WO2018232632A1 - 物质检测方法、装置和检测设备 - Google Patents

物质检测方法、装置和检测设备 Download PDF

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Publication number
WO2018232632A1
WO2018232632A1 PCT/CN2017/089384 CN2017089384W WO2018232632A1 WO 2018232632 A1 WO2018232632 A1 WO 2018232632A1 CN 2017089384 W CN2017089384 W CN 2017089384W WO 2018232632 A1 WO2018232632 A1 WO 2018232632A1
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Prior art keywords
substance
detected
information
spectral
appearance state
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PCT/CN2017/089384
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English (en)
French (fr)
Inventor
骆磊
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深圳前海达闼云端智能科技有限公司
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Priority to PCT/CN2017/089384 priority Critical patent/WO2018232632A1/zh
Priority to CN201780003268.2A priority patent/CN108369184B/zh
Publication of WO2018232632A1 publication Critical patent/WO2018232632A1/zh
Priority to US16/256,822 priority patent/US11079332B2/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G01N2021/0181—Memory or computer-assisted visual determination
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/126—Microprocessor processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40—Document-oriented image-based pattern recognition
    • G06V30/41—Analysis of document content
    • G06V30/416—Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors

Definitions

  • the embodiments of the present application relate to the field of substance detection, for example, to a substance detecting method, device and detecting device.
  • the inventors have found that at least the following problems exist in the related art: limited by the computing power of the device, once the database is large, the calculation and matching speed will be too slow.
  • the application of cloud computing power can solve this problem to a certain extent, when the number of concurrent reaches reaches a certain level and the database is very large, the detection efficiency will be affected.
  • an embodiment of the present application provides a substance detecting method, where the detecting method is applied to a detecting device, and the method includes:
  • the embodiment of the present application further provides a substance detecting device, where the detecting device is applied to a detecting device, and the device includes:
  • An information acquiring module configured to acquire spectral information and image information of the substance to be detected
  • An image recognition module configured to perform image recognition according to the image information to obtain appearance state information of the substance to be detected
  • a data sub-library obtaining module configured to determine, according to the appearance state information, a data sub-library in the preset spectral database that matches the appearance state information, where the spectral database includes a plurality of data sub-libraries classified according to a physical appearance state;
  • an analysis module configured to match the spectral information to spectral information in the data sub-library to obtain a detection result of the substance to be detected.
  • the embodiment of the present application further provides a detecting device, including:
  • At least one processor and,
  • the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.
  • the embodiment of the present application further provides a detecting terminal, including a light source and a lens assembly, configured to collect light emitted by a light source and collect scattered light of the light through the substance to be detected, so as to enable the detecting terminal.
  • a detecting terminal including a light source and a lens assembly, configured to collect light emitted by a light source and collect scattered light of the light through the substance to be detected, so as to enable the detecting terminal.
  • the detecting terminal further includes:
  • the camera assembly is configured to capture a picture of the substance to be detected, so that the detecting terminal acquires image information of the substance to be detected.
  • the material detecting method, device and detecting device obtained by the embodiments of the present invention obtain the appearance information of the substance to be detected through the image information by acquiring the spectral information and the image information of the substance to be detected, and
  • the spectral database selects a data sub-library that matches the appearance state information, thereby performing spectral comparison directly in the data sub-library according to the spectral information of the substance to be detected, greatly reducing the comparison range, improving the comparison efficiency, and accelerating the substance. Detection speed.
  • 1a is a schematic diagram of an application scenario of the method and apparatus of the present application.
  • FIG. 1b is a schematic diagram of an application scenario of the method and apparatus of the present application.
  • FIG. 2 is a schematic structural diagram of an embodiment of a detection terminal of the present application.
  • FIG. 3 is a schematic structural diagram of an embodiment of a detection terminal of the present application.
  • FIG. 4 is a flow chart of one embodiment of a detection method of the present application.
  • Figure 5 is a flow chart of one embodiment of the detection method of the present application.
  • FIG. 6 is a schematic structural view of an embodiment of a detecting device of the present application.
  • Figure 7 is a schematic structural view of an embodiment of the detecting device of the present application.
  • FIG. 8 is a schematic structural diagram of hardware of a detecting device provided by an embodiment of the present application.
  • the embodiment of the present application provides a substance detection scheme, which is applicable to the application scenarios shown in FIG. 1a and FIG. 1b.
  • the substance to be detected 10 the detecting terminal 21 and the detecting device 20 are included, wherein the detecting terminal 21 comprises a lens assembly 211, a camera assembly 212 and a light source (not shown), and the lens assembly 211
  • the detection terminal 21 acquires the spectral information of the substance to be detected 10 according to the scattered light.
  • the camera assembly 212 is configured to capture a picture of the substance 10 to be detected, so that the detection terminal 21 acquires image information of the substance 10 to be detected.
  • the detecting terminal 21 transmits the spectral information and the image information of the substance to be detected 10 to the detecting device 20, and the detecting device 20 is configured to perform substance detection based on the spectral information and the image information of the substance to be detected.
  • the detecting terminal 21 and the detecting device 20 can communicate with each other through the network 30, wherein the network
  • the network 30 can be, for example, a local area network of a home or company, or a specific network or the like.
  • the detecting terminal 21 and the detecting device 20 have at least one network interface to establish a communication connection with the network 30.
  • the detecting device 20 may be a cloud server or other server connected to the detecting terminal 21 via a network.
  • the detecting device 20 can also integrate the function of the detecting terminal 21 in the detecting device 20, and the detecting device 20 separately obtains the spectral information and the image information of the substance to be detected 10 from the substance to be detected 10, and passes the The spectral information and the image information are subjected to substance detection by the substance to be detected 10.
  • the detecting device 20 performs image recognition based on the image information, and obtains appearance state information of the substance 10 to be detected, for example, white particles having a particle size of 0.07 mm.
  • the detecting device 20 determines a data sub-library in the spectral database that matches the appearance state information based on the appearance state information.
  • the spectral database may be a Raman spectroscopy database.
  • the spectral database is also preserved as the appearance state of the substance (eg, white powder, transparent liquid, pale yellow solid, white particles, etc.). The way the database is indexed, and the substances in the spectral database are placed in these appearance state categories.
  • the detecting device 20 matches the data sub-library in the spectral database according to the appearance state information, and then can perform spectral comparison directly in the data sub-library according to the spectral information of the substance to be detected 10 to determine the name of the substance to be detected. This eliminates the need for spectral alignment across the spectral database, greatly reducing the range of alignment and increasing alignment efficiency.
  • the application scenario may further include more substances to be detected 10 and the detecting device 20 and the detecting terminal 21.
  • FIG. 2 is a schematic structural diagram of an embodiment of a detecting terminal 21 including a light source (not shown) and a lens assembly for causing the detecting terminal 11 to obtain spectral information of the substance 10 to be detected ( Not shown in the drawing, and a camera assembly (not shown) that causes the detecting terminal 11 to obtain image information of the substance 10 to be detected.
  • the lens of the lens assembly and the camera assembly may be a fixed focus lens or a zoom lens.
  • the embodiment of the present application obtains the spectral information and the image information of the substance to be detected, so that the detecting device can obtain the appearance state information of the substance to be detected through the image information, and select the matching with the appearance state information in the spectral database through the appearance state information.
  • Data sub-library depending on the spectrum of the substance to be detected The information is directly compared in the data sub-library, which greatly reduces the range of comparison, improves the efficiency of comparison, and speeds up the detection of substances.
  • the lens assembly comprises a first fixed focus lens 2111
  • the camera assembly comprises a second fixed focus lens 2121
  • the first fixed focus lens 2111 and
  • the mounting direction of the second fixed focus lens 2121 is the same.
  • the focus of the first fixed focus lens 2111 can be located within the depth of field of the second fixed focus lens 2121, so that the depth of field of the second fixed focus lens can be located when the substance to be detected 10 is detected.
  • the substance to be detected 10 can be held on the precise focus plane of the second fixed focus lens 2121, for example, the focus of the first fixed focus lens 2111 and the focus of the second fixed focus lens 2121 are at the same point. ,as shown in picture 2.
  • the focal length of the second fixed focus lens 2121 may be determined according to the focal length of the first fixed focus lens 2111, if the distance between the first fixed focus lens 2111 and the second fixed focus lens is represented as a (ie, the first fixed focus lens 2111) The distance of the center point from the center point of the second fixed focus lens 2121), the fixed focal length of the first fixed focus lens 2111 is represented as d, the installation angle of the second fixed focus lens 2121 is represented as ⁇ , and the second fixed focus lens 2121 is determined The focal length is expressed as f, which is known from the geometric relationship in Figure 2:
  • the focal length of the second fixed focus lens 2121 may be appropriately larger or smaller as long as the focus of the first fixed focus lens 2111 is located at the second fixed focus lens 2121.
  • the depth of field range that is, when the substance to be detected 10 is detected, it is located within the depth of field of the second fixed focus lens 2121.
  • the lens of the lens component may also adopt a zoom lens (for example, an autofocus lens), and the lens of the corresponding camera component also adopts a zoom lens, and then according to the lens component.
  • the focal length of the lens determines the focal length of the lens in the camera assembly.
  • the lens assembly includes a first zoom lens 2112
  • the camera assembly includes a second zoom lens 2122
  • the mounting directions of the first zoom lens 2112 and the second zoom lens 2122 are the same.
  • the geometric relationship between the first zoom lens 2112 and the second zoom lens 2122 needs to be as shown in FIG. 3, as shown in FIG.
  • the pitch a and the tilt angle ⁇ of the second zoom lens 2122 are both fixed values, which may vary with the focal length d of the first zoom lens 2112 (the detection terminal itself may obtain the real-time focal length value of the first zoom lens)
  • the focal length f of the second zoom lens 2122 is adjusted such that the substance to be detected 10 is on the precise focus plane of the second zoom lens 2122.
  • the camera assembly will not be able to detect when detected.
  • the substance to be detected 10 is seen.
  • the field of view angle (FOV angle) of the second zoom lens 2122 is expressed as ⁇ , which is known from the geometric relationship in FIG.
  • the condition that the angle of view of the second zoom lens needs to be satisfied can be determined according to the minimum focal length of the first zoom lens.
  • the embodiment of the present application provides a substance detecting method, which can be performed by the detecting device 20 in FIG. 1a and FIG. 1b.
  • the substance detecting method includes:
  • Step 101 Obtain spectral information and image information of the substance to be detected
  • the spectral recognition method in the embodiment of the present application may be a Raman spectroscopy method, an infrared spectroscopy method, or any other spectral recognition method, that is, the spectral information may be a Raman spectrum, an infrared spectrum, or the like.
  • Step 102 Perform image recognition according to the image information to obtain appearance state information of the substance to be detected;
  • the appearance state of the substance to be detected can be obtained according to the image data of the substance to be detected, such as liquid, solid, powder, granule, color and size, for example, the image recognition result is a granularity of 0.07 mm. White particles.
  • Step 103 Determine, according to the appearance state information, a data sub-library in the preset spectrum database that matches the appearance state information, where the spectrum database includes a plurality of data sub-libraries classified according to the appearance state of the material;
  • the preset spectral database stores the names of the plurality of substances and the corresponding spectral information.
  • the appearance state of the material is also used as an index of the database, and the substances in the spectral database are Corresponding to these appearance status categories.
  • the spectral database is divided into data sub-libraries such as white powder, transparent liquid, and white particles.
  • the detecting device 20 matches the data sub-library classified as white particles in the spectral database based on the appearance state information, that is, the image recognition result "white particles having a granularity of 0.07 mm".
  • Step 104 Match the spectral information to the acquired spectral information in the data sub-library to obtain a detection result of the substance to be detected.
  • the detection device 20 confirms that the "white particle" data sub-library in which the substance to be detected is located, the spectral information in the data sub-library is compared according to the spectral information of the substance to be detected, thereby determining the name of the substance to be detected. .
  • the embodiment of the present application obtains the spectral information and the image information of the substance to be detected, so that the detecting device can obtain the appearance state information of the substance to be detected through the image information, and select the matching with the appearance state information in the spectral database through the appearance state information.
  • the data sub-library according to the spectral information of the substance to be detected, directly performs spectral comparison in the data sub-library, which greatly reduces the comparison range, improves the comparison efficiency, and speeds up the detection speed of the substance.
  • the method further includes:
  • Step 205 Record appearance state information of the substance to be detected obtained by the image recognition and a detection result of the substance to be detected;
  • the detecting device 20 records the result of each image recognition, that is, the appearance state information of the substance to be detected, and the detection result of the substance to be detected, such as the name of the substance to be detected.
  • Step 206 Update the classification of the data sub-libraries in the spectral database according to the plurality of recorded appearance state information and the detection result.
  • the image recognition result is "white particles with a granularity of 0.07 mm", and the database index is "white particles”.
  • the detecting device 20 counts the plurality of appearance state information recorded, if the number of times in the image recognition result appears in a new, more refined appearance classification, for example, the image recognition result appears in the "granularity at 0.05"
  • the number of -0.1 mm white particles exceeds a certain number of times (this number can be preset, for example, 5000), and the "white particle” data sub-library is further subdivided, and the lower-level index "particles" is set under the index "white particles”
  • the white particles of degree 0.05-0.1 mm and the corresponding substances are placed under the category corresponding to the index according to the substance name.
  • the above can make the index in the spectral database more accurate.
  • it can ensure that the number of substances in the comparative data sub-library is less, that is, the comparison efficiency is higher.
  • the color can be more accurately subdivided by RGB values, such as powder with a color of (200, 230, 225), or the color value is indexed within a certain range, such as (200 ⁇ 10,230 ⁇ 10,225 ⁇ 10).
  • the embodiment of the present application further provides a substance detecting device, which is used in the detecting device shown in FIG. 1a or 1b.
  • the detecting device 300 includes:
  • the information acquiring module 301 is configured to acquire spectral information and image information of the substance to be detected;
  • the image recognition module 302 is configured to perform image recognition according to the image information to obtain appearance state information of the substance to be detected;
  • a data sub-library obtaining module 303 configured to determine, according to the appearance state information, a data sub-library in the preset spectral database that matches the appearance state information, where the spectral database includes a plurality of physical appearances Data sub-library of state classification;
  • the analyzing module 304 is configured to match the spectral information with the spectral information in the data sub-library to obtain a detection result of the substance to be detected.
  • the embodiment of the present application obtains the spectral information and the image information of the substance to be detected, so that the detecting device can obtain the appearance state information of the substance to be detected through the image information, and select the matching with the appearance state information in the spectral database through the appearance state information.
  • the data sub-library according to the spectral information of the substance to be detected, directly performs spectral comparison in the data sub-library, which greatly reduces the comparison range, improves the comparison efficiency, and speeds up the detection speed of the substance.
  • the detecting apparatus 400 includes: in addition to the modules 401, 402, 403, and 404,
  • a recording module 405, configured to record appearance state information of the substance to be detected obtained by the image recognition and a detection result of the substance to be detected;
  • the updating module 406 is configured to update the classification of the data sub-libraries in the spectral database according to the plurality of recorded appearance state information and the detection result.
  • the foregoing detecting apparatus can perform the detecting method provided by the embodiment of the present application, and has the corresponding functional modules and beneficial effects of the executing method.
  • the detection method provided by the embodiment of the present application.
  • FIG. 8 is a schematic diagram showing the hardware structure of the detecting device 20 according to the embodiment of the present application. As shown in FIG. 8, the detecting device 20 includes:
  • processors 22 and memory 23, one processor 22 is exemplified in FIG.
  • the processor 22 and the memory 23 can be connected by a bus or other means, as exemplified by a bus connection in FIG.
  • the memory 23 is used as a non-volatile computer readable storage medium, and can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions corresponding to the detection methods in the embodiments of the present application.
  • the module (for example, the information acquisition module 301, the image recognition module 302, the data sub-library acquisition module 303, and the analysis module 304 shown in FIG. 6).
  • the processor 22 executes various functional applications and data of the server by running non-volatile software programs, instructions, and modules stored in the memory 23. Processing, that is, the detection method of the above method embodiment is implemented.
  • the memory 23 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to usage of the detecting device, and the like. Further, the memory 23 may include a high speed random access memory, and may also include a nonvolatile memory such as at least one magnetic disk storage device, flash memory device, or other nonvolatile solid state storage device. In some embodiments, memory 23 can optionally include memory remotely located relative to processor 22, which can be connected to the detection device over a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the one or more modules are stored in the memory 23, and when executed by the one or more processors 22, perform the detection method in any of the above method embodiments, for example, performing the above described FIG. Method step 101 to step 104, method step 201 to step 206 in FIG. 5; implement the functions of modules 301-304 in FIG. 6, and modules 401-406 in FIG.
  • the detecting device 20 further integrates the functions of the detecting terminal 11 described above, please refer to FIG. 1b.
  • the embodiment of the present application provides a non-transitory computer readable storage medium storing computer-executable instructions that are executed by one or more processors, such as in FIG. a processor 22, which may cause the one or more processors to perform the detection method in any of the above method embodiments, for example, to perform the method steps 101 to 104 in FIG. 4 described above, the method steps in FIG. 201 to 206; implementing the functions of the modules 301-304 in FIG. 6, and the modules 401-406 in FIG.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • the implementation can be implemented by means of software plus a general hardware platform, and of course also by hardware.
  • a person skilled in the art can understand that all or part of the process of implementing the above embodiments can be completed by a computer program to instruct related hardware, and the program can be stored in a computer readable storage medium. When executed, the flow of an embodiment of the methods as described above may be included.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

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Abstract

一种物质检测方法、装置和检测设备（20），所述方法包括：获取待检测物质（10）的光谱信息和图像信息；根据所述图像信息进行图像识别，以获得所述待检测物质（10）的外观状态信息；根据所述外观状态信息确定预设光谱数据库中与所述外观状态信息匹配的数据子库，所述光谱数据库包括多个根据物质外观状态分类的数据子库；将所述光谱信息匹配获取的所述数据子库中的光谱信息，以获得待检测物质（10）的检测结果。通过获取待检测物质（10）的光谱信息和图像信息，使检测设备（20）能通过图像信息获得外观状态信息，从而在光谱数据库中选择与外观状态信息匹配的数据子库，直接在数据子库中进行光谱比对，缩小了比对范围，加快了检测速度。

Description

物质检测方法、装置和检测设备 技术领域
本申请实施例涉及物质检测领域，例如涉及一种物质检测方法、装置和检测设备。
背景技术
近年来，物质检测设备应用日趋广泛，包括安检中检测可疑物品、药监局检测药品成分、防化部队进行***现场勘查等专业领域，也包括检测农药残留、检测是否存在三聚氰胺、检测地沟油和真假酒等民用领域，尤其在食品安全领域得到广泛应用。目前的检测设备，例如拉曼检测终端，采用拉曼光谱分析的方法，能够比较快速和准确的检测出物质成分。
在实现本申请过程中，发明人发现相关技术中至少存在如下问题：受限于设备的计算能力，一旦数据库较大时将造成计算和匹配速度过慢的问题。应用云端强大的计算能力虽然可以一定程度上解决此问题，但当并发数达到一定程度且数据库很庞大时，检测效率也会受到一定影响。
发明内容
本申请实施例的一个目的是提供一种新的物质检测方法、装置和检测设备，能加快物质检测的速度。
第一方面，本申请实施例提供了一种物质检测方法，所述检测方法应用于检测设备，所述方法包括：
获取待检测物质的光谱信息和图像信息；
根据所述图像信息进行图像识别，以获得所述待检测物质的外观状态信息；
根据所述外观状态信息确定预设光谱数据库中与所述外观状态信息匹配的数据子库，所述光谱数据库包括多个根据物质外观状态分类的数据子库；
将所述光谱信息匹配获取的所述数据子库中的光谱信息，以获得待检测物 质的检测结果。
第二方面，本申请实施例还提供了一种物质检测装置，所述检测装置应用于检测设备，所述装置包括：
信息获取模块，用于获取待检测物质的光谱信息和图像信息；
图像识别模块，用于根据所述图像信息进行图像识别，以获得所述待检测物质的外观状态信息；
数据子库获取模块，用于根据所述外观状态信息确定预设光谱数据库中与所述外观状态信息匹配的数据子库，所述光谱数据库包括多个根据物质外观状态分类的数据子库；
分析模块，用于将所述光谱信息匹配所述数据子库中的光谱信息，以获得待检测物质的检测结果。
第三方面，本申请实施例还提供了一种检测设备，包括：
至少一个处理器；以及，
与所述至少一个处理器通信连接的存储器；其中，
所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行上述的方法。
第四方面，本申请实施例还提供了一种检测终端，包括光源和透镜组件，所述透镜组件用于汇聚光源发射的光线和收集所述光线经待检测物质的散射光线，以使检测终端根据所述散射光线获取待检测物质的光谱信息；所述检测终端还包括：
摄像头组件，用于拍摄待检测物质的图片，以使检测终端获取待检测物质的图像信息。
本申请实施例提供的物质检测方法、装置和检测设备，通过获取待检测物质的光谱信息和图像信息，使检测设备能通过图像信息获得待检测物质的外观状态信息，并通过该外观状态信息在光谱数据库选择与所述外观状态信息匹配的数据子库，从而根据待检测物质的光谱信息直接在数据子库中进行光谱比对，大大缩小了比对范围，提高了比对效率，加快了物质的检测速度。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。
图1a是本申请方法和装置的应用场景示意图；
图1b是本申请方法和装置的应用场景示意图；
图2是本申请检测终端的一个实施例的结构示意图；
图3是本申请检测终端的一个实施例的结构示意图；
图4是本申请检测方法的一个实施例的流程图；
图5是本申请检测方法的一个实施例的流程图；
图6是本申请检测装置的一个实施例的结构示意图；
图7是本申请检测装置的一个实施例的结构示意图；以及
图8是本申请实施例提供的检测设备的硬件结构示意图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请实施例提供了一种物质检测方案，适用于图1a和图1b所示的应用场景。在图1a所示的应用场景中，包括待检测物质10、检测终端21和检测设备20，其中，检测终端21包括透镜组件211、摄像头组件212和光源(图中未示出)，透镜组件211用于汇聚光源发射的光线和收集所述光线经待检测物质10的散射光线，以使检测终端21根据该散射光线获取待检测物质10的光谱信息。摄像头组件212，用于拍摄待检测物质10的图片，以使检测终端21获取待检测物质10的图像信息。检测终端21将待检测物质10的光谱信息和图像信息传送给检测设备20，检测设备20用于根据待检测物质的光谱信息和图像信息进行物质检测。检测终端21与检测设备20之间可以通过网络30互相通信，其中，网 络30可以是例如家庭或公司的局域网，或一个特定网络等。检测终端21和检测设备20具有至少一个网络接口，与网络30建立通信连接。在该实施例中，检测设备20可以是与检测终端21通过网络相连的云端服务器或者其他服务器。
如图1b所示，检测设备20也可以将检测终端21的功能集成在检测设备20中，由检测设备20单独完成从待检测物质10获取待检测物质10的光谱信息和图像信息，并通过该光谱信息和图像信息对待检测物质10进行物质检测。
检测设备20根据图像信息进行图像识别，获得待检测物质10的外观状态信息，例如颗粒度为0.07mm的白色颗粒。检测设备20根据所述外观状态信息确定光谱数据库中与所述外观状态信息匹配的数据子库。所述光谱数据库可以为拉曼光谱数据库，在光谱数据库构建过程中，除了保存光谱和物质名称外，同时也保存物质的外观状态(例如白色粉末，透明液体，淡黄色固体、白色颗粒等)作为数据库的索引方式，并将光谱数据库中的物质分别对应放到这些外观状态类别中。
检测设备20根据该外观状态信息匹配到光谱数据库中的数据子库，然后可以根据待检测物质10的光谱信息直接在该数据子库中进行光谱比对，以确定待检测物质的名称。这样无需在整个光谱数据库中进行光谱比对，大大缩小了比对范围，提高比对效率。
需要说明的是，在实际应用过程中，该应用场景还可以包括更多的待检测物质10和检测设备20以及检测终端21。
请参照图2，图2为检测终端21的一个实施例的结构示意图，所述检测终端21包括光源(图中未示出)、使检测终端11获得待检测物质10的光谱信息的透镜组件(图中未示出)，以及使检测终端11获得待检测物质10的图像信息的摄像头组件(图中未示出)。其中，所述透镜组件和摄像头组件的镜头可以采用定焦镜头也可以采用变焦镜头。
本申请实施例通过获取待检测物质的光谱信息和图像信息，使检测设备能通过图像信息获得待检测物质的外观状态信息，并通过该外观状态信息在光谱数据库选择与所述外观状态信息匹配的数据子库，从而根据待检测物质的光谱 信息直接在数据子库中进行光谱比对，大大缩小了比对范围，提高了比对效率，加快了物质的检测速度。
可选的，在所述检测终端11的某些实施例中，所述透镜组件包括第一定焦镜头2111，所述摄像头组件包括第二定焦镜头2121，所述第一定焦镜头2111和第二定焦镜头2121的安装方向相同。为了获得待检测物质10的清晰图像，可以使第一定焦镜头2111的焦点位于第二定焦镜头2121的景深范围内，这样待检测物质10被检测时，可以位于第二定焦镜头的景深范围内。如果想获得更清晰的图像，可以使待检测物质10保持在第二定焦镜头2121的精确对焦平面上，例如使第一定焦镜头2111的焦点和第二定焦镜头2121的焦点位于同一点，如图2所示。
可以根据第一定焦镜头2111的焦距确定第二定焦镜头2121的焦距，如果所述第一定焦镜头2111与所述第二定焦镜头的间距表示为a(即第一定焦镜头2111中心点距第二定焦镜头2121中心点的距离)，第一定焦镜头2111的定焦焦距表示为d，第二定焦镜头2121的安装角度表示为α，第二定焦镜头2121的定焦焦距表示为f，则由图2中的几何关系可知：
tanα＝a/d；
a2+d2＝f2
进而可推导出，第二定焦镜头2121的安装角度α＝arctan(a/d)，第二定焦镜头2121的定焦焦距为
Figure PCTCN2017089384-appb-000001
在实际应用中，如果对图片的清晰度要求不是特别高，第二定焦镜头2121的焦距可以适当大些或者小些，只要能使第一定焦镜头2111的焦点位于第二定焦镜头2121的景深范围内，即待检测物质10被检测时，位于第二定焦镜头2121的景深范围内即可。
可选的，在所述检测终端11的其他实施例中，所述透镜组件的镜头也可以采用变焦镜头(例如自动对焦的镜头)，对应的摄像头组件的镜头也采用变焦镜头，然后根据透镜组件镜头的焦距确定摄像头组件中镜头的焦距。如图3所示，所述透镜组件包括第一变焦镜头2112，所述摄像头组件包括第二变焦镜头2122， 所述第一变焦镜头2112和第二变焦镜头2122的安装方向相同。为了使待检测物质10保持在第二定焦镜头2122的精确对焦平面上，第一变焦镜头2112和第二变焦镜头2122的几何关系需如图3所示，由图3可知：
OB＝a*tanα；
AB＝OA+OB＝d+a*tanα；
f＝AB*cosα＝d*cosα+a*tanα*cosα＝d*cosα+a*sinα。
当检测终端制造完成后，间距a和第二变焦镜头2122的倾角α均为固定值，可以随着第一变焦镜头2112焦距d的变化(检测终端自身可得到第一变焦镜头的实时焦距数值)，调整第二变焦镜头2122的焦距f，从而使待检测物质10处于第二变焦镜头2122的精确对焦平面上。检测终端11还包括控制单元213，控制单元213用于在物质检测过程中，获取第一变焦镜头2112的实时焦距d，并调整第二变焦镜头2122的实时焦距f＝d*cosα+a*s i nα。
从图3中可看出，假如待检测物质10与第一变焦镜头2112的直线距离小于图中OC的长度且假设第一变焦镜头2112的最小焦距可以小于OC，则检测时，摄像头组件将无法看到待检测物质10。
假如第一变焦镜头2112的最小焦距d1等于OC,第二变焦镜头2122的视场角(FOV角度)表示为β，从图3中几何关系可知：
tan(90°-α–β/2)＝d1/a；
进而，可推导出：
β＝(90°-α–arctan(d1/a))*2；
也就是说，当第一变焦镜头的最小对焦距离为OC时，则β大于等于(90°-α–arctan(d1/a))*2时，才能保证待检测物质10处于第二变焦镜头2122的视角中。因此，在实际产品设计时，可以根据第一变焦镜头的最小焦距确定第二变焦镜头的视场角需要满足的条件。
本申请实施例提供了一种物质检测方法，所述物质检测方法可由图1a和图1b中的检测设备20执行，如图4所示，所述物质检测方法包括：
步骤101：获取待检测物质的光谱信息和图像信息；
本申请实施例中的光谱识别方法可以采用拉曼光谱识别方法、红外光谱识别方法或者其他任何一种光谱识别方法，即所述光谱信息可以为拉曼光谱、红外光谱等。
步骤102：根据所述图像信息进行图像识别，以获得所述待检测物质的外观状态信息；
基于现有的图像识别技术可以根据待检测物质的图像数据获得其所呈现的外观状态，如液体、固体、粉末状、颗粒状、颜色和大小等，例如，图像识别结果为颗粒度为0.07mm的白色颗粒。
步骤103：根据所述外观状态信息确定预设光谱数据库中与所述外观状态信息匹配的数据子库，所述光谱数据库包括多个根据物质外观状态分类的数据子库；
所述预设的光谱数据库存储有多种物质的名称和其对应的光谱信息，在预设光谱数据库的构建过程中，将物质的外观状态也作为数据库的索引方式，并将光谱数据库中的物质分别对应放到这些外观状态类别中。例如，光谱数据库分为白色粉末、透明液体、白色颗粒等数据子库。检测设备20根据所述外观状态信息即图像识别结果“颗粒度为0.07mm的白色颗粒”，匹配到光谱数据库中的分类为白色颗粒的数据子库。
步骤104：将所述光谱信息匹配获取的所述数据子库中的光谱信息，以获得待检测物质的检测结果。
仍以上例说明，检测设备20确认所述待检测物质位于的“白色颗粒”数据子库后，根据待检测物质的光谱信息比对该数据子库中的光谱信息，从而确定待检测物质的名称。
本申请实施例通过获取待检测物质的光谱信息和图像信息，使检测设备能通过图像信息获得待检测物质的外观状态信息，并通过该外观状态信息在光谱数据库选择与所述外观状态信息匹配的数据子库，从而根据待检测物质的光谱信息直接在数据子库中进行光谱比对，大大缩小了比对范围，提高了比对效率，加快了物质的检测速度。
如图5所示，为所述方法的另一实施例的流程示意图，在该实施例中，所 述方法除了步骤201、202、203和204之外，还包括：
步骤205：记录所述图像识别获得的待检测物质的外观状态信息和对待检测物质的检测结果；
检测设备20会记录每一次图像识别的结果，即待检测物质的外观状态信息，及对待检测物质的检测结果，例如待检测物质的名称。
步骤206：根据多个被记录的所述外观状态信息和检测结果更新所述光谱数据库中数据子库的分类。
因为图像识别的结果可能包含比当前光谱数据库的外观状态索引更详细的信息，例如，图像识别结果为“颗粒度为0.07mm的白色颗粒”，数据库索引为“白色颗粒”。检测设备20会统计被记录的多个外观状态信息，如果当图像识别结果中出现在某一新的更细化的外观分类中的次数较多时，例如图像识别结果中出现在“颗粒度在0.05-0.1mm的白色颗粒”的次数超过一定次数(该次数可以预先设置，例如5000)，则对该“白色颗粒”数据子库进行进一步细分，在索引“白色颗粒”下设置下级索引“颗粒度为0.05-0.1mm的白色颗粒”，并根据物质名称将对应的物质放置到该索引对应的类别下。
如上可以使光谱数据库中的索引更加精确，进行光谱对比时，可以保证对比的数据子库中的物质数量更少，即比对效率会更高。除颗粒度大小外，还可以对色彩按RGB值进行更精确细分，例如色彩在(200，230，225)的粉末，或者色彩值在一定范围内作为索引，如(200±10，230±10，225±10)。
相应的，本申请实施例还提供了一种物质检测装置，所述检测装置用于图1a或者图1b所示的检测设备，如图6所示，所述检测装置300包括：
信息获取模块301，用于获取待检测物质的光谱信息和图像信息；
图像识别模块302，用于根据所述图像信息进行图像识别，以获得所述待检测物质的外观状态信息；
数据子库获取模块303，用于根据所述外观状态信息确定预设光谱数据库中与所述外观状态信息匹配的数据子库，所述光谱数据库包括多个根据物质外观 状态分类的数据子库；
分析模块304，用于将所述光谱信息匹配所述数据子库中的光谱信息，以获得待检测物质的检测结果。
本申请实施例通过获取待检测物质的光谱信息和图像信息，使检测设备能通过图像信息获得待检测物质的外观状态信息，并通过该外观状态信息在光谱数据库选择与所述外观状态信息匹配的数据子库，从而根据待检测物质的光谱信息直接在数据子库中进行光谱比对，大大缩小了比对范围，提高了比对效率，加快了物质的检测速度。
可选的，在所述装置的其他实施例中，如图7所示，所述检测装置400除了模块401、402、403和404之外，还包括：
记录模块405，用于记录所述图像识别获得的待检测物质的外观状态信息和对待检测物质的检测结果；
更新模块406，用于根据多个被记录的所述外观状态信息和所述检测结果更新所述光谱数据库中数据子库的分类。
需要说明的是，上述检测装置可执行本申请实施例所提供的检测方法，具备执行方法相应的功能模块和有益效果。未在检测装置实施例中详尽描述的技术细节，可参见本申请实施例所提供的检测方法。
图8是本申请实施例提供的检测设备20的硬件结构示意图，如图8所示，该检测设备20包括：
一个或多个处理器22以及存储器23，图8中以一个处理器22为例。
处理器22和存储器23可以通过总线或者其他方式连接，图8中以通过总线连接为例。
存储器23作为一种非易失性计算机可读存储介质，可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块，如本申请实施例中的检测方法对应的程序指令/模块(例如，附图6所示的信息获取模块301、图像识别模块302、数据子库获取模块303和分析模块304)。处理器22通过运行存储在存储器23中的非易失性软件程序、指令以及模块，从而执行服务器的各种功能应用以及数据 处理，即实现上述方法实施例的检测方法。
存储器23可以包括存储程序区和存储数据区，其中，存储程序区可存储操作***、至少一个功能所需要的应用程序；存储数据区可存储根据检测装置的使用所创建的数据等。此外，存储器23可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器23可选包括相对于处理器22远程设置的存储器，这些远程存储器可以通过网络连接至检测装置。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述一个或者多个模块存储在所述存储器23中，当被所述一个或者多个处理器22执行时，执行上述任意方法实施例中的检测方法，例如，执行以上描述的图4中的方法步骤101至步骤104，图5中的方法步骤201至步骤206；实现图6中的模块301-304、图7中模块401-406的功能。
可选的，在所述检测设备20的其他实施例中，所述检测设备20还集成了上述检测终端11的功能，请参照图1b。
上述产品可执行本申请实施例所提供的方法，具备执行方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节，可参见本申请实施例所提供的方法。
本申请实施例提供了一种非易失性计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行指令，该计算机可执行指令被一个或多个处理器执行，例如图8中的一个处理器22，可使得上述一个或多个处理器可执行上述任意方法实施例中的检测方法，例如，执行以上描述的图4中的方法步骤101至步骤104，图5中的方法步骤201至步骤206；实现图6中的模块301-304、图7中模块401-406的功能。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
通过以上的实施方式的描述，本领域普通技术人员可以清楚地了解到各实 施方式可借助软件加通用硬件平台的方式来实现，当然也可以通过硬件。本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory,ROM)或随机存储记忆体(Random Access Memory,RAM)等。
最后应说明的是：以上实施例仅用以说明本申请的技术方案，而非对其限制；在本申请的思路下，以上实施例或者不同实施例中的技术特征之间也可以进行组合，步骤可以以任意顺序实现，并存在如上所述的本申请的不同方面的许多其它变化，为了简明，它们没有在细节中提供；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。

Claims (12)

  1. 一种物质检测方法，所述检测方法应用于检测设备，其特征在于，所述方法包括：
    获取待检测物质的光谱信息和图像信息；
    根据所述图像信息进行图像识别，以获得所述待检测物质的外观状态信息；
    根据所述外观状态信息确定预设光谱数据库中与所述外观状态信息匹配的数据子库，所述光谱数据库包括多个根据物质外观状态分类的数据子库；
    将所述光谱信息匹配获取的所述数据子库中的光谱信息，以获得待检测物质的检测结果。
  2. 根据权利要求1所述的检测方法，其特征在于，所述方法还包括：
    记录所述图像识别获得的待检测物质的外观状态信息和对待检测物质的检测结果；
    根据多个被记录的所述外观状态信息和所述检测结果更新所述光谱数据库中数据子库的分类。
  3. 一种检测终端，包括光源和透镜组件，所述透镜组件用于汇聚光源发射的光线和收集所述光线经待检测物质的散射光线，以使检测终端根据所述散射光线获取待检测物质的光谱信息，其特征在于，所述检测终端还包括：
    摄像头组件，用于拍摄待检测物质的图片，以使检测终端获取待检测物质的图像信息。
  4. 根据权利要求3所述的检测终端，其特征在于，所述透镜组件包括第一定焦镜头，所述摄像头组件包括第二定焦镜头；
    所述第一定焦镜头与所述第二定焦镜头的间距表示为a，第一定焦镜头的定焦焦距表示为d，则第二定焦镜头的安装角度α＝arctan(a/d)，第二定焦镜头的定焦焦距为
    Figure PCTCN2017089384-appb-100001
  5. 根据权利要求3所述的检测终端，其特征在于，所述透镜组件包括第一变焦镜头，所述摄像头组件包括第二变焦镜头；
    所述第一变焦镜头与所述第二变焦镜头的间距表示为a，第一变焦镜头的实时焦距表示为d，第二变焦镜头的安装角度表示为α，第二变焦镜头的实时焦距表示为f；
    所述检测终端还包括控制单元，用于获取第一变焦镜头的实时焦距d，并调整第二变焦镜头的实时焦距f＝d*cosα+a*sinα。
  6. 根据权利要求5所述的检测终端，其特征在于，所述第一变焦镜头的最小焦距表示为d1，则所述第二变焦镜头的视场角β≥(90°-α–arctan(d1/a))*2。
  7. 一种物质检测设备，其特征在于，包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行权利要求1-2任意一项所述的方法。
  8. 根据权利要求1所述的检测设备，其特征在于，所述检测设备还包括：权利要求3-6任意一项所述的检测终端。
  9. 一种物质检测装置，所述检测装置应用于检测设备，其特征在于，所述装置包括：
    信息获取模块，用于获取待检测物质的光谱信息和图像信息；
    图像识别模块，用于根据所述图像信息进行图像识别，以获得所述待检测物质的外观状态信息；
    数据子库获取模块，用于根据所述外观状态信息确定预设光谱数据库中与所述外观状态信息匹配的数据子库，所述光谱数据库包括多个根据物质外观状态分类的数据子库；
    分析模块，用于将所述光谱信息匹配所述数据子库中的光谱信息，以获得待检测物质的检测结果。
  10. 根据权利要求9所述的检测装置，其特征在于，所述装置还包括：
    记录模块，用于记录所述图像识别获得的待检测物质的外观状态信息和对待检测物质的检测结果；
    更新模块，用于根据多个被记录的所述外观状态信息和所述检测结果更新所述光谱数据库中数据子库的分类。
  11. 一种非易失性计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机可执行指令，当所述计算机可执行指令被检测设备执行时，使所述检测设备执行执行权利要求1-2任一项所述的方法。
  12. 一种计算机程序产品，其特征在于，所述计算机程序产品包括存储在非易失性计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被检测设备执行时，使所述检测设备执行权利要求1-2任一项所述的方法。
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