CN113762439A - Supply big data system based on RFID - Google Patents
Supply big data system based on RFID Download PDFInfo
- Publication number
- CN113762439A CN113762439A CN202110995642.XA CN202110995642A CN113762439A CN 113762439 A CN113762439 A CN 113762439A CN 202110995642 A CN202110995642 A CN 202110995642A CN 113762439 A CN113762439 A CN 113762439A
- Authority
- CN
- China
- Prior art keywords
- data
- rfid
- product
- writer
- acquisition system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013500 data storage Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000007781 pre-processing Methods 0.000 claims abstract description 15
- 230000005540 biological transmission Effects 0.000 claims abstract description 14
- 238000007405 data analysis Methods 0.000 claims abstract description 11
- 238000000034 method Methods 0.000 claims description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 230000009466 transformation Effects 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000003066 decision tree Methods 0.000 claims description 3
- 238000012847 principal component analysis method Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 5
- 239000002994 raw material Substances 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 230000006872 improvement Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000003860 storage Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 230000004931 aggregating effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012517 data analytics Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000008267 milk Substances 0.000 description 1
- 210000004080 milk Anatomy 0.000 description 1
- 235000013336 milk Nutrition 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device
- G06K17/0029—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device the arrangement being specially adapted for wireless interrogation of grouped or bundled articles tagged with wireless record carriers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Entrepreneurship & Innovation (AREA)
- Evolutionary Biology (AREA)
- Software Systems (AREA)
- Economics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Business, Economics & Management (AREA)
- Bioinformatics & Computational Biology (AREA)
- Game Theory and Decision Science (AREA)
- Medical Informatics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a supply big data system based on RFID, which comprises a data acquisition system, a data preprocessing system, a data transmission system, a data storage system, a data analysis processing system and a supply data system, wherein the data acquisition system is used for acquiring data; the data acquisition system comprises an electronic tag data acquisition system and a service data acquisition system; the data preprocessing system cleans and compresses the data acquired by the data acquisition system; the data transmission system transmits the data processed by the data preprocessing system to the data storage system; the data storage system is used for storing the data transmitted by the data transmission system; the data processing system is used for analyzing, feeding back and intelligently predicting the data in the data storage system.
Description
Technical Field
The embodiment of the invention belongs to the technical field of electronic equipment, and particularly relates to a supply big data system based on RFID.
Background
Big data analytics has been an important means for many enterprises to increase supply chain competitiveness. For example, the electronic commerce enterprise, the kyoto shopping mall analyzes and predicts commodity demands of various regions in advance through big data, so that the delivery and storage efficiency is improved, and the customer experience of next day goods is guaranteed. Product electronic identification technologies such as RFID, internet of things and mobile internet technologies can help industrial enterprises to obtain complete big data of a product supply chain, and the data are used for analysis, so that the storage, distribution and sale efficiency is greatly improved, and the cost is greatly reduced. Taking the hail company as an example, the supply chain system of the hail company is perfect, and the supply chain system takes a market chain as a link and takes an order information flow as a center to drive the movement of logistics and fund flows and integrate global supply chain resources and global user resources. In each link of the hail supply chain, customer data, enterprise internal data and supplier data are gathered into a supply chain system, and through large data collection and analysis on the supply chain, the hail company can continuously improve and optimize the supply chain, so that the sharp response of hail to customers is guaranteed. Larger OEM suppliers in the united states, over thousands, offer over 1 million different products to manufacturing companies, each selling their product depending on market forecasts and other variables such as sales data, market information, exhibitions, news, competitor's data, and even weather forecasts. Using sales data, product sensor data, and data from the supplier database, industrial manufacturing enterprises can accurately predict the needs of different regions of the world. Because inventory and sales prices can be tracked and bought in when prices drop, manufacturing enterprises can save a great deal of cost. If the data generated by the sensors in the product is reused, they can also predict where and when parts are needed, knowing what the product has failed and where to need the parts. This will greatly reduce inventory and optimize the supply chain.
At present, products sold in the market are mostly fragmented from raw materials, production, sales and logistics to final customers and then to product data in the whole process of raw material selection; the RFID technology is a radio frequency identification technology, and realizes communication between an electronic tag and a reader/writer by using an inductive coupling or electromagnetic anti-reflection scattering coupling principle. The bar code identification device can identify raw materials or finished products in links such as a raw material supply bin, a warehouse, a store and the like, and has the advantages of high efficiency, rapidness, reliability, non-line-of-sight reading, multi-target identification, capability of working in severe environment and the like by utilizing the bar code identification device and the RFID reader-writer to collect data.
On the other hand, personalized customization of products (e.g., clothing, jewelry) is one of the sources of new round of development power in various industries, for example, in the clothing industry, data shows that 50% of male consumers cannot find completely fit clothing, and 84% of women suffer from the problem of difficulty in fitting clothing after purchasing, which directly results in the loss of nearly 1100 ten thousand dollars per year in the clothing market. The size difference between individuals is large, and the simple S, L, M code does not meet the requirements of consumers well. Thus, more detailed customer portrayal and more personalized product customization are becoming more and more important.
The RFID electronic tag can acquire key data of all links of raw materials, production, sales and logistics to final customers, so that a solid foundation is provided for big data analysis, product recommendation and customization and process improvement of products, and meanwhile, the data can be utilized to track and trace the product quality.
Disclosure of the invention
The invention aims to provide a product supply big data system based on RFID (radio frequency identification devices), which provides a solid foundation for big data analysis, product recommendation and customization, process improvement and quality tracking and tracing of products.
The invention has the technical scheme that the RFID-based big data supply system comprises a data acquisition system, a data preprocessing system, a data transmission system, a data storage system, a data analysis processing system and a data supply system;
the data acquisition system comprises an electronic tag (RFID) data acquisition system and a service data acquisition system; the electronic tag data acquisition system scans the RFID electronic tag adhered to the product or the product container through the RFID reader-writer and acquires RFID electronic tag data; the service data acquisition system acquires service order information data of a product field through a standardized interface according to the service requirement of the supply data system;
the initial RFID data set contains a large amount of redundant information, because the RFID reader adopts interval scanning, and the reading and writing frequency is not unified with the product position, therefore, in order to ensure the sufficiency and the integrity of the product information, the reading and writing frequency of the reader is higher than the product moving frequency, and redundant data is formed. Therefore, the initial data set is required to be compressed, and the same information of the same product is removed; the data preprocessing system cleans and compresses the data acquired by the data acquisition system;
the data transmission system transmits the data processed by the data preprocessing system to the data storage system;
the data storage system is used for storing the data transmitted by the data transmission system;
the data processing system is used for analyzing, feeding back and predicting the data in the data storage system;
and the supply data system constructs a driving model based on a service instruction according to the data information processed by the data processing system, and feeds back a final calculation result to the data analysis processing system and an information receiving terminal of a worker, so that the worker can know the current product supply condition and the future supply trend.
Further, the method for compressing the data acquired by the data acquisition system by the data preprocessing system comprises the following steps:
step 2.1, a single tuple is adopted to represent a large number of products which move in batches or stay at the same position in different stages;
step 2.2. summarize the data by replacing the product attribute values of the relatively low level with the higher level attribute values;
and 2.3, ignoring or combining the specific product path sections into a simple product path.
Furthermore, the method for the data processing system to perform intelligent prediction processing on the data in the data storage system is as follows; extracting the characteristics of one or more product event data segmented in a data storage system, and respectively obtaining an abstract form model and specific detail characteristics of one or more product event integrals through statistical characteristics and wavelet transformation; and generating a feature vector by using the extracted statistical features and wavelet transformation features, constructing a classifier model by using a machine learning algorithm, and predicting and identifying future events of the product.
Further, the method for extracting the features of one or more product event data segmented in the data storage system is a principal component analysis method.
Further, the machine learning algorithm is a decision tree or a support vector machine algorithm.
Furthermore, the electronic tag data acquisition system comprises an RFID reader-writer and an RFID antenna connected with the RFID reader-writer, and the RFID reader-writer reads the data of the RFID electronic tag of the product according to the reading range of the RFID antenna of the RFID reader-writer.
Further, the RFID reader-writer is a UHF frequency band passive RFID reader-writer.
In general, by the above technical solution of the present invention, compared with the prior art, the following beneficial effects can be obtained:
1. the RFID-based supply big data system improves the data reusability, realizes the high-efficiency integration and integrated application of the RFID supply chain data, and is beneficial to the large-scale application and popularization of the RFID technology.
2. According to the RFID-based supply big data system, the RFID acquired data is compressed by adopting a novel compression method, so that the transmission efficiency and the transmission quantity are greatly improved.
3. According to the RFID-based supply big data system, the future events of the product are predicted and identified by constructing the machine learning classifier model of the product events, and a solution is provided for product recommendation and customization, process improvement, quality tracking and tracing.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
A supply big data system based on RFID comprises a data acquisition system, a data preprocessing system, a data transmission system, a data storage system, a data analysis processing system and a supply data system;
the data acquisition system comprises an electronic tag (RFID) data acquisition system and a service data acquisition system;
the electronic tag data acquisition system scans the RFID electronic tag adhered to the product or the product container through the RFID reader-writer and acquires RFID electronic tag data; the electronic tag data acquisition system comprises an RFID reader-writer (UHF frequency band passive RFID reader-writer) and an RFID antenna connected with the RFID reader-writer, wherein the RFID reader-writer reads data of the RFID electronic tag of a product according to the reading range of the RFID antenna of the RFID reader-writer.
The service data acquisition system acquires service order information data of a product field through a standardized interface according to the service requirement of the supply data system;
the initial RFID data set contains a large amount of redundant information, because the RFID reader adopts interval scanning, and the reading and writing frequency is not unified with the product position, therefore, in order to ensure the sufficiency and the integrity of the product information, the reading and writing frequency of the reader is higher than the product moving frequency, and redundant data is formed. Therefore, the initial data set is required to be compressed, and the same information of the same product is removed; the data preprocessing system cleans and compresses the data acquired by the data acquisition system;
the data transmission system transmits the data processed by the data preprocessing system to the data storage system;
the data storage system is used for storing the data transmitted by the data transmission system;
the data processing system is used for analyzing, feeding back and intelligently predicting the data in the data storage system; the specific intelligent prediction processing method comprises the following steps of; performing feature extraction on one or more product event data segmented in a data storage system by a principal component analysis method, and respectively obtaining an abstract morphological model and specific detail features of one or more product event integrals by statistical features and wavelet transformation; and generating a feature vector by using the extracted statistical features and wavelet transformation features, constructing a classifier model by using a decision tree or a support vector machine algorithm, and predicting and identifying future events of the product.
The supply data system designs a single-grade entity object and a composite-grade business object according to the RFID electronic tag data reported by the data acquisition system in real time and the received business order information, constructs a field driving model based on a business instruction, and feeds back a final calculation result to the data analysis processing system.
The method for compressing the data acquired by the data acquisition system by the data preprocessing system comprises the following steps:
2.1 using a single tuple to represent a large number of products that have different phases moved in batches or stayed in the same location; there are a large number of products moving in batches or staying in the same place at different stages, so it is not necessary to store all records, and the products can be represented by a single tuple, no matter how many. For example: assuming that 1000 boxes of milk stay at the position a in the time interval of (t1, t2), 1000 (prod-list, a, t1, t2) quadruple records do not need to be stored, and only one quintuple (pro-list, a, t1, t2) is used for replacing the four quadruple records, so that the storage space is greatly saved.
2.2 many workers are only interested in some data at higher abstraction levels and therefore can employ data generalization techniques for compression. For example: if the minimum time interval granularity is hour, then the movement of a large number of products within the same hour can be considered to move together and can be combined into one movement. Similarly, if the smallest granularity of location is shelf, then moving a large number of products to different shelfs can be viewed as moving to the same location, or can be merged into one record. Aggregating data by replacing relatively low-level product attribute values (e.g., values of attribute age) with higher-level attribute values (e.g., young, middle, and old);
2.3 in many analysis efforts, certain path segments can be ignored or merged into a simple path. For example, unwanted movement of some products, such as from one shelf to another, in a particular analysis may be ignored altogether. Some partial paths may be merged together without affecting the overall analysis structure. The merging of these path segments may substantially reduce the overall amount of data and speed up the data analysis.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. An RFID-based provisioning big data system, characterized by: the system comprises a data acquisition system, a data preprocessing system, a data transmission system, a data storage system, a data analysis processing system and a data supply system;
the data acquisition system comprises an electronic tag (RFID) data acquisition system and a service data acquisition system; the electronic tag data acquisition system scans the RFID electronic tag adhered to the product or the product container through the RFID reader-writer and acquires RFID electronic tag data; the service data acquisition system acquires service order information data of a product field through a standardized interface according to the service requirement of the supply data system;
the data preprocessing system cleans and compresses the data acquired by the data acquisition system;
the data transmission system transmits the data processed by the data preprocessing system to the data storage system;
the data storage system is used for storing the data transmitted by the data transmission system;
the data processing system is used for analyzing, feeding back and intelligently predicting the data in the data storage system;
and the supply data system constructs a driving model based on a service instruction according to the data information processed by the data processing system, and feeds back a final calculation result to the data analysis processing system and an information receiving terminal of a worker.
2. The RFID-based provisioning big data system of claim 1, wherein: the method for compressing the data acquired by the data acquisition system by the data preprocessing system comprises the following steps:
step 2.1, a single tuple is adopted to represent a large number of products which move in batches or stay at the same position in different stages;
step 2.2. summarize the data by replacing the product attribute values of the relatively low level with the higher level attribute values;
and 2.3, ignoring or combining the specific product path sections into a simple product path.
3. The RFID-based supply big data system of claim 1, wherein the data processing system is used for the intelligent prediction processing of the data in the data storage system by the method of; extracting the characteristics of one or more product event data segmented in a data storage system, and respectively obtaining an abstract form model and specific detail characteristics of one or more product event integrals through statistical characteristics and wavelet transformation; and generating a feature vector by using the extracted statistical features and wavelet transformation features, constructing a classifier model by using a machine learning algorithm, and predicting and identifying future events of the product.
4. The RFID-based provisioning big data system of claim 3, wherein: the method of feature extraction of one or more product event data segmented in a data storage system is a principal component analysis method.
5. The RFID-based provisioning big data system of claim 3, wherein: the machine learning algorithm is a decision tree or a support vector machine algorithm.
6. The RFID-based provisioning big data system of claim 1, wherein: the electronic tag data acquisition system comprises an RFID reader-writer and an RFID antenna connected with the RFID reader-writer, and the RFID reader-writer reads data of the RFID electronic tag of the product according to the reading range of the RFID antenna of the RFID reader-writer.
7. The RFID-based provisioning big data system of claim 6, wherein: the RFID reader-writer is a UHF frequency band passive RFID reader-writer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110995642.XA CN113762439A (en) | 2021-08-27 | 2021-08-27 | Supply big data system based on RFID |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110995642.XA CN113762439A (en) | 2021-08-27 | 2021-08-27 | Supply big data system based on RFID |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113762439A true CN113762439A (en) | 2021-12-07 |
Family
ID=78791528
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110995642.XA Pending CN113762439A (en) | 2021-08-27 | 2021-08-27 | Supply big data system based on RFID |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113762439A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109697488A (en) * | 2018-12-21 | 2019-04-30 | 深圳市远望谷锐泰科技有限公司 | A kind of the RFID Internet of Things application system and method for supply chain orientation management |
| CN110619294A (en) * | 2019-09-06 | 2019-12-27 | 中南大学 | Personalized mouth shape identification method based on RFID system customization |
| CN111144523A (en) * | 2019-12-26 | 2020-05-12 | 江苏孚登物联网技术有限公司 | Data analysis method based on RFID |
-
2021
- 2021-08-27 CN CN202110995642.XA patent/CN113762439A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109697488A (en) * | 2018-12-21 | 2019-04-30 | 深圳市远望谷锐泰科技有限公司 | A kind of the RFID Internet of Things application system and method for supply chain orientation management |
| CN110619294A (en) * | 2019-09-06 | 2019-12-27 | 中南大学 | Personalized mouth shape identification method based on RFID system customization |
| CN111144523A (en) * | 2019-12-26 | 2020-05-12 | 江苏孚登物联网技术有限公司 | Data analysis method based on RFID |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lee et al. | RFID‐based traceability in the supply chain | |
| CN109978332A (en) | A kind of extensive personalized customization method based on RFID | |
| CN108537635A (en) | A kind of recommendation method and device of product | |
| Thiesse et al. | RFID data sharing in supply chains: What is the value of the EPC network? | |
| Freichel et al. | Challenges of supply chain visibility in distribution logistics–a literature review | |
| Chaudhari | Impact of automation technology on logistics and supply chain management | |
| Bose et al. | Facing the challenges of RFID data management | |
| Zare Mehrjerdi | RFID‐enabled supply chain systems with computer simulation | |
| Fleisch et al. | On the management implications of ubiquitous computing: An IS perspective | |
| CN107944805A (en) | A kind of method and apparatus of Internet of product storage | |
| CN113762439A (en) | Supply big data system based on RFID | |
| Hardgrave et al. | RFID assimilation hierarchy | |
| Behl et al. | Role of IoT in supply chain innovation: A survey analysis | |
| Cinicioglu et al. | Use of radio frequency identification for targeted advertising: A collaborative filtering approach using bayesian networks | |
| Jardine et al. | Wireless smart product tracking using radio frequency identification | |
| CN115917574A (en) | System and method for increasing migration and accessibility of data | |
| Gerst et al. | Current issues in RFID standardisation | |
| Jiang et al. | Information technology support system of supply chain management | |
| Dash | Supply Chain Management: The RFID Advantage. | |
| Stojkic et al. | The enchanting of information systems with digital technologies | |
| Zeimpekis et al. | Real-time logistics management of dried figs using RFID technology: case study in a Greek cooperative company | |
| Mohebi et al. | Application of machine learning and RFID in the stability optimization of perishable foods | |
| Zhao et al. | Study of the lean logistics operating model based on RFID and its application in auto industry | |
| Tyagi et al. | RFID Data Management | |
| KR102645566B1 (en) | System for providing international freight service |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |