CN110363626B

CN110363626B - Behavior mode monitoring method for unmanned container, electronic equipment and unmanned container

Info

Publication number: CN110363626B
Application number: CN201910628656.0A
Authority: CN
Inventors: 张发恩; 王炬; 柯政远
Original assignee: Ainnovation Hefei Technology Co ltd
Current assignee: Ainnovation Hefei Technology Co ltd
Priority date: 2019-07-11
Filing date: 2019-07-11
Publication date: 2022-04-12
Anticipated expiration: 2039-07-11
Also published as: CN110363626A

Abstract

The invention relates to the technical field of vending of unmanned containers, in particular to a behavior mode monitoring method for an unmanned container, electronic equipment and the unmanned container. The method comprises the following steps: s1, providing commodity weight information prestored in an unmanned container, and starting to collect gravity signals based on door opening signals, wherein the gravity signals comprise a first signal and a second signal; s2, analyzing the collected gravity signal to separate the second signal from the gravity signal; s3, calculating to obtain a third signal based on the separated second signal and the pre-stored commodity weight information; s4, judging whether abnormal shopping behaviors occur or not based on the third signal, starting to collect the gravity signal when a door opening signal is received, analyzing the weight signal collected in real time in the shopping process of a consumer to judge whether the abnormal shopping behaviors occur or not, and well avoiding the defect of goods loss caused by judgment lag when a fault occurs in the shopping process.

Description

Behavior mode monitoring method for unmanned container, electronic equipment and unmanned container

[ technical field ] A method for producing a semiconductor device

The invention relates to the technical field of vending of unmanned containers, in particular to a behavior mode monitoring method for an unmanned container, electronic equipment and the unmanned container.

[ background of the invention ]

An unmanned container is an intelligent container which is completed by utilizing big data, intelligent technology and algorithm. Unmanned containers eliminate goods sold on containers later by using big data, and distribute different goods according to different regional preferences. The unmanned container changes the traditional shopping mode, improves the user satisfaction degree, and although the unmanned container enriches the shopping function, the unmanned container is difficult to avoid generating wrong orders to cause goods damage. The existing goods settlement methods generally include the following two methods:

the first method is to check the type and quantity of commodities selected and purchased by a user through an image recognition algorithm by comparing the difference of the weight values of the unmanned container before and after the door is opened. The disadvantage of this method is that the information obtained is relatively simple and it is difficult to identify various purchasing and selecting actions of the purchaser, and abnormal situations.

The second method is to monitor the purchasing and selecting behavior of the user by analyzing the video information collected by the camera built in the container when the shopper purchases. The disadvantage of this method is that the amount of processing for analyzing the video and the storage space required for storing the video information are both large, and the processing algorithm for the video is also very complex.

The method has the defect that the abnormal condition feedback is not timely in the shopping process.

[ summary of the invention ]

In order to overcome the defect that the existing unmanned container cannot feed back abnormal conditions in the shopping behavior process in time, the invention provides an unmanned container behavior mode monitoring method, electronic equipment and an unmanned container.

In order to solve the technical problem, the invention provides a behavior pattern monitoring method for an unmanned container, which comprises the following steps: s1, providing commodity weight information prestored in an unmanned container, and starting to collect gravity signals based on door opening signals, wherein the gravity signals comprise a first signal and a second signal; s2, analyzing the collected gravity signal to separate the second signal from the gravity signal; s3, calculating to obtain a third signal based on the separated second signal and the pre-stored commodity weight information; s4, judging whether abnormal shopping behavior occurs or not based on the third signal, wherein the abnormal shopping behavior occurs before the order is submitted; the first signal is a vibration signal generated when a door of the unmanned container is opened and closed; the second signal is a vibration signal acted on the unmanned container when the consumer takes up or puts down the commodity; the third signal is a static signal obtained by calculation based on the second signal, wherein the static signal is the weight of the unmanned container when the consumer takes and places the goods.

Preferably, in the step S4, the determination of whether the abnormal shopping behavior occurs based on the third signal is performed according to the following rule: when the weight of the unmanned cargo container increases; or when the weight of the unmanned container is reduced and the reduction value is not matched with the weight information of the pre-stored commodity.

Preferably, the analyzing the collected gravity signal in the step S2 to separate the second signal from the gravity signal specifically includes: the vibration frequency, vibration time, and amplitude of the acquired gravity signal are analyzed as characteristic values.

Preferably, a plurality of sets of vibration frequencies, vibration times and amplitudes corresponding to the first signal and the second signal are collected to establish an analysis model, and the gravity signal is analyzed based on the established analysis model to separate the second signal.

Preferably, in the step S2, if it is determined that abnormal shopping behavior occurs, the method for monitoring behavior pattern of unmanned containers further comprises the steps of: step S5, sending out an alarm signal; if no abnormal shopping behavior occurs, correspondingly executing the following steps: and step S6, calculating the commodity taking and placing times information of the consumer and the weight increasing and decreasing information of the unmanned container in real time.

Preferably, after the step S5 is executed, the step of: step S5', whether the abnormal shopping behavior is eliminated is judged based on the third signal, if yes, step S6 is executed correspondingly; if not, go to step S7: and step S7, informing the merchant of solving the abnormal shopping behavior and finishing the shopping behavior.

Preferably, the method for monitoring behavior patterns of unmanned containers further includes step S8, determining whether a door closing signal is received, and if a door closing signal is received, further executing step S9: generating commodity combination information by using the commodity taking and placing frequency information and the container weight increase and decrease information which are obtained by calculation in the step S6; if no door closing signal is received, the process continues to step S2.

Preferably, the method for monitoring behavior patterns of the unmanned container based on the dynamic weight signal further comprises the steps of: and S10, collecting image information when the cargo cabinet door is opened and closed based on the first signal, generating a shopping order by combining the commodity combination information obtained in the step S9, and settling the commodity price according to the shopping order.

The present invention further provides an electronic device for solving the above technical problem, wherein the electronic device comprises: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method as described above.

The invention also provides an unmanned container for solving the technical problems, which adopts the behavior mode detection method of the unmanned container to detect, wherein the unmanned container comprises a plurality of accommodating layers, gravity sensors and the processor, and at least one gravity sensor is correspondingly arranged in each accommodating layer; the gravity sensor is used for collecting gravity signals.

Preferably, the acquisition frequency of each gravity sensor is 15-30 times/second.

Compared with the prior art, when the server receives the door opening signal, the gravity sensor is started to start to collect the gravity signal, wherein the gravity signal comprises a first signal and a second signal, in the process of shopping of a consumer, the gravity sensor is started to collect the gravity signal based on the door opening signal, so that the gravity signal can be collected in time, the collected gravity signal can be analyzed in time, the gravity signal is collected in real time, the second signal is separated from the gravity signal based on the analysis of the weight signal collected in real time, a third signal is obtained based on the second signal and prestored commodity information, whether abnormal shopping behavior occurs or not is judged according to the third signal, and the defect that goods are damaged due to delay in the process of fault judgment can be well avoided.

The second signal is a vibration signal which acts on the unmanned container when a consumer takes up or puts down a commodity and is a real-time dynamic signal, the third signal obtained through calculation based on the second signal and prestored commodity information is a static signal, and the static signal obtained through calculation based on the real-time dynamic signal is used for judging whether an abnormal shopping behavior occurs or not, so that the abnormal shopping behavior can be found in time, and the accuracy of judging the abnormal shopping behavior can be improved.

The electronic equipment and the unmanned container provided by the invention have the same beneficial effects as the unmanned container monitoring method.

[ description of the drawings ]

FIG. 1 is a schematic view of the structure of an unmanned container provided in a first embodiment of the invention;

FIG. 2 is a schematic diagram of an electronic device module provided in a second embodiment of the invention;

FIG. 3 is a flow chart of the steps of a method for monitoring behavior patterns of an unmanned container provided in a third embodiment of the invention;

FIG. 4 is a flow chart of another step of the method for monitoring behavior patterns of an unmanned container provided in the third embodiment of the present invention;

FIG. 5 is a flow chart of yet another step of the unmanned container behavior pattern monitoring method provided in the third embodiment of the present invention;

FIG. 6 is a schematic block diagram of a computer system suitable for use as a server for implementing embodiments of the present invention;

description of reference numerals:

10. an unmanned cargo container; 101. a cabinet body; 102. a cabinet door; 120. a server; 130. a communication connection; 140. a mobile terminal; 1021. placing a layer; 1022. a gravity sensor; 103. an image recognition device; 20. an electronic device; 201. a processor; 202. a communication interface; 205. a memory.

[ detailed description ] embodiments

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and 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.

Referring to FIG. 1, a first embodiment of the present invention provides an unmanned cargo container 10. The unmanned cargo container 10 includes a body 101 and a door 102. The unmanned cargo container 10 is generally enclosed, for example having a door with a lock. The unmanned cargo container 10 stores one or more kinds of commodities to be sold. The unmanned container 10 is connected to the server 120 via a communication connection 130. When a user wishes to purchase goods in the unmanned container 10, the door 102 of the unmanned container 10 is unlocked. For example, the user may scan the identification code on the unmanned container 10 using the mobile terminal 140 and then log in to the server 120 using the mobile terminal. After successful login, if the user is eligible, the server 120 instructs the unmanned container 10 to unlock the door 102. Those skilled in the art will appreciate that the above is only one way to unlock the cabinet door 102. Other ways, such as scanning the identification code of the mobile terminal of the user, scanning the fingerprint of the user, scanning the palm print of the user, scanning the iris of the user, scanning the face of the user, and the like, can also unlock the door 102 of the unmanned container 10. Therefore, when the consumer unlocks the cabinet door 102 by any one of the above manners, the corresponding door opening signal can be sent out.

After the door 102 of the unmanned container 10 is unlocked, the user can open the door 102 by pulling the door 102 to take the goods of the unmanned container 10. A certain vibration effect is generated when the cabinet door 102 is pulled. After the user finishes shopping, for example, after the consumer closes the cabinet door 102, a commodity order is generated according to commodities taken by the consumer. After the user pays the amount of the order, shopping using the unmanned container 10 is completed once. It will be appreciated that when the user closes the door 102, it is assumed that a door closing signal is emitted and a corresponding door closing shock signal is generated.

Referring to fig. 2, the unmanned container 10 further includes a plurality of storage layers 1021 and a plurality of gravity sensors 1022, wherein the plurality of storage layers 1021 are arranged at intervals along the height direction of the unmanned container 10. At least one gravity sensor 1022 is correspondingly disposed in each of the storage layers 1021. Optionally, when a plurality of gravity sensors 1022 are correspondingly disposed in each of the storage layers 1021, the gravity sensors 1022 are disposed on each of the storage layers 1021 at equal intervals, and the gravity sensor 1022 is disposed on each of the storage layers 1021 for acquiring a gravity signal, where the gravity signal includes a first signal and a second signal. The first signal is a vibration signal generated when a consumer pulls or pushes the cabinet door 102 to open or close after receiving a door opening signal or a door closing signal, and the second signal is a vibration signal acted on the accommodating layer 1021 of the unmanned container 10 when the consumer picks up or puts down a commodity. The acquisition frequency of each gravity sensor 1022 is set to 15-30 times/second.

With continued reference to FIG. 1, the unmanned cargo container 10 further includes an image recognition device 103 disposed within the container body 101. Optionally, the image recognition device 103 is multiple, and at least one image recognition device 103 is correspondingly arranged on each storage layer 1021. The image recognition device 103 may be a camera. The camera can be a 360-degree panoramic camera or a wide-angle camera, and can also be a common camera. The image recognition device 103 is configured to take pictures when the consumer opens the cabinet door 102 and closes the cabinet door 102, that is, when the first signal is recognized.

Referring to fig. 2, a second embodiment of the invention provides an electronic device 20, where the electronic device 20 includes a processor 201, a communication interface 202, and a memory 205.

The processor 201 is electrically connected to the image recognition device 103, the communication interface 202, the memory 205, and the gravity sensor 1022. The processor 201 can include one or more Central Processing Units (CPUs), Graphics Processing Units (GPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or combinations thereof. The processor 201 is capable of executing software or computer readable instructions stored in the memory 205 to perform the methods or operations described herein. The processor 201 can be implemented in a number of different ways. For example, the processor 201 can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware Finite State Machines (FSMs), Digital Signal Processors (DSPs), or a combination thereof.

Wherein, the gravity sensor 1022 and the processor 201 are connected to be configured to collect the gravity signal. When the processor 201 receives the door opening signal, the processor 201 starts the gravity sensor 1022 to operate, and collects the gravity signal. The processor 201 analyzes the gravity signal collected by the gravity sensor 1022 at the same time, separates the second signal from the gravity signal, and the processor 201 further analyzes the second signal and the commodity weight information prestored in the unmanned container 10 to obtain a third signal. Wherein the third signal is: and analyzing the obtained static signal due to the increase and decrease of the weight of the unmanned container when the consumer takes and places the goods based on the second signal and the weight information of the goods prestored in the unmanned container 10.

The processor 201 also determines whether abnormal shopping behavior occurs based on the obtained third signal. And if the abnormal shopping behavior occurs, sending an alarm signal to inform the merchant of solving the fault. It will be appreciated that the unmanned container 10 further comprises an alarm connected to the processor 201. The abnormal shopping behavior may be understood as a behavior in which a customer puts foreign matter into the unmanned container 10 or takes out and returns a part of the articles in the unmanned container. At this time, the third signal corresponding to the foreign matter put in by the consumer is the weight gain value of the container weight. The consumer enjoys a third signal that is partly replaced and corresponds to a reduction in the weight of the container, and the weight reduction value does not match the weight reduction value due to the reduction in the goods selected by the user, then an abnormal shopping behavior is considered to have occurred. For example, when the cabinet door 102 is opened, the weight of each layer 1021 is W1, W2, and W3 … …. And taking a bottle of soda water on the first layer, drinking several mouths of soda water, and putting back the soda water, wherein the weight of the commodity in the first layer is W1 ', the reduced weight is W1 ″ -W1-W1', and if the weight information of the single commodity in the first layer, W1 ″, which is prestored, is not matched, abnormal shopping behaviors are considered to occur. It should also be understood that, since the collection frequency of the gravity sensor 1022 is set to be 15-30 times/second, which is equivalent to that the gravity sensor 1022 collects the gravity signal in real time after starting to work, it is easy to understand that the weight reduction value of the user can be accurately sensed in time when the user takes the commodity normally; if the user picks up the article and partially replaces it, the second signal collected between pick and place operations will often be delayed in time, and thus the calculated weight loss information will often be abnormal.

In some other embodiments, the processor 201 may also be part of the unmanned container 10.

In some embodiments, the processor 201 analyzes the gravity signal based on the vibration frequency, vibration time, and amplitude of the gravity signal as characteristic values. An analysis model is established by collecting a plurality of groups of vibration frequencies, vibration times and amplitudes corresponding to the first signal and the second signal, and the processor 201 analyzes and identifies the gravity signal based on the established analysis model so as to separate the second signal from the gravity signal.

Usually, the first signal that produces when normally opening and closing the goods cabinet door has higher vibration frequency and vibrations time shorter, and the second signal vibration frequency that produces when picking up or putting down goods is lower and the amplitude is great.

The communication interface 202 can include one or more wired or wireless communication interfaces. Such as a communications interface network interface card, wireless modem, or wired modem. In one application, the communication interface 202 can be a WiFi modem. In other applications, the communication interface 202 can be a 3G modem, a 4G modem, an LTE modem, a bluetooth component, a radio frequency receiver, an antenna, or a combination thereof.

The memory 205 can store software, data, logs, or a combination thereof. The memory 205 can be an internal memory or an external memory. For example, the memory can be volatile memory or non-volatile memory, such as non-volatile random access memory (NVRAM), flash memory, disk storage, or volatile memory such as Static Random Access Memory (SRAM).

Referring to fig. 3, a third embodiment of the present invention provides a method for monitoring behavior patterns of an unmanned container, which includes the following steps:

it should be noted that the definitions of the first signal, the second signal and the third signal in the following steps are the same as those in the second embodiment, and are not described herein again.

S1, providing commodity weight information prestored in an unmanned container, and starting to collect gravity signals based on door opening signals, wherein the gravity signals comprise a first signal and a second signal;

s2, analyzing the collected gravity signal to separate the second signal from the gravity signal;

s3, calculating to obtain a third signal based on the separated second signal and the pre-stored commodity weight information; and

and S4, judging whether abnormal shopping behavior occurs or not based on the third signal, wherein the abnormal shopping behavior occurs before the order is submitted.

In step S1, the door opening signal is that when the customer needs to shop, the identification code on the unmanned container 10 is scanned using the mobile terminal, and then the server 120 is logged in using the mobile terminal. After successful login, if the user is eligible, the server 120 instructs the unmanned container 10 to unlock the door 102. Those skilled in the art will appreciate that the above is only one way to unlock the cabinet door 102. Other ways, such as scanning the identification code of the mobile terminal of the user, scanning the fingerprint of the user, scanning the palm print of the user, scanning the iris of the user, scanning the face of the user, and the like, can also unlock the door 102 of the unmanned container 10. Thus, when the consumer unlocks the cabinet door 102 in either of the above manners, it is considered that a door open signal is issued. That is, when the processor 201 receives the door opening signal, the processor 201 starts the gravity sensor 1022 to collect the gravity signal.

In the above step S2, the analysis of the collected gravity signal to separate the second signal from the gravity signal is performed based on the vibration frequency, the vibration time, and the amplitude of the collected gravity signal as characteristic values. In some embodiments, an analysis model may be established by collecting a plurality of sets of vibration frequencies, vibration times, and amplitudes corresponding to the first signal and the second signal, and the first signal and the second signal may be analyzed based on the established analysis model to distinguish the weight signal as the first signal or the second signal.

In the above step S3, the third signal is calculated and obtained based on the separated second signal and the pre-stored weight information of the commodity, and is determined by combining the second signal and the pre-stored weight information of the unmanned container. The weight information of the unmanned container is understood as: each layer has corresponding weight information, namely corresponding total weight information in each layer and weight information corresponding to each article, when a user takes up or puts down the articles, the user has a dynamic action component which is generated and is also corresponding to a second signal, and the processor analyzes the second signal and the weight information of the articles preset in the unmanned container to obtain a static signal of the increase and decrease of the weight of the unmanned container when the consumer takes in or puts out the articles, namely to obtain a third signal. For example, when the cabinet door is opened, the weight of each layer is W1, W2, and W3 … …. After a bottle of soda in the first layer is selected, the gravity sensor senses the gravity signal, the gravity signal is analyzed by the processor to be a second signal, the processor calculates weight reduction information based on the second signal, and the weight reduction information corresponds to the weight of the commodity in the first layer being W1 ', so that the reduced weight W1 is W1-W1'.

After the third signal is obtained, step S4 is further executed to determine whether an abnormal shopping behavior occurs based on the third signal, and in step S4, if the obtained third signal is the weight information added, it indicates that the user has put a foreign object into the unmanned container, and it is determined that an abnormal behavior occurs. When the third signal is the reduced weight information, it is further required to compare the pre-stored weight information of the single sample stored in each layer to determine whether the reduced weight information matches with the weight information of the single cargo, if the reduced weight information matches with the weight information of the single cargo, it indicates that no abnormal shopping behavior occurs, and if the reduced weight information matches with the weight information of the single cargo, it indicates that an abnormal behavior occurs, for example, the user enjoys a part of the commodity and then puts the commodity back into an unmanned container. It can be understood that the determination may be made by combining the third information with the number of times the user has taken and placed the item, because if the weight is reduced due to enjoying a part of the item, the number of times the item has been taken and placed should be the same, that is, when the number of times the item has been taken and the third signal corresponds to a reduction in weight, it indicates that an abnormal shopping behavior has occurred.

Referring to fig. 4, after step S4 is completed, if it is determined that an abnormal shopping behavior occurs, the method for monitoring behavior pattern of unmanned container further includes the steps of:

step S5, sending out an alarm signal;

if no abnormal shopping behavior occurs, correspondingly executing the following steps:

and step S6, calculating the commodity taking and placing times information of the consumer and the weight increasing and decreasing information of the unmanned container in real time.

After step S5 is executed, the step of:

step S5', determining whether the abnormal shopping behavior is eliminated based on the third signal,

if yes, go to step S6;

if not, go to step S7:

and step S7, informing the merchant of solving the abnormal shopping behavior.

It is understood that, in step S5', it is determined whether the abnormal shopping behavior is eliminated based on the third signal, for example, when the weight of the unmanned container increases due to the user placing the foreign object in the unmanned container, that is, the third signal corresponds to the weight gain value, and when the user takes away the foreign object, the weight of the unmanned container returns to normal, the abnormal shopping behavior is considered to be eliminated, and it is also understood that when the user takes away part of the commodity after hearing the alarm and the user takes away the part of the commodity due to the user enjoying part of the commodity, the abnormal shopping behavior is considered to be eliminated, and of course, if the user does not purchase the commodity after enjoying part of the commodity and puts the commodity back in the unmanned container, the abnormal shopping behavior is not eliminated.

In step S7, the step of informing the merchant of resolving the abnormal shopping behavior may be based on the processor issuing a door closing driving signal to drive the container door to automatically close, so that the shopping behavior is ended.

Referring to fig. 5, the method for monitoring behavior pattern of an unmanned container further includes step S8, determining whether a door closing signal is received, and if so, further executing step S9: generating commodity combination information by using the commodity taking and placing frequency information and the container weight increase and decrease information which are obtained by calculation in the step S6; if no door closing signal is received, the process continues to step S2.

In the step S9, the generation of the commodity combination information by the number of times of taking and placing the commodity and the container weight increase and decrease information calculated in the step S6 may be performed based on the following rules, which are exemplified as follows:

the amount of the commercial product is reduced by 1000g,

the corresponding times are: 1 time, 1000g is taken each time; or

2 times, 500g each time.

Referring to fig. 5, the behavior pattern of the unmanned container based on the dynamic weight signal further includes the following steps:

and S10, collecting image information when the cargo cabinet door is opened and closed based on the first signal, generating a shopping order by combining the commodity combination information obtained in the step S9, and settling the commodity price according to the shopping order.

In this step, when the user starts shopping, the acquired first signal corresponds to the first signal generated when the door is opened, and at this time, image information when the user does not start shopping is obtained, and when the user finishes shopping and closes the door, the first signal corresponds to the first signal generated when the door is closed, and at this time, corresponding image information when the user finishes shopping is obtained.

And identifying the image information obtained by opening and closing the door by using an artificial intelligence model, namely matching the commodities in the image information with the trained commodities in the model, and sequencing the possible commodities according to the matching degree. In some examples, if the matching degree is greater than a predetermined threshold, the matching is considered to be successful, and a commodity list is generated according to all commodities which are successfully matched. Optionally, after sorting according to the matching degree from large to small, the predetermined first few goods are considered to be successfully matched, and then a goods list is generated according to all goods successfully matched. Therefore, in step S10, the product list is matched with the product combination information, and the type and number of products taken by the user are obtained to generate a shopping list.

For example, the commodity types and the similarity thereof identified in step S10 are as follows:

80% of cola, 60% of potato chips and 40% of mineral water;

if the similarity setting threshold is 50%, generating a commodity list as follows:

cola, potato chips;

the change in the weight of the commodity in step S9 is as follows:

weight change before and after opening the door: 455 g; the commodity combination information is as follows:

1 bottle of cola;

1 bag of the dried plum and 1 bag of the melon seeds;

2 bags of melon seeds and 1 bag of chewing gum.

And matching the commodity list with the types in the commodity combination information one by one to obtain the types and the number of the commodities taken by the user, namely 1 bottle of cola of the commodities taken by the user, then generating a shopping list, and settling the commodities by the consumer according to the shopping list.

Referring now to fig. 6, a block diagram of a computer system 800 suitable for use in implementing a terminal device/server of an embodiment of the present application is shown. The terminal device/server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the system 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.

According to embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program performs the above-described functions defined in the method of the present application when executed by the Central Processing Unit (CPU) 801. It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "for example" programming language or similar programming languages. The program code may execute entirely on the management-side computer, partly on the management-side computer, as a stand-alone software package, partly on the management-side computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the administrative side computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit of the present invention are intended to be included within the scope of the present invention.

Claims

1. A behavior pattern monitoring method for an unmanned container is characterized by comprising the following steps: the behavior pattern monitoring method of the unmanned container comprises the following steps:

s4, judging whether abnormal shopping behaviors occur or not based on the third signal, wherein the abnormal shopping behaviors occur before the order is submitted;

the first signal is a vibration signal generated when a door of the unmanned container is opened and closed; the second signal is a vibration signal acted on the unmanned container when the consumer takes up or puts down the commodity; the third signal is a static signal obtained by calculation based on the second signal, wherein the static signal is the weight of the unmanned container when the consumer takes and places the goods.

2. The unmanned container behavior pattern monitoring method of claim 1, wherein: in step S4, the determination of whether or not the abnormal shopping behavior occurs based on the third signal is performed according to the following rule:

when the weight of the unmanned cargo container increases; or when the weight of the unmanned container is reduced and the reduction value is not matched with the weight information of the pre-stored commodity.

3. The unmanned container behavior pattern monitoring method of claim 1, wherein: analyzing the collected gravity signal in the step S2 to separate the second signal from the gravity signal specifically includes: the vibration frequency, vibration time, and amplitude of the acquired gravity signal are analyzed as characteristic values.

4. The unmanned container behavior pattern monitoring method of claim 3, wherein: and acquiring a plurality of groups of vibration frequencies, vibration times and amplitudes corresponding to the first signal and the second signal to establish an analysis model, and analyzing the gravity signal based on the established analysis model to separate the second signal.

5. The unmanned container behavior pattern monitoring method of claim 1, wherein: in the step S2, if it is determined that the abnormal shopping behavior occurs, the method for monitoring behavior pattern of unmanned container further includes the steps of:

step S5, sending out an alarm signal;

6. The unmanned container behavior pattern monitoring method of claim 5, wherein: after step S5 is executed, the step of:

step S5', whether the abnormal shopping behavior is eliminated is determined based on the third signal,

if yes, go to step S6;

if not, go to step S7:

and step S7, informing the merchant of solving the abnormal shopping behavior and finishing the shopping behavior.

7. The unmanned container behavior pattern monitoring method of claim 6, wherein: the method for monitoring behavior patterns of an unmanned container further includes step S8, determining whether a door closing signal is received, and if a door closing signal is received, further executing step S9: generating commodity combination information by using the commodity taking and placing frequency information and the container weight increase and decrease information which are obtained by calculation in the step S6; if no door closing signal is received, the process continues to step S2.

8. The unmanned container behavior pattern monitoring method of claim 7, wherein: the method for monitoring the behavior pattern of the unmanned container based on the dynamic weight signal further comprises the following steps:

9. An electronic device, characterized in that: the electronic device includes:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.

10. An unmanned container, which is detected by the behavior pattern detection method of the unmanned container according to any one of claims 1 to 8, wherein: the unmanned container comprises a plurality of accommodating layers, gravity sensors and a processor, wherein at least one gravity sensor is correspondingly arranged in each accommodating layer;

the gravity sensor is used for collecting gravity signals.

11. The unmanned cargo container of claim 10, wherein: the acquisition frequency of each gravity sensor is 15-30 times/second.