Gas recognition method, device and equipment suitable for home environment and medium

Abstract

The invention relates to the technical field of odor recognition, and discloses a gas recognition method, device and equipment suitable for a home environment and a medium, the method comprises the following steps: acquiring an odor real-time measured value collected by a multi-factor gas sensor, and constructing a real-time measured value curve based on the odor real-time measured value; performing legality verification on the real-time measurement value curve, and generating a virtual quantity measurement value sequence based on a legality verification result; configuring a kernel function for the virtual quantity measurement value sequence, and generating kernel space distribution; and generating a home environment gas recognition result by using a kernel linear discriminant analysis method based on the kernel space distribution. According to the invention, accurate identification of the gas in the home environment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of odor recognition, and in particular to a gas recognition method, device, equipment and medium suitable for home environments. Background Art

[0002] An electronic nose is an electronic instrument that uses an array of gas sensors to measure data and then identifies the type of odor by performing pattern matching in an odor database.

[0003] Electronic noses were initially used in beverages, meat products, aquatic products, dairy products, tobacco, grain storage, food processing, chemical industry and other professional fields. Now, some manufacturers are trying to introduce electronic noses into the field of toC (Total Organic Carbon) home health services, trying to solve the problem of olfactory quantification and create a new business model.

[0004] However, the application cost of electronic noses in home environments is relatively high, and the gas identification in home environments is not accurate. Summary of the invention

[0005] In view of this, the present invention provides a gas identification method, device, equipment and medium suitable for a home environment, so as to solve the problem that the electronic nose is not accurate in identifying the gas in the home environment.

[0006] In a first aspect, the present invention provides a gas identification method suitable for a home environment, comprising:

[0007] Acquire real-time measurement values ​​of odor collected by a multi-factor gas sensor, and construct a real-time measurement value curve based on the real-time measurement values ​​of odor;

[0008] Performing a validity check on the real-time measurement value curve, and generating a virtual quantity measurement value sequence based on the validity check result;

[0009] Configure the kernel function for the virtual quantity measurement value sequence to generate the kernel space distribution;

[0010] Based on the kernel spatial distribution, the kernel linear discriminant analysis method is used to generate the home environment gas identification results.

[0011] The present embodiment provides a gas identification method suitable for home environments, which adopts low-cost sensors and improves the recognition of common gases in the bedroom by the electronic nose. The validity of the real-time measurement value curve is verified, thereby ensuring the validity of the real-time measurement value. In addition, based on the kernel space distribution, the kernel linear discriminant analysis method is used to generate the home environment gas identification result, which greatly reduces the calculation amount of odor identification in the home environment and improves the accuracy of gas identification in the home environment.

[0012] In an optional implementation, performing a validity check on the real-time measurement value curve and generating a virtual quantity measurement value sequence based on the validity check result includes:

[0013] Performing a validity check on the real-time measurement value curve. When the validity check result shows that there is an illegal measurement value, performing a weighted dimensionality increase process on the real-time measurement value curve to generate an extended value curve.

[0014] The extended value curve is cleaned to generate a virtual quantity measurement value sequence.

[0015] In an optional implementation, performing a validity check on the real-time measurement value curve and generating a virtual quantity measurement value sequence based on the validity check result further includes:

[0016] When the result of the validity check is that the real-time measurement value is valid, the real-time measurement value curve is cleaned to generate a virtual quantity measurement value sequence.

[0017] In an optional implementation, configuring a kernel function for a virtual quantity measurement value sequence to generate a kernel space distribution includes:

[0018] Perform Laplace transformation on the virtual quantity measurement value sequence to generate a measurement value linear space;

[0019] The Gaussian kernel function is used to map the measured value linear space to the Gaussian transformation kernel space to generate the kernel space distribution.

[0020] In an optional implementation, based on the kernel space distribution, a kernel linear discriminant analysis method is used to generate a home environment gas identification result, including:

[0021] The nonlinear feature extraction of the kernel spatial distribution is performed using the kernel linear discriminant analysis method to generate gas features;

[0022] The gas characteristics are input into the classifier to determine the gas category and generate home environment gas identification results.

[0023] In an optional implementation, it also includes:

[0024] The report quality score is given to the home environment gas identification results to generate the home environment gas quality, and the current home environment is adjusted based on the home environment gas quality.

[0025] In an optional implementation, the home environment gas identification result is scored for report quality, the home environment gas quality is generated, and the current home environment is adjusted based on the home environment gas quality, including:

[0026] When the home environment gas quality is unqualified, the home environment gas quality is sent to the user end so that the user can analyze the breath status report and adjust the current home environment based on the analysis results;

[0027] Alternatively, when the home environment gas quality is qualified, a breath status report is generated and stored.

[0028] In a second aspect, the present invention provides a gas identification device suitable for a home environment, comprising:

[0029] An acquisition module is used to acquire the real-time measurement value of the odor collected by the multi-factor gas sensor, and to construct a real-time measurement value curve based on the real-time measurement value of the odor;

[0030] A validity check module is used to check the validity of the real-time measurement value curve and generate a virtual quantity measurement value sequence based on the validity check result;

[0031] A configuration module, used for configuring a kernel function for a virtual quantity measurement value sequence to generate a kernel space distribution;

[0032] The generation module is used to generate home environment gas identification results based on kernel spatial distribution using kernel linear discriminant analysis.

[0033] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the gas identification method suitable for a home environment according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the gas identification method applicable to a home environment according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 is a flow chart of a gas identification method applicable to a home environment according to an embodiment of the present invention;

[0037] Figure 2is a flow chart of another gas identification method applicable to a home environment according to an embodiment of the present invention;

[0038] Figure 3 is a flow chart of another gas identification method applicable to a home environment according to an embodiment of the present invention;

[0039] Figure 4 is a schematic diagram of a linear space of measured values ​​according to an embodiment of the present invention;

[0040] Figure 5 is a schematic diagram of a Gaussian transform kernel space according to an embodiment of the present invention;

[0041] Figure 6 is a flow chart of another gas identification method applicable to a home environment according to an embodiment of the present invention;

[0042] Figure 7 is a schematic diagram of a process of a kernel LDA algorithm according to an embodiment of the present invention;

[0043] Figure 8 is a structural block diagram of a gas identification device suitable for a home environment according to an embodiment of the present invention;

[0044] Fig. 9 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0046] The electronic nose is introduced into the field of home health services to solve the problem of olfactory quantification. Among them, the common gas sources in the living environment are shown in Table 1 below:

[0047] Table 1:

[0048]

[0049] The use of electronic noses can solve the problem of olfactory quantification, but there are also certain problems.

[0050] First, the electronic nose was introduced into the field of home health services, resulting in few supporting facilities and high production and R&D costs.

[0051] Secondly, there are certain ethical and regulatory difficulties: on the one hand, indicators such as body odor and bad breath are personal privacy and contain a lot of information; on the other hand, if high-end perfumes, red wines and other products are indexed, some disruptions will occur. Therefore, in the stage where supervision is unclear, investing too much in research and development actually has certain risks.

[0052] At present, the most successful commercial application in the relevant market is formaldehyde removal service. Since the time-limited equipment for measuring time is expensive, this is the main reason that restricts the large-scale deployment of electronic noses in the field of home health services.

[0053] Therefore, in order to solve the above technical problems, an embodiment of the present invention provides a gas identification method suitable for home environment.

[0054] According to an embodiment of the present invention, a gas identification method embodiment suitable for home environments is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0055] In this embodiment, a gas identification method suitable for a home environment is provided, which can be used in the above-mentioned mobile terminals, such as mobile phones, tablet computers, etc. Figure 1 is a flow chart of a gas identification method applicable to a home environment according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0056] Step S101, obtaining real-time measurement values ​​of odor collected by the multi-factor gas sensor, and constructing a real-time measurement value curve based on the real-time measurement values ​​of odor.

[0057] Specifically, the main interferences in the home environment can be evaluated through multi-factor weighted gas-sensitive material products; for example, relevant meteorological PM2.5 sensors (sensors used to monitor the dust concentration in the surrounding air, that is, the PM2.5 value) generally use an oscillating balance method or a weighing method, which has high measurement accuracy and relatively high cost. However, in order to reduce costs, household PM2.5 sensors use a light scattering method. Since the relationship between light scattering and particle concentration is easily affected by other factors such as temperature, its accuracy is not as good as the oscillating balance method. Therefore, 10 light scattering PM2.5 sensors and 1 temperature and humidity sensor are used to form a multi-factor gas sensor. Since the cost of 10 light scattering PM2.5 sensors and 1 temperature and humidity sensor is much lower than that of 30 balance sensors, the accuracy of gas identification in the home environment is greatly improved while reducing costs.

[0058] Step S102, performing a validity check on the real-time measurement value curve, and generating a virtual quantity measurement value sequence based on the validity check result.

[0059] Specifically, due to electrical interference, the real-time measurement value of odor deviates. Therefore, it is necessary to verify the legitimacy of the real-time measurement value curve, and then perform data filtering, and directly discard obviously unreasonable data. For example, the light scattering PM2.5 sensor has deviations in real-time measurement value due to fluctuations in the gas flow rate of wind blowing, so it is necessary to verify the legitimacy of the real-time measurement value curve, and then eliminate the periodic fluctuations of three to five seconds in the real-time measurement value curve.

[0060] Step S103: configuring a kernel function for the virtual quantity measurement value sequence to generate a kernel space distribution.

[0061] Specifically, after experiments, it is found that the X-axis discreteness corresponding to the Gaussian kernel function is obviously the largest, so the kernel function adopts the Gaussian kernel function.

[0062] Step S104, based on the kernel space distribution, a kernel linear discriminant analysis (LDA) method is used to generate a home environment gas recognition result.

[0063] The present embodiment provides a gas identification method suitable for home environments, which adopts low-cost sensors and improves the recognition of common gases in the bedroom by the electronic nose. The validity of the real-time measurement value curve is verified, thereby ensuring the validity of the real-time measurement value. In addition, based on the kernel space distribution, the kernel linear discriminant analysis method is used to generate the home environment gas identification result, which greatly reduces the calculation amount of odor identification in the home environment and improves the accuracy of gas identification in the home environment.

[0064] In this embodiment, a gas identification method suitable for a home environment is provided, which can be used in the above-mentioned mobile terminals, such as mobile phones, tablet computers, etc. Figure 2 is a flow chart of a gas identification method suitable for a home environment according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0065] Step S201, obtain the real-time measurement value of the odor collected by the multi-factor gas sensor, and construct a real-time measurement value curve based on the real-time measurement value of the odor. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0066] Step S202: Perform a validity check on the real-time measurement value curve, and generate a virtual quantity measurement value sequence based on the validity check result.

[0067] Specifically, the above step S202 includes:

[0068] Step S2021, performing a validity check on the real-time measurement value curve. When the validity check result shows that there are illegal measurement values, performing a weighted dimensionality increase process on the real-time measurement value curve to generate an extended value curve.

[0069] Specifically, a better pattern matching experience is provided by classifying the calibration data. By utilizing the serial numbers corresponding to the multi-factor gas sensors of the electronic nose, weighting can be performed according to the data types, i.e., calibration grouping. For example, the sensor reports two data sequences, i.e., D1 formaldehyde data sequence and D2 carbon dioxide data sequence. After processing the difference between D1 and D2, an extended value curve D3 is added, where D3=D1+0.1*D2.

[0070] Furthermore, when the result of the validity check is that the real-time measurement value is valid, the real-time measurement value curve is cleaned to generate a virtual quantity measurement value sequence.

[0071] Step S2022: Clean the extended value curve to generate a virtual quantity measurement value sequence.

[0072] Step S203: configure a kernel function for the virtual quantity measurement value sequence to generate a kernel space distribution. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0073] Step S204: Based on the kernel space distribution, the kernel linear discriminant analysis method is used to generate the home environment gas identification result. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0074] The present embodiment provides a gas identification method suitable for home environments. The serial numbers in the sensor array of the electronic nose can be weighted according to the data type, which is mathematically called calibration grouping. Further, a better pattern matching experience is provided through the classification of calibration data, which greatly reduces the computational complexity of odor identification in the home environment and improves the accuracy of gas identification in the home environment.

[0075] In this embodiment, a gas identification method suitable for a home environment is provided, which can be used in the above-mentioned mobile terminals, such as mobile phones, tablet computers, etc. Figure 3 is a flow chart of a gas identification method applicable to a home environment according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0076] Step S301, obtain the real-time measurement value of the odor collected by the multi-factor gas sensor, and construct a real-time measurement value curve based on the real-time measurement value of the odor. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0077] Step S302: Perform a validity check on the real-time measurement value curve, and generate a virtual quantity measurement value sequence based on the validity check result. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0078] Step S303: configuring a kernel function for the virtual quantity measurement value sequence to generate a kernel space distribution.

[0079] Specifically, the above step S303 includes:

[0080] Step S3031, performing Laplace transform on the virtual quantity measurement value sequence to generate a measurement value linear space.

[0081] Specifically, Figure 4 As shown, the linear space of the measured values ​​presents a temporal change of a gas from being detected by the sensor to dissipation. The linear measured value can be expressed as x1, x2, … x N .

[0082] Step S3032: Use the Gaussian kernel function to map the measured value linear space to the Gaussian transformation kernel space to generate a kernel space distribution.

[0083] Specifically, the Gaussian kernel function is used to perform Gaussian transformation on the linear space of the measured values, and the Gaussian transformation kernel space distribution can be obtained, as shown in Figure 5 As shown, the expression is as follows:

[0084]

[0085] Among them, β represents the parameter, and x and y represent the data in the linear space of the measured values.

[0086] Step S304, based on the kernel space distribution, a kernel linear discriminant analysis method is used to generate a home environment gas identification result.

[0087] Specifically, the above step S304 includes:

[0088] Step S3041, using the kernel linear discriminant analysis method to perform nonlinear feature extraction on the kernel space distribution to generate gas features.

[0089] Specifically, the data in the input kernel space distribution is mapped to the high-dimensional feature space through nonlinear transformation. The data points after nonlinear transformation are φ(x1), φ(x2), …, φ(x N ); Calculate the kernel matrix K = [K(i, j)] of the training sample set according to the determined kernel function and the optimized kernel function parameters, where,

[0090]

[0091] Furthermore, the Fisher criterion function maximization is transformed into the problem of solving the generalized eigenvalue. The characteristic value of And solve for the eigenvector z r =[α1,α2,…α N ] T , where λ r Represents a constant.

[0092] Furthermore, the eigenvalues ​​are sorted in descending order; the φ(x i ) is projected onto the kth eigenvector to obtain the nonlinear feature y of the sample r :

[0093]

[0094] Furthermore, the kernel matrix K' between the test sample and the training set sample is calculated, and then the test sample is projected onto the feature vector to obtain the gas characteristics.

[0095] Step S3042, input the gas characteristics into the classifier, determine the gas category, and generate the home environment gas identification result.

[0096] Specifically, the similarity between the gas characteristics and the training gas samples is calculated through the classifier, and the training gas samples most similar to the gas characteristics are determined. Then, the category corresponding to the training gas samples is used as the category corresponding to the gas characteristics to obtain the home environment gas category.

[0097] By constructing a dimensional kernel function suitable for gas recognition patterns in modern bedroom environments, a kernel function LDA pattern matching was used in the home environment.

[0098] This embodiment provides a gas identification method suitable for home environments. The kernel linear discriminant analysis method can better characterize the nonlinear characteristics of different types of gases, improve the signal difference of samples after nonlinear mapping in the high-dimensional feature space, and thus improve the accuracy of gas identification in the home environment.

[0099] In this embodiment, a gas identification method suitable for a home environment is provided, which can be used in the above-mentioned mobile terminals, such as mobile phones, tablet computers, etc. Figure 6 is a flow chart of a gas identification method suitable for a home environment according to an embodiment of the present invention. Figure 6 As shown, the process includes the following steps:

[0100] Step S601, obtain the real-time measurement value of the odor collected by the multi-factor gas sensor, and construct a real-time measurement value curve based on the real-time measurement value of the odor. Figure 5Step S301 of the illustrated embodiment will not be described in detail here.

[0101] Step S602: Perform a validity check on the real-time measurement value curve, and generate a virtual quantity measurement value sequence based on the validity check result. Figure 5 Step S302 of the illustrated embodiment will not be described in detail here.

[0102] Step S603: configure a kernel function for the virtual quantity measurement value sequence to generate a kernel space distribution. Figure 5 Step S303 of the illustrated embodiment will not be described in detail here.

[0103] Step S604: Based on the kernel spatial distribution, the kernel linear discriminant analysis method is used to generate the home environment gas identification result. Figure 5 Step S304 of the illustrated embodiment will not be described in detail here.

[0104] Step S605, scoring the report quality of the home environment gas identification result, generating the home environment gas quality, and adjusting the current home environment based on the home environment gas quality.

[0105] Specifically, the above step S605 includes:

[0106] Step S6051, when the home environment gas quality is unqualified, the home environment gas quality is sent to the user end so that the user can analyze the breath status report and adjust the current home environment based on the analysis results.

[0107] Specifically, Figure 7 As shown, when the home environment gas quality is unqualified, the user performs manual analysis and global tuning, and uses a cleaning strategy with greater separation to clean the extended value curve, and reconfigures the kernel function of the virtual quantity measurement value sequence.

[0108] Step S6052, or, when the home environment gas quality is qualified, a breath status report is generated and stored.

[0109] For example, when applied in a dormitory environment, the breath status report is submitted to the sleep aid platform.

[0110] In this embodiment, a gas identification device suitable for a home environment is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated hereafter. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0111] This embodiment provides a gas identification device suitable for home environment, such as Figure 8 As shown, including:

[0112] An acquisition module 801 is used to acquire real-time measurement values ​​of odors collected by a multi-factor gas sensor, and to construct a real-time measurement value curve based on the real-time measurement values ​​of odors;

[0113] A validity check module 802 is used to perform a validity check on the real-time measurement value curve and generate a virtual quantity measurement value sequence based on the validity check result;

[0114] A configuration module 803 is used to configure a kernel function for a virtual quantity measurement value sequence to generate a kernel space distribution;

[0115] The generation module 804 is used to generate the home environment gas identification result based on the kernel space distribution and using the kernel linear discriminant analysis method.

[0116] In some optional implementations, the legality verification module 802 includes:

[0117] A verification unit is used to perform a validity check on the real-time measurement value curve. When the validity check result shows that there is an illegal measurement value, a weighted dimensionality increase process is performed on the real-time measurement value curve to generate an extended value curve.

[0118] The first cleaning unit is used to clean the extended value curve to generate a virtual quantity measurement value sequence.

[0119] In some optional implementations, the legality verification module 802 further includes:

[0120] The second cleaning unit is used to clean the real-time measurement value curve to generate a virtual quantity measurement value sequence when the legitimacy check result shows that the real-time measurement value is legitimacy.

[0121] In some optional implementations, the configuration module 803 includes:

[0122] A transformation unit, used for performing Laplace transformation on the virtual quantity measurement value sequence to generate a measurement value linear space;

[0123] The mapping unit is used to map the measured value linear space to the Gaussian transformation kernel space by using the Gaussian kernel function to generate the kernel space distribution.

[0124] In some optional implementations, the generating module 804 includes:

[0125] An extraction unit, used for performing nonlinear feature extraction on the kernel spatial distribution by using a kernel linear discriminant analysis method to generate gas features;

[0126] The determination unit is used to input the gas characteristics into the classifier, determine the gas category, and generate the home environment gas identification result.

[0127] In some optional embodiments, the method further comprises:

[0128] The scoring module is used to score the report quality of the home environment gas identification results, generate the home environment gas quality, and adjust the current home environment based on the home environment gas quality.

[0129] In some optional implementations, the scoring module includes:

[0130] An analysis unit, for sending the home environment gas quality to a user terminal when the home environment gas quality is unqualified, so that the user can analyze the breath status report and adjust the current home environment based on the analysis result;

[0131] The storage unit is used to generate a breath status report and store the breath status report when the gas quality in the home environment is qualified.

[0132] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0133] In this embodiment, a gas identification device suitable for home environments is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0134] The embodiment of the present invention also provides a computer device having the above Figure 8 A gas identification device suitable for home environment is shown.

[0135] See also Fig. 9 , Fig. 9 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig. 9As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 A processor 10 is taken as an example.

[0136] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0137] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0138] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0139] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0140] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig. 9 The example of connecting through bus is taken in the following.

[0141] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0142] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0143] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A gas identification method suitable for home environment, characterized in that: The method comprises: Acquire real-time measurement values ​​of odor collected by a multi-factor gas sensor, and construct a real-time measurement value curve based on the real-time measurement values ​​of the odor; Performing a validity check on the real-time measurement value curve, and generating a virtual quantity measurement value sequence based on the validity check result; configuring a kernel function for the virtual quantity measurement value sequence to generate a kernel space distribution; Based on the kernel space distribution, a kernel linear discriminant analysis method is used to generate home environment gas identification results.

2. The method according to claim 1, characterized in that Performing a validity check on the real-time measurement value curve and generating a virtual quantity measurement value sequence based on the validity check result, including: Performing a validity check on the real-time measurement value curve, and when the validity check result shows that there is an illegal measurement value, performing a weighted dimensionality increase process on the real-time measurement value curve to generate an extended value curve; The extended value curve is cleaned to generate the virtual quantity measurement value sequence.

3. The method according to claim 1, characterized in that: Performing a validity check on the real-time measurement value curve and generating a virtual quantity measurement value sequence based on the validity check result, further comprising: When the result of the validity check is that the real-time measurement value is valid, the real-time measurement value curve is cleaned to generate the virtual quantity measurement value sequence.

4. The method according to claim 1, characterized in that Configuring a kernel function for the virtual quantity measurement value sequence to generate a kernel space distribution includes: Performing Laplace transformation on the virtual quantity measurement value sequence to generate a measurement value linear space; The measured value linear space is mapped to the Gaussian transformation kernel space using a Gaussian kernel function to generate the kernel space distribution.

5. The method according to claim 1, characterized in that Based on the kernel space distribution, the kernel linear discriminant analysis method is used to generate the home environment gas identification results, including: Using the kernel linear discriminant analysis method to perform nonlinear feature extraction on the kernel spatial distribution to generate gas features; The gas characteristics are input into a classifier to determine the gas category and generate the home environment gas identification result.

6. The method according to claim 1, characterized in that Also includes: The home environment gas identification result is scored for report quality, the home environment gas quality is generated, and the current home environment is adjusted based on the home environment gas quality.

7. The method according to claim 6, characterized in that The home environment gas identification result is scored for report quality, the home environment gas quality is generated, and the current home environment is adjusted based on the home environment gas quality, including: When the home environment gas quality is unqualified, the home environment gas quality is sent to the user end so that the user can analyze the breath status report and adjust the current home environment based on the analysis result; Alternatively, when the home environment gas quality is qualified, a breath status report is generated and stored.

8. A gas identification device suitable for home environment, characterized in that: The device comprises: An acquisition module, used to acquire real-time measurement values ​​of odor collected by the multi-factor gas sensor, and to construct a real-time measurement value curve based on the real-time measurement values ​​of odor; A validity check module, used to perform a validity check on the real-time measurement value curve and generate a virtual quantity measurement value sequence based on the validity check result; A configuration module, configured to configure a kernel function for the virtual quantity measurement value sequence to generate a kernel space distribution; A generation module is used to generate home environment gas identification results based on the kernel space distribution using a kernel linear discriminant analysis method.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the gas identification method suitable for a home environment as described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the gas identification method suitable for a home environment according to any one of claims 1 to 7.

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