CN111723702B - Data monitoring method, device and payment system - Google Patents
Data monitoring method, device and payment system Download PDFInfo
- Publication number
- CN111723702B CN111723702B CN202010515467.5A CN202010515467A CN111723702B CN 111723702 B CN111723702 B CN 111723702B CN 202010515467 A CN202010515467 A CN 202010515467A CN 111723702 B CN111723702 B CN 111723702B
- Authority
- CN
- China
- Prior art keywords
- target frame
- behavior
- timestamp
- data
- data monitoring
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000012544 monitoring process Methods 0.000 title claims abstract description 21
- 238000001514 detection method Methods 0.000 claims abstract description 24
- 230000002159 abnormal effect Effects 0.000 claims abstract description 20
- 238000012806 monitoring device Methods 0.000 claims abstract description 9
- 230000006399 behavior Effects 0.000 claims description 53
- 238000013527 convolutional neural network Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G1/00—Cash registers
Abstract
The invention provides a data monitoring method, a data monitoring device and a payment system, wherein the method comprises the following steps: collecting a target frame from a video image; detecting the target frame to obtain a behavior category corresponding to the target frame; when the behavior category is a preset behavior, acquiring a first timestamp corresponding to the target frame; if transaction data uploaded by the POS machine is not queried in the time range corresponding to the first timestamp, marking the first timestamp and the corresponding target frame as abnormal data. Therefore, the transaction data of the POS machine can be automatically detected, manual intervention is not needed, the detection efficiency is high, abnormal transaction data can be accurately detected, and staff bill leakage and bill stealing actions are avoided.
Description
Technical Field
The invention relates to the technical field of POS (point of sale) machines, in particular to a data monitoring method, a data monitoring device and a payment system.
Background
With the rise of electronic commerce, a POS (Point of Sales) machine is used as a common electronic payment tool, has functions of card reading, password keyboard and the like, can interact with a background server remotely to complete transactions, and is widely applied to various shops, gas stations and some consumer places.
The POS machine is used as a financial application tool and is usually managed and issued by banks, unions and third-party financial institutions with corresponding qualification. The merchant clerk then uses the POS to complete the payment with the off-line customer.
Because the POS machine is used as an off-line operation behavior, the POS machine is difficult to monitor, and the situations of ticket stealing and ticket leakage of a store staff are easy to occur, so that the loss is caused to a merchant.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a data monitoring method, a data monitoring device and a payment system so as to realize effective monitoring of POS transaction data.
In a first aspect, an embodiment of the present invention provides a data monitoring method, including:
collecting a target frame from a video image;
detecting the target frame to obtain a behavior category corresponding to the target frame;
when the behavior category is a preset behavior, acquiring a first timestamp corresponding to the target frame;
if transaction data uploaded by the POS machine is not queried in the time range corresponding to the first timestamp, marking the first timestamp and the corresponding target frame as abnormal data.
Optionally, the capturing the target frame from the video image includes:
at least one target frame is extracted from the video image according to a preset sampling period.
Optionally, the detecting the target frame to obtain a behavior category corresponding to the target frame includes:
detecting the target frame by adopting a real-time object detection network model YOLO to obtain a behavior class corresponding to the target frame; wherein the behavior categories include: payment behavior based on POS, other behavior.
Optionally, the acquiring the first timestamp corresponding to the target frame includes:
and acquiring the shooting time of the target frame by adopting a convolutional neural network, and marking the shooting time as the first time stamp.
Optionally, the method further comprises:
counting the abnormal data according to a preset period and/or employee information to obtain a statistical table;
and sending the statistical table to a terminal corresponding to the administrator.
In a second aspect, an embodiment of the present invention provides a data monitoring apparatus, including:
the acquisition module is used for acquiring a target frame from the video image;
the first detection module is used for detecting the target frame to obtain a behavior category corresponding to the target frame;
the second detection module is used for acquiring a first timestamp corresponding to the target frame when the behavior type is a preset behavior;
and the processing module is used for marking the first time stamp and the corresponding target frame as abnormal data when the transaction data uploaded by the POS machine are not queried in the time range corresponding to the first time stamp.
Optionally, the acquisition module is specifically configured to:
at least one target frame is extracted from the video image according to a preset sampling period.
Optionally, the first detection module is specifically configured to:
detecting the target frame by adopting a real-time object detection network model YOLO to obtain a behavior class corresponding to the target frame; wherein the behavior categories include: payment behavior based on POS, other behavior.
Optionally, the second detection module is specifically configured to:
and acquiring the shooting time of the target frame by adopting a convolutional neural network, and marking the shooting time as the first time stamp.
Optionally, the processing module is further configured to:
counting the abnormal data according to a preset period and/or employee information to obtain a statistical table;
and sending the statistical table to a terminal corresponding to the administrator.
In a third aspect, an embodiment of the present invention provides a payment system, including: the device comprises a camera, a memory and at least one processor;
the camera is used for shooting video images;
the memory is used for storing executable instructions of the processor;
wherein the processor is configured to perform the data monitoring method of any of the first aspects via execution of the executable instructions.
In a fourth aspect, an embodiment of the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the data monitoring method according to any one of the first aspects.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a data monitoring method, a data monitoring device and a payment system, which are characterized in that a target frame is acquired from a video image; detecting the target frame to obtain a behavior category corresponding to the target frame; when the behavior category is a preset behavior, acquiring a first timestamp corresponding to the target frame; if transaction data uploaded by the POS machine is not queried in the time range corresponding to the first timestamp, marking the first timestamp and the corresponding target frame as abnormal data. Therefore, the transaction data of the POS machine can be automatically detected, manual intervention is not needed, the detection efficiency is high, abnormal transaction data can be accurately detected, and staff bill leakage and bill stealing actions are avoided.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
fig. 1 is a schematic view of an application scenario provided in an embodiment of the present invention;
FIG. 2 is a flow chart of a data monitoring method according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a data monitoring device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a payment system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present invention; as shown in fig. 1, at least one camera is provided at the counter position of the clerk, and the camera is used for shooting the transaction behavior between the clerk and the customer. The camera sends the shot video image to a background server, and the background server collects a target frame from the video image; detecting the target frame to obtain a behavior class corresponding to the target frame; when the behavior category is a preset behavior, a first timestamp corresponding to the target frame is obtained (see fig. 1, the first timestamp is 2019-06-13, 09:39:50). A time range is set based on the first timestamp, and transaction data in the time range is extracted from the POS machine system based on the time range. If the transaction data uploaded by the POS machine is not queried in the time range corresponding to the first timestamp, marking the first timestamp and the corresponding target frame as abnormal data. Therefore, the transaction data of the POS machine can be automatically detected, manual intervention is not needed, the detection efficiency is high, abnormal transaction data can be accurately detected, and staff bill leakage and bill stealing actions are avoided.
FIG. 2 is a flow chart of a data monitoring method according to an embodiment of the invention; as shown in fig. 2, the method in this embodiment may include the following steps:
s101, collecting a target frame from a video image.
S102, detecting the target frame to obtain a behavior category corresponding to the target frame.
And S103, when the behavior type is a preset behavior, acquiring a first timestamp corresponding to the target frame.
And S104, if the transaction data uploaded by the POS machine is not queried within the time range corresponding to the first time stamp, marking the first time stamp and the corresponding target frame as abnormal data.
Illustratively, in step S101, at least one target frame may be extracted from a video image according to a preset sampling period. E.g., every half of the number of video frames (F) (F/2), the target frame is decimated.
For example, in step S102, a real-time object detection network model YOLO (a concise architecture based on CNN and anchor frame, and a real-time object detection technology capable of aiming at the common use problem) may be used to detect the target frame, so as to obtain a behavior class corresponding to the target frame; wherein, the behavior category includes: payment behavior based on POS, other behavior. The object detection network model YOLO is adopted to detect the target frame, so that the image processing efficiency and accuracy can be effectively improved.
Illustratively, in step S103, a time of capturing the target frame may be acquired by using a time stamp technique of the convolutional neural network, and denoted as a first time stamp. The time stamp is extracted, so that the extraction of transaction data can be limited in the range corresponding to the time stamp, the number of data extraction is effectively reduced, and the detection efficiency is effectively improved on the premise of ensuring the data monitoring accuracy.
According to the embodiment, the transaction data of the POS machine can be automatically detected, manual intervention is not needed, the detection efficiency is high, abnormal transaction data can be accurately detected, and staff bill leakage and bill stealing actions are avoided.
Optionally, after obtaining the abnormal data, counting the abnormal data according to a preset period and/or employee information to obtain a statistical table; and sending the statistical table to a terminal corresponding to the administrator. Therefore, an administrator can conveniently count abnormal data, the administrator can timely find out employee bill leakage and bill stealing behaviors, and merchant loss is reduced.
FIG. 3 is a schematic diagram of a data monitoring device according to an embodiment of the present invention; as shown in fig. 3, the apparatus in this embodiment may include:
an acquisition module 21 for acquiring a target frame from a video image;
the first detection module 22 is configured to detect a target frame, so as to obtain a behavior class corresponding to the target frame;
the second detection module 23 is configured to obtain a first timestamp corresponding to the target frame when the behavior class is a preset behavior;
and the processing module 24 is configured to mark the first timestamp and the corresponding target frame as abnormal data when the transaction data uploaded by the POS machine is not queried within the time range corresponding to the first timestamp.
Optionally, the acquisition module 21 is specifically configured to:
at least one target frame is extracted from the video image according to a preset sampling period.
Optionally, the first detection module 22 is specifically configured to:
detecting a target frame by adopting a real-time object detection network model YOLO to obtain a behavior category corresponding to the target frame; wherein, the behavior category includes: payment behavior based on POS, other behavior.
Optionally, the second detection module 23 is specifically configured to:
and acquiring the shooting time of the target frame by adopting a convolutional neural network, and marking the shooting time as a first time stamp.
Optionally, the processing module 24 is further configured to:
counting abnormal data according to a preset period and/or employee information to obtain a statistical table;
and sending the statistical table to a terminal corresponding to the administrator.
The apparatus in this embodiment may perform the data monitoring method shown in fig. 2, and the specific implementation process and technical effect thereof are not described herein.
Fig. 4 is a schematic structural diagram of a payment system according to an embodiment of the present invention, as shown in fig. 4, the payment system 30 in this embodiment includes:
a processor 31; the method comprises the steps of,
a memory 32 for storing executable instructions of the processor, which may also be a flash memory;
wherein the processor 31 is configured to perform the steps of the above-described method via execution of executable instructions. Reference may be made in particular to the description of the embodiments of the method described above.
Alternatively, the memory 32 may be separate or integrated with the processor 31.
When the memory 32 is a device separate from the processor 31, the payment system 30 may further include:
a bus 33 for connecting the processor 31 and the memory 32.
The payment system 30 provided in this embodiment may be used to implement the technical solution of the method embodiment shown in fig. 2, and its implementation principle and technical effects are similar, and will not be described herein again. It should be noted that, the payment system 30 may be in communication connection with an external camera, or may be integrated with a camera, so that a video image may be captured.
It should be noted that, the steps in the data monitoring method provided by the present invention may be implemented by using corresponding modules, devices, units, etc. in the payment system, and those skilled in the art may refer to a technical scheme of the system to implement a step flow of the method, that is, an embodiment in the system may be understood as a preferred embodiment of the implementation method, which is not described herein.
Those skilled in the art will appreciate that the invention provides a system and its individual devices that can be implemented entirely by logic programming of method steps, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc., in addition to the system and its individual devices being implemented in pure computer readable program code. Therefore, the system and various devices thereof provided by the present invention may be considered as a hardware component, and the devices included therein for implementing various functions may also be considered as structures within the hardware component; means for achieving the various functions may also be considered as being either a software module that implements the method or a structure within a hardware component.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.
Claims (9)
1. A method of data monitoring, comprising:
collecting a target frame from a video image;
detecting the target frame to obtain a behavior category corresponding to the target frame;
when the behavior category is payment behavior, acquiring a first timestamp corresponding to the target frame; the obtaining the first timestamp corresponding to the target frame includes: acquiring the shooting time of the target frame by adopting a convolutional neural network, and marking the shooting time as the first time stamp;
if transaction data uploaded by the POS machine is not queried in the time range corresponding to the first timestamp, marking the first timestamp and the corresponding target frame as abnormal data.
2. The method of claim 1, wherein the capturing the target frame from the video image comprises:
at least one target frame is extracted from the video image according to a preset sampling period.
3. The method for monitoring data according to claim 1, wherein the detecting the target frame to obtain the behavior class corresponding to the target frame includes:
detecting the target frame by adopting a real-time object detection network model YOLO to obtain a behavior class corresponding to the target frame; wherein the behavior categories include: payment behavior based on POS, other behavior.
4. A data monitoring method according to any one of claims 1 to 3, further comprising:
counting the abnormal data according to a preset period and/or employee information to obtain a statistical table;
and sending the statistical table to a terminal corresponding to the administrator.
5. A data monitoring device, comprising:
the acquisition module is used for acquiring a target frame from the video image;
the first detection module is used for detecting the target frame to obtain a behavior category corresponding to the target frame;
the second detection module is used for acquiring a first timestamp corresponding to the target frame when the behavior class is payment behavior; the obtaining the first timestamp corresponding to the target frame includes: acquiring the shooting time of the target frame by adopting a convolutional neural network, and marking the shooting time as the first time stamp;
and the processing module is used for marking the first time stamp and the corresponding target frame as abnormal data when the transaction data uploaded by the POS machine are not queried in the time range corresponding to the first time stamp.
6. The data monitoring device according to claim 5, wherein the acquisition module is specifically configured to:
at least one target frame is extracted from the video image according to a preset sampling period.
7. The data monitoring device of claim 5, wherein the first detection module is specifically configured to:
detecting the target frame by adopting a real-time object detection network model YOLO to obtain a behavior class corresponding to the target frame; wherein the behavior categories include: payment behavior based on POS, other behavior.
8. A payment system, comprising: the device comprises a camera, a memory and at least one processor;
the camera is used for shooting video images;
the memory is used for storing executable instructions of the processor;
wherein the processor is configured to perform the data monitoring method of any one of claims 1 to 4 via execution of the executable instructions.
9. A storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the data monitoring method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010515467.5A CN111723702B (en) | 2020-06-08 | 2020-06-08 | Data monitoring method, device and payment system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010515467.5A CN111723702B (en) | 2020-06-08 | 2020-06-08 | Data monitoring method, device and payment system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111723702A CN111723702A (en) | 2020-09-29 |
| CN111723702B true CN111723702B (en) | 2024-02-27 |
Family
ID=72566256
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010515467.5A Active CN111723702B (en) | 2020-06-08 | 2020-06-08 | Data monitoring method, device and payment system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111723702B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101268478A (en) * | 2005-03-29 | 2008-09-17 | 斯达普力特有限公司 | Method and apparatus for detecting suspicious activity using video analysis |
| CN102714713A (en) * | 2010-02-01 | 2012-10-03 | 国际商业机器公司 | Video stream processing |
| CN103733633A (en) * | 2011-05-12 | 2014-04-16 | 索林科集团 | Video analytics system |
| CN104574954A (en) * | 2014-11-26 | 2015-04-29 | 深圳市金溢科技股份有限公司 | Vehicle checking method and system based on free flow system as well as control equipment |
| CN107005599A (en) * | 2015-07-24 | 2017-08-01 | 谷歌公司 | Improve the system and method for position sensor precision ratio |
| CN108183929A (en) * | 2017-11-28 | 2018-06-19 | 特斯联（北京）科技有限公司 | A kind of target association sensory perceptual system and method based on Intelligentized internet-of-thing |
| CN110415295A (en) * | 2019-07-03 | 2019-11-05 | 平安科技（深圳）有限公司 | Image processing method and image-data processing apparatus |
| CN110688522A (en) * | 2019-09-25 | 2020-01-14 | 神州数码融信软件有限公司 | Key frame based transaction flow recording method and device |
| CN110866429A (en) * | 2018-08-28 | 2020-03-06 | 阿里巴巴集团控股有限公司 | Missed scanning identification method and device, self-service cash register terminal and system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2008262268B2 (en) * | 2007-06-09 | 2012-05-03 | Sensormatic Electronics, LLC | System and method for integrating video analytics and data analytics/mining |
| US8448859B2 (en) * | 2008-09-05 | 2013-05-28 | Datalogic ADC, Inc. | System and method for preventing cashier and customer fraud at retail checkout |
| CA3091605A1 (en) * | 2013-03-15 | 2014-09-15 | James Carey | Investigation generation in an observation and surveillance system |
| US10650365B2 (en) * | 2017-06-02 | 2020-05-12 | Visa International Service Association | Automated point of sale system |
-
2020
- 2020-06-08 CN CN202010515467.5A patent/CN111723702B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101268478A (en) * | 2005-03-29 | 2008-09-17 | 斯达普力特有限公司 | Method and apparatus for detecting suspicious activity using video analysis |
| CN102714713A (en) * | 2010-02-01 | 2012-10-03 | 国际商业机器公司 | Video stream processing |
| CN103733633A (en) * | 2011-05-12 | 2014-04-16 | 索林科集团 | Video analytics system |
| CN104574954A (en) * | 2014-11-26 | 2015-04-29 | 深圳市金溢科技股份有限公司 | Vehicle checking method and system based on free flow system as well as control equipment |
| CN107005599A (en) * | 2015-07-24 | 2017-08-01 | 谷歌公司 | Improve the system and method for position sensor precision ratio |
| CN108183929A (en) * | 2017-11-28 | 2018-06-19 | 特斯联（北京）科技有限公司 | A kind of target association sensory perceptual system and method based on Intelligentized internet-of-thing |
| CN110866429A (en) * | 2018-08-28 | 2020-03-06 | 阿里巴巴集团控股有限公司 | Missed scanning identification method and device, self-service cash register terminal and system |
| CN110415295A (en) * | 2019-07-03 | 2019-11-05 | 平安科技（深圳）有限公司 | Image processing method and image-data processing apparatus |
| CN110688522A (en) * | 2019-09-25 | 2020-01-14 | 神州数码融信软件有限公司 | Key frame based transaction flow recording method and device |
Non-Patent Citations (4)
| Title |
|---|
| Sequence Matching for Suspicious Activity Detection in Anti-Money Laundering;Xuan Liu 等;《Intelligence and Security Informatics》;50-61 * |
| 小型超市收银台视频监控系统;孙妍妍;《中国优秀硕士学位论文全文数据库信息科技辑》(第第(2009)09期期);I140-505 * |
| 建立时间戳识别模型的一种新方法;姚文杰 等;《福建电脑》;第35卷(第11期);84-86 * |
| 银行自助设备监控系统联网设计方案;滕家亮 等;《中国金融电脑》(第07期);46-49 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111723702A (en) | 2020-09-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11069141B2 (en) | Dynamic configuration of an augmented reality overlay | |
| CN107995458B (en) | Method and device for shooting packaging process | |
| CN110704426B (en) | Data verification method, electronic device and computer readable storage medium | |
| CN102214378A (en) | Remote monitoring maintenance method of tax-control cash register | |
| CN112258507B (en) | Target object detection method and device of internet data center and electronic equipment | |
| CN113205037A (en) | Event detection method and device, electronic equipment and readable storage medium | |
| CN110619308A (en) | Aisle sundry detection method, device, system and equipment | |
| US20210272170A1 (en) | Method, Device, Electronic Apparatus and Storage Medium for Generating Order | |
| CN111723702B (en) | Data monitoring method, device and payment system | |
| CN111625418A (en) | Process monitoring method and device | |
| CN104750600A (en) | Equipment state recording method and equipment state recording system | |
| CN112223297A (en) | Goods shelf inspection method and device based on robot, storage medium and robot | |
| CN111369703A (en) | Online time determination method, device, server and medium | |
| CN104318283A (en) | Mobile terminal type test instrument management system and method | |
| WO2020107744A1 (en) | Fabric detection recording method and apparatus, device, and storage medium | |
| CN114500316B (en) | Method and system for inspecting equipment of Internet of things | |
| CN207067999U (en) | A kind of intelligent mobile cruising inspection system | |
| CN114611967A (en) | Object detection method, object detection apparatus, and computer-readable storage medium | |
| CN203870674U (en) | Clothes detection device | |
| JP2015101424A (en) | Commodity inventory system | |
| CN112288961A (en) | Automatic teller machine inspection device, inspection system and inspection method | |
| US20190378389A1 (en) | System and Method of Detecting a Potential Cashier Fraud | |
| CN113327114A (en) | Information processing method and device and electronic equipment | |
| CN215769830U (en) | Intelligent detection timing system | |
| CN113743547B (en) | Asset supervision method and platform based on RFID tag and storage medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |