CN115965324B - Commodity sales method and system based on vending machine - Google Patents
Commodity sales method and system based on vending machine Download PDFInfo
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Abstract
The invention provides a commodity selling method and system based on a vending machine, which belongs to the technical field of vending machines and specifically comprises the following steps: when the vending machine does not complete the shipment of the target commodity within the set time and the customer does not leave, the shipment success probability is determined based on the shipment success rate in the latest preset time, the single average shipment attempt number in the shipment success, and the single average shipment time in the shipment success, and when the shipment success probability is greater than a certain value, the shipment of the target commodity is performed again, and if and only if the customer has left and does not complete the shipment, the vending machine is output in an abnormal state, and the operation state is obtained based on the shipment success probability of the vending machine, the inconsistent number of times of the shipment commodity and the target commodity in the latest preset time, and the incomplete shipment number, and the vending machine is managed accordingly, so that the commodity loss rate is further reduced.
Description
Technical Field
The invention belongs to the technical field of vending machines, and particularly relates to a commodity selling method and system based on a vending machine.
Background
In order to realize the sales management of goods based on the vending machine, a shipment instruction is received in the patent grant bulletin No. CN110838205B, automatic vending method, vending machine and server; the shipment instruction comprises a sending time and a shipment instruction; calculating the time difference between the receiving time and the sending time of the shipment instruction; when the time difference is larger than the time difference threshold value, returning a shipment result containing a shipment state as failure; when the time difference is not greater than the time difference threshold, carrying out shipment operation according to shipment indication and returning a shipment result with successful shipment state, but the following technical problems exist:
1. the output of the recommended commodity according to the historical shopping condition of the user is not considered, and if the commodity cannot be recommended according to the commodity type or the commodity type of the historical shopping of the user during shopping, the shopping experience of the user is reduced.
2. Although the customer status is not recognized, in some cases, the customer may be waiting even though the shipment is not completed within a predetermined time, and thus if the shipment is not stopped based on the recognition of the customer status, there is a possibility that the customer may be damaged or the customer shopping experience may be poor.
3. The operation state of the vending machine is not considered based on the number of inconformities between the shipment goods and the target goods, the average number of times of single trial shipment of the shipment goods when the shipment is successful, the average shipment time of the shipment goods, and the number of times of incorrect shipment of the shipment goods, and when the vending machine completes shipment each time, but the average number of times of trial shipment or the average shipment time is long, or when the number of times of incorrect completion or the number of inconformities is large, if the operation state of the vending machine cannot be judged and targeted processing is performed according to the condition of the operation state, the problems such as poor shopping efficiency and goods loss may be caused.
Aiming at the technical problems, the invention provides a commodity selling method and system based on a vending machine.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to one aspect of the present invention, a vending machine-based commodity sales method is provided.
The commodity selling method based on the vending machine is characterized by comprising the following steps:
s11, recommending commodities based on shopping history data of a user, judging whether the vending machine finishes the shipment of target commodities within a set time, if so, entering a step S12, and if not, entering a step S13;
s12, judging whether the goods are consistent with the target goods, if so, outputting successful goods, otherwise, entering step S16;
s13, acquiring the state of a customer based on an image pickup device of the vending machine in real time, judging whether the customer does not leave, if so, entering a step S14, and if not, entering a step S16;
s14, obtaining the probability of successful shipment based on the success rate of shipment of the vending machine in the latest preset time, the single average shipment attempt number when the shipment is successful and the single average shipment time when the shipment is successful, judging whether the vending machine is normal or not based on the probability of successful shipment, if so, entering step S15, and if not, entering step S16;
s15, carrying out shipment of the target commodity again based on the vending machine, and if and only if the customer has left and the vending machine does not complete shipment, entering step S16;
s16, outputting that the vending machine is in an abnormal state and can not finish shipment, obtaining the running state of the vending machine by adopting a running state evaluation model based on a machine learning algorithm based on the probability of successful shipment of the vending machine, the inconsistent times of the shipment goods and the target goods in the latest preset time and the incomplete shipment times, and managing the vending machine according to the running state.
Through judging the consistency of the goods and the target goods and acquiring the states of the customers, the consistency of the goods and the target goods is ensured, the problem of goods loss caused by inconsistent goods and the target goods is reduced, and the shopping experience of the customers is improved on the basis of ensuring that no abnormal goods loss exists through acquiring the states of the customers.
The probability of successful shipment is obtained through the success rate of shipment to the vending machine, the single average shipment attempt times when the successful shipment is achieved in the latest preset time and the single average shipment time when the successful shipment is achieved in the latest preset time, so that the probability of successful shipment to the vending machine from multiple angles is judged, unnecessary attempts and goods losses are avoided, and meanwhile, the shopping experience of customers is also improved.
Through the judgment of the running state of the vending machine, the technical problems of increased goods loss and poor shopping experience caused by continuous vending after the original single fault is finished are avoided, the accurate and comprehensive evaluation of the running state of the vending machine is realized, and the technical problems of goods loss or poor shipment efficiency caused by the running fault of the vending machine are avoided
In another aspect, embodiments of the present application provide a computer system, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor runs the computer program to realize the commodity selling method based on the vending machine.
In another aspect, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer causes the computer to perform a vending machine-based commodity vending method as described above.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings;
FIG. 1 is a flow chart of a vending machine-based commodity sales method according to embodiment 1;
FIG. 2 is a flowchart of specific steps for determining whether the shipment is consistent with the target good according to embodiment 1;
FIG. 3 is a flowchart of specific steps for obtaining a probability of success in shipment according to example 1;
FIG. 4 is a flowchart of specific steps for determining the operational status of the vending machine according to embodiment 1;
fig. 5 is a frame diagram of a computer storage medium according to embodiment 3.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
In the commodity selling process of the original vending machine, the fault state of the vending machine is judged through judging the shipment time, and the occurrence of goods damage is reduced, but the identification of the customer state and the shipment goods is ignored, even if the shipment goods are inconsistent or the customer leaves in the set time, the goods damage problem is caused, meanwhile, the state of the vending machine is not evaluated, and the problems such as the goods damage are further caused.
Example 1
In order to solve the above-mentioned problems, according to an aspect of the present invention, as shown in fig. 1, there is provided a vending machine-based commodity sales method, comprising:
s11, recommending commodities based on shopping history data of a user, judging whether the vending machine finishes the shipment of target commodities within a set time, if so, entering a step S12, and if not, entering a step S13;
specifically, the set time is determined according to the single average shipment time of the vending machine when shipment is successful, wherein the longer the single average shipment time of the vending machine when shipment is successful, the longer the set time.
Specifically, for example, the state of the shipment of the target commodity is determined by the video monitoring device of the shipment port or the gravity sensing device, and when the time between the generation of the shipment command and the completion of the shipment of the target is 10S, the process proceeds to step S12, and when the shipment of the target commodity is not completed within 10S, the process proceeds to step S13, and the customer state is determined.
S12, judging whether the goods are consistent with the target goods, if so, outputting successful goods, otherwise, entering step S16;
specifically, as shown in fig. 2, the specific steps of determining whether the shipment goods are consistent with the target goods are as follows:
s21, acquiring an image of the goods on the basis of an image recognition device of the vending machine;
for example, the image recognition device may be placed in a delivery channel of a delivery commodity or a storage place of a final delivery commodity, so as to obtain an image of the delivery commodity.
S22, extracting HOG features of the images of the goods to obtain HOG features of the goods;
specifically, besides extracting the HOG features, the image of the goods to be delivered can be transmitted to an image recognition model based on a CNN algorithm to obtain the goods types of the goods to be delivered, and the consistency of the two goods can be judged according to the goods types of the target goods of the instruction of the vending machine.
S23, based on the HOG characteristics of the goods and the HOG characteristics of the original image of the target goods, judging whether the goods are consistent with the target goods.
Specifically, when the similarity of the HOG features of the images of the two is greater than a certain threshold, determining that the outgoing commodity and the target commodity belong to the same object.
Through judging the consistency of the goods and the target goods and acquiring the states of the customers, the consistency of the goods and the target goods is ensured, the problem of goods loss caused by inconsistent goods and the target goods is reduced, and the shopping experience of the customers is improved on the basis of ensuring that no abnormal goods loss exists through acquiring the states of the customers.
S13, acquiring the state of a customer based on an image pickup device of the vending machine in real time, judging whether the customer does not leave, if so, entering a step S14, and if not, entering a step S16;
for example, the customer's status may be determined by an infrared sensing device or an imaging device.
S14, obtaining the probability of successful shipment based on the success rate of shipment of the vending machine in the latest preset time, the single average shipment attempt number when the shipment is successful and the single average shipment time when the shipment is successful, judging whether the vending machine is normal or not based on the probability of successful shipment, if so, entering step S15, and if not, entering step S16;
specifically, the specific steps for obtaining the probability of successful shipment as shown in fig. 3 are:
s31, determining whether the vending machine operates normally or not based on the shipment success rate of the vending machine in the latest preset time, if so, taking the shipment success rate of the vending machine as the probability of successful shipment of the vending machine, and if not, entering step S32;
for example, if the vending machine has a delivery success rate of 97%, when it is greater than 96%, it is determined that it is operating properly, and the delivery success rate through the vending machine is 97%.
S32, judging whether the single average shipment attempt times when the shipment is successful in the latest preset time are smaller than the preset times or the single average shipment time when the shipment is successful in the latest preset time is smaller than the set time, if so, entering step S33, and if not, entering step S34;
specifically, for example, if the number of single-time average shipment attempts is 3 when shipment is successful in the latest preset time, the preset number is 5, or the average single-time shipment time is 20S when shipment is successful in the latest preset time, and the set time is 30S, the process proceeds to step S33.
S33, determining whether the vending machine is reliable to operate or not based on the shipment success rate of the vending machine in the latest preset time, if so, constructing a shipment success probability correction amount based on the single average shipment attempt number in the latest preset time when the shipment is successful and the single average shipment time in the latest preset time when the shipment is successful, and constructing the shipment success probability of the vending machine based on the shipment success probability correction amount and the shipment success rate, otherwise, entering step S34;
for example, if the vending machine has a success rate of 95.5% and a second power threshold is 95%, the vending machine is determined to be reliable in operation, and a success probability correction amount is constructed based on a single average number of delivery attempts when the vending machine has succeeded in the latest preset time and a single average delivery time when the vending machine has succeeded in the latest preset time.
For example, the calculation formula of the probability of successful shipment of the vending machine is:
S34, constructing an input set based on the shipment success rate of the vending machine in the latest preset time, the single average shipment attempt times when the shipment is successful and the single average shipment time when the shipment is successful, and obtaining the shipment success probability rate by adopting a prediction model based on an HHO-GRU algorithm.
For a specific example, the input set is x= { P1, T1}, where P1, T1 are respectively the delivery success rate of the vending machine in the last preset time, the single average delivery attempt number when the delivery is successful in the last preset time, and the single average delivery time when the delivery is successful in the last preset time.
For a specific example, the specific steps of constructing the prediction model based on the HHO-GRU algorithm are as follows:
step 1: the initialization parameters comprise the population individual scale N of the algorithm, the dimension D of the problem variable and the maximum iteration number Tmax of the algorithm.
Step 2: and determining an adaptation function and a LOSS function of LOSS after GRU training, and calculating a mean square error to obtain an evaluation index.
Step 3: and in the parameter vector space, performing optimal GRU parameter searching.
Step 4: and (3) completing the optimizing process of the HHO algorithm, and obtaining an optimal solution. And the result is used as a parameter to be imported into the GRU neural network to complete the prediction of the data.
Step 5: and calculating an error value according to the prediction result, returning the error value to the HHO as an evaluation index, and updating the optimal solution position according to the evaluation index.
In order to clearly show the error condition of the predicted data and the true value, introducing an MSE function and a LOSS function, namely, expressing the difference between the predicted result value and the original value; the language description of the function is that the sum of squares of the distances on the corresponding point positions of the predicted data and the original data is calculated; the calculation formula of the evaluation index is as follows:
Step 6: and (3) iterating the steps 3, 4 and 5 until the iteration requirement is met or the maximum iteration times are reached.
Step 7: and obtaining optimal solution network parameters, and leading the optimal solution network parameters into a neural network to complete model training.
For example, two situations of over-fitting and under-fitting may occur in the GRU nerve synthesis result, and parameters affecting the two results include the learning rate of the neural network and drop parameters; larger prediction errors occur when the configuration of such parameters is not appropriate for the current network structure and data attributes.
Step 8: predictive analysis is performed on the test data using the model.
Step 9: and obtaining a predicted result value.
For a specific example, for a meta-heuristic algorithm based on group intelligence, the global searching capability marks the instability of an optimal solution of the algorithm, namely the algorithm happens to avoid a local area where the optimal solution is located when searching an area, so that the algorithm does not capture the optimal solution in an iterative process; in the HHO algorithm, the magnitude of the hunting Energy Escaping_energy reflects the optimal solution searching capability of the Harris eagle algorithm on the problem, and the larger E is the stronger the searching capability of the HHO algorithm on the global range, and the stronger the searching capability on the local range is on the contrary; when the global scope is large, the algorithm cannot fully explore the solving space due to the search of the limited space, so that the global searching capacity is further weakened. Eventually, the algorithm optimizing capability is insufficient.
For example, the calculation formula of the E value of the HHO algorithm is:
Specifically, the value range of the probability of successful shipment is between 0 and 1, wherein the greater the probability of successful shipment, the greater the probability that the automatic vending machine can normally complete shipment.
Specifically, the first power forming threshold is greater than the second power forming threshold, and the first power forming threshold and the second power forming threshold are determined according to the average price of goods of the vending machine and the historical goods loss rate of the vending machine, wherein the higher the average price of goods of the vending machine and the greater the historical goods loss rate, the greater the first power forming threshold and the second power forming threshold are.
For example, in the actual operation, the first power threshold and the second power threshold may be constructed by using expert scoring or an expert system, or by using a neural network algorithm.
The probability of successful shipment is obtained through the success rate of shipment to the vending machine, the single average shipment attempt times when the successful shipment is achieved in the latest preset time and the single average shipment time when the successful shipment is achieved in the latest preset time, so that the probability of successful shipment to the vending machine from multiple angles is judged, unnecessary attempts and goods losses are avoided, and meanwhile, the shopping experience of customers is also improved.
S15, carrying out shipment of the target commodity again based on the vending machine, and if and only if the customer has left and the vending machine does not complete shipment, entering step S16;
s16, outputting that the vending machine is in an abnormal state and can not finish shipment, obtaining the running state of the vending machine by adopting a running state evaluation model based on a machine learning algorithm based on the probability of successful shipment of the vending machine, the inconsistent times of the shipment goods and the target goods in the latest preset time and the incomplete shipment times, and managing the vending machine according to the running state.
Specifically, as shown in fig. 4, the specific steps for determining the operation state of the vending machine are as follows:
s41, obtaining a fault state value of the vending machine by adopting a fault evaluation model based on a GRU algorithm based on inconsistent times, incomplete times and damage value of the vending machine of the goods discharged from the goods in the latest preset time and the target goods;
for a specific example, the fault status value of the vending machine ranges from 0 to 1, wherein the greater the fault status value, the greater the likelihood of the automatic harvester being faulty.
S42, determining whether the vending machine has a suspected fault or not based on the fault state value of the vending machine, if not, entering a step S43, and if so, entering a step S44;
for example, when the fault status value of the vending machine is 0.4 and is smaller than 0.6, it is determined that the vending machine has no suspected fault, and the process proceeds to step S43, where the vending success rate is continuously determined.
S43, determining whether the vending machine is normal or not based on the automatic shipment success rate of the vending machine, if so, judging that the operation state of the vending machine is normal, and normally opening the vending machine for use, and if not, entering step S44;
when the automatic delivery success rate is greater than a certain value, the automatic delivery success rate is determined to be normal.
S43, constructing an input set based on the fault state value and the probability of successful shipment of the vending machine, and obtaining a fault probability evaluation value of the vending machine by adopting a state evaluation model based on an HHO-GRU algorithm.
For a specific example, the value of the failure probability evaluation value ranges from 0 to 1, wherein the larger the failure probability evaluation value is, the larger the failure probability of the vending machine is.
Specifically, when the failure probability evaluation value of the vending machine is greater than a first probability threshold, determining that the operation state of the vending machine is abnormal, setting the vending machine to be in a failure state, locking the vending machine, and not vending any more; and when the fault probability evaluation value of the vending machine is smaller than or equal to a first probability threshold value, determining that the running state of the vending machine is in a normal running state, opening the vending machine, and restarting vending.
Through the judgment to the running state of automatic vending machine to avoided original single trouble to finish the back, still continue goods damage increase and the poor technical problem of shopping experience that lead to of selling goods, realized the accurate and comprehensive evaluation to the running state of automatic vending machine, avoided the goods damage that automatic vending machine running failure led to or the poor technical problem of shipment efficiency.
Example 2
In an embodiment of the present application, a computer system is provided, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor runs the computer program to obtain the vending machine-based commodity sales method for the internal components of the oil immersed transformer.
Specifically, the embodiment also provides a computer system, which comprises a processor, a memory, a network interface and a database which are connected through a system bus; wherein the processor of the computer system is configured to provide computing and control capabilities; the memory of the computer system includes nonvolatile storage medium, internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The computer device network interface is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a vending machine based commodity vending method for an internal component of an oil immersed transformer as described above.
Example 3
As shown in fig. 5, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer, causes the computer to perform a vending machine-based commodity sales method for an oil immersed transformer internal component as described above.
In particular, it will be understood by those skilled in the art that implementing all or part of the above-described methods of the embodiments may be implemented by a computer program, which may be stored in a non-volatile computer readable storage medium, and the computer program may include the steps of the embodiments of the above-described methods when executed. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. 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.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (9)
1. The commodity selling method based on the vending machine is characterized by comprising the following steps:
s11, recommending commodities based on shopping history data of a user, judging whether the vending machine finishes the shipment of target commodities within a set time, if so, entering a step S12, and if not, entering a step S13;
s12, judging whether the goods are consistent with the target goods, if so, outputting successful goods, otherwise, entering step S16;
s13, acquiring the state of a customer based on an image pickup device of the vending machine in real time, judging whether the customer does not leave, if so, entering a step S14, and if not, entering a step S16;
s14, obtaining the probability of successful shipment based on the success rate of shipment of the vending machine in the latest preset time, the single average shipment attempt number when the shipment is successful and the single average shipment time when the shipment is successful, judging whether the vending machine is normal or not based on the probability of successful shipment, if so, entering step S15, and if not, entering step S16;
the specific steps for obtaining the probability of successful shipment are as follows:
s31, determining whether the vending machine operates normally or not based on the shipment success rate of the vending machine in the latest preset time, if so, taking the shipment success rate of the vending machine as the probability of successful shipment of the vending machine, and if not, entering step S32;
s32, judging whether the single average shipment attempt times when the shipment is successful in the latest preset time are smaller than the preset times or the single average shipment time when the shipment is successful in the latest preset time is smaller than the set time, if so, entering step S33, and if not, entering step S34;
s33, determining whether the vending machine is reliable to operate or not based on the shipment success rate of the vending machine in the latest preset time, if so, constructing a shipment success probability correction amount based on the single average shipment attempt number in the latest preset time when the shipment is successful and the single average shipment time in the latest preset time when the shipment is successful, and constructing the shipment success probability of the vending machine based on the shipment success probability correction amount and the shipment success rate, otherwise, entering step S34;
s34, constructing an input set based on the shipment success rate of the vending machine in the latest preset time, the single average shipment attempt times when the shipment is successful and the single average shipment time when the shipment is successful, and obtaining the shipment success probability rate by adopting a prediction model based on an HHO-GRU algorithm;
s15, carrying out shipment of the target commodity again based on the vending machine, and if and only if the customer has left and the vending machine does not complete shipment, entering step S16;
s16, outputting that the vending machine is in an abnormal state and can not finish shipment, obtaining the running state of the vending machine by adopting a running state evaluation model based on a machine learning algorithm based on the probability of successful shipment of the vending machine, the inconsistent times of the shipment goods and the target goods in the latest preset time and the incomplete shipment times, and managing the vending machine according to the running state.
2. The vending machine-based item selling method of claim 1, wherein the set time is determined based on a single average shipment time of the vending machine when shipment is successful, wherein the longer the single average shipment time of the vending machine when shipment is successful, the greater the set time.
3. The vending machine-based item sales method of claim 1, wherein the specific step of determining whether the shipment item is consistent with the target item is:
acquiring an image of the shipment item based on an image recognition device of the vending machine;
extracting HOG characteristics of the images of the goods to obtain HOG characteristics of the goods;
and judging whether the goods and the target goods are consistent based on the HOG characteristics of the goods and the HOG characteristics of the original image of the target goods.
4. The vending machine-based item sales method of claim 1, wherein the value of the probability of successful shipment is in a range of 0 to 1, wherein the greater the probability of successful shipment, the greater the probability that the vending machine can normally complete shipment.
5. The vending machine-based item selling method of claim 1, wherein a first power threshold is greater than a second power threshold, the first power threshold and the second power threshold being determined based on a price of the vending machine item average and a historical rate of the vending machine item loss, wherein the higher the price of the vending machine item average and the greater the historical rate of loss, the greater the first power threshold and the second power threshold.
6. The vending machine-based item sales method of claim 1, wherein the determining the operational status of the vending machine comprises the specific steps of:
s41, obtaining a fault state value of the vending machine by adopting a fault evaluation model based on a GRU algorithm based on inconsistent times, incomplete times and damage value of the vending machine of the goods discharged from the goods in the latest preset time and the target goods;
s42, determining whether the vending machine has a suspected fault or not based on the fault state value of the vending machine, if so, entering a step S43, and if not, entering a step S44;
s43, determining whether the vending machine is normal or not based on the automatic shipment success rate of the vending machine, if so, judging that the operation state of the vending machine is normal, and normally opening the vending machine for use, and if not, entering step S44;
s43, constructing an input set based on the fault state value and the probability of successful shipment of the vending machine, and obtaining a fault probability evaluation value of the vending machine by adopting a state evaluation model based on an HHO-GRU algorithm.
7. The vending machine-based commodity sales method according to claim 1, wherein when the failure probability evaluation value of the vending machine is greater than a first probability threshold value, it is determined that there is an abnormality in the operation state of the vending machine, and the vending machine is set to the failure state, and locked, and vending is no longer performed; and when the fault probability evaluation value of the vending machine is smaller than or equal to a first probability threshold value, determining that the running state of the vending machine is in a normal running state, opening the vending machine, and restarting vending.
8. A computer system, comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor, when executing the computer program, performs a vending machine-based merchandising method as claimed in any one of claims 1-7.
9. A computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a vending machine based commodity vending method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310251675.2A CN115965324B (en) | 2023-03-16 | 2023-03-16 | Commodity sales method and system based on vending machine |
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