KR20220034970A - A system and method for lifelong TMS of industrial electric vehicles by big data - Google Patents

Abstract

The present invention relates to an industrial rail motor vehicle lifetime total maintenance service (TMS) system using big data and an operation method thereof. The system comprises: a step of providing a shortened URL and QR code by a system administrator who has performed maintenance and repair of an electric forklift; a step of providing details of the electric forklift to a big data distributed data node after trading, leasing, maintenance and repair of the electric forklift; and a step of communicating in a system using the big data. Through the above process, there is an effect of providing an industrial rail motor vehicle lifetime TMS system using big data and an operation method thereof.

Description

Industrial electric vehicle lifetime TMS system using big data and its operation method {A system and method for lifelong TMS of industrial electric vehicles by big data}

The present invention relates to an industrial train management system and an operating method thereof.

The present invention relates to an industrial electric vehicle lifetime total maintenance service (TMS) system using big data and a method for operating the same. It relates to a system for lifelong management of storage batteries of industrial electric vehicles mounted on forklifts using big data and a method for operating the same.

In general, industrial forklifts are widely used in most industrial sites due to the advantage of being able to move around the site freely while transporting and unloading heavy cargo instead of hoists or cranes. However, the above-mentioned conventional industrial forklift trucks are sold or leased and used in the field, but it is not easy to secure a customer. Even in the process of receiving /S, there was a problem that action could not be taken quickly. In particular, in the case of industrial electric forklifts, the installed batteries often fail and require very careful management of electrolytes. In addition, it is also experiencing serious difficulties in the continuous acquisition and management of customer information according to the purchase and sale of industrial electric forklifts, leases, and parts purchases.

This problem is related to the long-distance diagnosis and failure warning system of forklifts by the domestic patent application "Remote diagnosis and failure alarm system for forklifts (registration number 20-0319142, extinct)", the main purpose of which is to predict the operation status of forklifts in advance. This is to prevent malfunctions in advance by inspecting the system, and at the same time to promptly repair forklifts that have received fault signals through wired/wireless signals. To this end, the long-distance diagnosis and failure warning system of the forklift of the present invention includes a failure signal detection device that detects failures occurring in each part of the forklift, and a failure signal wirelessly transmitted from the failure signal detection device, and the failure signal It stores the relay terminal provided by the forklift user company and the data transmitted from the relay terminal to transmit the data to the central management unit by wire, and transmits the error handling information accordingly to the communication means such as the Internet or fax provided at the forklift sales agency by wire or wireless. It is to include a central management unit consisting of a main server that can be quickly dispatched and repaired by sending it to Accordingly, it is possible to prevent the failure of the forklift in advance, and it is a design to have the advantage of increasing the productivity of transportation and unloading by enabling rapid repair of failures in the event of a failure. It can be used without restrictions.

In relation to lease management, the domestic patent application technology "Rent billing system and method for electric devices and batteries (Publication No. 10-2015-0055649, rejected)" is a remote control of battery usage of industrial electric vehicles such as electric forklifts, golf carts and electric wheelchairs. It relates to a system and method for charging a rent for electric vehicles and batteries that collect and automatically charge rent based on usage. It is to provide a rent billing system and method for electric vehicles and batteries that can charge rents fairly for all tenants.

However, the above technologies have both considerable functionality and effectiveness in that they can quickly repair breakdowns and charge rental fees in the sense that they centrally manage breakdown or usage information from a distance and take comprehensive follow-up measures from the center. something to do. And in the conventional industrial field practice, it is a common case that the sales and rental of electric forklifts are divided from the accounting department to the trading boss or rental business operator, and the maintenance and repair are handled by the on-site manager to the related company. In the case of a small company, even if the business was integrated, there was hardly any case of easy communication or lifelong management between mutual companies and managers.

The present invention is to solve the conventional problems as described above, and more specifically, as an industrial electric train lifetime TMS system using big data and its operation method, more specifically, the sale and rental of industrial electric forklifts, electric trains and electric vehicle storage batteries, especially electric forklifts It relates to a system and its operation method for lifelong management of storage battery maintenance and failure history of industrial electric forklifts installed in the company using big data. The step of granting a shortened URL and QR code by the system administrator who performed the maintenance and repair of the requested electric forklift, the steps of entering the details into the big data system after trading, leasing, maintenance and repair of the electric forklift; After sale, lease, maintenance and repair, the industrial site manager or system manager uses SNS and SMS to access the information on the electric forklift truck and its storage battery status through the Internet with a shortened URL and QR code to provide big data distributed data nodes In the big data system, there is a task to provide functionally and economically each step of communication, such as answering via SNS or SMS with smart phones of each relevant person, such as an electric forklift manager at an industrial site, and providing appropriate recommendations.

In addition, through the above process, it is possible to provide appropriate information and secure management status to end users such as system administrators, sales and leasing of electric forklifts, maintenance and repair companies, industrial site managers, and institutional users, or big data to promote related businesses. There is a task to effectively provide a lifetime TMS system for industrial trains using

The above object is achieved by the present invention, and the present invention relates to a lifetime TMS system for an industrial electric vehicle using big data and a method for operating the same. It relates to a lifetime TMS system for an electric forklift, in particular, a lifelong TMS system for an industrial electric forklift that manages battery maintenance and failure history of an industrial electric vehicle mounted on an electric forklift using big data, and its operation method. there was no

Accordingly, in the present invention, when receiving a request for sales and leasing of an electric forklift, and maintenance and repair of the electric forklift from an industry that requires an electric forklift, the system administrator first gives a shortened URL and QR code to the electric forklift, and the maintenance and repair After performing the task, managing the details in the big data distributed data node and after the above steps, connect to SNS or SMS through the shortened URL and QR code with the smart phone of the industrial electric forklift manager to receive information, but the TMS To ensure that the system continuously provides appropriate recommendations, functionally and economically, and through the above recommendation process, system administrators, electric forklift sales and rentals, and end users such as maintenance and repair companies, industrial site managers and institutional users It is intended to effectively provide the industrial train lifetime TMS system and its operation method using big data so that they can provide appropriate information and secure management status or promote related businesses.

The present invention is to solve the conventional problems as described above, and an object of the present invention is not only the sales and rental services of industrial electric vehicles, particularly industrial electric forklifts, but also industrial electric vehicles and their storage batteries, especially storage batteries and malfunctions mounted on electric forklifts. It has the effect of providing a TMS system that manages repair and maintenance history for a lifetime using big data and its operation method.

In addition, the industrial electric vehicle lifetime TMS system using big data according to the present invention and its operation method are requested by the system manager, the relevant company, and the manager when the purchase and rental of the industrial electric forklift, the maintenance and repair of the industrial electric forklift and its storage battery are requested at the industrial site. And it has the effect of providing a method for lifelong management of maintenance and breakdown services of industrial electric trains to ensure proper information provision and management status to institutional users as a functional and economical solution.

In addition, the present invention provides related information and recommendations so that industries can determine and maintain strategies for selling and leasing electric forklifts appropriately to the site and business conditions through the above process, and electric forklifts purchased or leased by industries It has the effect of receiving A/S, such as timely repair and maintenance.

1 is a statistical table of the global forklift market size from 2001 to 2011.
2 is a conceptual diagram of a big data server configuration of an industrial train lifetime TMS system using big data according to the present invention and a method for operating the same.
3 is a flow chart of each subject-centered work by the industrial train lifetime TMS system and its operating method using big data according to the present invention.
4 is a flowchart illustrating an industrial train lifetime TMS system using big data and an operating method thereof according to the present invention.
5 is a block diagram of a big data system by an industrial train lifetime TMS system using big data according to the present invention and an operating method thereof.
6 is a conceptual diagram of a big data ecosystem (Hadoop) by an industrial train lifetime TMS system using big data according to the present invention and its operating method.
7 is a conceptual diagram of map reduce by a big data tool of an industrial train lifetime TMS system and its operating method using big data according to the present invention.
8A and 8B are examples of communication by SMS API of an industrial train lifetime TMS system using big data according to the present invention and an operating method thereof.
9 is a co-branded embodiment "88 TMS" of the industrial train lifetime TMS system and its operating method using big data according to the present invention.
And FIG. 10 is a shortened URL and QR code generated by the related tool of the industrial train lifetime TMS system and its operating method using big data according to the present invention.

Industrial electric vehicle lifetime total maintenance service (TMS) system and its operating method using big data of the present invention for achieving this object;

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

First of all, Figure 1 is a statistical table of the global forklift market size from 2001 to 2011.

According to the above statistical table, electric forklifts are counted slightly more than internal combustion engine-type forklifts, which is the same as the domestic trend.

Therefore, in the present invention, for industrial electric vehicles, particularly industrial electric forklifts, sales companies, rental companies, and repair and maintenance companies form collaborations or cooperatives, and independently use big data to create a distributed file system for regional industrial train lifetime TMS systems. When established, the manufacturing, sales, and repair companies of the above-mentioned collaboration or association can secure stable demand for the relevant life period of the train, and among the end users who use the big data system, the industrial company can provide stable maintenance while using the train. It provides a convenient and reliable way to receive services and to initiate and terminate the purchase or lease of the train, and provide effective policy and statistical data to other system administrators, local and central government agencies and public users. that you want to be able to do.

The description of the present invention will be comprehensively described using FIGS. 2 to 10, but will be described in more detail focusing on the big data ecosystem (Hadoop) of FIGS. 2 and 6, which are representative drawings.

First, as shown in Figs. 2 and 4 to 5, the participating subject according to the present invention is a system administrator (U1S) who manages the system according to the present invention, in which an end user (U00) is a power user (Power user). ), local government and employment-related civil servants, such as institutional users (U50), electric forklift-related organizations and vocational education organizations, and public users (U51) such as schools and employment insurance, and I am a union member (U20), and as a casual user, I would like to classify the electric forklift manager (U30) and the electric forklift consumer (U00). The end users each access the system according to the present invention, that is, each regional distributed management system composed of an Internet portal site, and perform the steps of providing and changing predetermined self-information. However, the system according to the present invention extracts electric forklift appraisal or sales standard price data directly input by end users (U00) or the electric forklift reference price information of each region from the government electric forklift information site when constructing the system according to the present invention. It is designed to be brought into the system according to the present invention and moved to the name node and data node (Load), and the above process will be described in more detail later.

The data management system according to the present invention is a distributed file system, which eliminates the inconvenience of the conventional system in which end users (U00) have to input information generated or retained by themselves in duplicate, and as shown in FIG. 5, end users ( U00) inputs the data from a terminal connected to the Internet, such as a smartphone or desktop, to a connected distributed data node, each data node manages it along with history (history) information so that the final data revision can always be used as valid data.

As shown in FIG. 6, the implementation of the big data distributed file system as described above uses the big data basic tool Hadoop to directly input input data (I00) or externally extracted end users (U00) ( U00) Relevant data, that is, each transaction log file and summary key values such as input data I01, I02, I03, I04, and I05 shown in FIG. 6 are loaded in the name node, and main data files are distributed in the data node. Nodes are managed in the form of Meta DB. Here, the meta DB is an abbreviation of a meta database, as shown in FIG. 5, and it can be seen as a catalog of a database. At this time, the owner (Dbadmin) of each database is not limited to one metadb to facilitate parallel processing in the future. A meta DB can be created and utilized in the system according to the present invention.

In addition, the data node loads or retrieves the main data as each database in parallel processing at a speed more than 6 times faster than that of the existing DBMS processing. It is an open source HDFS (Hadoop distributed file system) based on Google's distributed file system in the United States. Design and build the data in blocks ranging from 64MB to 1GB in order to speed up the access speed and to repeatedly access large amounts of data in a stable manner. It is desirable to set the block designed as above to 128 MB, which shows a huge structural advantage compared to a block of 4 KB unit of the conventional Unix operating system. It is characterized in that it is operated as a block of

As shown in FIG. 5, the HDFS has the first and second structures, that is, the master/slave structure. An HDFS cluster consists of a single name node and a master server that manages the file system and controls client access. In addition, there is one Datanode on each node in the cluster, and this Datanode manages the storage added to the node as it runs.

The HDFS discloses a namespace to allow each user's data to be stored in a file. Internally, a file is divided into one or more blocks, and these blocks are stored in data nodes. Namenode performs various functions of the namespace of the file system, such as reading (Open), closing (Close), and renaming (Rename) of files and directories. In addition, the mapping between data nodes and blocks is determined. And the data node is in charge of the read and write functions requested by each client of the file system. In addition, a data node can also perform functions such as creation, deletion, and duplication in the name node.

The namenode and datanode shown in FIG. 5 are parts of software designed to be executed on a commercial Linux machine based on GNU/Linux OS.

And, since the HDFS uses the Java language, NameNode or DataNode software can be executed on any computer running Java.

When input data is extracted from external data, or when additional input data is loaded into the name node and data node, the job tracker (E20), the main execution tool for performing and managing the map reduce operation Map reduce tasks are performed using Task tracker (E10), a tool that subordinately performs and manages Map and Reduce tasks as distributed processing tasks. The big data system according to the present invention can secure personal information protection measures by replacing or separating and merging information that conflicts with personal information protection.

The map reduce operation will be described in more detail with reference to FIG. 7 .

7 is a conceptual diagram of a map reduce process of Hadoop adopted among an industrial train lifetime TMS system using big data and an operating method thereof according to an embodiment of the present invention.

As shown in FIG. 5, the input data (Input data, I00) is data that end users (U00) directly input from among the generated or held data, the structured data (I01) and the existing public and related institutions. Semi-structured data (I02 to I03) that can be easily processed as imported ones, those generated by end users (U00) through social media, data transmitted/received with the system according to the present invention, or data that have been consulted. It includes all of the unstructured data (I04), which is not at all, and the unstructured collection data (I05) collected from newspaper articles or the Internet.

The industrial train lifetime TMS system using the big data system according to the present invention and its operation method are performed by performing the data smelting step of the input data (I00) in accordance with the present system framework, and each data node on the data node rack (Data node rack). to be saved in The above data refining step is a map reduce step of Hadoop shown in FIGS. 5 and 7 .

MapReduce is a software framework originally developed by Google for the purpose of processing large-scale data processing in distributed parallel computing and released in 2004. The framework was initially developed to support parallel processing of large-capacity data of more than petabytes in a cluster environment composed of servers with low reliability. The framework is mainly composed of a map and a reduce function that are generally used as a functional type.

7 conceptually shows the data processing process of the map reduce. The map reduce operation is divided into two stages: Map and Reduce. The map stage is a stage of smelting data in advance before processing the data used in the application in the reduce stage. In the map stage, the input data is transformed into a matrix of <key, value> to organize the data, and at this time, abnormal data is excluded. In the Reduce step, it is a step to process using the <key, value> data generated in the Map step.

In the Map step, a large amount of <key, value> combination of data is generated, and among the data, data with the same key is a node that executes one Reduce. Transmit the generated data. The node executing this reduce step executes the user-defined task with the received data. In the above process, the <key> value is one of several such as the vehicle manager's name, date of birth, electric forklift type and capacity, and lot number.

However, in the Hadoop ecosystem E00 shown in FIG. 6, a unit of work is a Job. Each unit job consists of input data I00, a map reduce framework, and other environment setting information. In addition, each job is further classified into a task of a map task and a reduce task. In the Map Reduce framework programming model, computers acting as each node are gathered to form one cluster as shown in FIG. 5 . Each node is composed of two types of nodes, a job tracker (E20) and a task tracker (E10), and one job tracker (E20) and several task tracker (E10) nodes are aggregated to form another cluster. The task tracker E10 serves to execute each task, and the job tracker E20 executes each job by scheduling task assignment in several task trackers E10. And in order to distribute the data, there is a method of balancing the execution time between the task trackers E10 by transferring the data back to the task tracker E10. However, in this method, there is a lot of data that is sent again in the middle of execution, and congestion appears in the entire network and the overall performance of Hadoop may decrease. It is preferable to use a method of distributing data of size.

In the Hadoop ecosystem (E00) according to the present invention, it is preferable that source computers having data directly input to each task tracker (E10) are arranged based on the closest thing on the network. And when the input data I00 is transmitted to each data node, the data is first transmitted to the closest task tracker E10 according to the instruction of the job tracker E20.

In the embodiment of the system using big data according to the present invention, it is preferable to schedule by distributing data in consideration of the following three before transmitting the input data I00 to the task tracker E10. That is, (1) the time to transfer data from the data source computer to the task tracker (E10), (2) the amount of data to be processed in the queue of the task tracker (E10), and (3) the task tracker (E10) is a unit The data is distributed in consideration of the time required to process the data. This is to assign an ID (ID) to each data group made up of data blocks when transmitting data, so that the group can be distinguished. Record and save the time when sending to (E10). After that, when the task tracker E10 completes the mapping operation, when a completion signal is sent to the job tracker E20 together with each job record, the work throughput is calculated and stored. Through the above method, the above three considerations proposed by the present invention are integrated and calculated so that the throughput of each server system is relatively reduced, and the performance is ranked among the task trackers (E10) for server management and It is desirable to refer to this for future work allocation.

However, it is desirable to exclude data distribution based on information that is inaccurate to grasp the status and performance of the task tracker E10 by only recording one or two tasks of the task tracker E10.

Therefore, it is preferable to use a moving average value that is considered together with the currently measured value without erasing the previous performance measurement value of the task tracker E10 to use the application method. In addition, it is desirable to determine the value showing the optimal performance by periodically analyzing the trend through repeated application in the future as well as application of the moving average value. Since the rest of the values in the list other than the newly entered values are already sorted, when insertion sort is used, the execution time of this algorithm becomes 0(n) for the number of nodes n constituting the HDFS, which enables quick sorting. There is this. And as a map reduce application used in an embodiment of the present invention, a word count for calculating the number of input data I00 and a historical format using the year and date as key values (History records) to get the input data. First, the word count application receives the document files as input data I00. If these document files are distributed to the task tracker (E10), the task tracker (E10) that executes the map task creates a word based on the space character for all characters in the input data file. It becomes the key value in the program. The reduce task receives only data with this specific key value and adds up the number of data to calculate the number of words. And the history records (History records) application uses a data file consisting of a record of a specific format having a year as a key value as the input data (I00). The Map task gets the records in year and date format from the records in the received data file, sorts them, and loads them into the data node. Most of the above application tasks are performed as direct batch tasks. You can use the Hadoop-related tools Pig or Hive packages to directly use the conventional SQL (Ansi SQL) syntax or indirectly search and select queries. there is.

The <key> value assigned to the input/output data generated by the present invention functions like a fingerprint of each data, such as the data input date and assigned data group ID (ID). In addition to solving data protection and security problems through map reduce deployment and real-time processing, some of the names to be protected among the information extracted for the protection of personal information of end users, which are the most recent issue through the above process, are is replaced with other characters, and the first six characters of the resident registration number are saved. As an example, the first digit of the resident registration number 600126' is partitioned into 'born on January 26, 1960', and the last seven digits are identified by code, merged into gender (1/2) and birthplace, and stored separately in the data node. If stored, public data can be legally collected while satisfying the personal identification element deletion or de-identification processing technique according to the Personal Information Protection Act and the Act on the Promotion of Provision and Use of Public Data, and output data from the system according to the present invention ( 000) can be used legally. If we explain the above steps of personal information protection in more detail by applying each big data tool, it is a package in which Hadoop's Zookeeper manages and adjusts Mappers that delete and de-identify personally identifiable elements. make it function. Flamingo package is used as a package that manages the entire workflow as above. As an alternative, there are Oozie, Luigi, Azkaban, Falcon, and the like. And it is preferable to design Hbase as the main package as the main tool of the HDFS or Hadoop ecosystem (E00) is the job tracker (E20) in terms of hardware for the management of the distributed management system (HDFS) described above.

6 is a Hadoop Ecosystem (E00) of an industrial train lifetime TMS system and its operating method using big data according to an embodiment of the present invention.

The Hadoop Distributed File System (HDFS) (E10) or Hadoop ecosystem (E00) shown in FIG. 6 consists of a name node and a data node (Ata node) as shown in FIG. 5 as a hardware structure. Name nodes store information about the directory structure of data, and data nodes store actual data. In HDFS (E10) or Hadoop ecosystem (E00), structured storage is required with the existing DBMS, but it is designed to store unstructured and parallel processing of hardware and data.

Each processing step of the industrial train lifetime TMS system using big data according to an embodiment of the present invention according to the present invention and its operation method is characterized in that the conventional database building step consists of three steps such as data extraction, conversion, and loading For the database construction according to the present invention, as shown in FIG. 6, (1) Extract, (2) Load, (3) Transform, and (4) Load of input data I00 It is preferable (E50) consisting of five steps, such as (Load) and (5) data mining (Ata mining). And (1) in the extraction stage, in the case of structured, semi-structured and unstructured data (I01, I02, I03, I04, I05) according to the type of input data (I00), It is used as a package for each Flume, Chukwa, etc., and in the case of SNS or SMS short message transmission and reception or collection data, batch work is processed using the Sqoop and Hiho packages. And (2) In the Load stage, the MetaDB operation and the data storage operation are performed in batches through a direct map reduce operation using Pig, Hive, and MongoDB.

In addition, (3) In order to perform the transformation operation, the structure of MetaDB can be performed in real time with SQL query operations such as Impala and Tajo, and through the above operation, the name node and data node As a method of updating the DBMS structure and actually (4) loading data into each data node, and characteristically performing the loaded data according to Mahout, Ankus, or the present invention, (5) There is data mining (Ata mining), which means data analysis and processing into information for prediction, description, and recommendation. It is preferable to consist of 5 steps to utilize.

And as the application program (A00) shown in Fig. 6, there is an SNS API (A11) for interworking with social media and an SMS (A12) for sending and receiving short messages, and MySQL (A11) for data statistics, analysis and report writing. However, it is preferable to use NodeXL, SAS, or program R (A12).

However, since all of the above applications except SAS are open source, there is little to no cost burden.

8A is a screen showing a result of sending a short message to a level 1 electric forklift consumer U40 of the industrial train lifetime TMS system using big data and its operating method according to an embodiment of the present invention.

First, to the electric forklift manager (U30) with a short letter according to the embodiment of the present invention, "TMS electric forklift information] PM of Asan Plant! This is electric forklift information (3 units) suitable for you as of today! For details, go to 'www.88tms Please refer to '.co.kr/asp1004.asp'." provides the electric forklift manager with an Internet URL with customer-specific electric forklift information at the reference point. At this time, the electric forklift manager U30 may conduct electric forklift transaction activities by referring to the information provided first, and may wish to receive electric forklift information by changing conditions. Immediately re-create and tell the electric forklift manager (U30) as shown in Figure 8a above, "TMS electric forklift information] This is the domestic electric forklift information (5 units) as of today that you requested." It is preferable to reply.

The above embodiment is not limited to the electric forklift manager U30, but also allows other end users U00, such as electric forklift users U40, to manage customized information with the same procedures and steps.

However, the customized electric forklift information is a problem in which there are both the electric forklift consumer (U40) and the electric vehicle provider, that is, the electric forklift manager (U30) and the seller. Therefore, the above train management process is not a problem that can be solved only with the efforts of the electric forklift manager (U30), but a problem that can be solved only when there is a mutual response from the system manager and the electric forklift consumer (U40). Nevertheless, the existing electric forklift management system is still only a one-way condition search system. In contrast, in the system according to the present invention, an attempt may be made to introduce a bi-directional fuzzy matching (BDFM) method that replaces the unidirectional search condition, which fuzzy the preference input value (weight) of the conventional matching algorithm. It is possible to design the automatic bi-directional fuzzy matching of the automatically calculated matching rate value through the algorithm.

However, since the above attempts are ultimately made for the entire electric forklift data, the search speed is slowed and the results are also increased, so it is rather difficult to exceed the functionality and efficiency of the existing one-way conditional search. As described above, designing a system with the same structure as the existing electric forklift sales transaction or information database, namely, the electric forklift manager (U30) and all companies that own all electric vehicles owned by the trader, is a significant amount of data. Since the part is filled with imaginary numbers and non-current data, it is a factor that reduces the processing speed and efficiency of the computer system by that much, as well as the evidence of a vicious cycle in which the imaginary and non-current data are again false and lower the reliability of the market, such as bait for sale is used

Accordingly, in the industrial train lifetime TMS system using big data according to an embodiment of the present invention and its operation method, as a first step, for each electric forklift consumer U40, the distance calculation (Dist) function using the meta DB of the name node and Bi-directional fuzzy matching (BDFM) algorithm is preferably used.

Conventional information retrieval systems generally provide information only when all of the presentation conditions are exactly matched by using the intersection between each condition. That is, if even one of the input conditions was not satisfied, information could not be provided and, from the point of view of the information consumer, it was difficult to obtain other relevant information that could consider other possibilities. In order to solve the above problems, a fuzzy matching method is generally introduced, and the search condition and similarity information among all information as well as information matching the conditions directly requested by the consumer are calculated with probability and sequentially as information with high relevance. Introduce a method to show The fuzzy matching method has the advantage of effectively narrowing the difference between the viewpoints of the information consumer and the information provider by providing highly relevant information in consideration of various conditions.

Therefore, in the present invention as well, examining each variable in order to introduce the fuzzy matching method as described above, the desired purchase (price) condition p of the electric forklift consumer U40 in the electric forklift manager U30, who is the consumer of the information, and the information provider side The sale (value) condition c of the member U20 each has the same attribute a _k for matching. That is, when expressed as an equation, pa = a ₁ , a ₂ , ..., a _k , ca = a ₁ , a ₂ , ..., a _k . In this case, the matching rate may be expressed as a weighted average according to the importance of each item for the degree of matching (similarity) information. And the desired purchase condition of the electric forklift manager (U30) or consumer (U40) p . The degree of similarity between a _i and the sales condition ca _i of the member U20 is calculated using the following DIST function formula to calculate the probability result.

if a _i = nominal or a _j = sequence

DIST ( pa _i , ca _j ) = AffTable ( pa _i , ca _j )

if else, DIST ( pa _i , ca _j ) = AffTable ( pa _i , -ca _j )

When the information item of the DIST function is a nominal or sequence scale, the similarity recorded in the similarity table ( AffTable ) is used. The items of information include region, electric forklift type and capacity, lot number, vehicle price, year, mileage, accident status, maintenance/repair history, quality assurance, electric forklift manager or demand type and its name, etc. When each item is a sequence or an equal scale, the degree of similarity is calculated according to a probability calculation formula. As an example, if the desired price of the electric forklift manager (U30) or the consumer (U40) is 15 million won and the union member (U40)'s proposed selling price is 14 million won, the degree of similarity is 78% (=1- (1,500-1,400)/( 1,500*30%)). In this case, 30% is a range in which the similarity defined by the user is 0%. The value of the similarity table and the formula for calculating the probability are preferably determined in advance by using the expert's experience or past usage history, and it is also preferable to always use the final numerical value corrected by machine learning. Bidirectional fuzzy matching (BDFM), an algorithm used in the system according to the present invention, is an algorithm that increases the matching rate between information consumers and information providers by automatically calculating the mutual preferences of each user.

However, when the BDFM algorithm is initially introduced or used poorly, the user-defined data is not established and the result value may be distorted. will be

The detailed code of the BDFM algorithm adopted in the present invention is as follows.

BDFM (Electric Forklift Manager (U30) to Consumer (U40)'s Desired Condition p')

* The matching rate between the desired condition p' of the electric forklift manager (U30) or the consumer (U40) and the sale condition c of the seller union member (U20) is calculated and the matching rate with all electric forklifts owned is stored.

1. p_r ← RANK(S(p',C))

* n is the total electric forklift

2. For(j = 0, j < n, j++)

c_r ← SEARCHR(RANK(S(c) _It's , P)), p')

End for

3. a = w ₁ * p_r + w ₂ * c_r

Determines how the screen is displayed and provides the results.

End Function

The first step in the detailed code of the BDFM algorithm is the matching rate between the desired condition, p' of a specific electric forklift manager (U30) or consumer (U40) and all electric forklift vehicles C = c ₁ , c ₂ ..., c _n Calculate and store each rank in p_r. p is the desired price condition described in the electric forklift data input into the system, p' is the desired price condition of a specific electric forklift user (U40) who uses the service provided by the system according to the present invention, and P is the entire electric forklift data pool (Pool).

The RANK function is a function that calculates a ranking according to a matching rate. When the number of electric forklift data is n , the calculation amount is 0(n) . The matching rate is preferably calculated using fuzzy matching as described above. And in the second step, the ranking of the electric forklift manager U40 to the consumer U40 is calculated according to the desired sales conditions of the seller-side union member U20.

According to the desired sales conditions of each member (U20), search for the desired conditions of the electric forklift manager (U30) or the consumer (U40), and between the conditions between the electric forklift manager (U30) and the consumer (U40) according to the desired conditions of the union member (U20) It is obtained by sorting the matching rate. The SEARCHR function is inputted by the electric forklift manager (U30) or the consumer (U40) when the matching rate is calculated with the p of all electric forklift managers (U30) or consumers (U40) according to the desired condition c of the union member (U20). The rank of the desired purchase condition p' is calculated.

And when there are m pieces of information on the electric forklift data input into the system, the calculation amount of the matching rate becomes 0(n*m) . The third step is a weighted average of the rank p_r from the side of the electric forklift manager (U30) or the consumer (U40) and the rank c_r from the side of the union member (U20) in order to display the information showing high preference from both sides, and the consensus value It gives the results in ascending order. In aligning the rankings by applying weights to the individual preferences of the electric forklift manager (U30) or the consumer (U40) and the preferences of the union members (U20) according to the priority ranking, it is better to apply the same weight at the initial stage of application of the system according to the present invention. Therefore, the calculation amount becomes 0 (Max(m, n)) . However, on the assumption that the total amount of electric forklift information and the amount of electric forklift data possessed by the system according to the present invention are almost the same, the total matching rate calculation amount is 0(n ² )=0(n)+0(n*m)+0(Max (m, n)) becomes

However, in the computerized system according to the present invention, it is intended to provide a method for enabling the information user to easily understand the preferences of both the electric forklift manager (U30), the consumer (U40), and the seller union member (U20). Accordingly, the first method provides one data by averaging and combining the two preferences, which has the advantage of minimizing confusion after receiving only one result to the information user. In both cases, we would like to introduce and review a method that enhances visibility by emphasizing information with high preference. Among the above methods, it would be better for the information user to make their own decisions by referring to both preferences, so it is preferable to adopt the second method in the present invention. However, it is preferable to limit the number of information to be provided to about 3 to 5 and to use SNS or SMS media as the method of providing the information.

As described above, in the big data platform according to the present invention, the desired conditions of the union member (U20), who is the seller of the electric forklift, and the electric forklift manager (U30) to the consumer (U40), who are the consumer of the electric forklift, are recommended by the BDFM algorithm. In step 1, the actual data within 10 are first designated, and in step 2, the comparison order of the designated real data is determined. At this time, at each stage, weights are applied to each data field, such as region, electric forklift type and capacity, lot number, vehicle price, year, mileage, accident status, maintenance/repair history, quality assurance, job type of electric forklift manager or consumer, and its name. By assigning it differently, the expected matching rate is calculated. Since there is no actual data in the initial stage, the weight for each data field can be directly input from the union member (U20) and the electric forklift user (U40), and can also be arbitrarily designated and entered in the system according to the present invention. You may. In step 3, the expected matching rate calculated in step 2 is adjusted in consideration of the rankings of the seller union member (U20) and the electric forklift manager (U30) to the consumer (U40) side. That is, the case where the union member U20 does not enter the ranking of the electric forklift manager U30 or the consumer U40 who are in the designated rank of the union member U20 and vice versa, that is, the seller union member U20 and the electric forklift truck If there is a mismatch between the manager (U30) or the consumer (U40), the area of the mismatch data, the electric forklift type and capacity, lot number, vehicle price, year, mileage, accident status, maintenance/repair history, quality assurance, By adding weights again in the order of job type and name of the electric forklift manager or consumer, the union member (U20) and the electric forklift manager (U30) to the consumer (U40) through batch and shuffle until they match Adjust so that information is extracted by a predetermined amount (this is preferably 3 to 5 as described above). In step 4, the electric forklift information adjusted in the above process is simultaneously provided to the union member (U20) side and the electric forklift manager (U30) to the consumer (U40) side as short text information through SNS and SMS media. And in step 5, the data responding from the union member (U20) side and the electric forklift manager (U30) to the consumer (U40) side is extracted and again the union member (U20) and the electric forklift manager (U30) to the consumer (U40) data and metaDB to update

8B is a screen showing the result of sending a short message to the electric forklift manager U30 of level 2 of the industrial train lifetime TMS system and its operating method using big data according to an embodiment of the present invention. 8B is a screen showing a result of sending a short message to the electric forklift manager U30 of the second level of the system and its operating method according to an embodiment of the present invention. Looking at the screen of Figure 8b above, the electric forklift information is the same at the present time, but additionally, "[TMS Recommendation] 88 If you receive quality assurance through TMS, you can buy and sell for an additional 2 million won." provides additional information. Here, the electric forklift TMS system according to the present invention provides a more extended recommendation service by providing the above recommendation information to the electric forklift manager (U30) who is an electric forklift owner while additionally expanding the data field of quality assurance and sales price increase. designed to provide And as shown in Fig. 8b, when the electric forklift quality assurance and sales period are added as new data fields to the union member (U20) and the electric forklift manager (U30) to the consumer (U40) side, more electric forklift data pool (Data pool) The above recommendation short letter shows that it will have

Figure 8a is a level 1 step, only based on the current point in time, by going through the comparison step of the meta DB to calculate the ranking of the expected matching rate of actual data, and to the union member (U20) and the electric forklift manager (U30) to the consumer (U40) as the upper standard Regarding the provision of information, FIG. 8B is a level 2 step, and the process of expanding and changing the metaDB in the level 1 process and comparing it is designed to repeat the process again using the added data field as a medium. At this time, it is desirable to design the information on the quality assurance for electric forklifts to refer to the database of a separate quality assurance section of the cooperative constituted by the present invention. In the above, the medium for mutual communication between each end user (U00) is the internet and social media, but the basic internet medium is a hybrid app (Hybrid App, URL1 to URL2) using HTML5, but with a smartphone It is desirable to jointly use SNS/SMS API resources on the desktop and to ensure that maintenance is performed collectively. Hybrid app (A21), as the name suggests, a hybrid app is a mixture of web and native app. In a simple form of mixing, when a native app is started, a set web page is called. In a more complex form, HTML or images are put in the native app in advance, and some of the HTML codes embedded in the native app are read and sprayed on the screen. The rest of the information is retrieved from the server after accessing the Internet. Whichever method you choose, the basic thing is that the native app is executed first and then the code created with web technology is called again. The disadvantage of hybrid apps is that it has much lower performance than native apps, but it may be slower than web apps.

However, the advantage of the hybrid app is that by using HTML5, the product of the latest web technology, the kernels can easily and quickly create with web technology and add only a few objects such as store registration or camera operation as native apps, so that the Internet web, iPhone app, and Android app can be separated from each other. There is no need to create it separately, so if you modify one Internet web, it is applied to the entire platform, so maintenance is easy, so it is a technology that will become a trend in the future.

A URL shortening function is a technique for shortening a long URL on the Internet World Wide Web. The server that provides the URL shortening function shortens the forwarding of the client to a long URL in the original HTTP forwarding technique. Although there may be a URL of several hundred bytes in length, the shortened URL usually does not exceed 30 bytes at most because a query of 6-7 digits is attached to the address of the URL shortening server, saving resources and increasing interest from viewers. can be turned off Thanks to this, it was developed in a micro blog service such as Twitter and used a lot. On the other hand, since the client trusts the explanation of the URL provider and can be led to a dangerous site until directly accessing it, it can also bear risks such as phishing, fraud, click inducement, and spam. For this reason, the URL shortening server according to the present invention has a feature of rejecting a specific URL shortening. In addition, addresses containing malicious code are automatically filtered by malicious URL check, and the shortening is limited, so you can use it safely without worry. It should have features designed to be used not only as a shortened URL but also as a QR code. In addition, the Internet term, Uniform Resource Locator (URL), is a protocol for informing where a network resource is located, and has a predetermined structure for locating a network resource. A URL can consist of several elements, but most of it consists of a protocol, a host, a domain, and a subdirectory.

The data serial number "ASP1004" in the database of the electric forklift management big data server used in the embodiment according to the present invention is a URL that uses it as a subdirectory, that is, "http://www.88tms.co.kr/ASP1004" It is designed in the structure of .asp/" and it is desirable to use them together when generating QR codes.

In the shortened URL according to the present invention, the Internet address is input as a string to getShortenUrl(), which is a static method of Java (URL according to the present invention "http://www.88tms. com/ASP1004.asp", it is automatically returned as a shortened URL string converted by the shortened URL API. Since this is a fairly long string, the operator directly combines 5-8 characters consisting of numbers and lowercase/uppercase letters. , for example, a method of directly inputting "ASP1004" in accordance with a subdirectory according to the present invention to create a shortened URL is adopted.

In addition, when generating a shortened URL according to the present invention, it is designed to generate a QR code at the same time. A QR code is a matrix type two-dimensional barcode that displays information in a black and white grid pattern. Structure of QR Code While general barcodes can store numeric or character information one-dimensionally, that is, QR codes have a two-dimensional shape in both vertical and horizontal directions, allowing more information to be stored. Information can be stored more conveniently. A QR code can contain up to 7,089 numeric characters, 4,296 alphanumeric characters (there is a separate code table), 2,953 bytes of 8-bit bytes, and 1,817 Chinese characters.

An embodiment of the shortened URL and QR code generated by the present invention is shown in FIG. 'ASP1004' at the bottom of the QR code is a shortened URL, which means 'Asan Factory No. 1004 Electric Forklift'. If the user scans the QR code or enters the shortened URL in the URL according to the present invention, it can be directly connected to the electric forklift management book. It is desirable to provide a separate ID and password to the administrator or system administrator so that more detailed information can be inquired or inputted.

As described above, the input data I00 among the input and output data shown in FIG. 7 is the industrial train inspection result stereotyped data information generated after the inspection is performed by the manufacturer, the certified industrial electric vehicle inspection station, and the private inspection company of the electric train received from the inspector. As a result of the visual inspection, image data taken inside or outside is transmitted to each data node, and the other data nodes are electric vehicle investigation reports of the government and local governments, tax and fine information, etc., accident and maintenance history of the Insurance Development Institute, insurance-related data, etc. Public information of the relevant train is provided from the public database as basic data, and in addition to telephone consultation data with the electric forklift manager (U30), e-mail, and social media data such as opinions of vendors and dealers in charge, each standard (I01), semi-standard ( Big data in the form of I02, I03) and unstructured (I04, I05) data is received from each data node in a separated form without any distinction, and is used as output data (000) by map reduction in the future.

The output data may be generated in the form of a predetermined check list or a standardized report to store the data, or may be stored in the data node immediately before documenting as semi-processed data in a state that can be documented at any time.

When collecting the input data, the personal information protection policy such as obtaining and securing the consent of the electric forklift manager (U30) or the consumer (U40) is established and strictly complied with, while the stored data is managed for life until the electric vehicle is scrapped. do. In addition, the output data may be generated in the form of a predetermined check list or a standardized report to store the data, or may be stored in the data node as semi-processed data in a state that can be documented at any time immediately before documenting. In addition, when collecting the input data, a personal information protection policy such as obtaining and securing the consent of the forklift manager (U30) or the consumer (U40) is established and strictly observed, while the stored data is managed for life until the vehicle is scrapped. .

However, the success of the system according to the present invention can be a key factor in securing the quantity of input data, and the successful securing of the input data is to form a cooperative with dealers and trading firms as members and jointly purchase the cooperative as the main body. It should be resolved as a result of long-term accumulation, such as securing data on electric vehicles that have been purchased based on the concept, voluntary data input by the electric vehicle manager (U30), securing existing electric forklift sales data, and processing the data. In the process, electric forklift quality It is desirable to provide various benefits to each user or customer in a way that guarantees, points, and manages as a lifetime customer.

In the industrial train lifetime TMS system using big data according to the present invention and its operation method, the information generated or received from the outside in the diagnostic evaluation process by a professional diagnosis and evaluation person as described above is distributed to each data node, and strict whenever necessary. It is desirable to collect and use the map reduce method while observing the privacy policy. And it is desirable to obtain the permission of the information owner in advance or from time to time when using and utilizing the activated information as well as the process of collecting the above information. In addition, in the industrial train lifetime maintenance and failure history management system and the operating method thereof according to the present invention, joint purchases are made mainly by cooperatives or members. Dealers can overcome the limitations of independent purchase, lack of financial power and technical insight, or overcome limitations by purchasing only the desired account for the electric vehicle that has been decided to purchase. Even if you don't use it, you can perform maintenance and repair of purchased electric vehicles, and further tuning, in a separate business area at the professional level, and members themselves can sell electric forklifts without a separate sale price determination and negotiation process with the already determined sales price. It is desirable to introduce a characteristic completely different from the conventional transaction method, such as only having to focus on work.

More specifically, according to the system according to the present invention, the union members (U20) in charge of the sales task intervene from the beginning of the purchase price determination and evaluation procedure of the electric forklift, but after that, the precise evaluation, maintenance and repair, and the electric forklift quality It is desirable to be involved in the guarantee granting process as well. However, it is desirable to introduce an artificial intelligence machine learning module using big data according to the present invention to perform precision appraisal and maintenance and repair, grant of quality assurance for electric forklifts and purchase decision, and to determine purchase price and expected sale price. The price is determined by increasing 9% from the provisional purchase price. The provisional purchase price is determined by including the cost of precision appraisal, maintenance and repair cost, quality assurance cost, and the cooperative's price policy, which reflects the results of the machine learning in real time. It is preferable to go through the process of adjustment.

In addition, in the industrial train lifetime TMS system using big data according to the present invention and its operation method, it is necessary to secure not only online transactions but also offline transactions due to the basic properties of the above-described electric forklift sales transaction. The system and organization based on the system initially collects each member's investment accounts and forms them in the form of a cooperative, so it is easy to escape the conventional disadvantage of small size. It is preferable to use co-brands such as those shown in FIG. 9 that can be used.

The embodiment of the physical structure of the industrial train lifetime TMS system using big data according to the present invention gives a joint brand to the independent sales and rental, repair and maintenance part operation departments in the cooperative as described above, and also guarantees electric forklift quality It is desirable to manage as a lifetime customer once a customer becomes a customer with the same marketing concept as the sale of a new car with a free service warranty period. And in the above process, a cooperative is formed with electric forklift dealers and trading firms, etc., and a separate big data system management section is placed in the cooperative to recommend and manage customers and provide various benefits such as providing points to electric forklifts. It is characterized by building and operating an electric forklift TMS system using big data, which manages customers as lifelong customers who actively participate.

In addition, in the industrial train lifetime TMS system and its operating method using big data according to the present invention, it is characterized in that a recommendation system using big data for electric forklift sales and rental as described above is provided. At a certain point in time, a customer who has rented a vehicle becomes a customer who has to purchase or lease a vehicle again. In other words, since industrial sites can hardly be operated without forklifts, most of them will be in a position to stop using their own forklifts and purchase or lease another forklift at the same time. It is preferable to do

In addition, the storage battery restoration management method of the electric vehicle is managed by the "Industrial Lead Acid Battery Restoration Management Method" for which applicants have already filed a domestic application. It is desirable to manage by "Lifetime management maintenance and failure history management system and its operation method".

Therefore, in the big data system according to the present invention, mutual communication is constantly maintained in terms of lifelong customer management through SNS or SMS to the customers, and information and recommendations are provided to the customer and the electric forklift from time to time, and communication as described above. and electric forklift-related transactions, i.e., sales and leasing, maintenance, repair and maintenance, etc. upon request, certain points are provided to provide various benefits when making other transactions in the future. It is preferable to adopt a system and operating method characterized in that the customer and the electric forklift are managed as active lifetime customers who actively participate in the TMS system according to the present invention.

The present invention is not limited to the specific preferred embodiments described above, and without departing from the gist of the present invention as claimed in the claims, anyone with ordinary skill in the art to which the invention pertains can implement various modifications Of course, such modifications are intended to be within the scope of the appended claims.

Explanation of symbols in FIG. 2
U11: distributed server (data node) U12: big data server system,
U13: related business server, U20: seller cooperative member server,
U30: Electric (forklift) manager, U40: Electric (forklift) consumer,
URL1: Hybrid App Web Server 1, URL2: Hybrid App Web Server 2,
This is the same as the description of the corresponding reference numerals in FIG. 3 and other drawings.
Explanation of symbols in FIG. 4
S10: Repair and maintenance request stage;
S20: Shortened URL/QR code grant step,
S30: maintenance and repair phase;
S40: Enter history information;
S50: Big data system provision stage,
S60: advisory/communication phase;
S70: information request and provision stage;
S80: Big data system management stage
Explanation of symbols in FIG. 6
I00: input data,
I01: Direct input data (structured data),
I02: Public data (semi-structured data),
I03: Institutional data (semi-structured data),
I04: SNS/transmission data (unstructured data),
I05: Collected data (unstructured data),
E00: Big data hadoop ecosystem,
E10: Job tracker HDFS/Mapper
E20: Task tracker tools;
E30: Classification of big data job processing methods,
E31: Hadoop job management tools;
E40: Privacy and Reconciliation (Zookeeper);
E50: Separation of big data work stages;
A00: application,
A11: RDB programs such as MySQL, Oracle, etc.;
A12: Analysis, Statisticalization and Urbanization Package;
A13: SNS API,
A14: SMS API,
U00: end user, U1S: system administrator,
U11: Distributed Server (Data Node) U12: Big Data Server System
U13: Associated Business Server (Data Node), U20: Merchant Cooperative Member Server,
U30: electric (forklift) vehicle manager, U40: electric (forklift) vehicle consumer
U50: institutional users, U51: public users;
This is the same as the description of the corresponding reference numerals in FIG. 3 and other drawings.
Explanation of symbols in FIG. 7
I00: input data (Input data, I01, I02, i01, i02),
000: Output data (001, 002)

Claims (14)

The present invention comprises the steps of: granting a shortened URL and QR code by a system administrator who has been requested to purchase and lease an electric forklift from a person in charge of an industrial site, and to maintain and repair an electric forklift from an electric forklift manager; inputting the details of the electric forklift into a big data system after trading, leasing, maintenance, and repair of the electric forklift; After the sale, lease, maintenance and repair, the industrial site manager or system manager uses SNS and SMS to access the information on the status of the electric forklift and storage battery via the shortened URL and QR code to the big data distributed data node step of; communicating in the big data system, such as replying via SNS or SMS to the smart phones of each relevant person, such as an industrial site manager, and providing appropriate recommendations; and
Through the above process, it is possible to provide appropriate information to end users such as system administrators, sales and rental of electric forklifts, maintenance and repair companies, industrial site managers and institutional users, etc. Lifetime TMS system for industrial trains using. The method of claim 1,
Structured data such as direct input data of end users such as system administrators, public users, institutional users, train managers and consumers, and members of train trading cooperatives, semi-structured data such as institutional data and public data, and unstructured data such as SNS/transmission data and income data extracting by using a package of Hadoop, which is a big data platform; creating a metaDB for the data extracted in the above step and loading it into a name node and a data node; A method for meeting personal information protection and public data management guidelines, and converting it into a predetermined data format required by the system; and modifying the meta-DB for the converted data and loading it into a name node and a data node; data mining the loaded data as described above; And after the data mining goes through the steps of level 1 and level 2, it is characterized in that it comprises the step of generating predictions, descriptions, and recommendation data with an application program, and communicating with end users using social media and short text APIs. A method of operating an industrial train lifetime TMS system using big data. 3. The method according to claim 1 and 2,
In each of the above steps, the concept of independent joint purchase is introduced in the system linking offline and online, where a cooperative made up of train dealers and bosses is formed to jointly purchase trains and then sell the cars to make a profit. A lifetime TMS system operation method for industrial trains managed by a system using big data. 3. The method according to claim 1 and 2,
In the step of precisely appraising the train, a specialized team for precision appraisal of trains is separately configured in the cooperative association configured above to perform precise appraisal using big data. A method of operating an industrial train lifetime TMS system using big data. 3. The method according to claim 1 and 2,
When extracting input data, by using Big Data Map reduce and HDFS package, it deletes and de-identifies personally identifiable elements in accordance with the national regulations and guidelines related to personal information protection and public data. A method of operating a lifetime TMS system for an industrial train using big data, characterized in that by generating a DB and distributing the input data to a name node and a data node. 3. The method according to claim 1 and 2,
When managing input data, using the Big Data HDFS package, metadata is placed on several name nodes and the actual data is composed of data nodes (servers) that are processed in parallel to speed up data processing and dramatically reduce server installation costs. An industrial train lifetime TMS system operation method using big data, characterized in that 3. The method according to claim 1 and 2,
A method of operating an industrial train lifetime TMS system using big data, characterized in that it provides differentiated two-way fuzzy matching information at a certain time point using MetaDB to end users such as public and institutional users, train managers, train consumers, and union members . 8. The method according to any one of claims 1 and 2, 5 to 7,
A method of operating an industrial train lifetime TMS system using big data, characterized in that matching and request information to end users is transmitted to a client PC, social media, and short text message. The method according to any one of claims 1 to 2,
Matching and request information to end users are sent to client PC and social media short text messages. Responses and requests for results, sales and diagnostic evaluation consultation materials, etc. are also collected using big data collection package tools and map reduce framework. A lifelong TMS system operation method for industrial trains using big data, characterized in that it is collected and processed, stored as related data of the corresponding train, and utilized. The method according to any one of claims 1 to 2,
A method of operating an industrial train lifetime TMS system using big data, characterized in that end users directly connect to the train management ledger constructed according to the present invention through the input of a shortened URL provided by the system administrator or scanning the QR code. The method according to any one of claims 1 to 2,
A lifelong TMS system operation method for industrial trains using big data, characterized in that it is a lifelong management system that maintains the information management related to the train from the time it starts to when the car is scrapped. The method according to any one of claims 1 to 2,
A server written as a hybrid app for communication of procedures and methods, such as collecting, managing, and utilizing the transaction procedures of sales and lease, maintenance and repair according to the present invention, and management and analysis data according to the procedures in the entire process of the electric vehicle business. A lifelong TMS system operation method for industrial trains using big data, characterized in that the site is centered. The method according to any one of claims 1 to 2,
Industrial train lifetime using big data, characterized in that the quality certification procedure according to the present invention and management and analysis data according to the procedure are collected, managed, and utilized in the entire process of the electric vehicle business and utilized as a new electric vehicle business model How to operate the TMS system. The method according to any one of claims 1 to 2,
A lifetime TMS system operation method for industrial trains using big data, characterized by providing statistical and analysis data of the electric car and electric car market to public and institutional users.

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