CN109975481A - Volatile organic component detection method, device, storage medium and terminal - Google Patents

Abstract

The present application provides a volatile organic component detection method, device, storage medium and terminal, which include: acquiring volatile component detection data of multiple batches of samples of a material to be tested, so as to form a volatile component data set of the material to be tested; The detection reference data of each batch of samples of the material to be tested is obtained according to the volatile component data set, and the similarity between the volatile component detection data of each batch of samples and the corresponding detection reference data is calculated respectively. Determine the upper limit threshold, center threshold, and lower threshold of similarity of the material to be tested; visually output the comparison result information between the similarity of each batch of samples and the reference data and the upper threshold, center threshold, and lower threshold. This application adopts the visual similarity quality control chart, and calculates the upper and lower limits and the central limit of the quality control chart with the similarity mean value and the preset multiple similarity standard deviation or similarity limit, so as to quickly and effectively evaluate the quality stability of packaging paper.

Description

Volatile organic component detection method, device, storage medium and terminal

technical field

The present application relates to the technical field of quality stability detection of volatile organic components, and in particular, to a method, device, storage medium and terminal for detection of volatile organic components.

Background technique

In industrial production, the preparation process of packaging paper is complicated, and chemical components including volatile organic compounds (VOCs) will inevitably be introduced due to the application of printing process. Excessive VOCs content in packaging paper will affect the user's sensory experience. For food, cosmetics, tobacco and other industries, the level of VOCs content in packaging materials and their stability have always been the key points of product quality control. There is still no comprehensive and effective method for stability assessment.

Application content

In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a volatile organic component detection method, device, storage medium and terminal for solving the problems in the prior art.

In order to achieve the above-mentioned purpose and other related purposes, a first aspect of the present application provides a volatile organic component detection method, which includes: acquiring volatile component detection data of multiple batches of samples of a material to be tested, so as to form the The volatile component data set of the material; the detection benchmark data of each batch of samples of the material to be tested is obtained according to the volatile component data set, and the volatile component detection data of each batch of samples and the corresponding detection benchmark data are calculated respectively. The similarity between the samples to determine the upper threshold, the center threshold, and the lower threshold of the material to be tested; the comparison between the similarity of each batch of samples and the upper threshold, the center threshold, and the lower threshold is visualized result information.

In some embodiments of the first aspect of the present application, the manner of forming the volatile component data set of the material to be tested in the method includes: detecting data from volatile components of multiple batches of samples of the material to be tested Eliminate outlier data; establish a volatile component database of the material to be tested according to the volatile component detection data after removing the outlier data; wherein the volatile component database includes the data of each batch of samples of the material to be tested Each volatile component detection index and detection data.

In some embodiments of the first aspect of the present application, the detection reference data of each batch of samples of the material to be tested includes an average value of all volatile component detection data of each batch of samples.

In some embodiments of the first aspect of the present application, the cosine similarity algorithm is used to calculate the similarity between the volatile component detection data of each batch of samples and the detection reference data.

In some embodiments of the first aspect of the present application, the method includes: calculating the similarity of the batches of samples according to the similarity between the volatile component detection data of each batch of samples and the detection reference data The mean serves as the central threshold.

In some embodiments of the first aspect of the present application, the method includes: determining the lower threshold value by the difference between the central threshold value and a first preset multiple standard deviation, but the lower threshold value is not lower than 0.

In some embodiments of the first aspect of the present application, the method includes: determining the upper threshold as the sum of the central threshold and a second predetermined multiple standard deviation, but the upper threshold is not higher than 1.

In order to achieve the above object and other related purposes, a second aspect of the present application provides a volatile organic component detection device, which includes: a data acquisition module for acquiring volatile component detection data of multiple batches of samples of a material to be tested, According to this, a volatile component data set of the material to be tested is formed; a data processing module is used to obtain the detection reference data of the tested material according to the volatile component data set, and calculate the volatile component detection of each batch of samples respectively. The similarity between the data and the detection benchmark data is used to determine the upper threshold, center threshold, and lower threshold of the similarity of the material to be tested; the visual output module is used to visually output the similarity of each batch of samples and The comparison result information among the upper threshold, the central threshold, and the lower threshold.

In order to achieve the above object and other related objects, a third aspect of the present application provides a computer-readable storage medium on which a computer program is stored, and the computer program implements the volatile organic component detection method when executed by a processor.

In order to achieve the above object and other related purposes, a fourth aspect of the present application provides an electronic terminal, 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 , so that the terminal executes the volatile organic component detection method.

As mentioned above, the volatile organic component detection method, device, storage medium and terminal of the present application have the following beneficial effects: the present application adopts the similarity algorithm and the single-value quality control chart to realize the evaluation of the quality stability of VOCs in packaging paper , using the outlier detection method to establish a basic database of various VOCs components in packaging paper, and using the average detection value of each VOCs component as the benchmark, the similarity between each batch of samples and the benchmark is calculated by the similarity algorithm, and the mean value of the similarity is calculated. and the preset multiple similarity standard deviation or similarity limit to calculate the upper and lower limit and center limit of the quality control chart, and draw the similarity quality control chart, which can quickly and effectively evaluate the quality stability of packaging paper.

Description of drawings

FIG. 1 shows a schematic flowchart of a method for detecting volatile organic components in an embodiment of the present application.

FIG. 2 is a schematic diagram of a similarity quality control chart in an embodiment of the present application.

FIG. 3 is a schematic diagram of a similarity quality control chart in an embodiment of the present application.

FIG. 4 is a schematic diagram of a similarity quality control chart in an embodiment of the present application.

FIG. 5 is a schematic diagram of a volatile organic component detection device according to an embodiment of the present application.

FIG. 6 is a schematic structural diagram of a detection terminal in an embodiment of the present application.

Detailed ways

The embodiments of the present application are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application 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 application. It should be noted that the following embodiments and features in the embodiments may be combined with each other under the condition of no conflict.

It should be noted that, in the following description, reference is made to the accompanying drawings, which describe several embodiments of the present application. It is to be understood that other embodiments may be utilized and mechanical, structural, electrical, as well as operational changes may be made without departing from the spirit and scope of the present application. The following detailed description should not be considered limiting, and the scope of embodiments of the present application is limited only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application. Spatially related terms, such as "upper," "lower," "left," "right," "below," "below," "lower," "above," "upper," etc., may be used in the text for ease of description The relationship of one element or feature shown in the figures to another element or feature.

In this application, unless otherwise expressly specified and limited, terms such as "installation", "connection", "connection", "fixing", "fixing" and other terms should be understood in a broad sense, for example, it may be a fixed connection or a It is a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication between two components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific situations.

Also, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context dictates otherwise. It should be further understood that the terms "comprising", "comprising" indicate the presence of a stated feature, operation, element, component, item, kind, and/or group, but do not exclude one or more other features, operations, elements, components, The existence, appearance or addition of items, categories, and/or groups. The terms "or" and "and/or" as used herein are to be construed to be inclusive or to mean any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C" . Exceptions to this definition arise only when combinations of elements, functions, or operations are inherently mutually exclusive in some way.

In industrial production, the preparation process of packaging paper is complicated, and chemical components including volatile organic compounds (VOCs) will inevitably be introduced due to the application of printing process. Excessive VOCs content in packaging paper will affect the user's sensory experience. For food, cosmetics, tobacco and other industries, the level of VOCs content in packaging materials and their stability have always been the key points of product quality control. There is still no comprehensive and effective method for stability assessment.

In view of various problems in the prior art, the present application provides a volatile organic component detection method, device, storage medium and terminal to effectively solve these technical problems. The main idea of this application is to use the similarity algorithm and the single-value quality control chart to realize the evaluation of the quality stability of VOCs in packaging paper, and to use the outlier detection method to establish a basic database of various VOCs components in packaging paper. And based on the average detection of each VOCs component, the similarity between each batch of samples and the benchmark is calculated by the similarity algorithm, and the upper and lower limits of the quality control chart and The central limit and the similarity quality control chart can be drawn, which can quickly and effectively evaluate the quality stability of packaging paper.

It should be noted that the technical solutions provided in this application can not only be used to detect the VOC components of packaging paper, but also can be used to detect furniture decoration materials, auto parts material paints, textile products, toys, or volatilization when used at higher temperatures. VOC components such as electronic products that produce organic compounds. Hereinafter, the technical solutions of the present application will be explained in detail with reference to specific embodiments.

As shown in FIG. 1 , a schematic flowchart of a method for detecting volatile organic components in an embodiment of the present application is shown.

In some embodiments, the method may be applied to a controller, such as an ARM controller, an FPGA controller, a SoC controller, a DSP controller, or an MCU controller, among others. In some embodiments, the method may also be applied to include a memory, a memory controller, one or more processing units (CPUs), peripheral interfaces, RF circuits, audio circuits, speakers, microphones, input/output (I/ O) Computers with components such as subsystems, display screens, other output or control devices, and external ports; such computers include, but are not limited to, such as desktop computers, notebook computers, tablet computers, smart phones, smart TVs, personal digital assistants (Personal Digital Assistants) Digital Assistant, referred to as PDA) and other personal computers. In other embodiments, the method can also be applied to a server, and the server can be arranged on one or more entity servers according to various factors such as function and load, and can also be composed of a distributed or centralized server cluster.

In this embodiment, the volatile organic component detection method includes step S11, step S12, and step S13.

In step S11, the volatile component detection data of multiple batches of samples of the material to be tested are acquired, so as to form a volatile component data set of the material to be tested.

VOC is the English abbreviation of Volatile Organic Compounds. Outdoors are mainly from fuel combustion and transportation, indoors are mainly from combustion products such as coal and natural gas, smoking, heating and cooking smoke, building and decoration materials, furniture, etc. , household appliances, cleaning agents and the human body itself emissions and so on. The volatile organic compounds are for example: inks, organic solvents, etc. in the tobacco industry, glues used in footwear products in the textile industry, etc., correction fluids or fragrances in the toy industry, etc., coatings, paints, or Adhesives, etc., glue or paint used in auto parts materials, cleaning solvents used in the electrical and electronic industry, etc.

In one embodiment, the method of forming the volatile component data set of the material to be tested includes: removing outlier data from the volatile component detection data of multiple batches of samples of the material to be tested; After the volatile component detection data, a volatile component database of the material to be tested is established; wherein the volatile component database includes each volatile component detection index and detection data of each batch of samples of the material to be tested. In this embodiment, the method of eliminating outlier data can perform data preprocessing on the acquired volatile component detection data, preventing outlier data from interfering with VOC detection, and effectively improving the accuracy of detection.

Specifically, the Grubbs test method is used to detect whether there is outlier data in each volatile component detection data in each batch of samples one by one, and the detected outlier data is eliminated without participating in the calculation of the average value. The mean value of the detection of the volatile components in the sub-sample is used instead. The significance level α may take data such as 0.01 or 0.05, which is not limited in this embodiment.

It should be noted that the detection method of outlier data includes, but is not limited to, the Grubbs test method. In other embodiments, detections such as proximity-based outlier detection, density-based detection, and clustering-based technology can also be selected. method, which is not limited in this application.

In step S12, the detection reference data of each batch of samples of the material to be tested is acquired according to the volatile component data set, and the difference between the volatile component detection data of each batch of samples and the corresponding detection reference data is calculated respectively. The similarity is used to determine the upper threshold, center threshold, and lower threshold of the material to be tested.

In one embodiment, the detection reference data of each batch of samples of the material to be tested includes an average value of all volatile component detection data of each batch of samples. It should be noted that the detection benchmark data of each batch of samples includes, but is not limited to, the mean value of the detection data. In other embodiments, the median value or the root mean square value of the detection data can also be selected, which is not made in this application. limited.

In one embodiment, the cosine similarity algorithm is used to calculate the similarity between the volatile component detection data of each batch of samples and the detection reference data. The cosine similarity algorithm refers to using the cosine value of the angle between two vectors in the vector space as a calculation method to measure the difference between two individuals. The closer the cosine value is to 1, the closer the surface angle is to 0 degrees, that is, the two vectors The more similar, the smaller the difference between the two individuals.

For example, the cosine similarity calculation can be performed using the following formula 1):

Among them, S represents the similarity, a _k is the detected value of the k-th volatile organic compound component of the paper sample a, b _k is the detected value of the smoke component k of the paper sample detection benchmark b, and n is the selected volatile organic compound Total number of ingredients.

In one embodiment, according to the similarity between the volatile component detection data of each batch of samples and the detection reference data, the average similarity of the batches of samples is calculated as the central threshold. That is, first calculate the similarity value of each batch of samples, then calculate the average value of these similarity values, and use the average value as the central threshold value, specifically, the following formula 2) can be used to calculate:

Among them, CL represents the center threshold, n represents the total number of batches of all samples of the material to be tested, and _Sk represents the similarity of the kth batch of samples.

In one embodiment, the lower threshold is determined by the difference between the central threshold and a first preset multiple standard deviation. For example, the lower threshold is determined by the difference between the central threshold and 3 times the standard deviation, and can be calculated specifically by using the following formula 3) and formula 4):

LCL=CL-3σ; Equation 3)

Among them, LCL represents the lower limit threshold, σ represents the standard deviation, n represents the total number of batches of all samples of the material to be tested, _Sk represents the similarity of the kth batch of samples, represents the mean similarity.

In one embodiment, the upper threshold is determined by the sum of the central threshold and a second preset multiple standard deviation. For example: the upper threshold is determined by the sum of the central threshold and 3 times the standard deviation, which can be calculated by using the following formula 5):

UCL=CL+3σ; Equation 5)

Among them, UCL represents the upper limit threshold, and the calculation methods of CL and σ have been explained above, so they will not be repeated.

It should be noted that the values of the first preset multiple and the second preset multiple may be the same, for example, both may be 3 times the value, or may be different, which is not limited in this application.

In step S13, the comparison result information between the similarity of each batch of samples and the upper threshold, the center threshold, and the lower threshold is output visually.

In one embodiment, the similarity between the volatile component detection data of each batch of samples and the detection reference data and the upper threshold, the center threshold, and the lower threshold are output in a two-dimensional coordinate view. Compare the result information.

The detection results of each VOCs component in each batch of samples should be calculated for similarity with the detection benchmark, and the calculated results should be plotted in the established similarity quality control chart in chronological order to evaluate the VOCs components in each batch of samples. The overall volatility and sample quality stability of the material to be tested.

Preferably, the detection method of VOCs components in each subsequent batch of samples, the detected VOCs components and the production process formula should be consistent with the detection methods, similarity evaluation parameters and paper production parameters used in establishing the detection benchmark. If any of the above conditions are changed, the similarity quality control chart should be redrawn and evaluated.

For ease of understanding, the similarity quality control chart in this embodiment is further explained with reference to FIG. 2 . Figure 2 shows the VOC detection data of 6 batches of samples of a certain material to be tested. The horizontal axis represents the 6 batches of samples, and the vertical axis represents the similarity value. In this embodiment, the range of similarity from 0.6 to 1.0 is used as an example. The three solid lines from the top right to the bottom in the figure represent the upper threshold, center threshold, and lower threshold of similarity, respectively. The similarity of each batch of samples is represented by dots, and the similarity dots of each batch of samples are connected into a polyline.

Therefore, the visual view provided by this embodiment can clearly and directly display the comparison results between the similarity of each batch of samples and the upper threshold, the center threshold, and the lower threshold, which is convenient for analysis and summarization. For example, it can be seen from Figure 2 that the similarity corresponding to the batch 4 samples is lower than the lower threshold LCL, so it can be intuitively known that the quality stability of the batch 4 samples is poor.

Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments may be completed by hardware related to computer programs. The aforementioned computer program may be stored in a computer-readable storage medium. When the program is executed, the steps including the above method embodiments are executed; and the foregoing storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

As shown in FIG. 3 , a schematic diagram of a similarity quality control graph in an embodiment of the present application is shown. The similarity quality control chart provided in this example is used to evaluate the quality stability of 30 batches of samples of packaging paper A1. Specifically, the similarity quality control chart shown in FIG. 3 is generated through the following step process.

First, take the qualitative and quantitative detection data of volatile components in 30 batches of samples of packaging paper A1. The Grubbs test was used to test the outliers of the VOCs detection results in each batch of samples. After eliminating the outliers, the basic VOCs database of packaging paper A1 was established.

Secondly, the average detection value of all VOCs components in packaging paper A1 is calculated as the detection reference value, and the cosine similarity algorithm is used to calculate the similarity between the VOCs detection value and the detection reference value of 30 batches of samples in turn, and the similarity center is statistically analyzed. Threshold value and standard deviation, the average similarity of the measured packaging paper A1 is 0.931, and the standard deviation is 0.070, where A1* is the detection benchmark. In this embodiment, the upper limit threshold value 1.0 is calculated based on about 1 times the standard deviation, and the lower limit threshold value 0.722 is calculated based on about 3 times the standard deviation. Therefore, the quality control limit range of packaging paper A1 is [0.722, 1], and the center value is 0.931.

It can be seen from Figure 3 that the similarity values of all samples of packaging paper A1 are distributed on the upper and lower sides of the central threshold, and they all fluctuate within the quality control range, and there are no out-of-limit abnormal points, indicating that the sample quality of packaging paper A1 is more uniform. Well, the detection data all fluctuate within a controllable range.

As shown in FIG. 4 , a schematic diagram of a similarity quality control graph in another embodiment of the present application is shown. The similarity quality control chart provided in this example is used to evaluate the quality stability of 20 batches of samples of packaging paper B1. Specifically, the similarity quality control chart shown in FIG. 4 is generated through the following step process.

First, qualitative and quantitative detection data of volatile components in 20 batches of samples of packaging paper B1 were obtained. The Grubbs test was used to test the outliers of the VOCs detection results in each batch of samples. After eliminating the outliers, the basic VOCs database of trademark paper B1 was established.

Secondly, the average detection value of all VOCs components in packaging paper B1 is calculated as the detection reference value, and the cosine similarity algorithm is used to calculate the similarity between the detection value of VOCs in 20 batches of samples and the detection reference value in turn, and the similarity center is statistically analyzed. Threshold value and standard deviation, the average value of B1 similarity of packaging paper is measured to be 0.891, and the standard deviation is 0.113, of which B1* is the detection benchmark. In this embodiment, the upper limit threshold value 1.0 is calculated based on about 1 times the standard deviation, and the lower limit threshold value 0.552 is calculated based on about 3 times the standard deviation. Therefore, the quality control limit range of packaging paper A1 is [0.552, 1], and the center value is 0.891.

It can be seen from Figure 4 that most of the sample similarity values of the guaranteed paper B1 are distributed on the upper and lower sides of the central threshold, and all fluctuate within the quality control range. However, the similarity of B1*-8 sample pair is 0.462, which is lower than the lower limit of quality control and judged as an abnormal point. The traceability of the VOCs detection results of the 8# sample shows that two volatile components, butanone and ethyl acetate, are in the sample. The detected value was the minimum value in the group and was significantly lower than the reference value, among which ethyl acetate was the main volatile component in the paper. This shows that the overall volatility of the volatile components in the 8# sample is large, and the reason for the fluctuation is mainly due to the large deviation of the detection amount of butanone and ethyl acetate, which may be due to abnormality or abnormality in the detection process of the paper sample. This sample was produced during formulation changes resulting in quality differences between samples.

As shown in FIG. 5 , a schematic diagram of a volatile organic component detection device in an embodiment of the present application is shown. The detection device includes a data acquisition module 51 , a data processing module 52 , and a visual output module 52 .

The data acquisition module 51 is configured to acquire volatile component detection data of multiple batches of samples of the material to be tested, so as to form a volatile component data set of the material to be tested. The data processing module 52 is configured to obtain the detection reference data of the material to be tested according to the volatile component data set, and calculate the similarity between the volatile component detection data of each batch of samples and the detection reference data, according to the data. To determine the upper threshold, center threshold, and lower threshold of the material to be tested. The visual output module 52 is configured to visually output the comparison result information between the similarity of each batch of samples and the upper threshold, the center threshold, and the lower threshold.

It should be noted that, the implementation of the volatile organic component detection device provided in this embodiment is similar to the implementation of the volatile organic component detection method provided above, so it will not be repeated. In addition, it should be noted that it should be understood that the division of each module of the above apparatus is only a division of logical functions, and may be fully or partially integrated into a physical entity in actual implementation, or may be physically separated. And these modules can all be implemented in the form of software calling through processing elements; they can also all be implemented in hardware; some modules can also be implemented in the form of calling software through processing elements, and some modules can be implemented in hardware. For example, the data processing module can be a separately established processing element, or can be integrated into a certain chip of the above-mentioned device to realize, in addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and processed by one of the above-mentioned devices. The element calls and executes the functions of the above data processing modules. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, and can also be implemented independently. The processing element described here may be an integrated circuit with signal processing capability. In the implementation process, each step of the above-mentioned method or each of the above-mentioned modules can be completed by an integrated logic circuit of hardware in the processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), or one or more microprocessors ( digital signal processor, referred to as DSP), or, one or more Field Programmable Gate Array (Field Programmable Gate Array, referred to as FPGA) and the like. For another example, when one of the above modules is implemented in the form of processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU for short) or other processors that can call program codes. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC for short).

As shown in FIG. 6 , a schematic structural diagram of a detection terminal in an embodiment of the present application is shown. The detection terminal provided in this example includes: a processor 61, a memory 62, a transceiver 63, a communication interface 64, and a system bus 65; the memory 62 and the communication interface 64 are connected to the processor 61 and the transceiver 63 through the system bus 65 and complete each other Communication between, the memory 62 is used to store the computer program, the communication interface 64 and the transceiver 63 are used to communicate with other devices, the processor 61 is used to run the computer program, so that the electronic terminal executes the various steps of the above volatile organic component detection method .

The system bus mentioned above may be a Peripheral Component Interconnect (PCI for short) bus or an Extended Industry Standard Architecture (Extended Industry Standard Architecture, EISA for short) bus or the like. The system bus can be divided into address bus, data bus, control bus and so on. For ease of presentation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used to realize the communication between the database access device and other devices (eg client, read-write library and read-only library). The memory may include random access memory (Random Access Memory, RAM for short), and may also include non-volatile memory (non-volatile memory), such as at least one disk storage.

The above-mentioned processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, referred to as CPU), a network processor (Network Processor, referred to as NP), etc.; may also be a digital signal processor (Digital Signal Processing, referred to as DSP) , Application Specific Integrated Circuit (ASIC for short), Field-Programmable Gate Array (FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

To sum up, the present application provides a volatile organic component detection method, device, storage medium and terminal. The similarity algorithm and the single-value quality control chart are used to realize the evaluation of the quality stability of VOCs in packaging paper, and the outlier detection is used. Methods The basic database of various VOCs components in packaging paper was established, and the average detection value of each VOCs component was used as the benchmark, and the similarity between each batch of samples and the benchmark was calculated by the similarity algorithm. The standard deviation or similarity limit can be used to calculate the upper and lower limits and the center limit of the quality control chart, and draw the similarity quality control chart, which can quickly and effectively evaluate the quality stability of packaging paper. Therefore, the present application effectively overcomes various shortcomings in the prior art and has high industrial application value.

The above-mentioned embodiments merely illustrate the principles and effects of the present application, but are not intended to limit the present application. Anyone skilled in the art can make modifications or changes to the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed in this application should still be covered by the claims of this application.

Claims (10)

1. a volatile organic component detection method, is characterized in that, comprises: Acquiring volatile component detection data of multiple batches of samples of the material to be tested, so as to form a volatile component data set of the material to be tested; The detection benchmark data of each batch of samples of the material to be tested is obtained according to the volatile component data set, and the similarity between the volatile component detection data of each batch of samples and the corresponding detection benchmark data is calculated respectively. Determine the upper threshold, center threshold, and lower threshold of similarity of the material to be tested; Visually output the comparison result information between the similarity of each batch of samples and the upper threshold, center threshold, and lower threshold. 2. The method according to claim 1, wherein the method of forming the volatile component data set of the material to be tested in the method comprises: Eliminate outlier data from the volatile component detection data of the multiple batches of samples of the material to be tested; According to the volatile component detection data after removing outlier data, a volatile component database of the material to be tested is established; wherein the volatile component database includes each volatile component detection index of each batch of samples of the material to be tested and test data. 3 . The method according to claim 1 , wherein the detection reference data of each batch of samples of the material to be tested comprises an average value of all volatile component detection data of each batch of samples. 4 . 4 . The method according to claim 1 , wherein the method comprises: using a cosine similarity algorithm to calculate the similarity between the volatile component detection data of each batch of samples and the detection reference data. 5 . 5. The method of claim 1, wherein the method comprises: According to the similarity between the volatile component detection data of each batch of samples and the detection reference data, the average similarity of the batches of samples is calculated as the central threshold. 6. The method according to claim 1 or 5, wherein the method comprises: determining the lower threshold by the difference between the central threshold and a first preset multiple standard deviation, but the lower threshold is not lower than 0 . 7. The method according to claim 1 or 5, characterized in that, the method comprises: determining the upper threshold by the sum of the central threshold and the second preset multiple standard deviation, but the upper threshold is not higher than 1 . 8. A volatile organic component detection device, characterized in that, comprising: a data acquisition module, used for acquiring volatile component detection data of multiple batches of samples of the material to be tested, so as to form a data set of volatile components of the material to be tested; The data processing module is used to obtain the detection reference data of the material to be tested according to the volatile component data set, and calculate the similarity between the volatile component detection data of each batch of samples and the detection reference data, according to to determine the upper threshold, center threshold, and lower threshold of the material to be tested; The visual output module is used to visually output the comparison result information between the similarity of each batch of samples and the upper threshold, the central threshold, and the lower threshold. 9 . A computer-readable storage medium on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the method for detecting a volatile organic component according to any one of claims 1 to 7 is implemented. 10. A detection terminal, comprising: a processor and a memory; the memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory, so that the terminal executes the volatile organic component detection method according to any one of claims 1 to 7.