maintain it according to the authority information of the development system. That is, the history information, which is the authority to adjust the authority information of which the authority information is regarded as the highest authority information among the history information corresponding to the middle section among the three sections, is maintained, and all the remaining history information is deleted. The history information database can be updated.

The development environment management system can maintain history information corresponding to the section including the highest index among the three sections. The history information corresponding to the section can be regarded as a relatively recent creation/modification date, and thus it is highly likely that the history information will be referenced again, so it can be maintained.

The asset transaction system 10 having the configuration as described above may include a backup file decentralization module (not shown in the drawings for convenience of description).

It may include a backup file decentralization module (not shown in the drawing for convenience of description).

The backup file decentralization module generates important information such as user information or system information to be protected from external attacks such as hacking or ransomware as a backup file, and then the generated backup file is the primary backup file containing the same data. Creates and saves the secondary backup in turn, but stores the primary backup file and secondary backup file in different storage locations.

However, the creation of the backup file is not limited to the primary and secondary, and multiple backup files of the third or higher may be generated in consideration of the performance of the system.

In addition, the backup file decentralization module is set at a predetermined cycle (for example, once every 3 hours, which is basically set on the system, once every 5 hours, etc., but is not limited to the set cycle) It can be set even if it is set) Change the storage location of the saved primary backup file and secondary backup file to a preset location on the system or a newly created location.

At this time, it is preferable that the moving location of the backup file is designated as a folder or a sub-folder generated according to an arbitrary random variable, not a location previously set or designated by a user on the system.

Accordingly, it is possible to predict the existence of a folder in which an attacking program, such as hacking or ransomware, to which the file to be attacked is located or the location of the folder is located and easily prevent the attack, as well as prevent customer information that should not be deleted. The same important data can be prevented from being accidentally deleted or modified by the user.

In the present invention, the primary backup file and the secondary backup file are files that contain data of the same content, and there is no superiority between them, and the secondary backup after the primary backup file is moved even in the movement of the file The file may be moved, or the primary backup file may be moved after the secondary backup file is moved.

In one embodiment, when the intrusion is detected from the outside, the backup file distribution module continuously performs replication from previously generated primary backup files and secondary backup files to generate a plurality of sub backup files of each backup file. In addition, the generated multiple sub-backup files may also be individually stored in different locations created according to random variables.

Accordingly, by dividing and storing a plurality of backup files sporadically generated on the system in an arbitrary location, necessary data, etc., are used by using backup files sporadically present on the system even when some backup files are lost or deleted by an attack. Can be easily recovered.

Next, the backup file decentralization module permanently deletes a file determined to be an attacked backup file among a plurality of backup files on the system.

Accordingly, in the present invention, a normal function on a system may not be performed due to a hacking or ransomware attack, or a zombie program that may expose other files on the system to risk due to the attack. By deleting the file on the system in advance, it is possible to prevent the entire system from being attacked by some files.

In one embodiment, when the backup file decentralization module stores a backup file in a synchronization folder linked to a cloud service, when the backup file is stored in the synchronization folder and the stored backup file is uploaded on the cloud, the synchronization is performed. You can turn off synchronization for a folder.

For example, when the cloud service for synchronization of a user is referred to as "Dropbox", the function of the backup file decentralization module as described above is implemented using the "optional synchronization service" provided by "Dropbox".

That is, when the backup file decentralization module creates a "backup folder" on the system as a space for storing the backup file, the cloud service also creates the newly created "backup folder" on the cloud in the same way.

Next, the backup file decentralization module will store the backup file in the corresponding folder, and accordingly, the backup file is also uploaded on the cloud.

Finally, when the upload of the corresponding backup file on the cloud is completed, the backup file decentralization module selectively releases only the synchronization for the "backup folder" used for uploading the backup file, and deletes the "backup folder" on the system.

In this case, instead of turning off synchronization for the entire system, by turning off synchronization for only the "backup folder" that was temporarily created for use in uploading backup files, a stable synchronization service with the cloud service is continuously performed. On the other hand, as the backup file is uploaded on the cloud and then deleted on the system, the backup file is safely stored on the cloud, and it is possible to fundamentally prevent continuous exposure to attacks that have infiltrated the system.

In one embodiment, the backup file decentralization module downloads a backup file that has been uploaded to the cloud service by re-synchronizing a folder in which selective synchronization has been canceled, when it is the order to change the storage location of the backup file that has been uploaded on the cloud. Thereafter, the downloaded backup file may be moved to a newly created location according to random variables as described above.

Some of the components of the asset trading system 10 having the above-described configuration may be implemented by artificial intelligence, and may further include a decision reason presentation module (not shown in the drawings for convenience of description).

The decision reason presentation module not only classifies and predicts the data given or input by the user, but also analyzes the causality of the decision to find an appropriate basis, and why the artificial intelligence suggests the result. The reason can be explained at the user level. By enabling reliable decision-making between the user and artificial intelligence through the decision reason presentation module, feedback from the user can be appropriately reflected when problems or errors occur. In addition, by placing a decision-making reason presentation module, it is possible to clearly explain the cause of why such an outcome is caused by the AI-provided result, thereby eliminating the distrust that the user may have in the AI, and excessive In the case of learning, it is possible to prevent the overfitting problem that the optimal solution in the region may be selected rather than the optimal solution from the overall perspective.

In one embodiment, the decision reason presentation module may further include a model building module and a reason description interface module. The model building module may be implemented as an in-depth description learning module, an interpretable model generation module, and a model induction module.

The deep description learning module is a modified deep learning technology that enables deep neural networks to learn features that can be described. Nodes in the hidden layer can be trained to represent meaningful properties. For example, if you are learning a model that distinguishes the images of the arms and legs, each hidden node can be a finger or toenail, finger or toe, palm or sole. By learning to indicate the location, etc., when the model determines that an image is a hand, it can know the basis of the judgment through an activated hidden node. The basis for this determination may also be expressed linguistically through a natural language generation model such as RNN (Recurrent Neural Network). RNN is a model of deep learning and is a type of artificial neural network. It is used to learn data that changes over time, such as time series data. It uses input control vectors, oblivion vectors, and output control vectors to input and output data. Get In the input control vector, the input signal accepts the value after going through the connection layer with the activation function, and the oblivion vector serves to reflect a part of the past input into the current input. And the output control vector takes in the value using the activation function considering the past value and the modified input value. And the final result goes back to the input. Such a circulatory neural network is mainly used to classify document emotions or recognize handwriting, and may also be mainly used for speech recognition, time series prediction, or waveform generation. This is because input data can be processed in a proper order even if it has a fixed shape without order.

In addition, in one embodiment, the in-depth description learning module may visually display a portion based on an image. For example, when the artificial intelligence system categorizes a cat image, the existing system derives only whether or not the input image is a cat, but the in-depth learning module derives whether it is a cat, and uses the image of its basis (hair, whiskers, etc.) Can provide.

The interpretable model generation module can construct structured data into an interpretable causality model. According to one embodiment, a model generation module that can be interpreted can be built using Bayesian program learning (BPL), and BPL is a method of learning to express a combination of small pieces, for example, learning a model that generates letters. When you do, divide the letters into strokes to create the most reasonable combination of strokes. BPL can be imitated as if it is a human being without a large amount of data, and it is an evolution of the Neural Network, and when a new event is given, the probability value can be changed based on the event. That is, BPL is not a method of changing only the weights of virtual variables, but also includes creating other virtual variables in the middle. If a new environment is given, it is to understand the phenomenon in a different way.For example, if you throw a coin 100 times, and you get 60% of the face and 40 times of the back, the probability that the face will come out is 60%. It is a method of lowering the probability that the head will come out to 59.4%.

In addition, in one embodiment, the interpretable model generation module may be implemented through a stochastic approach. The probabilistic approach can be learned with just a few samples, and is similar to learning that, for example, showing long and short chairs, there are also chairs of medium length. In other words, it is a technique of learning by filling in insufficient data by itself. Depending on the embodiment, the stochastic approach may include the ability to calibrate the probability and program itself through mathematical calculations.

In addition, in one embodiment, the interpretable model generation module may be implemented using And-Or-Graph. And-Or-Graph is an AND/OR graph, which shows the condition of rule and conclusion relationship and AND/OR relationship in graph form. As the intermediate and final data derived by artificial intelligence are structured, the model decision process is logical It has the advantage of being easy to explain. That is, the graph is represented by the AND node and the OR node, and all the AND nodes must be processed, and only one OR node can be processed. Using the AND/OR graph, a set of rules interspersed with each other can be viewed as a structure, and the logical relationship between each sentence can be easily identified.

The model induction module can infer any black box model into a descriptive model. In one embodiment, the model induction module may be implemented with local interpretable model-agnostic explanations (LIME), which make any black box model locally descriptive through sparse linear combinations around already descriptive data. Can. For example, if a black box model classifying an image determines that an image is a heart, a description of the heart of another model that can already be explained, that is, the pixel representing the heart is compared with a given image to determine which part is the heart. Can be presented.

In addition, in one embodiment, the model induction module may be implemented as bayesian rule lists (BRL) representing a model as a series of if-then conditional statements. BRL divides high-dimensional, multi-variable feature spaces into simple and already interpretable conditional statements to understand complex models.

The above-described in-depth description learning module, an interpretable model generation module, and a model induction module may operate independently or in combination with each other, and the implementation order thereof may also vary according to embodiments.

Next, the reason description interface module can express the description of the artificial intelligence decision in a way that the user can understand. Reason description The interface module may include essential items such that the presented description is repetitive, includes all necessary descriptions, does not include unnecessary descriptions, and appropriate amounts. That is, the user can easily provide the user with the language, tables, images, graphs, formulas, etc. which process and reason the artificial intelligence has produced the final result and what factors or data influenced each step. .

In addition, the reason description interface module may receive a user's correction command. To this end, the reason description interface module may include corrections as essential items such that the explanation is flexible, respects user feedback, and watches for gradual change. The user can evaluate and develop the effectiveness of the reason description interface module by receiving feedback from the user about the clarity and utilization of the description.

In another embodiment, the decision reason presentation module may be formed as a causality model. The causal model can be formed by combining deep learning and a Markov random field. First, the probability distribution of the deep Markov random field model is modeled from the training data, and the structure of the Markov random field indicating conditional independence between random variables is studied. Deducing the latent function of the structured Markov random field into a deep neural network alleviates the problem that the number of parameters required for the latent function increases exponentially as the number of input variables increases, and it is complex without constraints on variable association. You can learn the relationship. According to an embodiment, after class learning problems such as attributes and super-categories as auxiliary tasks are learned, linear expression can be combined at the output stage to enable effective expression. It can also include an interactive learning algorithm that allows a person to check whether the causal relationship has been correctly learned and give feedback to correct it.

In another embodiment, the decision reason presentation module may be implemented as an analysis module. As a technique for regressing a time series function to multivariate Gaussian based on various kernels, the optimal kernel combination representing the kernel in a Gaussian process can be learned to explain the given time series data based on the kernel combination found above. Furthermore, by learning a combination of a kernel that is commonly expressed even when there are multiple time series data and a kernel that expresses characteristics of each time series data, features that are common in multiple time series data can be described. By writing the combination of kernels found through the time series data analysis model in natural language, it is possible to explain to users the process of deriving the decision made by artificial intelligence and the reason in natural language.

Through this decision reason presentation module, by visualizing and texting the decision-making process of artificial intelligence from the user's point of view, it is possible to explain the components involved in the decision-making process and at the same time analyze the correlation of complex models to cause It can be explained by dividing it into elements and result elements. In particular, it is created in the form of an automatic report that can be easily understood by the user, thereby providing not only a result of analyzing data, but also a reason, so that artificial intelligence can interact with humans more precisely.

FIG. 2 is a view showing the physical property brokering system of FIG. 1.

2, the tangible asset brokering system 200 includes a tangible asset brokering server 210, a big data construction unit 220, an AI unit 230 and a management terminal 240.

The tangible asset brokerage server 210 stores information of each institution or individual, receives the scanned content from the management terminal, issues a unique QR code corresponding to the product, and manages it, and manages the institution, purchase, and status information, or It constructs a new transaction using consumer, delivery, type, and status information, blocks it through a data verification step, and retrieves the information that has been blockchained.

First, the tangible asset brokerage server 210 serves to store information of each institution or individual as a QR code management server, and receives information scanned by the QR code scan terminal to manage the information management server or product information according to the type It plays a role of delivering to the server, and in the case of products, it issues unique QR codes for each product and manages them.

Next, the tangible asset brokerage server 210 is a blockchain-based product information management server, which receives the institution, purchase, and status information received as a QR code management server, constructs a new transaction, and becomes a blockchain through a data verification step. It plays a role in order to inquire, and it also plays a role of inquiring information on blockchain.

Lastly, the tangible asset brokerage server 210 is a blockchain-based information management server, receives consumer, delivery, type, and status information received as a QR code management server, constructs a new transaction, and becomes a blockchain through a data verification step. It plays a role in order to inquire, and it also plays a role of inquiring information on blockchain.

The collected information can query the occurrence, distribution, and arrival history of products through the tangible asset brokerage server 210 as a QR code management server.

3 and 4 are diagrams illustrating history tracking management according to the present invention.

Referring to FIGS. 3 and 4, in the case of delivery of a purchased product, information of a product prior to initial delivery is transmitted to a server through an application, and a QR code is issued to be issued in a print format to the product. The sender scans the printed QR code to inform the start of the delivery and proceeds with the delivery. When the delivery is completed, the termination of the delivery is registered through QR code scan, and the buyer registers the delivery via application scan.

The collected information can be viewed in real time through the tangible asset brokerage server 210 as a QR code management server, and the total amount and distribution history of purchased purchases can be viewed through the app or the web provided separately.

The big data construction unit 220 collects at least one production element among temperature, humidity, water supply, and water quality to build big data related to production environment information, and uses applications, QR codes, and GPS To build big data related to logistics and delivery environment information.

The big data construction unit 220 uses the IOT technology to create a temperature/humidity sensor, soil EC/PH sensor, soil moisture sensor, nutrient solution information sensor, sunlight amount sensor, wind direction/wind speed sensor, rainfall sensor, etc. It collects information by detecting digital factors and changes in products and digitalizing them.

To this end, the big data construction unit 220 should be equipped with video recording equipment that stores video information such as drones, CCTVs, and web cameras to make big data.

In addition, the big data construction unit 220 is a system for joint use of information, and provides information necessary for the production of products, such as the agricultural information ICT convergence information joint utilization system of the Rural Development Administration, the National Meteorological Agency joint utilization system, and integrated disaster safety information system. It functions as a system for collecting and storing in big data.

The AI unit 230 collects demand from consumers and analyzes the tendency and previous orders for each consumer, clustering/sampling through a k-means algorithm, and using the sentiment analysis techniques for the words used in the collected evaluations. It analyzes the degree of positive and negative using a machine learning algorithm to predict the future demand of consumers, and uses the deep learning technology to infer production by region by collecting data.

Accordingly, the AI unit 230 may help to accurately supply data where there is demand by predicting precise data in supply and demand through these data.

The management terminal 240 installs a history management application, is paid to each institution or individual, and transmits the information of the person, the QR code information and the status value to the tangible asset brokerage server 210 according to the type of the scanned QR code. And query the information that has been blockchainized.

The physical property brokering system 200 having the above-described configuration has the following configuration to achieve the purpose.

First, build big data as IOT IoT configuration, collect temperature, humidity, water supply, water quality, etc. to build big data related to production factors, and obtain logistics and delivery environment information through applications, QR codes, GPS To build big data.

By providing information through a co-purchase system/delivery artificial intelligence control/tracking system/survey system/application, the O2O co-purchase platform (real-time big data, AI close) can be constructed.

Delivery can be controlled by artificial intelligence by controlling location, article, hourly, loadable amount by location, and delivery completion.

In order to manage distribution history, to manage the distribution process of products, QR codes are automatically generated and issued to identify organizations at each location of producers, shippers, and consumers, and products are distributed to distribute each product to manage products. Issue a QR code that can be checked.

Accordingly, the producer receives and attaches the QR code related to the product prior to packaging and delivery from the main server, and the shipper scans the QR code attached by the producer through the separately provided application at the beginning and end of delivery to deliver the delivery history. This data is stored in a blockchain through a server to store information.

When the buyer receives the delivery, the QR code attached by the producer is scanned through the application provided separately, and the completion of delivery is transmitted to the server.

It is possible to build a history tracking system through a series of processes such as automatic granting of QR code of the certification authority, automatic generation of a log invoice, and real-time history tracking through apps and the web.

Production, distribution, consumption process and logistics details can be blocked into blockchain data to prevent data corruption, and Q-code can be used to store blockchain data and block data and artificial intelligence decisions to block hacking.

Lastly, it is possible to rescue the survey system through consumer reviews, evaluation and big data making, and production and quality, service improvement, and data and technology support.

The term'~unit' used in the above embodiments means software or a hardware component such as a field programmable gate array (FPGA) or an ASIC, and'~unit' performs certain roles. However,'~bu' is not limited to software or hardware. The'~ unit' may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors. Thus, as an example,'~ unit' refers to components such as software components, object-oriented software components, class components and task components, processes, functions, attributes, and procedures. , Subroutines, segments of program patent code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays, and variables.

The functionality provided within components and'~units' may be combined into a smaller number of components and'~units', or separated from additional components and'~units'.

In addition, the components and'~ unit' may be implemented to play one or more CPUs in the device or secure multimedia card.

The above-described embodiments are for illustration only, and those skilled in the art to which the above-described embodiments belong can easily be modified into other specific forms without changing the technical idea or essential characteristics of the above-described embodiments. You will understand. Therefore, it should be understood that the above-described embodiments are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope to be protected through the present specification is indicated by the claims, which will be described later, rather than the detailed description, and should be interpreted to include all modified or modified forms derived from the meaning and scope of the claims and the equivalent concept. .

According to the present invention, it can be used immediately like cash to download or stream a sound source from the platform.

Through the present invention, members can support the production of producers (creators) belonging to the Korea Music Industry Association, and the musicians can provide music, goods, performance tickets, fan meetings, and other goods to investors according to the conditions proposed at the time of planning, or You can make additional purchases, and donate and purchase like cash through coins.

Reward for sound source registration and play to the right holder (creator), GAS profit compensation through participation in the blockchain network of the present invention to network participants (aPos), reward to community participants to C block chain project of the present invention and community activity , Reward for P-ICO participants' reward for activities and contributions of ecosystem participants of the present invention, such as reward for P-ICO participation and promotional activities, and reward for content service profit model provided to 3rd-Party dApp developers/operators. It can be done.

10: Blockchain based asset trading and history tracking system
100: intangible asset brokering system
110: intangible asset brokerage server
120: input terminal
130: consumer terminal
140: development terminal
150: project terminal
200: physical property brokerage system
210: Tangible asset brokerage server
220: Big Data Construction Department
230: AI section
240: management terminal

Claims (2)

In the asset trading and history tracking system based on a blockchain that dataizes the transaction process of tangible assets using a blockchain,
A blockchain-based asset trading and history tracking system, including a tangible asset brokering system that mediates the transaction of tangible assets between producers and consumers of tangible assets.
According to claim 1, wherein the physical property brokering system,
Stores the information of each institution or individual, receives the scanned contents from the management terminal, issues a unique QR code corresponding to the product, and manages it, and the institution, purchase and status information, or consumer, delivery, type and status information Intangible asset brokerage server that constructs a new transaction by using, blocks it through the data verification step, and inquires the information that is in block chain;
Collects at least one production element among temperature, humidity, water supply and water quality to build big data related to production environment information, and uses application, QR code, and GPS to log big information related to logistics and delivery environment information. A big data construction unit that builds data;
It collects demand from consumers and analyzes the tendency of each consumer and previous orders and clusters/samples them through the k-means algorithm, and affirms the words used in the collected evaluations using sentiment analysis technology and machine learning algorithm. An AI unit that predicts future demands of consumers by analyzing the degree of injustice and injustice, and infers production by region by using deep learning technology through collected data; And
Install a history management application, be paid to each institution or individual, send your information, Qalcode information and status value to the intangible asset brokerage server according to the type of Qal code scanned, and view the blockchain information Blockchain-based asset transaction and history tracking system, including a management terminal.

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