Intelligent settlement method, device and equipment of full-automatic vending machine and medium

Abstract

The invention relates to an intelligent settlement method, device and equipment of a full-automatic vending machine and a medium, and belongs to the field of vending machines. The method comprises the following steps: triggering a physical sign detection instruction, obtaining a physical sign library, and generating a post-price parameter and commodity recommendation matrix on the basis of a reference price parameter according to the physical sign library; a dynamic price adjustment model is constructed, and after the user completes option marking in the commodity recommendation matrix, dynamic discount parameters are output on the basis of the post-price parameters according to the dynamic price adjustment model; and the user completes payment operation based on the dynamic discount parameters, activates a taboo screening protocol after obtaining financial account authorization, selects whether to generate a transaction voucher or not according to the taboo screening protocol, and starts a beverage making process. Personalized full-automatic vending machine settlement is achieved, physical sign data can be converted into effective recommendations and preferences, the buying desire of a user is improved, consumption is promoted, meanwhile, a passive response mode of a traditional vending machine is broken through, and an active health intervention function is provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic vending machines, and in particular relates to an intelligent settlement method, device, equipment and medium for a fully automatic vending machine. Background Art

[0002] With the development of industrial automation, fully automatic vending machines have gradually occupied a certain market share. They can make a variety of drinks according to customer needs, and customers can freely choose the drink temperature, drink taste and whether to add small ingredients. After the customer selects the drink by clicking on the screen, the machine can automatically dispense the cup and make the drink.

[0003] At present, the fully automatic vending machines for freshly made beverages on the market still have certain defects, such as rigid settlement mechanisms: fixed prices lead to an imbalance between supply and demand, and are unable to respond to environmental changes or inventory shelf life; health risk blind spots: lack of active intervention mechanisms for users' physiological conditions and drug contraindications, etc. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides an intelligent settlement method, device, equipment and medium for a fully automatic vending machine.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An intelligent settlement method for a fully automatic vending machine, wherein the implementation of the intelligent settlement method comprises the following steps: When it is detected that the user is in the terminal interaction field of the fully automatic vending machine, a vital sign detection instruction is triggered and a vital sign library is obtained, and a post-price parameter and a product recommendation matrix are generated based on the benchmark price parameter according to the vital sign library; A dynamic price adjustment model is constructed by integrating a micro-climate sensor and an internal raw material monitoring module into the terminal body. After the user completes the selection in the product recommendation matrix, the dynamic price adjustment model outputs dynamic discount parameters based on the post-price parameters. The user completes the payment operation based on the dynamic discount parameters, activates the taboo screening protocol after obtaining financial account authorization, and chooses whether to generate a transaction voucher and start the beverage preparation process according to the taboo screening protocol.

[0006] Preferably, triggering a vital sign detection instruction and acquiring a vital sign library includes: The terminal receives the vital sign detection instruction and obtains a multimodal physiological signal, where the multimodal physiological signal is a multi-category physiological signal associated with the user; generating a vital sign library based on the multimodal physiological signals, presetting physiological signal thresholds for the multimodal physiological signals, and generating a priority mark list by arranging the signals in descending order of deviation when any of the multimodal physiological signals is detected to have exceeded the corresponding physiological signal threshold. The priority mark list includes first-level abnormal physiological signals, second-level abnormal physiological signals, and so on; The post-price parameters and the product recommendation matrix are generated based on the benchmark price parameters according to the priority mark list. The spatial topological distribution of beverages on the product recommendation matrix is positively correlated with the corresponding abnormal physiological signal priority, and the post-price parameters are negatively correlated with the corresponding abnormal physiological signal priority.

[0007] Preferably, the terminal receiving the vital sign detection instruction and acquiring the multimodal physiological signal includes: Acquire the user's heart rate oscillation signal through non-contact method and obtain fatigue quantitative index; Obtaining epidermal water loss rate through a skin moisture testing device, wherein the epidermal water loss rate is negatively correlated with the user's skin moisture content; Acquire a core temperature characteristic value through a temperature sensor, wherein the core temperature characteristic value is positively correlated with the user's body temperature; The user's voiceprint frequency is obtained and a voiceprint recognition vector is obtained. The voiceprint recognition vector is positively correlated with the proportion of the low-frequency part in the user's voiceprint frequency.

[0008] Preferably, the step of obtaining the user's heart rate oscillation signal by a non-contact method and obtaining the fatigue quantification index includes: The original ECG shock signal A={A1,A2,…,A N}; Extracting R wave feature points from the original ECG signal and constructing an R wave detection sequence, encoding the rising edge phase of the R wave as 1 and the falling edge phase as -1; The peak point of the R wave is identified according to the peak recognition formula, which is: , where I n is the nth value of the R wave detection sequence, I n-1 is the n-1th value of the R wave detection sequence, then when the peak recognition formula takes the value -2, the corresponding position is determined to be the R wave peak point; Reconstructing the R wave peak according to the R wave peak point to obtain a heart rate oscillation signal extraction sequence, so that the R wave feature point takes the value of 1 at the R wave peak point and the other sampling points are set to 0; Extracting a heart rate oscillation signal according to the heart rate oscillation signal extraction sequence, that is, extracting a point with a value of 1 in the heart rate oscillation signal extraction sequence; The fatigue quantification index is assigned to the user according to the low-frequency power of the heart rate oscillation signal, and the low-frequency power is positively correlated with the fatigue quantification index.

[0009] Preferably, the building of a dynamic price adjustment model includes: When it is detected that the user is carrying a recyclable container, the environmental protection additional coefficient a∈(0,1) is enabled, otherwise a=0; Obtaining real-time meteorological parameters through the micro-meteorological sensor, wherein the real-time meteorological parameters include ambient enthalpy value and aerosol mass concentration, presetting meteorological condition boundary values, and defining a meteorological adjustment coefficient b∈[0,1) based on the meteorological parameter deviation; Obtaining the remaining validity period of the required raw materials through the in-machine raw material monitoring module, and activating the near-expiry attenuation factor c∈(0,1) when the remaining validity period deviates from a preset threshold; The dynamic discount parameter is calculated based on the environmental protection additional coefficient, the meteorological adjustment coefficient and the near-expiry attenuation factor and the dynamic price adjustment model is constructed. The calculation formula of the dynamic discount parameter is T=(1-abc)T0, wherein T is the dynamic discount parameter, a is the environmental protection additional coefficient, b is the meteorological adjustment coefficient, c is the near-expiry attenuation factor, and T0 is the post-price parameter.

[0010] Preferably, the step of defining a meteorological adjustment coefficient b∈[0,1) based on parameter deviation includes: When the ambient enthalpy value exceeds the corresponding meteorological condition boundary value and the product category belongs to low-temperature beverages, the meteorological adjustment coefficient is activated; When the aerosol mass concentration exceeds the corresponding meteorological condition boundary value and the product contains tea polyphenol derivatives, activating the meteorological adjustment coefficient; When the real-time meteorological parameters exceed the corresponding meteorological condition boundary values and the product is a low-temperature beverage containing tea polyphenol derivatives, the meteorological adjustment coefficient takes the average value.

[0011] Preferably, the selecting whether to generate a transaction voucher and start a beverage preparation process according to the taboo screening protocol includes: Parse the payment channel type. When the payment channel type is identified as a medical insurance settlement interface, trigger the contraindication screening protocol and activate the drug-food interaction database, obtain the user's recent medical records and output a list of contraindication items. If the product ID exists in the list of contraindication items, trigger the alarm protocol; otherwise, execute the standard product delivery process, generate a transaction voucher and start the beverage preparation process.

[0012] An intelligent settlement device for a fully automatic vending machine, used to implement the intelligent settlement method described above, comprising a vital sign detection module, a dynamic price adjustment module, and a contraindication screening module; The physical sign detection module is used to trigger a physical sign detection instruction and obtain a physical sign library when it detects that the user is in the terminal interaction field of the fully automatic vending machine, and generate a post-price parameter and a product recommendation matrix based on the benchmark price parameter according to the physical sign library; The dynamic price adjustment module is used to build a dynamic price adjustment model using a micro-climate sensor integrated into the terminal body and an in-machine raw material monitoring module. After the user selects a drink from the product recommendation matrix, the dynamic price adjustment model outputs a dynamic discount parameter based on the post-price parameter. The taboo screening module is used for the user to complete the payment operation based on the dynamic discount parameters, activate the taboo screening protocol after obtaining financial account authorization, and choose whether to generate a transaction voucher and start the beverage preparation process according to the taboo screening protocol.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the intelligent settlement method described above is implemented.

[0014] A storage medium containing computer-executable instructions, which are used to perform the above-mentioned smart settlement method when executed by a computer processor.

[0015] The beneficial effects of the present invention are: (1) By introducing a physical sign database and a dynamic price adjustment model, the user's physical signs, environment, and the effect of the shelf life of raw materials are comprehensively considered to achieve personalized recommendation and settlement of beverages. This makes the output beverage recommendation mechanism and dynamic discount parameters more reasonable, enhances customers' purchasing desire, promotes consumption, and at the same time breaks through the passive response mode of traditional vending machines and provides active health intervention functions.

[0016] (2) Through abnormal physiological signals, price parameters and product recommendation matrices are generated based on the baseline price parameters, so that different customers can see the drinks that are most suitable for their current physical condition first and get certain discounts, which is conducive to stimulating consumption and making customers feel humane care.

[0017] (3) Based on the environmental protection additional coefficient, meteorological adjustment coefficient and near-expiry attenuation factor, dynamic discount parameters are calculated and a dynamic price adjustment model is constructed to achieve dynamic price adjustment of beverages, improve the current situation where the settlement mechanism of fully automatic vending machines is rigid, resulting in an imbalance between supply and demand and unable to respond to environmental changes or inventory shelf life.

[0018] (4) Generate a list of contraindications through the medical records of the user's medical insurance financial account, generate an active intervention mechanism for the user's physiological state and drug contraindications, and improve the customer consumption experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0020] Figure 1 The figure is a flow chart of the steps of the intelligent settlement method for a fully automatic vending machine of the present invention. DETAILED DESCRIPTION

[0021] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0022] The working principle and use process of the present invention: See also Figure 1 , an intelligent settlement method for a fully automatic vending machine, comprising: S1: When it is detected that the user is in the terminal interaction field of the fully automatic vending machine, a vital sign detection instruction is triggered and a vital sign library is obtained. Based on the vital sign library, a post-price parameter and a product recommendation matrix are generated based on the benchmark price parameter; S2: A dynamic pricing model is constructed using a micro-climate sensor integrated into the terminal body and an in-machine raw material monitoring module. After a user selects a drink from the product recommendation matrix, the dynamic pricing model outputs dynamic discount parameters based on the post-price parameters. S3: The user completes the payment operation based on the dynamic discount parameters, activates the taboo screening protocol after obtaining financial account authorization, and chooses whether to generate a transaction voucher and start the beverage preparation process according to the taboo screening protocol.

[0023] In this embodiment, triggering the vital sign detection instruction and obtaining the vital sign library can be specifically implemented through the following steps: S101: The terminal receives the vital sign detection instruction and obtains multimodal physiological signals, where the multimodal physiological signals include but are not limited to fatigue quantification index, epidermal water loss rate, core temperature characteristic value, and voiceprint recognition vector; S102: Generating a vital sign library based on the multimodal physiological signals, presetting physiological signal thresholds for the multimodal physiological signals, and generating a priority mark list by arranging the signals in descending order of deviation when any of the multimodal physiological signals is detected to have exceeded the corresponding physiological signal threshold. The priority mark list includes first-level abnormal physiological signals, second-level abnormal physiological signals, and so on. S103: Generate the post-price parameter and the product recommendation matrix based on the benchmark price parameter according to the priority mark list. The spatial topological distribution of the beverage on the product recommendation matrix is positively correlated with the corresponding priority of the abnormal physiological signal, that is, the beverage corresponding to the abnormal physiological signal with a higher ranking is also positioned at the front in the product recommendation matrix. The post-price parameter is negatively correlated with the corresponding priority of the abnormal physiological signal, that is, the closer the abnormal physiological signal is positioned, the lower the value of the post-price parameter. For example, when the fatigue quantification index is the first abnormal physiological signal, caffeine-rich beverages can relieve fatigue and will be recommended to the user at the front of the product recommendation matrix with a greater discount. When the voiceprint recognition vector is the first abnormal physiological signal, the user may have a hoarse voice or a cold, and hot drinks will be recommended to the user at the front of the product recommendation matrix. When the core temperature characteristic value is the first abnormal physiological signal, cold drinks will be recommended to the user at the front of the product recommendation matrix with a greater discount based on the benchmark price parameter, and finally generate the post-price parameter. In this embodiment, if the low-frequency power of a user's heart rate oscillation signal is high, a fatigue quantification index of 1.3 is assigned to the user. At the same time, if the user's skin water content is low, an epidermal water loss rate of 1.8 is assigned to the user. At this time, both are greater than the corresponding thresholds and the epidermal water loss rate is higher than the fatigue quantification index. The epidermal water loss rate is marked as the first abnormal physiological signal, and the fatigue quantification index is marked as the second abnormal physiological signal. At this time, electrolyte drinks and other drinks with hydrating effects are at the forefront of the product recommendation matrix, and the discount is the largest. Caffeinated drinks and other drinks that can relieve fatigue are in the middle of the product recommendation matrix, and the discount is lower than that of electrolyte drinks.

[0024] In this embodiment, the terminal receives the vital sign detection instruction and obtains the multimodal physiological signal, which can be specifically implemented by the following steps: S101-1: Acquire the user's heart rate oscillation signal through a non-contact method and obtain the fatigue quantification index; S101-2: Obtaining the user's skin moisture content through a skin moisture testing device and obtaining the epidermal water loss rate, wherein the epidermal water loss rate is negatively correlated with the user's skin moisture content; S101-3: Obtaining the user's body temperature through a temperature sensor and obtaining the core temperature characteristic value, where the core temperature characteristic value is positively correlated with the user's body temperature; S101-4: Acquire the user's voiceprint frequency and obtain the voiceprint recognition vector, where the voiceprint recognition vector is positively correlated with the proportion of the low-frequency part in the user's voiceprint frequency.

[0025] In this embodiment, the user's heart rate oscillation signal is obtained by a non-contact method to obtain the fatigue quantification index, which can be specifically implemented by the following steps: The original ECG shock signal A={A1,A2,…,A N}; Extracting R wave feature points from the original ECG signal and constructing an R wave detection sequence, encoding the rising edge phase of the R wave as 1 and the falling edge phase as -1; The peak point of the R wave is identified according to the peak recognition formula, which is: , where I n is the nth value of the R wave detection sequence, I n-1 is the n-1th value of the R wave detection sequence, then when the peak recognition formula takes a value of -2, the corresponding position is the R wave peak point; Reconstructing the R wave peak according to the R wave peak point to obtain a heart rate oscillation signal extraction sequence, so that the R wave peak value is 1 at the R wave peak point and the remaining sampling points are reset to 0; Extracting a heart rate oscillation signal according to the heart rate oscillation signal extraction sequence, that is, extracting a point with a value of 1 in the heart rate oscillation signal extraction sequence; The fatigue quantification index is assigned to the user according to the low-frequency power of the heart rate oscillation signal, and the low-frequency power is positively correlated with the fatigue quantification index.

[0026] In this embodiment, the dynamic price adjustment model outputs dynamic discount parameters based on the post-price parameters, which can be specifically implemented by the following steps: S201: When it is detected that the user is carrying a recyclable container, the environmental protection additional coefficient a∈(0,1) is enabled, otherwise a=0; S202: obtaining real-time meteorological parameters through the micro meteorological sensor, wherein the real-time meteorological parameters include ambient enthalpy and aerosol mass concentration, preset meteorological condition boundary values, and defining a meteorological adjustment coefficient b∈[0,1) based on the meteorological parameter deviation; S203: Obtaining the remaining validity period of the ingredients required for the beverage selected by the user in the fully automatic vending machine through the in-machine ingredient monitoring module, and when the remaining validity period of the ingredients is less than a preset threshold, assigning a preset expiration attenuation factor between an open interval (0, 1) to the beverage; S204: Based on the environmental protection additional coefficient, the meteorological adjustment coefficient, and the near-expiry attenuation factor, the dynamic discount parameter is calculated and the dynamic price adjustment model is constructed. The calculation formula of the dynamic discount parameter is T=(1-abc)T0, where T is the dynamic discount parameter, a is the environmental protection additional coefficient, b is the meteorological adjustment coefficient, c is the near-expiry attenuation factor, and T0 is the post-price parameter. In this embodiment, when the ambient enthalpy value is greater than or equal to the ambient enthalpy value threshold and the aerosol mass concentration is greater than or equal to the aerosol concentration threshold, the user selects iced green tea with a post-price parameter of 13 yuan and brings their own cup. At this time, the green tea in the fully automatic vending machine is about to expire. At this time, it is assigned an environmental protection additional coefficient of 0.02, a meteorological adjustment coefficient of 0.1, and a near-expiry attenuation factor of 0.08. The dynamic discount parameter that the user needs to pay is (1-0.02-0.1-0.08)×13=10.4 yuan.

[0027] In this embodiment, obtaining a meteorological adjustment coefficient with a value between the open interval (0, 1) according to the real-time meteorological parameter can be specifically implemented by the following steps: When the ambient enthalpy value is greater than or equal to the ambient enthalpy value threshold and the beverage selected by the user is a cold drink, assigning the preset weather adjustment coefficient between the open interval (0, 1) to the beverage; When the aerosol mass concentration is greater than or equal to the aerosol mass concentration threshold and the beverage selected by the user contains tea polyphenols, the beverage is assigned the meteorological adjustment coefficient whose value is preset in the open interval (0, 1); When the real-time meteorological parameters are all greater than or equal to the corresponding meteorological condition boundary values and the beverage selected by the user is a cold drink containing tea polyphenols, the meteorological adjustment coefficient takes the average value.

[0028] In this embodiment, the selection of whether to generate an order and prepare a beverage according to the taboo detection instruction can be specifically implemented through the following steps: Parse the payment channel type. When the payment channel type is identified as a medical insurance settlement interface, trigger the contraindication screening protocol and activate the drug-food interaction database, obtain the user's medical records within a preset time range, and output a list of contraindications. When the drink selected by the user is on the list of contraindications, a warning message pops up. When the user's payment method is other accounts or the drink selected by the user is not on the list of contraindications, the fully automatic vending machine generates an order and prepares the drink. For example, when the user has a recent purchase record of cephalosporins, all alcoholic drinks are included in the list of contraindications. If the drink selected by the user is on the list of contraindications, a health warning is issued to the user.

[0029] An intelligent settlement device for a fully automatic vending machine, comprising a vital sign detection module, a dynamic price adjustment module, and a contraindication screening module; The physical sign detection module is used to trigger a physical sign detection instruction and obtain a physical sign library when it detects that the user is in the terminal interaction field of the fully automatic vending machine, and generate a post-price parameter and a product recommendation matrix based on the benchmark price parameter according to the physical sign library; The dynamic price adjustment module is used to build a dynamic price adjustment model using a micro-climate sensor integrated into the terminal body and an in-machine raw material monitoring module. After the user selects a drink from the product recommendation matrix, the dynamic price adjustment model outputs a dynamic discount parameter based on the post-price parameter. The taboo screening module is used for the user to complete the payment operation based on the dynamic discount parameters, activate the taboo screening protocol after obtaining financial account authorization, and choose whether to generate a transaction voucher and start the beverage preparation process according to the taboo screening protocol.

[0030] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0031] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0032] The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF or the like, or any suitable combination thereof. The computer program code for performing the operation of the present invention can be written in one or more programming languages or a combination thereof, and the programming language includes an object-oriented programming language such as Java, Smalltalk, C++, and also includes a conventional procedural programming language such as "C" language or similar programming language. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, utilizing an Internet service provider to connect through the Internet).

[0033] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An intelligent settlement method for a fully automatic vending machine, characterized in that: The implementation of the smart settlement method includes the following steps: When it is detected that the user is in the terminal interaction field of the fully automatic vending machine, a vital sign detection instruction is triggered and a vital sign library is obtained, and a post-price parameter and a product recommendation matrix are generated based on the benchmark price parameter according to the vital sign library; A dynamic price adjustment model is constructed by integrating a micro-climate sensor and an internal raw material monitoring module into the terminal body. After the user completes the selection in the product recommendation matrix, the dynamic price adjustment model outputs dynamic discount parameters based on the post-price parameters. The user completes the payment operation based on the dynamic discount parameters, activates the taboo screening protocol after obtaining financial account authorization, and chooses whether to generate a transaction voucher and start the beverage preparation process according to the taboo screening protocol.

2. The intelligent settlement method according to claim 1, characterized in that: The triggering of the vital sign detection instruction and obtaining the vital sign library includes: The terminal receives the vital sign detection instruction and obtains a multimodal physiological signal, where the multimodal physiological signal is a multi-category physiological signal associated with the user; generating a vital sign library based on the multimodal physiological signals, presetting physiological signal thresholds for the multimodal physiological signals, and generating a priority mark list by arranging the signals in descending order of deviation when any of the multimodal physiological signals is detected to have exceeded the corresponding physiological signal threshold. The priority mark list includes first-level abnormal physiological signals, second-level abnormal physiological signals, and so on; The post-price parameters and the product recommendation matrix are generated based on the benchmark price parameters according to the priority mark list. The spatial topological distribution of beverages on the product recommendation matrix is positively correlated with the corresponding abnormal physiological signal priority, and the post-price parameters are negatively correlated with the corresponding abnormal physiological signal priority.

3. The intelligent settlement method according to claim 2, characterized in that: The terminal receiving the vital sign detection instruction and acquiring the multimodal physiological signal includes: Acquire the user's heart rate oscillation signal through non-contact method and obtain fatigue quantitative index; Obtaining epidermal water loss rate through a skin moisture testing device, wherein the epidermal water loss rate is negatively correlated with the user's skin moisture content; Acquire a core temperature characteristic value through a temperature sensor, wherein the core temperature characteristic value is positively correlated with the user's body temperature; The user's voiceprint frequency is obtained and a voiceprint recognition vector is obtained. The voiceprint recognition vector is positively correlated with the proportion of the low-frequency part in the user's voiceprint frequency.

4. The intelligent settlement method according to claim 3, characterized in that: The step of obtaining the user's heart rate oscillation signal by a non-contact method and obtaining the fatigue quantification index includes: The original ECG shock signal A={A1,A2,…,A N }; Extracting R wave feature points from the original ECG signal and constructing an R wave detection sequence, encoding the rising edge phase of the R wave as 1 and the falling edge phase as -1; The peak point of the R wave is identified according to the peak recognition formula, which is: , where I n is the nth value of the R wave detection sequence, I n-1 is the n-1th value of the R wave detection sequence, then when the peak recognition formula takes the value -2, the corresponding position is determined to be the R wave peak point; Reconstructing the R wave peak according to the R wave peak point to obtain a heart rate oscillation signal extraction sequence, so that the R wave feature point takes the value of 1 at the R wave peak point and the other sampling points are set to 0; Extracting a heart rate oscillation signal according to the heart rate oscillation signal extraction sequence, that is, extracting a point with a value of 1 in the heart rate oscillation signal extraction sequence; The fatigue quantification index is assigned to the user according to the low-frequency power of the heart rate oscillation signal, and the low-frequency power is positively correlated with the fatigue quantification index.

5. The intelligent settlement method according to claim 1, characterized in that: The construction of the dynamic price adjustment model includes: When it is detected that the user is carrying a recyclable container, the environmental protection additional coefficient a∈(0,1) is enabled, otherwise a=0; Obtaining real-time meteorological parameters through the micro-meteorological sensor, wherein the real-time meteorological parameters include ambient enthalpy value and aerosol mass concentration, presetting meteorological condition boundary values, and defining a meteorological adjustment coefficient b∈[0,1) based on the meteorological parameter deviation; Obtaining the remaining validity period of the required raw materials through the in-machine raw material monitoring module, and activating the near-expiry attenuation factor c∈(0,1) when the remaining validity period deviates from a preset threshold; The dynamic discount parameter is calculated based on the environmental protection additional coefficient, the meteorological adjustment coefficient and the near-expiry attenuation factor and the dynamic price adjustment model is constructed. The calculation formula of the dynamic discount parameter is T=(1-abc)T0, wherein T is the dynamic discount parameter, a is the environmental protection additional coefficient, b is the meteorological adjustment coefficient, c is the near-expiry attenuation factor, and T0 is the post-price parameter.

6. The intelligent settlement method according to claim 5, characterized in that: The method of defining a meteorological adjustment coefficient b∈[0,1) based on parameter deviation includes: When the ambient enthalpy value exceeds the corresponding meteorological condition boundary value and the product category belongs to low-temperature beverages, the meteorological adjustment coefficient is activated; When the aerosol mass concentration exceeds the corresponding meteorological condition boundary value and the product contains tea polyphenol derivatives, activating the meteorological adjustment coefficient; When the real-time meteorological parameters exceed the corresponding meteorological condition boundary values and the product is a low-temperature beverage containing tea polyphenol derivatives, the meteorological adjustment coefficient is averaged.

7. The intelligent settlement method according to claim 1, characterized in that: The selecting whether to generate a transaction voucher and start a beverage preparation process according to the taboo screening protocol includes: Parse the payment channel type. When the payment channel type is identified as a medical insurance settlement interface, trigger the contraindication screening protocol and activate the drug-food interaction database, obtain the user's recent medical records and output a list of contraindication items. If the product ID exists in the list of contraindication items, trigger the alarm protocol; otherwise, execute the standard product delivery process, generate a transaction voucher and start the beverage preparation process.

8. An intelligent settlement device for a fully automatic vending machine, characterized in that: The device is applied to the intelligent settlement method as described in any one of claims 1 to 7, including a vital sign detection module, a dynamic price adjustment module, and a contraindication screening module; The physical sign detection module is used to trigger a physical sign detection instruction and obtain a physical sign library when it detects that the user is in the terminal interaction field of the fully automatic vending machine, and generate a post-price parameter and a product recommendation matrix based on the benchmark price parameter according to the physical sign library; The dynamic price adjustment module is used to build a dynamic price adjustment model using a micro-climate sensor integrated into the terminal body and an in-machine raw material monitoring module. After the user selects a drink from the product recommendation matrix, the dynamic price adjustment model outputs a dynamic discount parameter based on the post-price parameter. The taboo screening module is used for the user to complete the payment operation based on the dynamic discount parameters, activate the taboo screening protocol after obtaining financial account authorization, and choose whether to generate a transaction voucher and start the beverage preparation process according to the taboo screening protocol.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the intelligent settlement method as described in any one of claims 1 to 7 is implemented.

10. A storage medium containing computer-executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to execute the smart settlement method according to any one of claims 1 to 7.

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