CN112417911A - RFID-based intelligent optimization group inspection method - Google Patents
RFID-based intelligent optimization group inspection method Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
- G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
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- G06K7/10297—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves arrangements for handling protocols designed for non-contact record carriers such as RFIDs NFCs, e.g. ISO/IEC 14443 and 18092
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
- G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
- G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
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Abstract
The invention relates to the technical field of RFID (radio frequency identification devices), in particular to an RFID-based intelligent optimization group inspection method, which comprises the steps of firstly, carrying out data acquisition on an RFID chip through an RFID reader and storing the data into a server; carrying out feature model training on the imported data; and carrying out intelligent optimization group inspection on the imported data through the trained characteristic model. According to the invention, the data model is obtained by carrying out efficient characteristic training on the data read and stored by the RFID reader in the data server, and the data model obtained by training is subjected to real-time data screening, so that the data which needs to be utilized and is valuable pertinently is selected, and the real-time training and acquisition of the data of the large database which is subjected to data acquisition by the RFID technology are facilitated, so that the data with the required characteristics is obtained, and the purpose of efficiently utilizing the data is achieved.
Description
Technical Field
The invention relates to the technical field of RFID (radio frequency identification devices), in particular to an intelligent optimization group inspection method based on RFID.
Background
The understanding of the world and the construction of the world are the basis of all human activities. The world is recognized by establishing a model, and the world is established by optimizing the model. Particularly in the big data era, the model is more and more complex, the data is more and more, and the optimization model becomes more important. The algorithm of the optimization model and the corresponding optimization idea have the essential limitations, the efficiency is difficult to improve due to the calculation of a single point, the iteration towards the improvement direction is easy to fall into the local optimization, and the application range of the optimization algorithm is greatly limited due to various constraints on the objective function. Therefore, in a large-scale and distributed condition, in order to achieve better training effect, a plurality of models must be optimized, and a group intelligence algorithm is used.
There are many kinds of algorithms for crowd sourcing, but most have a common property, namely, emulation. For example, the concept of genetic algorithm comes from biological evolution, particle swarm algorithm comes from the motion law of bird swarm, fish swarm or bee swarm, simulated annealing algorithm comes from physics, and the like.
The rfid (auto Frequency identification) technology, also called rfid, is a communication technology that can identify a specific target and read and write related data through radio signals without establishing mechanical or optical contact between the identification system and the specific target. The common technologies comprise low frequency (125K-134.2K), high frequency (13.56Mhz), ultrahigh frequency, passive technology and the like. NFC is a short-range high-frequency RFID technology, and transmits signals by electromagnetic induction coupling in the radio frequency part of the frequency spectrum. The system is integrated and evolved by non-contact Radio Frequency Identification (RFID) and interconnection and intercommunication technologies, and realizes applications such as mobile payment, electronic ticketing, entrance guard, mobile identity identification, anti-counterfeiting and the like by integrating functions of an induction type card reader, an induction type card and point-to-point communication on a single chip and utilizing a mobile terminal. The E-paper Display technology, generally called electronic Display screen technology, replaces the traditional electronic Display screen with paper Display, and adopts electrophoretic Display (EPD) technology as the Display panel, and the Display effect is close to the natural paper effect. The display content can be randomly rewritten by the fingers sent by the computer, and after the rewriting is finished, the display panel does not need to be continuously powered, and the display content is always kept on the display panel. However, data obtained by performing identification scanning on a large batch of data through RFID is usually not further processed, and becomes original data, and in the data importing process, real-time data screening, discrimination and application are required to achieve efficient utilization of the data.
Disclosure of Invention
The invention provides an RFID-based intelligent optimization group inspection method, which comprises the following steps:
the RFID-based intelligent optimization group inspection method is realized by the following steps:
s1: firstly, data acquisition of an RFID chip is carried out through an RFID reader and is stored in a server;
s2: carrying out feature model training on the imported data;
s3: and carrying out intelligent optimization group inspection on the imported data through the trained characteristic model.
Further, S2 includes the steps of:
s2.1: firstly, manually kicking out data with useless characteristics in the data;
s2.2: determining the objective function as L (x): as shown in formula (1):
l ^ (x) ═ L (x) = L (x) + alpha | | | w | | |1 formula (1)
Where α is called a penalty factor, | | w | | |1 represents the L1 norm
S2.3: iterating the data obtained in the step S2.2, wherein a specific iteration formula is shown as a formula (2);
θ k +1 ═ θ k- α 1m ∑ i ═ 1m (yi-h θ (xi)) xik formula (2)
Wherein alpha is the learning rate
S2.4: step S2.3 is followed by the addition of an L2 norm to the objective function; specifically shown as formula (3);
l (θ) ═ 12m Σ i ═ 1m (yi-h θ (xi))2+ λ 2| | | θ | | |2 formula (3)
S2.5: and (4) carrying out group inspection calculation on the data substituted into the server by the training model obtained in the step (S2.4) so as to obtain the data screened by the characteristics obtained by the target function training.
Further, in step S2.1, a character with a different type of mark or a data string with data imperfections is kicked out first, leaving only data with a complete data string and with the character characteristics required.
Further, in step S2.2-step S2.4: and in the processes of iterative computation of data and import and acquisition of the data, a Python code is adopted for coding realization.
The invention has the advantages and positive effects that: the data model is obtained by carrying out efficient characteristic training on data read and stored by the RFID reader in the data server, and real-time data screening is carried out on the trained data model, so that data which need to be utilized and are valuable are selected, the real-time training and acquisition of data of a large database which is used for carrying out data acquisition through the RFID technology are facilitated, the data of the needed characteristics are obtained, and the purpose of efficiently utilizing the data is achieved.
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FIG. 1 is a flow chart of a method of the present invention;
Detailed Description
The invention will be described in detail below with reference to the following figures and specific examples:
in this embodiment, S1: firstly, data acquisition of an RFID chip is carried out through an RFID reader and is stored in a server;
s2: carrying out feature model training on the imported data;
s3: and carrying out intelligent optimization group inspection on the imported data through the trained characteristic model.
In this embodiment, S2 includes the following steps:
s2.1: firstly, manually kicking out data with useless characteristics in the data;
s2.2: determining the objective function as L (x): as shown in formula (1):
l ^ (x) ═ L (x) = L (x) + alpha | | | w | | |1 formula (1)
Where α is called a penalty factor, | | w | | |1 represents the L1 norm
S2.3: iterating the data obtained in the step S2.2, wherein a specific iteration formula is shown as a formula (2);
θ k +1 ═ θ k- α 1m ∑ i ═ 1m (yi-h θ (xi)) xik formula (2)
Wherein alpha is the learning rate
S2.4: step S2.3 is followed by the addition of an L2 norm to the objective function; specifically shown as formula (3);
l (θ) ═ 12m Σ i ═ 1m (yi-h θ (xi))2+ λ 2| | | θ | | |2 formula (3)
S2.5: and (4) carrying out group inspection calculation on the data substituted into the server by the training model obtained in the step (S2.4) so as to obtain the data screened by the characteristics obtained by the target function training.
In this embodiment, the first kicked out in step S2.1 is a string of characters with different types of flags or data that is incomplete, leaving only the data with the complete string and with the character characteristics that are needed.
In this embodiment, in step S2.2 to step S2.4: and in the processes of iterative computation of data and import and acquisition of the data, a Python code is adopted for coding realization.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.
Claims (4)
1. The RFID-based intelligent optimization group inspection method is characterized by comprising the following steps:
s1: firstly, data acquisition of an RFID chip is carried out through an RFID reader and is stored in a server;
s2: carrying out feature model training on the imported data;
s3: and carrying out intelligent optimization group inspection on the imported data through the trained characteristic model.
2. The RFID-based smart optimized cluster inspection method of claim 1, wherein S2 comprises the steps of:
s2.1: firstly, manually kicking out data with useless characteristics in the data;
s2.2: determining the objective function as L (x): as shown in formula (1):
l ^ (x) ═ L (x) = L (x) + alpha | | | w | | |1 formula (1)
Where α is called a penalty factor, | | w | | |1 represents the L1 norm
S2.3: iterating the data obtained in the step S2.2, wherein a specific iteration formula is shown as a formula (2);
θ k +1 ═ θ k- α 1m ∑ i ═ 1m (yi-h θ (xi)) xik formula (2)
Wherein alpha is the learning rate
S2.4: step S2.3 is followed by the addition of an L2 norm to the objective function; specifically shown as formula (3);
l (θ) ═ 12m Σ i ═ 1m (yi-h θ (xi))2+ λ 2| | | θ | | |2 formula (3)
S2.5: and (4) carrying out group inspection calculation on the data substituted into the server by the training model obtained in the step (S2.4) so as to obtain the data screened by the characteristics obtained by the target function training.
3. RFID-based smart optimized crowd inspection method according to claim 2, characterized in that in step S2.1 first kicked out is a character with different kind of flags or a data string with data imperfection, leaving only data with a complete data string and with the character characteristics needed.
4. The RFID-based smart optimized cluster inspection method of claim 2, wherein in step S2.2-step S2.4: and in the processes of iterative computation of data and import and acquisition of the data, a Python code is adopted for coding realization.
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| CN110503354A (en) * | 2019-07-02 | 2019-11-26 | 北京交通大学 | A kind of RFID label tag position estimation method based on deep learning |
| CN110866468A (en) * | 2019-10-30 | 2020-03-06 | 上海交通大学 | Gesture recognition system and method based on passive RFID |
| CN111275025A (en) * | 2020-03-23 | 2020-06-12 | 复旦大学 | Parking space detection method based on deep learning |
| CN111414982A (en) * | 2020-03-20 | 2020-07-14 | 上海哲山科技股份有限公司 | RFID label positioning method and device |
| CN111564160A (en) * | 2020-04-21 | 2020-08-21 | 重庆邮电大学 | Voice noise reduction method based on AEWGAN |
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- 2020-11-19 CN CN202011304557.6A patent/CN112417911A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190332854A1 (en) * | 2018-04-25 | 2019-10-31 | Shutterfly, Inc. | Hybrid deep learning method for recognizing facial expressions |
| CN110503354A (en) * | 2019-07-02 | 2019-11-26 | 北京交通大学 | A kind of RFID label tag position estimation method based on deep learning |
| CN110866468A (en) * | 2019-10-30 | 2020-03-06 | 上海交通大学 | Gesture recognition system and method based on passive RFID |
| CN111414982A (en) * | 2020-03-20 | 2020-07-14 | 上海哲山科技股份有限公司 | RFID label positioning method and device |
| CN111275025A (en) * | 2020-03-23 | 2020-06-12 | 复旦大学 | Parking space detection method based on deep learning |
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