CN112562182A - Method and apparatus for identifying a product removed from an unmanned vending machine - Google Patents
Method and apparatus for identifying a product removed from an unmanned vending machine Download PDFInfo
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Abstract
The invention provides a method and a device for identifying commodities taken out of an unmanned vending machine, wherein the method comprises the following steps: determining the transmission time difference of the microwaves transmitted from the first microwave module to the second microwave module when the commodity is in the two adjacent static states; when the transmission time length of the microwave transmitted from the first microwave module to the second microwave module is kept unchanged in a preset time period, the commodity is in a static state; inputting the transmission time difference into a pre-trained neural network model to obtain the time variation when the commodity is in the adjacent two static states; determining the weight of various commodities in the static state; the type and the number of the commodities taken out of the unmanned vending machine are identified according to the quantity variation of the commodities in the two adjacent static states, the total weight variation of the commodities in the two adjacent static states and the weight of various commodities in the static state. The scheme of the invention can improve the accuracy of identifying the commodities taken out of the vending machine.
Description
Technical Field
The invention relates to the technical field of vending machines, in particular to a method and a device for identifying commodities taken out of an unmanned vending machine.
Background
Open-door vending machine compares traditional non-open-door vending machine, owing to can make the consumer carry out more autonomous selection, and shopping earlier, the shopping mode of back settlement brings better user experience. The consumer opens through scanning the two-dimensional code or through other perception means and sells the quick-witted lock, then carries out shopping by oneself, and after shopping, closes the vending machine lock, sells the quick-witted generation shopping order, and the consumer accomplishes the payment. One of the major technical problems facing door-opening vending machines is how to quickly and accurately identify the goods and the quantity of goods purchased by a customer.
In the prior art, vending machines may accomplish the identification of merchandise through gravity sensing techniques. Specifically, the weight of the commodity on the shelf before the door is opened is detected by the gravity sensor, and then the weight of the commodity on the shelf after the door is closed is detected. The items purchased by the customer and the quantity of the items are determined based on the two weights. For example, patent publication No. CN108665623A discloses a vending machine, which recognizes commodities by gravity, and the recognition accuracy of the commodities is low, which is likely to cause the loss of the commodities for the merchants.
As can be seen from the above description, in the prior art, the purchased commodities and the quantity of the commodities are determined based on only the weight before the door is opened and the weight after the door is closed, and a plurality of different commodity combinations are possible based on the two weights, and the prior art cannot identify the combination closest to the real situation from the plurality of different commodity combinations. Thus, the prior art has less accuracy in identifying the articles removed from the merchandiser.
Disclosure of Invention
The embodiment of the invention provides a method and a device for identifying commodities taken out of an unmanned vending machine, which can improve the accuracy of identifying the commodities taken out of the vending machine.
In a first aspect, an embodiment of the present invention provides a method for identifying a commodity taken out of an unmanned vending machine, where the unmanned vending machine is provided with a first microwave module and a second microwave module, a commodity is arranged between the first microwave module and the second microwave module, the first microwave module is configured to emit microwaves, and the second microwave module is configured to receive the microwaves emitted by the first microwave module;
the method comprises the following steps:
determining the transmission time length of the microwave transmitted from the first microwave module to the second microwave module when the commodity is in the two adjacent static states, and determining the difference value of the two transmission time lengths as the transmission time difference; when the transmission time length of the microwave transmitted from the first microwave module to the second microwave module is kept unchanged in a preset time period, the commodity is in a static state;
inputting the transmission time difference into a pre-trained neural network model to obtain the time variation when the commodity is in the two adjacent static states;
determining the weight of various commodities in the static state;
and identifying the type and the quantity of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states, the total weight variation of the commodities in the two adjacent static states and the weight of various commodities in the static state.
In a possible design, the second microwave module is further configured to emit microwaves, and the first microwave module is further configured to receive the microwaves emitted by the second microwave module;
when the commodity is in a static state, the transmission time length is determined by the following steps:
sending microwaves with time stamps to the second microwave module by using the first microwave module, wherein the time stamps are the time when the first microwave module sends the microwaves to the second microwave module;
sending microwaves with first response time duration to the first microwave module by using the second microwave module, wherein the first response time duration is a difference value between the time when the second microwave module receives the microwaves sent by the first microwave module and the time when the second microwave module sends the microwaves to the first microwave module;
determining a first receiving time length, wherein the first receiving time length is a difference value between the time when the first microwave module receives the microwaves sent by the second microwave module and the timestamp;
sending microwaves with second response time duration to the second microwave module by using the first microwave module, wherein the second response time duration is a difference value between the time when the first microwave module receives the microwaves sent by the second microwave module and the time when the first microwave module sends the microwaves to the second microwave module;
determining a second receiving time length, wherein the second receiving time length is a difference value between the time when the second microwave module receives the microwaves sent by the first microwave module for the second time and the time when the second microwave module sends the microwaves to the first microwave module;
the transmission time length is determined by the following formula:
Tch＝(Tjs1*Tjs2-Txy1*Txy2)/(Tjs1+Tjs2+Txy1+Txy2)
wherein, TchFor characterizing the transmission duration, Tjs1For characterizing the first reception duration, Tjs2For characterizing the second reception duration, Txy1For characterizing the first response duration, Txy2For characterizing the second response duration.
In one possible design, the determining the weight of each of the commodities in the current static state includes:
determining the number variable of each type of commodity, wherein the number variable of each type of commodity does not exceed a value obtained by rounding up the ratio of the total weight variation of the commodity in two adjacent static states to the initial weight of the commodity, the sum of the number variables of each type of commodity is not less than the number variation of the commodity in two adjacent static states, and the initial weight of the commodity is the weight determined in the last adjacent static state;
and determining the weight of each kind of commodity in the current static state according to the quantity variation of the commodity in the two adjacent static states, the quantity variation of each kind of commodity and the weight error mean value determined before the static state of the commodity.
In a possible design, the determining the weight of each kind of goods in the current static state according to the quantity variation of the goods in the two adjacent static states, the quantity variation of each kind of goods, and the weight error mean value of the goods determined before the current static state includes:
the weight of each type of commodity in this static state is determined by the following formula:
wherein, Wn2The weight of the nth commodity in the static state is represented; wn1The weight of the nth commodity in the last adjacent static state or the initial weight of the nth commodity in the current static state is represented; xnError coefficients for characterizing the nth commodity; y is used for representing the compensation coefficient; wtotalThe weight change quantity of the commodity in two adjacent static states is represented; w11The weight of the 1 st commodity in the last adjacent static state or the initial weight of the 1 st commodity in the current static state is represented; c1The number variable is used for representing the number variable of the 1 st type commodity in the two adjacent static states; cnThe number variable is used for representing the number variable of the nth commodity in the two adjacent static states; t iscThe quantity variation quantity is used for representing the quantity variation quantity when the commodity is in two adjacent static states; sigmanAnd the weight error mean value is used for representing the weight error mean value determined before the static state of the commodity.
In one possible design, the identifying, according to a quantity variation of the commodities in the two adjacent static states, a total weight variation of the commodities in the two adjacent static states, and weights of various kinds of commodities in the current static state, a kind and a quantity of the commodities taken out of the vending machine includes:
identifying a taking-out result of the commodity taken out of the unmanned vending machine according to the total weight variation of the commodity in the two adjacent static states and the weight of each kind of commodity in the static state;
and determining the type and the quantity of the taken-out commodities in the taking-out result according to the quantity variation of the commodities in the two adjacent static states.
In one possible design, the identifying, according to a quantity variation of the commodities in the two adjacent static states, a total weight variation of the commodities in the two adjacent static states, and weights of various kinds of commodities in the current static state, a kind and a quantity of the commodities taken out of the vending machine includes:
identifying a taking-out result of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states;
and determining the type and the number of the taken-out commodities in the taking-out result according to the total weight variation of the commodities in the two adjacent static states and the weight of each type of commodities in the current static state.
In a second aspect, an embodiment of the present invention provides an apparatus for identifying a commodity taken out of an unmanned vending machine, where the unmanned vending machine is provided with a first microwave module and a second microwave module, a commodity is arranged between the first microwave module and the second microwave module, the first microwave module is configured to emit microwaves, and the second microwave module is configured to receive the microwaves emitted by the first microwave module;
the device comprises:
the transmission time difference determining module is used for determining the transmission time length for the first microwave module to transmit the microwaves to the second microwave module when the commodity is in the two adjacent static states, and determining the difference value of the two transmission time lengths as the transmission time difference; when the transmission time length of the microwave transmitted from the first microwave module to the second microwave module is kept unchanged in a preset time period, the commodity is in a static state;
the quantity variation determining module is used for inputting the transmission time difference into a pre-trained neural network model to obtain the quantity variation of the commodities in the two adjacent static states;
the weight determining module is used for determining the weight of various commodities in the static state;
and the identification module is used for identifying the types and the quantity of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states, the total weight variation of the commodities in the two adjacent static states and the weight of various commodities in the static state.
In one possible design, the weight determination module is configured to:
determining the number variable of each type of commodity, wherein the number variable of each type of commodity does not exceed a value obtained by rounding up the ratio of the total weight variation of the commodity in two adjacent static states to the initial weight of the commodity, the sum of the number variables of each type of commodity is not less than the number variation of the commodity in two adjacent static states, and the initial weight of the commodity is the weight determined in the last adjacent static state;
determining the weight of each kind of commodity in the static state according to the quantity variation of the commodity in the two adjacent static states, the quantity variation of each kind of commodity and the weight error mean value determined before the static state of the commodity;
the weight determining module is configured to, when determining the weight of each type of commodity in the current static state according to the quantity variation of the commodity in the two adjacent static states, the quantity variation of each type of commodity, and the weight error mean value determined before the static state of the commodity, perform the following operations:
the weight of each type of commodity in this static state is determined by the following formula:
wherein, Wn2The weight of the nth commodity in the static state is represented; wn1The weight of the nth commodity in the last adjacent static state or the initial weight of the nth commodity in the current static state is represented; xnError coefficients for characterizing the nth commodity; y is used for representing the compensation coefficient; wtotalThe weight change quantity of the commodity in two adjacent static states is represented; w11The weight of the 1 st commodity in the last adjacent static state or the initial weight of the 1 st commodity in the current static state is represented; c1The number variable is used for representing the number variable of the 1 st type commodity in the two adjacent static states; cnThe number variable is used for representing the number variable of the nth commodity in the two adjacent static states; t iscThe quantity variation quantity is used for representing the quantity variation quantity when the commodity is in two adjacent static states; sigmanAnd the weight error mean value is used for representing the weight error mean value determined before the static state of the commodity.
In a third aspect, an embodiment of the present invention provides an apparatus for identifying a product removed from an unmanned vending machine, including: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is configured to invoke the machine-readable program to perform the method described above.
In a fourth aspect, embodiments of the present invention provide a computer-readable medium having stored thereon computer instructions, which, when executed by a processor, cause the processor to perform the method described above.
According to the scheme, the method and the device for identifying the commodities taken out of the unmanned vending machine, provided by the invention, firstly determine the transmission time length for the first microwave module to transmit microwaves to the second microwave module when the commodities are in two adjacent static states, and determine the difference value of the two transmission time lengths as the transmission time difference; then inputting the transmission time difference into a pre-trained neural network model to obtain the time variation when the commodity is in the adjacent two static states; and identifying the type and the quantity of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states, the total weight variation of the commodities in the two adjacent static states and the weight of various commodities in the static state. According to the technical scheme, the type and the quantity of the commodities taken out of the unmanned vending machine are identified according to the quantity variation quantity when the commodities are in the two adjacent static states, the total weight variation quantity when the commodities are in the two adjacent static states and the weight of each type of commodities in the static state, so that compared with a scheme of identifying the commodities taken out only by means of weight, the accuracy of identifying the commodities taken out of the vending machine can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings 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 some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method of identifying merchandise being removed from an unmanned merchandiser, according to one embodiment of the present invention;
FIG. 2 is a schematic view of an apparatus for identifying articles removed from an unmanned merchandiser, according to one embodiment of the present invention;
fig. 3 is a schematic view of an apparatus for identifying articles removed from an unmanned merchandiser, according to one embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.
FIG. 1 is a flow chart of a method of identifying merchandise being removed from an unmanned vending machine according to one embodiment of the present invention. Above-mentioned unmanned machine of selling is provided with first microwave module and second microwave module, is provided with commodity between first microwave module and the second microwave module, and first microwave module is used for sending the microwave, and the second microwave module is used for receiving the microwave that first microwave module sent. For example, the first microwave module is arranged at a corner of a shelf, the second microwave module is arranged at another corner of the shelf and is positioned on the same diagonal line with the first microwave module, so that the microwaves can pass through all goods on the shelf comprehensively, and the sensitivity of the change of the microwave transmission duration can be improved.
As shown in fig. 1, the method may include the steps of:
step 101, determining the transmission time length of the microwave transmitted from the first microwave module to the second microwave module when the commodity is in the two adjacent static states, and determining the difference value of the two transmission time lengths as the transmission time difference.
Microwave (UWB) is a wireless carrier communication technology, which directly uses digital waveforms to transmit data, and measures the microwave transmission duration through the time stamps of the first microwave module and the second microwave module.
In this step, when the transmission duration of the microwave transmitted from the first microwave module to the second microwave module is kept constant within the preset time period, the commodity is in a static state. That is to say, before the commodity is not taken out of the unmanned vending machine, the commodity in the unmanned vending machine is in a static state, and at this time, the transmission time length for the first microwave module to transmit the microwave to the second microwave module is kept unchanged within a preset time period; after the commodity is taken out of the unmanned vending machine, the commodity in the unmanned vending machine is still, and the transmission time length of the microwave transmitted from the first microwave module to the second microwave module is kept unchanged within the preset time period. In this embodiment, whether the goods in the unmanned vending machine are in a static state is determined through the change of the transmission time length of the microwave transmitted from the first microwave module to the second microwave module.
Since the transmission time of the microwaves in the first microwave module and the second microwave module is small in magnitude, for example, generally in the order of several ms. When the user takes the commodity out of the unmanned vending machine, the transmission time length can be changed for multiple times, the selection of the preset time period can be in the order of magnitude of 10-30 times of the transmission time length, so that whether the transmission time length is unchanged or not can be determined, and whether the commodity in the unmanned vending machine is in a static state or not can be further determined.
It should be noted that the transmission duration of the microwave transmission in each period is determined as follows: the first microwave module sends out a first number (for example, 100) of microwaves to the second microwave module, and when the second microwave module receives a second number (the second number is a preset percentage of the first number, for example, the preset percentage is 90%, that is, the second number is 90) of microwaves, the average transmission time length used by the second microwave module to receive the second number of microwaves is calculated, and the average transmission time length is used as the transmission time length of each transmission of microwaves.
However, since the determined transmission time duration of the microwave transmitted in each period is only the transmission time duration measured when the first microwave module transmits the microwave to the second microwave module, the accuracy of the measurement method may be low.
In one embodiment of the present invention, to improve the measurement accuracy of the transmission duration, the transmission duration may be determined as follows. The second microwave module is also used for emitting microwaves, and the first microwave module is also used for receiving the microwaves emitted by the second microwave module; when the commodity is in a static state, the transmission time length is determined by the following steps:
the method comprises the steps that microwaves with time stamps are sent to a second microwave module by a first microwave module, wherein the time stamps are the time when the first microwave module sends the microwaves to the second microwave module;
the method comprises the steps that a second microwave module is utilized to send microwaves carrying first response time length to a first microwave module, wherein the first response time length is a difference value between the time when the second microwave module receives the microwaves sent by the first microwave module and the time when the second microwave module sends the microwaves to the first microwave module;
determining a first receiving time length, wherein the first receiving time length is a difference value between the time when the first microwave module receives the microwaves sent by the second microwave module and the timestamp;
the method comprises the steps that a first microwave module is utilized to send microwaves with second response time length to a second microwave module, wherein the second response time length is the difference value between the time when the first microwave module receives the microwaves sent by the second microwave module and the time when the first microwave module sends the microwaves to the second microwave module;
determining a second receiving time length, wherein the second receiving time length is a difference value between the moment when the second microwave module receives the microwaves sent by the first microwave module for the second time and the moment when the second microwave module sends the microwaves to the first microwave module;
the transmission time length is determined by the following formula:
Tch＝(Tjs1*Tjs2-Txy1*Txy2)/(Tjs1+Tjs2+Txy1+Txy2)
wherein, TchFor characterizing the transmission duration, Tjs1For characterizing a first receiving duration, Tjs2For characterizing a second receiving duration, Txy1For characterizing a first response duration, Txy2For characterizing the second response duration.
It should be noted that the derivation process of the above formula is as follows:
Tjs1＝2*Tch+Txy1… … equation (1)
Tjs2＝2*Tch+Txy2… … equation (2)
Equation (1) × equation (2) is cross-linked, and we can get:
Tjs1*Tjs2-Txy1*Txy2＝2*Tch(2*Tch+Txy1+Txy2) … … equation (3)
By combining equation (1) and equation (3), we can obtain:
Tjs1*Tjs2-Txy1*Txy2＝2*Tch(Tjs1+Txy2) … … equation (4)
By combining equation (2) and equation (3), we can obtain:
Tjs1*Tjs2-Txy1*Txy2＝2*Tch(Tjs2+Txy1) … … equation (5)
By combining equation (4) and equation (5), we can obtain:
Tch＝(Tjs1*Tjs2-Txy1*Txy2)/(2*(Tjs1+Txy2) … … equation (6)
Tch＝(Tjs1*Tjs2-Txy1*Txy2)/(2*(Tjs2+Txy1) … … equation (7)
And because:
Tjs1+Txy2+Tch＝Tjs2+Txy1+Tch；
namely:
Tjs1+Txy2＝Tjs2+Txy1；
thus:
2*(Tjs1+Txy2)＝(Tjs1+Txy2)+(Tjs1+Txy2)＝(2*(Tjs2+Txy1)；
thus: equation (6) and equation (7) can be converted simultaneously to:
Tch＝(Tjs1*Tjs2-Txy1*Txy2)/(Tjs1+Tjs2+Txy1+Txy2)。
the transmission time obtained through the calculation can greatly reduce the clock error between the first microwave module and the second microwave module, and is beneficial to improving the measurement accuracy of the transmission time.
And 102, inputting the transmission time difference into a pre-trained neural network model to obtain the time variation of the commodities in two adjacent static states.
In practice, for the product taken out from the unmanned vending machine, the quantity of change of the quantity of the product becomes a positive value when the product is in two adjacent static states, for example, the quantity of the product between the first microwave module and the second microwave module is 10, and the corresponding transmission time length is 10 ms; after 1 commodity is taken out, the number of the commodities is changed into 9, the corresponding transmission time length is 8.5ms (because the commodities are few, the transmission rate of microwave is increased, and the transmission time length is reduced), the transmission time difference is 1.5ms, and the number variation of the commodities in the adjacent two static states is 1 by inputting the 1.5ms into a pre-trained neural network model. It should be noted that if there are 2 or more commodities to be taken out and the difference of the transmission time is 1.5ms, the result (i.e. the amount change) output by the neural network model is not unique, so that the unique result can be determined by means of weight identification, which will be described in detail below.
And 103, determining the weight of each type of commodity in the static state.
Since the standard weight of each kind of product (i.e. the standard weight carried on the product package or the product information, such as a bag of instant noodles of a certain type, the standard weight is 100g) is fixed, but the actual weight may have an error from the standard weight, and the error will gradually increase as the number of products taken out increases, it is necessary to take the number of taken-out products (i.e. the number variation) into account.
In this step, the method specifically includes the following steps:
and A1, determining the number variable of each type of commodity.
In step a1, the number variable of each type of article does not exceed the value obtained by rounding up the ratio of the total weight change amount when the article is in the two adjacent stationary states to the initial weight of the article, and the sum of the number variables of each type of article is not less than the amount change amount when the article is in the two adjacent stationary states, and the initial weight of the article is the weight determined in the immediately preceding adjacent stationary state.
For example, the total weight change amount of the article in two adjacent static states is 1390g, the initial weight of the article a is 600g (the initial weight is the standard weight, such as 600g, in the first calculation), the initial weight of the article B is 500g (the initial weight is the standard weight, such as 300g, in the first calculation), the initial weight of the article C is 700g (the initial weight is the standard weight, such as 200g, in the first calculation), the number variable of the a commodities does not exceed 3 (where 3 is a value rounded up to the ratio of 1390 and 600), the number variable of the B commodities does not exceed 3 (where 3 is a value rounded up to the ratio of 1390 and 500), the number variable of the C commodities does not exceed 2 (where 2 is a value rounded up to the ratio of 1390 and 700), and the amount of change is 8 or less (i.e., 3+3+ 2).
And A2, determining the weight of each type of commodity in the current static state according to the quantity change quantity when the commodity is in the two adjacent static states, the quantity variable of each type of commodity and the weight error average value determined before the current static state of the commodity.
In step a2, following step a1, for example, since the weights of the two C commodities are closer to 1390g, the weight error at this time is 10g (i.e. 1400-.
In this embodiment, the amount of change in the number of the commodities in the two adjacent stationary states, the number variable of each kind of commodities, and the average value of the weight errors of the commodities determined before the present stationary state are taken into account in the weight of the commodities, so that the accuracy of commodity identification can be improved.
Further, step a2 is specifically implemented as follows.
For example, the weights of various kinds of commodities at the present static state are determined by the following formula sets:
wherein, Wn2For characterizing the weight of the nth kind of goods in the present static stateAn amount; wn1The weight of the nth commodity in the last adjacent static state or the initial weight of the nth commodity in the current static state is represented; xnError coefficients for characterizing the nth commodity; y is used for representing the compensation coefficient; wtotalThe weight change quantity of the commodity in two adjacent static states is represented; w11The weight of the 1 st commodity in the last adjacent static state or the initial weight of the 1 st commodity in the current static state is represented; c1The number variable is used for representing the number variable of the 1 st type commodity in the two adjacent static states; cnThe number variable is used for representing the number variable of the nth commodity in the two adjacent static states; t iscThe quantity variation quantity is used for representing the quantity variation quantity when the commodity is in two adjacent static states; and E, representing the weight error mean value determined before the static state of the commodity.
Following the example of steps a1 and a2, the mean value of the weight error determined before the current static state of the commercial product is 5g, the initial weight of the C commercial product at the current static state is 700g, the error coefficient of the C commercial product is 0.007 (i.e., 5/7), the compensation coefficient is-0.005 (i.e., (1390-. By correcting the weight of the product each time the product is removed from the unmanned merchandiser (i.e., taking into account the error and compensation coefficients), the accuracy of identifying the product removed from the merchandiser may be improved.
And 104, identifying the type and the quantity of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states, the total weight variation of the commodities in the two adjacent static states and the weight of various commodities in the static state.
In an embodiment of the present invention, the type and number of the goods taken out from the vending machine may be specifically identified by the following steps:
identifying a taking-out result of the commodity taken out of the unmanned vending machine according to the total weight variation of the commodity in the two adjacent static states and the weights of various commodities in the static state;
and determining the type and the quantity of the taken-out commodities in the taking-out result according to the quantity variation of the commodities in the two adjacent static states.
As an example, if the total weight change of the product in the two adjacent static states is 3000g, i.e. the weight of the product taken out from the vending machine is 3000g, the above step a1 can be performed as follows: 1) 5A commodities; 2) 6B commercial products.
Since the amount of change in the number of products is 5 in the extraction result 1), the amount of change in the number of products is 6 in the extraction result 2). Therefore, the unique commodity taking-out result can be accurately determined in the taking-out result by judging the quantity variation of the commodities in the two adjacent static states, so that the accuracy of identifying the commodities taken out of the vending machine can be improved.
In another embodiment of the present invention, the type and quantity of the goods taken out from the vending machine can be specifically identified by the following steps:
identifying a taking-out result of the commodity taken out of the unmanned vending machine according to the quantity variation of the commodity in the two adjacent static states;
and determining the type and the number of the taken-out commodities in the taking-out result according to the total weight change amount of the commodities in the two adjacent static states and the weight of each type of commodities in the current static state.
As an example, in step a1, if the difference between the transmission times of the two adjacent stationary states of the merchandise items is 300ms, the difference between the transmission times of 1 merchandise item a taken out of the vending machine is 300ms (the amount variation is 1), and the difference between the transmission times of 2 merchandise items B taken out of the vending machine is 300ms (the amount variation is 2), then the following take-out results are obtained: 1)1 commodity A; 2) 2B commodities.
Since the weight of the product A was 600g in the removal result 1), and the weight of the product B was 500g in the removal result 2). Therefore, the unique commodity taking-out result can be accurately determined in the taking-out result by judging the weight variation of the commodity in the two adjacent static states, so that the accuracy of identifying the commodity taken out of the vending machine can be improved.
In the embodiment of the invention, firstly, the transmission time length of the microwave transmitted from the first microwave module to the second microwave module when the commodity is in the two adjacent static states is determined, and the difference value of the two transmission time lengths is determined as the transmission time difference; then inputting the transmission time difference into a pre-trained neural network model to obtain the time variation when the commodity is in the adjacent two static states; and identifying the type and the quantity of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states, the total weight variation of the commodities in the two adjacent static states and the weight of various commodities in the static state. According to the technical scheme, the type and the quantity of the commodities taken out of the unmanned vending machine are identified according to the quantity variation quantity when the commodities are in the two adjacent static states, the total weight variation quantity when the commodities are in the two adjacent static states and the weight of each type of commodities in the static state, so that compared with a scheme of identifying the commodities taken out only by means of weight, the accuracy of identifying the commodities taken out of the vending machine can be improved.
As shown in fig. 2 and 3, embodiments of the present invention provide an apparatus in which a device for identifying an article removed from an unmanned vending machine is located and a device for identifying an article removed from an unmanned vending machine. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. From a hardware level, as shown in fig. 2, a hardware structure diagram of a device for identifying a commodity taken out of an unmanned vending machine according to an embodiment of the present invention is shown, where the device in the embodiment may generally include other hardware, such as a forwarding chip responsible for processing a message, in addition to the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 2. Taking a software implementation as an example, as shown in fig. 3, as a logical apparatus, the apparatus is formed by reading, by a CPU of a device in which the apparatus is located, corresponding computer program instructions in a non-volatile memory into a memory for execution.
As shown in fig. 3, in the apparatus for identifying a commodity taken out of an unmanned vending machine according to this embodiment, the unmanned vending machine is provided with a first microwave module and a second microwave module, the commodity is arranged between the first microwave module and the second microwave module, the first microwave module is configured to emit microwaves, and the second microwave module is configured to receive the microwaves emitted by the first microwave module;
the device comprises:
a transmission time difference determining module 301, configured to determine a transmission time length for the first microwave module to transmit microwaves to the second microwave module when the commodity is in a static state of two adjacent times, and determine a difference value between the two transmission time lengths as a transmission time difference; when the transmission time length for transmitting the microwaves to the second microwave module by the first microwave module is kept unchanged in a preset time period, the commodity is in a static state;
the quantity variation determining module 302 is configured to input the transmission time difference into a pre-trained neural network model to obtain a quantity variation when the commodity is in two adjacent static states;
a weight determination module 303, configured to determine weights of various kinds of commodities in the current static state;
the identification module 304 is configured to identify the type and the quantity of the goods taken out from the unmanned vending machine according to the quantity variation of the goods in the two adjacent static states, the total weight variation of the goods in the two adjacent static states, and the weight of each type of goods in the current static state.
In the embodiment of the present invention, the transmission time difference determining module 301 may be configured to perform step 101 in the above-described method embodiment, the quantity variation determining module 302 may be configured to perform step 102 in the above-described method embodiment, the weight determining module 303 may be configured to perform step 103 in the above-described method embodiment, and the identifying module 304 may be configured to perform step 104 in the above-described method embodiment.
In one embodiment of the present invention, the second microwave module is further configured to emit microwaves, and the first microwave module is further configured to receive the microwaves emitted by the second microwave module;
when the commodity is in a static state, the transmission time length is determined by the following steps:
the method comprises the steps that microwaves with time stamps are sent to a second microwave module by a first microwave module, wherein the time stamps are the time when the first microwave module sends the microwaves to the second microwave module;
the method comprises the steps that a second microwave module is utilized to send microwaves carrying first response time length to a first microwave module, wherein the first response time length is a difference value between the time when the second microwave module receives the microwaves sent by the first microwave module and the time when the second microwave module sends the microwaves to the first microwave module;
determining a first receiving time length, wherein the first receiving time length is a difference value between the time when the first microwave module receives the microwaves sent by the second microwave module and the timestamp;
the method comprises the steps that a first microwave module is utilized to send microwaves with second response time length to a second microwave module, wherein the second response time length is the difference value between the time when the first microwave module receives the microwaves sent by the second microwave module and the time when the first microwave module sends the microwaves to the second microwave module;
determining a second receiving time length, wherein the second receiving time length is a difference value between the moment when the second microwave module receives the microwaves sent by the first microwave module for the second time and the moment when the second microwave module sends the microwaves to the first microwave module;
the transmission time length is determined by the following formula:
Tch＝(Tjs1*Tjs2-Txy1*Txy2)/(Tjs1+Tjs2+Txy1+Txy2)
wherein, TchFor characterizing the transmission duration, Tjs1For characterizing a first receiving duration, Tjs2For characterizing a second receiving duration, Txy1For characterizing a first response duration, Txy2For characterizing the second response duration.
In one embodiment of the present invention, the weight determination module 303 is configured to perform the following operations:
determining the number variable of each type of commodity, wherein the number variable of each type of commodity does not exceed a value obtained by rounding up the ratio of the total weight variation of the commodity in two adjacent static states to the initial weight of the commodity, the sum of the number variables of each type of commodity is not less than the number variation of the commodity in two adjacent static states, and the initial weight of the commodity is the weight determined in the last adjacent static state;
and determining the weight of each kind of commodities in the current static state according to the quantity variation of the commodities in the two adjacent static states, the quantity variation of each kind of commodities and the weight error mean value of the commodities determined before the current static state.
In an embodiment of the present invention, the weight determining module 303, in determining the weight of each kind of goods in the current static state according to the quantity variation of the goods in the two adjacent static states, the quantity variation of each kind of goods, and the weight error average value determined before the current static state of the goods, is configured to perform the following operations:
the weight of each type of commodity in this static state is determined by the following formula:
wherein, Wn2The weight of the nth commodity in the static state is represented; wn1The weight of the nth commodity in the last adjacent static state or the initial weight of the nth commodity in the current static state is represented; xnError coefficients for characterizing the nth commodity; y is used for representing the compensation coefficient; wtotalThe weight change quantity of the commodity in two adjacent static states is represented; w11The weight of the 1 st commodity in the last adjacent static state or the initial weight of the 1 st commodity in the current static state is represented; c1The number variable is used for representing the number variable of the 1 st type commodity in the two adjacent static states; cnThe number variable is used for representing the number variable of the nth commodity in the two adjacent static states; t iscThe quantity variation quantity is used for representing the quantity variation quantity when the commodity is in two adjacent static states; e, representing the weight errors determined before the static state of the commodityThe value is obtained.
In an embodiment of the present invention, the identifying module 304 is configured to perform the following operations:
identifying a taking-out result of the commodity taken out of the unmanned vending machine according to the total weight variation of the commodity in the two adjacent static states and the weights of various commodities in the static state;
and determining the type and the quantity of the taken-out commodities in the taking-out result according to the quantity variation of the commodities in the two adjacent static states.
In an embodiment of the present invention, the identifying module 304 is configured to perform the following operations:
identifying a taking-out result of the commodity taken out of the unmanned vending machine according to the quantity variation of the commodity in the two adjacent static states;
and determining the type and the number of the taken-out commodities in the taking-out result according to the total weight change amount of the commodities in the two adjacent static states and the weight of each type of commodities in the current static state.
It is to be understood that the illustrated configuration of the embodiments of the present invention does not constitute a specific limitation on the means for identifying the articles removed from the vending machine. In other embodiments of the invention, the means for identifying the merchandise removed from the vending machine may include more or fewer components than shown, or some components may be combined, some components may be separated, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
Because the content of information interaction, execution process, and the like among the modules in the device is based on the same concept as the method embodiment of the present invention, specific content can be referred to the description in the method embodiment of the present invention, and is not described herein again.
An embodiment of the present invention further provides a device for identifying a commodity taken out of an unmanned vending machine, including: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is configured to invoke the machine readable program to perform a method of identifying an item removed from an unmanned merchandiser of any of the embodiments of the present invention.
Embodiments of the present invention also provide a computer-readable medium storing instructions for causing a computer to perform a method of identifying items removed from an unmanned merchandiser as described herein. Specifically, a method or an apparatus equipped with a storage medium on which a software program code that realizes the functions of any of the above-described embodiments is stored may be provided, and a computer (or a CPU or MPU) of the method or the apparatus is caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer via a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments can be implemented not only by executing the program code read out by the computer, but also by performing a part or all of the actual operations by an operation method or the like operating on the computer based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion unit connected to the computer, and then causes a CPU or the like mounted on the expansion board or the expansion unit to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the above-described embodiments.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (10)
1. The method for identifying the commodity taken out of the unmanned vending machine is characterized in that the unmanned vending machine is provided with a first microwave module and a second microwave module, the commodity is arranged between the first microwave module and the second microwave module, the first microwave module is used for emitting microwaves, and the second microwave module is used for receiving the microwaves emitted by the first microwave module;
the method comprises the following steps:
determining the transmission time length of the microwave transmitted from the first microwave module to the second microwave module when the commodity is in the two adjacent static states, and determining the difference value of the two transmission time lengths as the transmission time difference; when the transmission time length of the microwave transmitted from the first microwave module to the second microwave module is kept unchanged in a preset time period, the commodity is in a static state;
inputting the transmission time difference into a pre-trained neural network model to obtain the time variation when the commodity is in the two adjacent static states;
determining the weight of various commodities in the static state;
and identifying the type and the quantity of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states, the total weight variation of the commodities in the two adjacent static states and the weight of various commodities in the static state.
2. The method of claim 1, wherein the second microwave module is further configured to emit microwaves, and the first microwave module is further configured to receive the microwaves emitted by the second microwave module;
when the commodity is in a static state, the transmission time length is determined by the following steps:
sending microwaves with time stamps to the second microwave module by using the first microwave module, wherein the time stamps are the time when the first microwave module sends the microwaves to the second microwave module;
sending microwaves with first response time duration to the first microwave module by using the second microwave module, wherein the first response time duration is a difference value between the time when the second microwave module receives the microwaves sent by the first microwave module and the time when the second microwave module sends the microwaves to the first microwave module;
determining a first receiving time length, wherein the first receiving time length is a difference value between the time when the first microwave module receives the microwaves sent by the second microwave module and the timestamp;
sending microwaves with second response time duration to the second microwave module by using the first microwave module, wherein the second response time duration is a difference value between the time when the first microwave module receives the microwaves sent by the second microwave module and the time when the first microwave module sends the microwaves to the second microwave module;
determining a second receiving time length, wherein the second receiving time length is a difference value between the time when the second microwave module receives the microwaves sent by the first microwave module for the second time and the time when the second microwave module sends the microwaves to the first microwave module;
the transmission time length is determined by the following formula:
Tch＝(Tjs1*Tjs2-Txy1*Txy2)/(Tjs1+Tjs2+Txy1+Txy2)
wherein, TchFor characterizing the transmission duration, Tjs1For characterizing the first reception duration, Tjs2For characterizing the second reception duration, Txy1For characterizing the first response duration, Txy2For characterizing the second response duration.
3. The method of claim 1, wherein determining the weight of each type of item at the present static state comprises:
determining the number variable of each type of commodity, wherein the number variable of each type of commodity does not exceed a value obtained by rounding up the ratio of the total weight variation of the commodity in two adjacent static states to the initial weight of the commodity, the sum of the number variables of each type of commodity is not less than the number variation of the commodity in two adjacent static states, and the initial weight of the commodity is the weight determined in the last adjacent static state;
and determining the weight of each kind of commodity in the current static state according to the quantity variation of the commodity in the two adjacent static states, the quantity variation of each kind of commodity and the weight error mean value determined before the static state of the commodity.
4. The method as claimed in claim 3, wherein the determining the weight of each kind of goods in the current static state according to the quantity variation of the goods in the two adjacent static states, the quantity variation of each kind of goods, and the weight error average value of the goods determined before the current static state comprises:
the weight of each type of commodity in this static state is determined by the following formula:
wherein, Wn2The weight of the nth commodity in the static state is represented; wn1The weight of the nth commodity in the last adjacent static state or the initial weight of the nth commodity in the current static state is represented; xnError coefficients for characterizing the nth commodity; y is used for representing the compensation coefficient; wtotalThe weight change quantity of the commodity in two adjacent static states is represented; w11For characterizing the weight of the 1 st merchandise in the last adjacent quiescent state or the initial 1 st merchandise in this quiescent stateWeight; c1The number variable is used for representing the number variable of the 1 st type commodity in the two adjacent static states; cnThe number variable is used for representing the number variable of the nth commodity in the two adjacent static states; t iscThe quantity variation quantity is used for representing the quantity variation quantity when the commodity is in two adjacent static states; and E, representing the weight error mean value determined before the static state of the commodity.
5. The method according to any one of claims 1-4, wherein identifying the type and the number of the commodities taken out of the unmanned vending machine according to the amount of change in the number of the commodities in the two adjacent static states, the amount of change in the total weight of the commodities in the two adjacent static states, and the weights of the various commodities in the current static state comprises:
identifying a taking-out result of the commodity taken out of the unmanned vending machine according to the total weight variation of the commodity in the two adjacent static states and the weight of each kind of commodity in the static state;
and determining the type and the quantity of the taken-out commodities in the taking-out result according to the quantity variation of the commodities in the two adjacent static states.
6. The method according to any one of claims 1-4, wherein identifying the type and the number of the commodities taken out of the unmanned vending machine according to the amount of change in the number of the commodities in the two adjacent static states, the amount of change in the total weight of the commodities in the two adjacent static states, and the weights of the various commodities in the current static state comprises:
identifying a taking-out result of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states;
and determining the type and the number of the taken-out commodities in the taking-out result according to the total weight variation of the commodities in the two adjacent static states and the weight of each type of commodities in the current static state.
7. The device for identifying the commodities taken out of the unmanned vending machine is characterized in that the unmanned vending machine is provided with a first microwave module and a second microwave module, the commodities are arranged between the first microwave module and the second microwave module, the first microwave module is used for emitting microwaves, and the second microwave module is used for receiving the microwaves emitted by the first microwave module;
the device comprises:
the transmission time difference determining module is used for determining the transmission time length for the first microwave module to transmit the microwaves to the second microwave module when the commodity is in the two adjacent static states, and determining the difference value of the two transmission time lengths as the transmission time difference; when the transmission time length of the microwave transmitted from the first microwave module to the second microwave module is kept unchanged in a preset time period, the commodity is in a static state;
the quantity variation determining module is used for inputting the transmission time difference into a pre-trained neural network model to obtain the quantity variation of the commodities in the two adjacent static states;
the weight determining module is used for determining the weight of various commodities in the static state;
and the identification module is used for identifying the types and the quantity of the commodities taken out of the unmanned vending machine according to the quantity variation of the commodities in the two adjacent static states, the total weight variation of the commodities in the two adjacent static states and the weight of various commodities in the static state.
8. The apparatus of claim 7, wherein the weight determination module is configured to:
determining the number variable of each type of commodity, wherein the number variable of each type of commodity does not exceed a value obtained by rounding up the ratio of the total weight variation of the commodity in two adjacent static states to the initial weight of the commodity, the sum of the number variables of each type of commodity is not less than the number variation of the commodity in two adjacent static states, and the initial weight of the commodity is the weight determined in the last adjacent static state;
determining the weight of each kind of commodity in the static state according to the quantity variation of the commodity in the two adjacent static states, the quantity variation of each kind of commodity and the weight error mean value determined before the static state of the commodity;
the weight determining module is configured to, when determining the weight of each type of commodity in the current static state according to the quantity variation of the commodity in the two adjacent static states, the quantity variation of each type of commodity, and the weight error mean value determined before the static state of the commodity, perform the following operations:
the weight of each type of commodity in this static state is determined by the following formula:
wherein, Wn2The weight of the nth commodity in the static state is represented; wn1The weight of the nth commodity in the last adjacent static state or the initial weight of the nth commodity in the current static state is represented; xnError coefficients for characterizing the nth commodity; y is used for representing the compensation coefficient; wtotalThe weight change quantity of the commodity in two adjacent static states is represented; w11The weight of the 1 st commodity in the last adjacent static state or the initial weight of the 1 st commodity in the current static state is represented; c1The number variable is used for representing the number variable of the 1 st type commodity in the two adjacent static states; cnThe number variable is used for representing the number variable of the nth commodity in the two adjacent static states; t iscThe quantity variation quantity is used for representing the quantity variation quantity when the commodity is in two adjacent static states; sigmanAnd the weight error mean value is used for representing the weight error mean value determined before the static state of the commodity.
9. Device for identifying a product removed from an unmanned vending machine, comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor, configured to invoke the machine readable program, to perform the method of any of claims 1 to 6.
10. Computer readable medium, characterized in that it has stored thereon computer instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 6.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101726343A (en) * | 2008-10-23 | 2010-06-09 | 惠尔普尔公司 | Lid based amount sensor |
| WO2011010096A1 (en) * | 2009-07-24 | 2011-01-27 | Wayne Rudd | A method for determining the location of an interface region in a medium and associated apparatus |
| CN104111106A (en) * | 2014-07-01 | 2014-10-22 | 武汉领傲科技有限公司 | Internet of Things perception method and system based on article consumption and compositional variation |
| CN107341939A (en) * | 2017-07-12 | 2017-11-10 | 杨智勇 | The intelligent commodity shelf of merchandise sales is realized based on short-term resting space |
| CN108549851A (en) * | 2018-03-27 | 2018-09-18 | 合肥美的智能科技有限公司 | Method and device for identifying goods in intelligent container and intelligent container |
| CN108922084A (en) * | 2018-07-06 | 2018-11-30 | Ut斯达康通讯有限公司 | A kind of commodity recognition method and device |
| CN109405879A (en) * | 2018-10-16 | 2019-03-01 | 苏州中科先进技术研究院有限公司 | A kind of Intelligent unattended vending system and commodity recognition method |
| CN109858602A (en) * | 2019-01-04 | 2019-06-07 | 四川虹美智能科技有限公司 | A kind of commodity amount detection device, method and automatic vending machine |
| CN110428548A (en) * | 2019-08-09 | 2019-11-08 | 四川虹美智能科技有限公司 | A kind of method and device identifying the cargo taken out from vending machine |
| CN110470361A (en) * | 2019-09-19 | 2019-11-19 | 四川虹美智能科技有限公司 | A kind of liquid level detection system and its detection method |
-
2020
- 2020-12-07 CN CN202011417203.2A patent/CN112562182A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101726343A (en) * | 2008-10-23 | 2010-06-09 | 惠尔普尔公司 | Lid based amount sensor |
| WO2011010096A1 (en) * | 2009-07-24 | 2011-01-27 | Wayne Rudd | A method for determining the location of an interface region in a medium and associated apparatus |
| CN104111106A (en) * | 2014-07-01 | 2014-10-22 | 武汉领傲科技有限公司 | Internet of Things perception method and system based on article consumption and compositional variation |
| CN107341939A (en) * | 2017-07-12 | 2017-11-10 | 杨智勇 | The intelligent commodity shelf of merchandise sales is realized based on short-term resting space |
| CN108549851A (en) * | 2018-03-27 | 2018-09-18 | 合肥美的智能科技有限公司 | Method and device for identifying goods in intelligent container and intelligent container |
| CN108922084A (en) * | 2018-07-06 | 2018-11-30 | Ut斯达康通讯有限公司 | A kind of commodity recognition method and device |
| CN109405879A (en) * | 2018-10-16 | 2019-03-01 | 苏州中科先进技术研究院有限公司 | A kind of Intelligent unattended vending system and commodity recognition method |
| CN109858602A (en) * | 2019-01-04 | 2019-06-07 | 四川虹美智能科技有限公司 | A kind of commodity amount detection device, method and automatic vending machine |
| CN110428548A (en) * | 2019-08-09 | 2019-11-08 | 四川虹美智能科技有限公司 | A kind of method and device identifying the cargo taken out from vending machine |
| CN110470361A (en) * | 2019-09-19 | 2019-11-19 | 四川虹美智能科技有限公司 | A kind of liquid level detection system and its detection method |
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