Methods, systems, equipment, and computer storage media for forming cigarette smoking profiles

Abstract

This invention provides a method, system, device, and computer storage medium for forming a cigarette smoking curve. The method includes: collecting process data of a smoker smoking a brand of cigarette; the process data of the smoking port includes a smoking time series and an actual smoking flow series; the smoking time series consists of smoking time points; the actual smoking flow series consists of the smoking flow occurring at each smoking time point; randomly establishing a smoking flow fitting model; the smoking flow fitting model is used to characterize the relationship between smoking flow and smoking time; calculating the fitted smoking flow corresponding to each smoking port based on the collected process data and the smoking flow fitting model; comparing the similarity between the fitted smoking flow corresponding to each smoking port and the corresponding actual smoking flow to form a cigarette smoking curve for the smoker. This invention can quickly and accurately establish a smoking waveform, preparing for subsequent statistical analysis of the smoking characteristics of a target consumer group.

Description

Technical Field

[0001] This invention belongs to the field of cigarette smoking behavior analysis technology, and relates to a method and system for forming a cigarette smoking curve, particularly a method, system, device and computer storage medium for forming a cigarette smoking curve. Background Technology

[0002] Currently, there are many commercially available devices for acquiring parameters of smokers' smoking behavior, such as the CReSS Pocket from Borgwaldt (Germany), the SA7 from British American Tobacco (BAT), and the SPA / M from SODIM (France). These devices can record parameters such as the amount of cigarette smoked, the duration of smoke, the interval between smokes, and the frequency of smoke. These parameters can effectively describe the smoking characteristics of consumers, but they are insufficient to reproduce the entire smoking behavior. In particular, when replicating specific smoking characteristics as smoking parameters on conventional smoking machines for product design and development, it is also necessary to describe the smoking waveform. However, existing technologies cannot effectively establish a smoking waveform, making it impossible to analyze smoking behavior based on the waveform later.

[0003] Therefore, how to provide a method, system, device, and computer storage medium for forming a cigarette smoking curve to solve the shortcomings of existing technologies, such as the inability to effectively establish a smoking waveform, which leads to the inability to analyze smoking behavior based on the smoking waveform later, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method, system, device and computer storage medium for forming a cigarette smoking curve, so as to solve the problem that the prior art cannot effectively establish a smoking waveform, resulting in the inability to analyze the smoking behavior based on the smoking waveform in the later stage.

[0005] To achieve the above and other related objectives, the present invention provides a method for forming a cigarette smoking curve, comprising: collecting process data of a smoker smoking a brand of cigarette from the puff port; the process data of the puff port includes a smoking time series and an actual smoking flow rate series; the smoking time series consists of smoking time points; the actual smoking flow rate series consists of the smoking flow rate occurring at the smoking time points; randomly establishing a smoking flow rate fitting model; the smoking flow rate fitting model is used to characterize the relationship between smoking flow rate and smoking time; calculating the fitted smoking flow rate corresponding to each puff port based on the collected process data and the smoking flow rate fitting model; and comparing the similarity between the fitted smoking flow rate corresponding to each puff port and the corresponding actual smoking flow rate to form a cigarette smoking curve for the smoker.

[0006] In one embodiment of the present invention, the step of randomly establishing a suction flow fitting model includes: determining the fitting coefficients to be solved in the suction flow fitting model; solving the fitting coefficients to be solved based on preset constraints; and establishing the suction flow fitting model using the fitting coefficients to be solved; wherein, in the suction flow fitting model, the suction flow is the dependent variable and the suction time is the independent variable.

[0007] In one embodiment of the present invention, the preset limiting conditions include: when the suction time point is 0, the suction flow rate at that time point is 0; within the suction time series, the total amount of the fitted suction flow rate of the suction flow rate fitting model is equal to the total amount of the actual suction flow rate; the suction flow rate fitting model includes at least one preset extreme point; within the suction time series, the suction flow rate in the suction flow rate fitting model needs to remain continuous.

[0008] In one embodiment of the present invention, the suction flow rate fitting model includes a linear fitting model, a sinusoidal fitting model, a quadratic fitting model, an exponential fitting model, and / or a piecewise fitting model; wherein the piecewise fitting model includes at least two of the linear fitting model, the sinusoidal fitting model, the quadratic fitting model, and the exponential fitting model.

[0009] In one embodiment of the present invention, the step of calculating the fitted suction flow rate corresponding to each suction port based on the collected process data and the suction flow rate fitting model includes: substituting each suction time point in the process data of the suction port of a brand of cigarettes sucked by the smoker into the established suction flow rate fitting model to calculate the fitted suction flow rate matching the suction time point.

[0010] In one embodiment of the present invention, the step of comparing the similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate includes: calculating the suction similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate.

[0011] In one embodiment of the present invention, the method for forming the cigarette smoking curve further includes: repeatedly running a randomly established smoking flow fitting model; the smoking flow fitting model is used to characterize the relationship between smoking flow and smoking time; calculating the fitted smoking flow corresponding to each puff based on the collected process data and the smoking flow fitting model; comparing the similarity between the fitted smoking flow corresponding to each puff and the corresponding actual smoking flow several times to obtain cigarette smoking curves of several smokers; selecting the cigarette smoking curve with the lowest similarity from the cigarette smoking curves of several smokers as a representative of the cigarette smoking waveform of a smoker smoking a brand of cigarettes.

[0012] Another aspect of the present invention provides a system for forming a cigarette smoking curve, comprising: a data acquisition module for acquiring process data of a smoker smoking a brand of cigarette from the puff port; the process data of the puff port includes a smoking time series and an actual smoking flow series; the smoking time series consists of smoking time points; the actual smoking flow series consists of the smoking flow occurring at the smoking time points; a model building module for randomly establishing a smoking flow fitting model; the smoking flow fitting model is used to characterize the relationship between smoking flow and smoking time; a calculation module for calculating the fitted smoking flow corresponding to each puff port based on the acquired process data and the smoking flow fitting model; and a forming module for comparing the similarity between the fitted smoking flow corresponding to each puff port and the corresponding actual smoking flow to form a cigarette smoking curve for the smoker.

[0013] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for forming the cigarette smoking curve.

[0014] The final aspect of the present invention provides an apparatus for forming a cigarette smoking profile, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the apparatus for forming the cigarette smoking profile to perform the method for forming the cigarette smoking profile.

[0015] As described above, the method, system, device, and computer storage medium for forming cigarette smoking curves according to the present invention have the following beneficial effects:

[0016] The method, system, equipment, and computer storage medium for forming cigarette smoking curves described in this invention can quickly and accurately establish smoking waveforms, preparing for subsequent statistical analysis of the smoking characteristics of the target consumer group and more effectively describing the smoking characteristics of consumers. Attached Figure Description

[0017] Figure 1 The diagram shown is a flowchart of a method for forming a cigarette smoking curve according to an embodiment of the present invention.

[0018] Figure 2 The diagram shown is a flowchart of step S12 in the method for forming the cigarette smoking curve of the present invention.

[0019] Figure 3 The diagram shown is an example of a cigarette smoking curve for a smoker according to the present invention.

[0020] Figure 4 The diagram shows nine examples of cigarette smoking curves according to the present invention.

[0021] Figure 5The diagram shown is a schematic representation of the principle structure of the cigarette smoking curve formation system of the present invention in one embodiment.

[0022] Component designation explanation

[0023] 1. The system for forming cigarette smoking curves

[0024] 11. Data Acquisition Module

[0025] 12 Model Building Module

[0026] 13 Calculation Module

[0027] 14 Formation Module

[0028] 15. Repeated Execution Module

[0029] Steps S11 to S15 Detailed Implementation

[0030] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0031] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0032] Example 1

[0033] This embodiment provides a method for forming a cigarette smoking curve, including:

[0034] Data on the process of a smoker inhaling a brand of cigarette is collected. The process data includes a smoking time series and an actual smoking flow rate series. The smoking time series consists of smoking time points. The actual smoking flow rate series consists of the smoking flow rate occurring at each smoking time point.

[0035] A randomized suction flow rate fitting model is established; the suction flow rate fitting model is used to characterize the relationship between suction flow rate and suction time.

[0036] Based on the collected process data and the suction flow fitting model, calculate the fitted suction flow corresponding to each suction port.

[0037] The similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate is compared to form the cigarette smoking curve of the smoker.

[0038] The method for forming the cigarette smoking curve provided in this embodiment will be described in detail below with reference to the illustrations. Please refer to... Figure 1 The diagram shows a flowchart of a method for forming a cigarette smoking curve in one embodiment. Figure 1 As shown, the method for forming the cigarette smoking curve specifically includes the following steps:

[0039] S11, Collect process data of a smoker inhaling a brand of cigarette. The process data includes two dimensions: time and instantaneous flow rate, denoted as a data matrix [T:{t1,t2,…,tn},Q:{q1,q2,…,qn}], meaning the process data of the smoker includes the inhalation time series T. i and the actual suction flow rate sequence Q i The suction time series consists of suction time points, and the actual suction flow rate series consists of the suction flow rate occurring at the suction time points.

[0040] For example, the data on the process of a smoker taking a puff of a brand of cigarette is shown in Table 1.

[0041] Table 1: Example of Process Data

[0042]

[0043]

[0044] S12, Randomly establish a suction flow rate fitting model; the suction flow rate fitting model is used to characterize the relationship between suction flow rate and suction time.

[0045] Please see Figure 2 The diagram shows a flowchart of step S12. As shown, step S12 specifically includes the following steps:

[0046] S121, Determine the fitting coefficients to be solved in the suction flow fitting model.

[0047] In this embodiment, the suction flow rate fitting model is Y = f(k1,k2,…,k n ,T), where k1 to k n Let Y be the fitting coefficient to be solved in the suction flow rate fitting model, T be the suction time series, and Y be the flow rate series to be fitted. The suction flow rate fitting model is Y = f(k1,k2,…,k). nThe model (T) includes linear fitting model, sinusoidal fitting model, quadratic fitting model, exponential fitting model and / or piecewise fitting model; wherein the piecewise fitting model includes at least two of the following: linear fitting model, sinusoidal fitting model, quadratic fitting model and exponential fitting model.

[0048] For example, when the suction flow rate fitting model is a quadratic function, the suction flow rate fitting model is Y = k1(T-k2). 2 +k3, where k1, k2, and k3 are three fitting coefficients to be solved.

[0049] For example, when the suction flow rate fitting model is a piecewise function, the suction flow rate fitting model Among them, k1 to k 10 These are the three fitting coefficients to be solved.

[0050] S122, Based on preset constraints, solve for the fitting coefficients to be solved.

[0051] The preset restrictions include:

[0052] (1) When the suction time point is 0, the suction flow rate at that time point is 0.

[0053] Specifically, f(k1,k2,…,k) n ,T)|t=t0=0.

[0054] (2) Within the suction time series, the total amount of the fitted suction flow of the suction flow fitting model is equal to the total amount of the actual suction flow.

[0055] Specifically,

[0056] (3) The suction flow fitting model includes at least one preset extreme point.

[0057] In this embodiment, the preset extreme points are set as the midpoint or quartile of the time series of the function or piecewise function, according to the envisioned function shape. For example, in fitting a clock wave, the extreme point is taken as the midpoint of the time series.

[0058] (4) Within the suction time series, the suction flow rate in the suction flow rate fitting model must remain continuous, i.e., f i (t j )=f i+1 (t j ), i = 1, 2, ..., n-1, t j It represents the time point of the corresponding piecewise function.

[0059] In this embodiment, by simultaneously solving all the above equations, a system of equations matching the number of unknowns can be obtained, and solving it yields all the fitting coefficients to be solved.

[0060] For example, the solution yields k1 = -24.98, k2 = 2, and k3 = 26.24.

[0061] S123, establish the suction flow rate fitting model using the unsolved fitting coefficients; wherein, in the suction flow rate fitting model, the suction flow rate is the dependent variable and the suction time is the independent variable.

[0062] For example, the suction flow rate fitting model established using the unsolved fitting coefficients k1 = -24.98, k2 = 2, and k3 = 26.24 is: Y = -24.98(x-2). 2 +26.24.

[0063] S13. Based on the collected process data and the suction flow fitting model, calculate the fitted suction flow rate corresponding to each suction port.

[0064] Specifically, S13 includes substituting each smoking time point in the process data of a smoker smoking a brand of cigarette into an established smoking flow fitting model to calculate the fitted smoking flow rate that matches that smoking time point.

[0065] S14, compare the similarity between the fitted suction flow rate corresponding to each puff and the corresponding actual suction flow rate to form the smoker's cigarette smoking curve. The suction flow rate fitting model is: Y = -24.98(x-2). 2 The cigarette smoking curve of the smoker fitted by +26.24 is as follows: Figure 3 As shown.

[0066] In this embodiment, comparing the fitted suction flow rate and the corresponding actual suction flow rate for each suction port in S14 refers to calculating the suction similarity between the fitted suction flow rate and the corresponding actual suction flow rate for each suction port. The specific formula for calculating the suction similarity is as follows:

[0067]

[0068] Where R represents the similarity between a smoker's puff of a certain brand of cigarette and the puffing waveform of that particular smoker, y i Let q be the fitted suction flow rate corresponding to the i-th suction port. i Let R be the actual suction flow rate corresponding to the i-th suction port. In this embodiment, a smaller suction similarity R indicates a better fit.

[0069] S15, repeat S12 to S14 several times to obtain cigarette smoking curves of several smokers. Select the cigarette smoking curve with the lowest similarity from these smokers as the representative cigarette smoking waveform of a particular brand. These cigarette smoking curves can then be subjected to machine learning and statistical analysis to find the smoking characteristics of the corresponding target consumer group. This characteristic is then used in a conventional smoking machine for smoke and physical detection; the detected value represents the average smoking behavior of this group.

[0070] For example, by using nine different suction flow rate fitting models to fit the suction waveform, the following results were obtained: Figure 4 The two cigarette smoking curves shown are selected as the two curves with the smallest R-values ​​to characterize the smoking habits of the smoker who changed their smoking style. Figure 4 The data indicates that the most similar waveforms for the person performing the suction are half_sine_back and saw_slow_fall.

[0071] The method for forming cigarette smoking curves described in this embodiment can quickly and accurately establish smoking waveforms, preparing for subsequent statistical analysis of the smoking characteristics of the target consumer group and more effectively describing the smoking characteristics of consumers.

[0072] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following... Figure 1 The method for forming the cigarette smoking curve.

[0073] At any possible level of technical detail, this application can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this application.

[0074] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, (but not limited to) electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0075] The computer-readable program described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards these instructions to a computer-readable storage medium in the respective computing / processing device. The computer program instructions used to perform the operations of this application may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and procedural programming languages ​​such as "C" or similar programming languages. Computer-readable program instructions may execute entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of this application.

[0076] This embodiment further provides a system for forming a cigarette smoking curve, including:

[0077] The data acquisition module is used to collect process data of a smoker smoking a brand of cigarettes through the puff port; the process data of the puff port includes a smoking time series and an actual smoking flow rate series; the smoking time series consists of smoking time points; the actual smoking flow rate series consists of the smoking flow rate occurring at the smoking time points.

[0078] The model building module is used to randomly build a suction flow rate fitting model; the suction flow rate fitting model is used to characterize the relationship between suction flow rate and suction time.

[0079] The calculation module is used to calculate the fitted suction flow rate for each suction port based on the collected process data and suction flow rate fitting model.

[0080] The module is used to compare the similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate to form the cigarette smoking curve of the smoker.

[0081] The following will describe in detail the cigarette smoking curve formation system provided in this embodiment, with reference to the accompanying diagrams. Please refer to... Figure 5 The diagram shows the principle structure of a cigarette smoking curve formation system in one embodiment. Figure 5 As shown, the cigarette smoking curve formation system 1 includes: a data acquisition module 11, a model building module 12, a calculation module 13, a formation module 14, and a repetitive execution module 15.

[0082] The data acquisition module 11 is used to collect process data of a smoker inhaling a brand of cigarette. The process data includes two dimensions: time and instantaneous flow rate, denoted as a data matrix [T:{t1,t2,…,tn},Q:{q1,q2,…,qn}], meaning the process data from the smoker includes the inhalation time series T. i and the actual suction flow rate sequence Q i The suction time series consists of suction time points, and the actual suction flow rate series consists of the suction flow rate occurring at the suction time points.

[0083] The model building module 12 is used to randomly build a suction flow rate fitting model; the suction flow rate fitting model is used to characterize the relationship between suction flow rate and suction time.

[0084] Specifically, the model building module 12 determines the fitting coefficients to be solved in the suction flow fitting model; solves the fitting coefficients to be solved based on preset constraints; and establishes the suction flow fitting model through the fitting coefficients to be solved; wherein, in the suction flow fitting model, the suction flow is the dependent variable and the suction time is the independent variable.

[0085] In this embodiment, the preset limiting conditions include:

[0086] When the suction time point is 0, the suction flow rate at that time point is 0;

[0087] Within the aforementioned suction time series, the total amount of the fitted suction flow rate of the suction flow rate fitting model is equal to the total amount of the actual suction flow rate.

[0088] The suction flow rate fitting model includes at least one preset extreme point;

[0089] Within the suction time series, the suction flow rate in the suction flow rate fitting model must remain continuous.

[0090] The calculation module 13 is used to calculate the fitted suction flow rate corresponding to each suction port based on the collected process data and the suction flow rate fitting model.

[0091] Specifically, the calculation module 13 includes substituting each smoking time point in the process data of a smoker smoking a brand of cigarette into an established smoking flow fitting model to calculate the fitted smoking flow matching the smoking time point.

[0092] The forming module 14 is used to compare the similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate to form the cigarette smoking curve of the smoker.

[0093] In this embodiment, the similarity comparison between the fitted suction flow rate and the corresponding actual suction flow rate for each suction port in the forming module 14 refers to calculating the suction similarity between the fitted suction flow rate and the corresponding actual suction flow rate for each suction port. The specific formula for calculating the suction similarity is as follows:

[0094]

[0095] Where R represents the similarity between a smoker's puff of a certain brand of cigarette and the puffing waveform of that particular smoker, y i Let q be the fitted suction flow rate corresponding to the i-th suction port. i Let R be the actual suction flow rate corresponding to the i-th suction port. In this embodiment, a smaller suction similarity R indicates a better fit.

[0096] The repeated execution module 15 is used to repeatedly call the model establishment module 12, the calculation module 13, and the formation module 14 to obtain the cigarette smoking curves of several smokers. The cigarette smoking curve with the lowest similarity is selected from the cigarette smoking curves of several smokers as a representative of the cigarette smoking waveform of a smoker smoking a brand of cigarettes.

[0097] It should be noted that the division of the various modules in the above system is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls, entirely in hardware, or partially in software calls via processing element calls, with some modules implemented in hardware. For example, module x can be a separate processing element or integrated into a chip within the system. Additionally, module x can be stored as program code in the system's memory, invoked and executed by a processing element. The implementation of other modules is similar. These modules can be fully or partially integrated together or implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the processor element or through software instructions. These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Field Programmable Gate Arrays (FPGAs), etc. When a module is implemented through processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. These modules can be integrated together to form a System-on-a-Chip (SOC).

[0098] Example 2

[0099] This embodiment provides a device for forming a cigarette smoking profile. The device includes a processor, a memory, a transceiver, a communication interface and / or a system bus. The memory and the communication interface are connected to the processor and the transceiver via the system bus and communicate with each other. The memory is used to store computer programs, the communication interface is used to communicate with other devices, and the processor and the transceiver are used to run the computer programs, causing the device for forming the cigarette smoking profile to perform the various steps of the cigarette smoking profile forming method as described in Embodiment 1.

[0100] The system bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. The communication interface is used to enable communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries). Memory may include Random Access Memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0101] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0102] The scope of protection of the method for forming the cigarette smoking curve described in this invention is not limited to the order of steps listed in this embodiment. Any solution achieved by adding, subtracting, or replacing steps in the prior art based on the principles of this invention is included within the scope of protection of this invention.

[0103] The present invention also provides a cigarette smoking curve forming system, which can realize the cigarette smoking curve forming method of the present invention. However, the implementation device of the cigarette smoking curve forming method of the present invention includes, but is not limited to, the structure of the cigarette smoking curve forming system listed in this embodiment. All structural modifications and substitutions of the prior art made according to the principle of the present invention are included within the protection scope of the present invention.

[0104] In summary, the method, system, equipment, and computer storage medium for forming cigarette smoking curves described in this invention can quickly and accurately establish smoking waveforms, preparing for subsequent statistical analysis of the smoking characteristics of the target consumer group and more effectively describing consumers' smoking characteristics. Therefore, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0105] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for forming a cigarette smoking curve, characterized in that, include: Data on the process of a smoker inhaling a brand of cigarette is collected. The process data includes a smoking time series and an actual smoking flow rate series. The smoking time series consists of smoking time points. The actual smoking flow rate series consists of the smoking flow rate occurring at each smoking time point. A randomized suction flow rate fitting model is established; the suction flow rate fitting model is used to characterize the relationship between suction flow rate and suction time. Based on the collected process data and the suction flow fitting model, calculate the fitted suction flow corresponding to each suction port. The similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate is compared to form the cigarette smoking curve of the smoker. The step of comparing the similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate includes: calculating the suction similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate. The specific formula for calculating the similarity of the extraction is as follows: Where R represents the suction similarity. Let i be the fitted suction flow rate corresponding to the i-th suction port. Let R be the actual suction flow rate corresponding to the i-th suction port. The smaller the suction similarity R, the better the fit. The method for forming the cigarette smoking curve further includes: repeatedly running a randomly established smoking flow fitting model; the smoking flow fitting model is used to characterize the relationship between smoking flow and smoking time; calculating the fitted smoking flow corresponding to each puff based on the collected process data and the smoking flow fitting model; comparing the similarity between the fitted smoking flow corresponding to each puff and the corresponding actual smoking flow several times to obtain cigarette smoking curves of several smokers; selecting the cigarette smoking curve with the lowest similarity from the cigarette smoking curves of several smokers as a representative of the cigarette smoking waveform of a smoker smoking a brand of cigarettes.

2. The method for forming a cigarette smoking curve according to claim 1, characterized in that, The steps for randomly establishing a suction flow fitting model include: Determine the fitting coefficients to be solved in the suction flow rate fitting model; Based on preset constraints, the fitting coefficients to be solved are obtained; The suction flow rate fitting model is established using the unsolved fitting coefficients; wherein, in the suction flow rate fitting model, the suction flow rate is the dependent variable and the suction time is the independent variable.

3. The method for forming a cigarette smoking curve according to claim 2, characterized in that, The preset restrictions include: When the suction time point is 0, the suction flow rate at that time point is 0; Within the aforementioned suction time series, the total amount of the fitted suction flow rate of the suction flow rate fitting model is equal to the total amount of the actual suction flow rate. The suction flow rate fitting model includes at least one preset extreme point; Within the suction time series, the suction flow rate in the suction flow rate fitting model must remain continuous.

4. The method for forming a cigarette smoking curve according to claim 2, characterized in that, The suction flow rate fitting model includes a linear fitting model, a sinusoidal fitting model, a quadratic fitting model, an exponential fitting model, and / or a piecewise fitting model; wherein the piecewise fitting model includes at least two of the linear fitting model, the sinusoidal fitting model, the quadratic fitting model, and the exponential fitting model.

5. The method for forming a cigarette smoking curve according to claim 2, characterized in that, The steps for calculating the fitted suction flow rate corresponding to each suction port based on the collected process data and suction flow rate fitting model include: substituting each suction time point in the process data of the smoker sucking a brand of cigarette into the established suction flow rate fitting model to calculate the fitted suction flow rate matching that suction time point.

6. A cigarette smoking profile forming system employing the cigarette smoking profile forming method as described in claim 1, characterized in that, include: The data acquisition module is used to collect process data of a smoker smoking a brand of cigarettes through the puff port; the process data of the puff port includes a smoking time series and an actual smoking flow rate series; the smoking time series consists of smoking time points; the actual smoking flow rate series consists of the smoking flow rate occurring at the smoking time points. The model building module is used to randomly build a suction flow rate fitting model; the suction flow rate fitting model is used to characterize the relationship between suction flow rate and suction time. The calculation module is used to calculate the fitted suction flow rate for each suction port based on the collected process data and suction flow rate fitting model. The module is used to compare the similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate in order to form the cigarette smoking curve of the smoker. The step of comparing the similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate includes: calculating the suction similarity between the fitted suction flow rate corresponding to each suction port and the corresponding actual suction flow rate. The specific formula for calculating the similarity of the extraction is as follows: Where R represents the suction similarity. Let i be the fitted suction flow rate corresponding to the i-th suction port. Let R be the actual suction flow rate corresponding to the i-th suction port. The smaller the suction similarity R, the better the fit. The method for forming the cigarette smoking curve further includes: repeatedly running a randomly established smoking flow fitting model; the smoking flow fitting model is used to characterize the relationship between smoking flow and smoking time; calculating the fitted smoking flow corresponding to each puff based on the collected process data and the smoking flow fitting model; comparing the similarity between the fitted smoking flow corresponding to each puff and the corresponding actual smoking flow several times to obtain cigarette smoking curves of several smokers; selecting the cigarette smoking curve with the lowest similarity from the cigarette smoking curves of several smokers as a representative of the cigarette smoking waveform of a smoker smoking a brand of cigarettes.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method for forming a cigarette smoking curve as described in any one of claims 1 to 5.

8. A device for forming a cigarette smoking curve, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the cigarette smoking profile forming apparatus to perform the cigarette smoking profile forming method as described in any one of claims 1 to 5.

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