CN112196402A - Bank note box and management method thereof - Google Patents
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Abstract
The application provides a money box, which comprises a box body, a communication module, a motion module, an identification module and a handover module; the cash box comprises a box body, a communication module, a task management system and a cash delivery module, wherein the box body is used for containing cash, the communication module is used for receiving an escorting task issued by the task management system, and the escorting task is used for indicating to deliver the cash box to a delivery person; the motion module is used for moving according to the escort task; the identification module is used for identifying the handover person according to the identity information of the handover person in the escort task; the handover module is used for completing handover when the recognition module recognizes the handover person. Therefore, the transfer difficulty of the cash box is reduced, and the risk in the transfer process of the cash box is reduced.
Description
Technical Field
The application relates to the technical field of computers, in particular to a money box and a management method of the money box.
Background
Banks, as a currency distribution mechanism, often transfer bills through a bill box. The banknote cassette is a cassette for accommodating banknotes. The bank note boxes of a bank can hold a large amount of cash, for example, two million cash. It is very heavy when the cassette is full of cash. On the one hand, escort personnel need expend a large amount of efforts at escort in-process portable two paper money casees that fill with cash, have increased the paper money case transfer degree of difficulty, and on the other hand, the handing-over process also appears handing-over personnel very easily and does not verify the identity as required, gives the phenomenon that the handing-over process caused the risk.
In view of the above, it is desirable to provide a method for managing a cash box, so as to reduce the transfer difficulty of the cash box and reduce the risk in the handover process of the cash box.
Disclosure of Invention
The application provides a money box and a management method of the money box, the money box moves through the money box, transfer difficulty caused by carrying the money box by escort personnel is solved, identity verification is carried out on a transfer person, transfer is completed after verification, and risks in the process of transferring the money box are reduced.
In a first aspect, the present application provides a banknote box, which includes a box body, a communication module, a motion module, an identification module, and a handover module;
the cash box comprises a box body, a communication module, a task management system and a cash delivery module, wherein the box body is used for containing cash, the communication module is used for receiving an escorting task issued by the task management system, and the escorting task is used for indicating to deliver the cash box to a delivery person;
the motion module is used for moving according to the escort task;
the identification module is used for identifying the handover person according to the identity information of the handover person in the escort task;
the handover module is used for completing handover when the recognition module recognizes the handover person.
In some possible implementation manners, the banknote box further includes a wind control module, and the wind control module is configured to determine the risk of the banknote box by using a risk identification model according to the weight of the banknote box and/or the shape of the articles in the banknote box.
In some possible implementations, the weight of the cassette is obtained by a gravity sensor disposed in the cassette, and the shape information of the items in the cassette is obtained by an optical sensor disposed in the cassette.
In some possible implementation manners, the risk identification model is an error feedback neural network model optimized by a genetic algorithm, the risk identification model takes the weight of the banknote cassette and/or the shape of the articles in the banknote cassette as input, and takes a risk label as output, and the risk label is used for indicating whether the banknote cassette has a risk or not.
In some possible implementation manners, the money box further comprises an alarm module, and the alarm module is used for giving an alarm when the escorting process of the money box is abnormal.
In some possible implementations, the movement module is configured to move according to the escort task, including:
moving according to a path set by the escort task; or,
identifying the escorting person set by the escorting task, and moving according to the moving route of the escorting person.
In a second aspect, the application provides a method for managing a cash cassette. The method comprises the following steps:
receiving an escorting task issued by a task management system, wherein the escorting task is used for indicating the cash box to be handed over to a hand-over person;
moving according to the escort task;
identifying the handover person according to the identity information of the handover person in the escort task;
and when the handover person is identified, completing handover.
In some possible implementations, the method further includes:
and determining the risk of the money box by using a risk identification model according to the weight of the money box and/or the shape of the articles in the money box.
In some possible implementations, the weight of the cassette is obtained by a gravity sensor disposed in the cassette, and the shape information of the items in the cassette is obtained by an optical sensor disposed in the cassette.
In some possible implementation manners, the risk identification model is an error feedback neural network model optimized by a genetic algorithm, the risk identification model takes the weight of the banknote cassette and/or the shape of the articles in the banknote cassette as input, and takes a risk label as output, and the risk label is used for indicating whether the banknote cassette has a risk or not.
In some possible implementations, the method further includes:
and alarming when the escorting process of the cash box is abnormal.
In some possible implementations, the moving according to the escort task includes:
moving according to a path set by the escort task; or,
identifying the escorting person set by the escorting task, and moving according to the moving route of the escorting person.
In a third aspect, the present application provides an apparatus comprising a processor and a memory. The processor and the memory are in communication with each other. The processor is configured to execute the instructions stored in the memory, so as to cause the device to execute the banknote cassette management method according to the first aspect or any implementation manner of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and the instructions instruct an apparatus to execute the banknote cassette management method according to the first aspect or any implementation manner of the first aspect.
In a fifth aspect, the present application provides a computer program product comprising instructions which, when run on an apparatus, cause the apparatus to perform the method of banknote cassette management as described in the first aspect or any implementation manner of the first aspect.
The present application can further combine to provide more implementations on the basis of the implementations provided by the above aspects.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is an architecture diagram of a banknote box according to an embodiment of the present application;
fig. 2 is a flowchart of a banknote cassette management method according to an embodiment of the present application.
Detailed Description
The scheme in the embodiments provided in the present application will be described below with reference to the drawings in the present application.
The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely descriptive of the various embodiments of the application and how objects of the same nature can be distinguished.
In order to facilitate understanding of the technical solutions of the present application, some technical terms related to the present application are described below.
A cash cassette generally refers to a cassette for holding cash during the transport of the cash. At present, two materials, namely metal and nonmetal, are mainly used. The cash box can hold two million cash under the condition of full load, even if the self weight of the cash box is not calculated, the cash box also can weigh twenty kilograms or more, so that the escorting person is very painful to carry the two full cash boxes.
In the conveying process of the money box, the handing-over link is an accident easily-occurring link, if the handing-over personnel cannot verify the identity according to the requirements, risks are easily caused to the handing-over process, and the loss of bank property is easily caused.
In view of this, an embodiment of the present application provides a banknote box, which includes a box body, a communication module, a motion module, an identification module, and a handover module. Specifically, the box still receives the escort task that task management system issued except that holding the bank note, and the motion module removes according to the escort task, discerns the back at the identification module to handing-over people, accomplishes the handing-over, so reduced the degree of difficulty of paper money case transfer, reduced the risk of handing-over process.
Next, a banknote cassette provided in an embodiment of the present application will be described with reference to the accompanying drawings.
Referring to the architecture diagram of a banknote cassette shown in fig. 1, the banknote cassette 100 includes: a case 102, a communication module 104, a motion module 106, an identification module 108, and a handover module 110. The various components of the cassette 100 will now be described in detail.
The cassette 102 is particularly intended to receive banknotes therein. In some possible implementations, the housing 102 may be made of a non-metal material, for example, a plastic such as Polycarbonate (PC) or Polyvinyl Chloride (PVC). The non-metallic housing does not shield electrical signals and thus does not affect the communication between the interior and exterior of the housing 102. In other possible implementations, the housing 102 may also be made of a metal, for example, a low-density metal such as an aluminum alloy. In order to avoid the shielding effect of the box 102 on the electrical signals, the box 102 may be subjected to an anti-shielding process.
The communication module 104 is specifically configured to receive an escort task issued by the task management system, where the escort task is used to instruct the banknote box 100 to be handed over to the handing-over person. In consideration of the convenience of communication, the communication module 104 may specifically be a wireless communication module, such as any one or more of a bluetooth communication module, a Wireless Local Area Network (WLAN) communication module, or a cellular communication module.
In some possible implementations, the handover person may be a natural person or a virtual person having a function of performing the operation, for example, a robot having an identity recognition function. In some embodiments, the robot has a robotic arm that can be used to carry the cassette 100.
And the motion module 106 is used for moving according to the escort task. In some embodiments, the motion module 106 includes hardware with motion functionality, such as a roller or pulley that includes motion functionality. Further, the motion module 106 may further include a controller for controlling the motion direction, such as a steering engine.
Wherein, the moving parts such as pulleys or rollers can be installed on the box body 102. For example, when the casing 102 is a rectangular parallelepiped, the pulleys or rollers may be installed at the bottom of the casing 102 and outside the casing 102, so that the pulleys or rollers move to drive the casing to move together.
Further, the steering engine may also be mounted on the case 102. The steering engine can control the moving direction of the pulley or the roller, thereby controlling the moving direction of the box body.
When the escort task is set with an escort path, the motion module 106 can directly move according to the set path. Of course, the escort task may set an escort person instead of setting the escort path, and thus the motion module 106 may recognize the escort person set by the escort task and then move according to the moving route of the escort person. The escort task may set the active area of the cassette in view of security. When the movement module 106 moves according to the movement route of the escort person, whether the current position is in the activity area can be determined, and if not, the risk is indicated.
The escort person may be a natural person, or may be a virtual person having a function of executing the operation, for example, a robot having a path navigation function.
It should be noted that in some possible implementations, the motion module 106 may not be moved by a pulley or a roller. For example, the cassettes may be placed on a conveyor belt, with movement being effected by the conveyor belt.
The identification module 108 may be specifically configured to identify the handover person according to the identity information of the handover person in the escort task. The identity information may be any information that can characterize the identity of the handover person. Identity information is typically unique. In some possible implementations, the identity information may be any one or more of face information, fingerprint information, or voiceprint information.
Identity information of the transfer person, such as face information, fingerprint information or voiceprint information, can be carried in the escort task. When the cassette 100 reaches the destination, the recognition module 108 can acquire the identity information of "people" (including natural people or robots) on site and then compare the acquired identity information with the identity information of the escort task, thereby recognizing the hand-over person.
For ease of understanding, the present application exemplifies the identity information as the face information.
When the cash box 100 reaches the destination, the recognition module 108 may call a camera to collect a face image of a person on site, and then extract a face feature value from the face image, and similarly, the recognition module 08 may obtain a face image of an escort person in the escort task and extract a face feature value therefrom. The recognition module 108 may calculate the similarity between the face feature value of the person on the spot and the face feature value of the escort person corresponding to the escort task. The identification module 108 identifies the escort person based on the similarity. Specifically, when the similarity between the face feature value of a person in the scene and the face feature value in the escort task is greater than a preset threshold, the person can be determined as the escort person.
In some possible implementations, the identity information may also be a password, a passcode, or the like. The person in the field can input any one or more of a password or a verification code, and the identification module 108 identifies the escort person according to the password and the verification code input by the person in the field and the password and the verification code carried in the escort task.
The handover module 110 may be specifically configured to complete the handover when the identification module 108 identifies the handover person. The process of the handover module 110 completing the handover may be that when the recognition module 108 recognizes the handover, a handover person is allowed to move the banknote cassette 100, otherwise, the banknote cassette 100 is not allowed to be moved.
In view of security, in some possible implementations, the cash cassette 100 may also include a wind control module 112. The wind control module 112 is configured to determine a risk of the banknote cassette 100 by using a risk identification model according to the weight of the banknote cassette 100 and/or the shape of the articles in the banknote cassette 100.
Wherein the weight of the cassette 100 can be obtained by a gravity sensor disposed in the cassette 100. The shape information of the items in the cassette 100 can be obtained by an optical sensor disposed in the cassette 100. The optical sensor may specifically be a laser sensor, for example, a three-dimensional (3D) laser sensor.
The risk identification model in the wind control module 112 may be obtained through training. Specifically, the risk identification module may be a genetic algorithm optimized error back neural network (GA BP) model.
The genetic algorithm is an adaptive global optimization search algorithm formed by simulating the process of heredity and evolution of organisms in a natural environment. The genetic algorithm can simultaneously process a plurality of individuals in the group to achieve the purpose of quickly optimizing the weight and the threshold of the error back-transmission neural network, so that the optimized error back-transmission neural network has better prediction precision.
The training process of the risk identification model is explained in detail below. Firstly, a network structure of an error feedback neural network is constructed. According to theoretical research, in two groups of input and output data which are not linearly related, if other conditions are reasonably selected, any mapping from n dimension to m dimension can be realized by a common three-layer BP neural network, and the precision requirement of design is met. Based on the method, a BP neural network comprising an input layer, a hidden layer and an output layer can be constructed.
The number of nodes (neurons) in the hidden layer can be determined by trial and error. The trial and error method specifically means that the number of nodes in the hidden layer is determined through continuous experiments or trials. In some embodiments, the number of nodes in the hidden layer may be 5.
The input layer may comprise one or more nodes including the weight of the cassette 100 and/or the shape of the items within the cassette 100 and the output layer may comprise one node being a risk label. Wherein the risk label is used to indicate whether there is a risk in the cassette 100.
And then, collecting historical data to form sample data, and training the neural network model through the sample data to obtain a risk identification model. The historical data includes the weight of the past banknote box 100, the shape of the articles in the banknote box 100 and whether risks exist, wherein the weight of the banknote box 100 and/or the shape of the articles in the banknote box 100 can be used as input, and whether risks exist can be used as marking information, so that sample data can be obtained.
Further, the sample data may be partitioned into a training set and a test set. It should be noted that the sample data may be randomly divided into the training set test set. The ratio of the sample data in the training set and the test set may be a predetermined ratio, for example, 7: 3. After the neural network model is trained by the training set, the accuracy of model prediction can be verified by using the test set.
In some possible implementations, the wind control module 112 may output a risk label that the cassette 100 is at risk when the risk identification model identifies that a risk exists for the hand-off process. In addition, the wind control module 112 may also output a risk tag present in the banknote cassette 100 when the risk identification model identifies that the handover person identity information is incorrect. Similarly, the wind control module 112 may output a risk signature to the banknote cassette 100 when it is monitored that there is abnormal behavior of surrounding pedestrians.
In some possible implementations, the cassette 100 also includes an alert module 114. The alarm module is used for giving an alarm when the escort process of the cash box 100 is abnormal. Among them, there are many possibilities that the escort process of the banknote box 100 is abnormal. For example, when the banknote cassette 100 receives a risk label output by the risk identification model and indicating that the banknote cassette 100 has a risk, the alarm module 114 alarms. For another example, the alarm module 114 may alarm when the cassette 100 recognizes that the cassette is located in an area beyond the activity area. Of course, the alarm module 1114 may alarm when the banknote cassette 100 recognizes that a non-handover person has acquired the banknote cassette 100.
In some possible implementations, the alert module 114 may alert by sounding an alarm.
In some possible implementations, the alert module 114 may alert by sending information to the task manager.
In some possible implementations, the alert module 114 may alert by sounding an alert while sending information to the task manager.
The banknote box 100 can automatically convey banknotes, reduce transfer difficulty, reduce risks in the handing-over process of the banknote box 100 and guarantee fund safety.
The banknote box 100 provided in the embodiment of the present application is described in detail above with reference to fig. 1, and next, the banknote box management method provided in the embodiment of the present application will be described with reference to the drawings.
Referring to the flow chart of the banknote cassette management method shown in fig. 2, the method is applied to the banknote cassette 100 shown in fig. 1, the banknote cassette 100 includes a cassette body 102, a communication module 104, a motion module 106, an identification module 108 and a handover module 110, and the method includes:
s202: the communication module 104 receives an escort task issued by the task management system, wherein the escort task is used for indicating the cash box 100 to be handed over to a deliverer.
S204: the movement module 106 moves according to the escort task.
In some possible implementations, the moving according to the escort task may be moving according to a path set by the escort task.
In some possible implementations, the moving according to the escort task may be to identify an escort person set by the escort task, and move according to a moving route of the escort person.
S206: the identification module 108 identifies the handover person based on the identity information of the handover person in the escort mission.
S208: the handover module 110 completes the handover when the identification module 108 identifies a handover person.
In some possible implementation manners, the banknote box 100 further includes a wind control module 112, and in the process of escorting and transporting the banknote box 100, the wind control module 112 may further perform wind control management on the banknote box. Specifically, the air control module 112 determines the risk of the banknote cassette 100 using a risk identification model according to the weight of the banknote cassette 100 and/or the shape of the items within the banknote cassette 100.
The weight of the banknote box 100 can be obtained by a gravity sensor disposed in the banknote box 100, and the shape information of the articles in the banknote box 100 can be obtained by an optical sensor disposed in the banknote box 100.
In some possible implementations, the risk identification model is an error feedback neural network model optimized by a genetic algorithm, and the risk identification model takes the weight of the banknote cassette 100 and/or the shape of the articles in the banknote cassette 100 as input and takes a risk label as output, and the risk label is used for indicating whether the banknote cassette 100 has a risk.
In some possible implementations, the banknote cassette 100 further includes an alarm module 114, and the alarm module 114 may further alarm when an exception occurs in the escort process of the banknote cassette 100.
The banknote box 100 according to the embodiment of the present application may correspondingly execute the method described in the embodiment of the present application, and the above and other operations and/or functions of each module of the banknote box 100 are respectively for implementing corresponding processes of each method in fig. 2, and are not described herein again for brevity.
The application provides equipment used for a method for managing a money box. The apparatus includes a processor and a memory. The processor and the memory are in communication with each other. The processor is used for executing the instructions stored in the memory so as to enable the device to execute a management method of the cash box.
The application provides a computer-readable storage medium, which comprises instructions for instructing a computer to execute the management method of the cash box.
The present application provides a computer-readable storage medium having instructions stored therein, which, when run on an apparatus, cause the apparatus to perform the above-described method of managing a cassette.
The present application provides a computer program product comprising instructions which, when run on an apparatus, cause the apparatus to perform the above-described method of managing a cassette.
It should be noted that the above-described embodiments of the apparatus are merely schematic, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiments of the apparatus provided in the present application, the connection relationship between the modules indicates that there is a communication connection therebetween, and may be implemented as one or more communication buses or signal lines.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, an exercise device, or a network device) to execute the method according to the embodiments of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, training device, or data center to another website site, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a training device, a data center, etc., that incorporates one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Claims (14)
1. A money box is characterized by comprising a box body, a communication module, a motion module, an identification module and a cross-connecting module;
the cash box comprises a box body, a communication module, a task management system and a cash delivery module, wherein the box body is used for containing cash, the communication module is used for receiving an escorting task issued by the task management system, and the escorting task is used for indicating to deliver the cash box to a delivery person;
the motion module is used for moving according to the escort task;
the identification module is used for identifying the handover person according to the identity information of the handover person in the escort task;
the handover module is used for completing handover when the recognition module recognizes the handover person.
2. The method of claim 1, wherein the cassette further comprises a wind control module for determining the risk of the cassette using a risk identification model based on the weight of the cassette and/or the shape of the items within the cassette.
3. A banknote cassette as claimed in claim 2, wherein the weight of the cassette is obtained by a gravity sensor disposed in the cassette and the shape information of the contents of the cassette is obtained by an optical sensor disposed in the cassette.
4. A banknote cassette as claimed in claim 2, wherein the risk identification model is an error back neural network model optimised by a genetic algorithm, the risk identification model taking the weight of the cassette and/or the shape of the contents of the cassette as input and a risk label as output, the risk label being used to indicate whether there is a risk in the cassette.
5. The cash box according to any one of claims 1 to 4, characterized in that the cash box further comprises an alarm module, and the alarm module is used for alarming when the escort process of the cash box is abnormal.
6. The cash cassette of any one of claims 1 to 4, wherein the motion module is configured to move according to the escort task, and comprises:
moving according to a path set by the escort task; or,
identifying the escorting person set by the escorting task, and moving according to the moving route of the escorting person.
7. A method for managing a banknote cassette, the method comprising:
receiving an escorting task issued by a task management system, wherein the escorting task is used for indicating the cash box to be handed over to a hand-over person;
moving according to the escort task;
identifying the handover person according to the identity information of the handover person in the escort task;
and when the handover person is identified, completing handover.
8. The method of claim 7, further comprising:
and determining the risk of the money box by using a risk identification model according to the weight of the money box and/or the shape of the articles in the money box.
9. The method of claim 8, wherein the weight of the cassette is obtained by a gravity sensor disposed in the cassette and the shape information of the contents of the cassette is obtained by an optical sensor disposed in the cassette.
10. The method as claimed in claim 8, wherein the risk identification model is an error-resilient neural network model optimized by a genetic algorithm, the risk identification model taking the weight of the cassette and/or the shape of the items inside the cassette as input and a risk label as output, the risk label being used to indicate whether the cassette is at risk.
11. The method according to any one of claims 7 to 10, further comprising:
and alarming when the escorting process of the cash box is abnormal.
12. The method of any one of claims 7 to 10, wherein said moving according to said escort mission comprises:
moving according to a path set by the escort task; or,
identifying the escorting person set by the escorting task, and moving according to the moving route of the escorting person.
13. An apparatus, comprising a processor and a memory;
the processor is to execute instructions stored in the memory to cause the device to perform the method of any of claims 7 to 12.
14. A computer-readable storage medium comprising instructions that direct a device to perform the method of any of claims 7-12.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114037385A (en) * | 2021-08-16 | 2022-02-11 | 云钞机器人科技(北京)有限公司 | Escort processing method and escort processing system |
CN114780984A (en) * | 2022-06-21 | 2022-07-22 | 深圳市晨创科技有限公司 | Intelligent money box handover management method and system |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120005073A1 (en) * | 2010-06-30 | 2012-01-05 | Joseph Gnorski | Financial Transactions Processing System including Cash Automation Machine |
JP2014055480A (en) * | 2012-09-13 | 2014-03-27 | Tazen Yoshihiko | Money storage device and money storage device management system |
CN205507883U (en) * | 2016-01-28 | 2016-08-24 | 深圳市众能泰科智能技术有限公司 | Money case handing -over authentication intelligent terminal |
CN106529623A (en) * | 2016-10-28 | 2017-03-22 | 广州广电银通金融电子科技有限公司 | Internet of things technology-based money box transfer management system and method |
CN108457560A (en) * | 2017-02-21 | 2018-08-28 | 北京石油化工学院 | Bank's money case, telesecurity monitoring server and its safety monitoring system |
CN109877850A (en) * | 2019-04-01 | 2019-06-14 | 福州大学 | One kind being based on indoor intelligent delivery service system and device and its working method |
CN110059887A (en) * | 2019-04-25 | 2019-07-26 | 湖南大学 | BP neural network Risk Identification Method and system based on self-adapted genetic algorithm |
CN110675548A (en) * | 2019-09-25 | 2020-01-10 | 北京海益同展信息科技有限公司 | Method and device for handing over intelligent cash boxes |
-
2020
- 2020-09-30 CN CN202011064613.3A patent/CN112196402A/en active Pending
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120005073A1 (en) * | 2010-06-30 | 2012-01-05 | Joseph Gnorski | Financial Transactions Processing System including Cash Automation Machine |
JP2014055480A (en) * | 2012-09-13 | 2014-03-27 | Tazen Yoshihiko | Money storage device and money storage device management system |
CN205507883U (en) * | 2016-01-28 | 2016-08-24 | 深圳市众能泰科智能技术有限公司 | Money case handing -over authentication intelligent terminal |
CN106529623A (en) * | 2016-10-28 | 2017-03-22 | 广州广电银通金融电子科技有限公司 | Internet of things technology-based money box transfer management system and method |
CN108457560A (en) * | 2017-02-21 | 2018-08-28 | 北京石油化工学院 | Bank's money case, telesecurity monitoring server and its safety monitoring system |
CN109877850A (en) * | 2019-04-01 | 2019-06-14 | 福州大学 | One kind being based on indoor intelligent delivery service system and device and its working method |
CN110059887A (en) * | 2019-04-25 | 2019-07-26 | 湖南大学 | BP neural network Risk Identification Method and system based on self-adapted genetic algorithm |
CN110675548A (en) * | 2019-09-25 | 2020-01-10 | 北京海益同展信息科技有限公司 | Method and device for handing over intelligent cash boxes |
Non-Patent Citations (1)
Title |
---|
李云鹏等: "《旅游电子商务》", 31 January 2019 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114037385A (en) * | 2021-08-16 | 2022-02-11 | 云钞机器人科技(北京)有限公司 | Escort processing method and escort processing system |
CN114780984A (en) * | 2022-06-21 | 2022-07-22 | 深圳市晨创科技有限公司 | Intelligent money box handover management method and system |
CN114780984B (en) * | 2022-06-21 | 2022-09-16 | 深圳市晨创科技有限公司 | Intelligent money box handover management method and system |
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