WO2019036941A1 - 物质检测方法、装置、存储介质及电子设备 - Google Patents

物质检测方法、装置、存储介质及电子设备 Download PDF

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WO2019036941A1
WO2019036941A1 PCT/CN2017/098682 CN2017098682W WO2019036941A1 WO 2019036941 A1 WO2019036941 A1 WO 2019036941A1 CN 2017098682 W CN2017098682 W CN 2017098682W WO 2019036941 A1 WO2019036941 A1 WO 2019036941A1
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data
substance
sensor data
condition
sensor
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PCT/CN2017/098682
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English (en)
French (fr)
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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 CN201780002475.6A priority Critical patent/CN107995948B/zh
Priority to PCT/CN2017/098682 priority patent/WO2019036941A1/zh
Publication of WO2019036941A1 publication Critical patent/WO2019036941A1/zh

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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
    • G01N21/62—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

Definitions

  • the present disclosure relates to the field of substance detection, and in particular, to a substance detection method, device, storage medium, and electronic device.
  • Raman detection equipment uses Raman scattering to detect the composition of substances. It is currently used in many fields, including professional oil exploration, drug testing, common safety inspections in life, anti-narcotics, etc., and with Raman detection. The cost of equipment is declining and there are more applications in the civilian market, such as pesticide testing, real and fake inspection.
  • the current detection methods of Raman detection equipment are directed to the detection of a substance at a point, that is, the position where the Raman detection device emits laser focus, and the data collected at the point is collected for detection, for a single substance, the existing method Effective detection results can be obtained, but if it is necessary to detect the composition and distribution of substances in the heterogeneous mixture, the method of fixed-point acquisition will be used to select different detection points, and different detection results will be obtained, and leakage measurement and measurement uncertainty will occur. problem.
  • the present disclosure provides a method, a device, a storage medium, and an electronic device for detecting a substance, which are used to solve the problem that the measurement result is inaccurate and the partial scene is not applicable.
  • a substance detecting method comprising:
  • the current sensor data of the spectrometer sensor is recorded, and the spectrometer sensor is cleared to continue counting;
  • substance identification is performed according to the n sensor data
  • a substance detecting apparatus comprising:
  • a timing module configured to start timing when the Raman detecting device starts to emit laser light to the test substance and the Raman detecting device moves according to a preset route
  • a recording module configured to record current sensor data of the spectrometer sensor when the timing is detected to reach a preset time interval, and clear the spectrometer sensor, and continue counting by the timing module;
  • a determining module configured to determine, according to the currently recorded n sensor data, whether the preset data condition is met
  • An identification module configured to perform substance identification according to the n sensor data when the data condition is met
  • the step of recording the current sensor data of the spectrometer sensor to the output substance recognition result when the timing is detected to satisfy the preset time interval is performed again.
  • a computer readable storage medium including one or more programs for performing the first embodiment of the present disclosure The method described on the one hand.
  • a substance detecting apparatus comprising: the computer readable storage medium of the third aspect of the embodiments of the present disclosure;
  • One or more processors for executing a program in the computer readable storage medium.
  • the present disclosure starts the timing when the Raman detecting device moves and emits the laser according to the prescribed route, records the data collected by the spectrometer sensor according to the preset time interval, and linearly superimposes the collected data, and integrates
  • the time length or the signal-to-noise ratio detection result is a condition for judging the superimposed data, and when the condition is satisfied, the substance to be tested is detected, and the substance recognition result is output.
  • the timing is not interrupted, but the timing and acquisition of the next time interval are continued, and the continuous substance recognition result on the prescribed route can be output. It can avoid the problems of missed detection and uncertainty that may occur in the fixed-point detection method, and has the effect of improving the accuracy and output efficiency of the substance detection.
  • FIG. 1 is a flowchart of a substance detecting method according to an exemplary embodiment of the present disclosure
  • FIG. 2 is a flow chart of another substance detecting method according to an exemplary embodiment of the present disclosure.
  • FIG. 3 is a flowchart of another substance detecting method according to an exemplary embodiment of the present disclosure.
  • FIG. 4 is a flowchart of another substance detecting method according to an exemplary embodiment of the present disclosure.
  • FIG. 5 is a flowchart of still another substance detecting method according to an exemplary embodiment of the present disclosure.
  • FIG. 6 is a flowchart of still another substance detecting method according to an exemplary embodiment of the present disclosure.
  • FIG. 7 is a block diagram of a substance detecting apparatus according to an exemplary embodiment of the present disclosure.
  • FIG. 8 is a block diagram of another substance detecting apparatus according to an exemplary embodiment of the present disclosure.
  • FIG. 9 is a block diagram of still another substance detecting apparatus according to an exemplary embodiment of the present disclosure.
  • FIG. 10 is a block diagram of an electronic device, according to an exemplary embodiment.
  • the application scenario is to detect a substance to be tested according to a certain route by using a Raman detecting device.
  • a laser emitter and spectrometer sensor are provided on the Raman detection device.
  • FIG. 1 is a flowchart of a method for detecting a substance according to an exemplary embodiment of the present disclosure. As shown in FIG. 1 , the method includes:
  • Step 101 When the Raman detecting device starts to emit laser light and the Raman detecting device moves according to a preset route, timing is started.
  • the substance to be tested is detected, and the laser emitter of the Raman detecting device is first turned on, and the laser emitter is continuously emitted. Then, the user holds the Raman detecting device, or the Raman detecting device carried by the mechanical device such as the drone or the robot moves according to the preset route, and starts timing at this time.
  • the preset route can be set according to the shape and volume of the substance to be tested, or can be set by the user according to specific needs. If the information of the substance to be tested is small, the route cannot be determined, and the pre-stored default route can also be selected. Test.
  • Step 102 When it is detected that the timing reaches a preset time interval, record the current sensor data of the spectrometer sensor, and clear the spectrometer sensor to continue counting.
  • the data collected in the spectrometer sensor needs to be emptied after each acquisition, that is, each time the preset time interval is reached, the current sensor data of the spectrometer sensor is stored, and the data in the spectrometer sensor is emptied.
  • the preset time interval may be smaller than the general detection time of the existing detection method, and the general detection time is 2-3 s, and the preset time interval may be set to 0.5 s.
  • each 0.5 s sensor data and corresponding time can be stored as a record in a table, as shown in Table 1, or the sensor data can be stored in a LIFO according to the order of recording time (English: Last In First) Out, Chinese: Last in, first out).
  • Step 103 Determine whether the preset data condition is met according to the currently recorded n sensor data.
  • the preset data conditions can be classified into two types according to the integral of the fixed integration time and the automatic signal-to-noise ratio detection according to the Raman detection device. If the integral of the fixed integration time is adopted, the sensor data in the fixed integration time length may be superimposed, and the superposed data is detected by the signal-to-noise ratio, and the signal-to-noise ratio detection is used to satisfy the preset data condition. If the automatic signal-to-noise ratio detection method is adopted, the latest sensor data can be superimposed step by step, and the signal-to-noise ratio is detected for each superimposed data, and the signal-to-noise ratio detection is used to satisfy the preset data condition.
  • Step 104 When the data condition is met, the substance identification is performed based on the n sensor data.
  • step 105 the substance recognition result is output.
  • steps 102 to 105 are performed again.
  • step 102 while the sensor data is recorded, the timing is continued.
  • the substance recognition result on the preset route can be continuously output, so that the substance is compared with the fixed point detection method.
  • the recognition result is a discrete value, this embodiment
  • the method can display the substance recognition result in the form of a trend curve, a state diagram or a data table.
  • the Raman detecting device can be controlled to turn off the laser transmitter, and the current detection is terminated.
  • the end condition may be that the preset route has been moved or a detected closing time is set, and whether the laser transmitter is turned off is determined according to the closing time.
  • the laser transmitter can also be actively turned off by the user issuing a shutdown command.
  • the present disclosure starts the timing when the Raman detecting device moves and emits the laser according to the prescribed route, records the data collected by the spectrometer sensor according to the preset time interval, and linearly superimposes the collected data, and integrates
  • the time length or the signal-to-noise ratio detection result is a condition for judging the superimposed data, and when the condition is satisfied, the substance to be tested is detected, and the substance recognition result is output.
  • the timing is not interrupted, but the timing and acquisition of the next time interval are continued, and the continuous substance recognition result on the prescribed route can be output. It can avoid the problems of missed detection and uncertainty that may occur in the fixed-point detection method, and has the effect of improving the accuracy and output efficiency of the substance detection.
  • FIG. 2 is a flowchart of another method for detecting a substance according to an exemplary embodiment of the present disclosure. As shown in FIG. 2, step 103 includes:
  • step 1031 it is determined whether the number of n sensor data reaches p.
  • n is a positive integer
  • n is the total number of currently recorded sensor data
  • p is the number of specified data
  • the number of specified data is determined according to the ratio of the fixed integration duration to the time interval of the Raman detection device. , the time interval is less than the fixed integration time.
  • step 1032a when n ⁇ p, it is determined that the data condition is not satisfied.
  • Step 1032b when n ⁇ p, acquire superimposed data of the newly acquired p sensor data.
  • step 1033 the signal to noise ratio detection is performed on the superimposed data.
  • Step 1034 when the superimposed data is detected by the signal to noise ratio, it is determined that the data condition is satisfied. When the superimposed data does not pass the signal to noise ratio detection, it is determined that the data condition is not satisfied.
  • the Raman detection device when the Raman detection device is a detection device with a fixed integration duration, p can be determined according to the ratio of the fixed integration duration and the time interval, for example, p can be the fixed integration duration divided by the time interval, and rounded up, It should be noted that the time interval is less than the fixed integration time. For example, if the fixed integration time is 2s and the time interval is 0.5s, then p is 4, that is, whether n reaches 4 or not. When n ⁇ 4, it is determined that the data condition is not satisfied, that is, the time range corresponding to the n sensor data does not reach the fixed integration time, and the Raman detecting device cannot perform the detection.
  • the newly acquired p sensor data is linearly superimposed to obtain superimposed data.
  • the signal-to-noise ratio (SNR) detection is performed on the superimposed data, and the data condition is satisfied by the signal-to-noise ratio detection. If the signal-to-noise ratio is not detected, the data condition is not satisfied.
  • the newly acquired p sensor data is p sensor data in which the time difference between the recording time and the current time is the smallest among the n sensor data.
  • the current time is 5s, at which time n is 10, the sensor data collected at 0.5s is the first one, and the sensor data collected at 1s is the second and so on to the tenth.
  • ⁇ p then the superimposed data is the result of superimposing the latest 4 sensor data (tenth, ninth, eighth, and seventh) among the 10 data.
  • n is 11, which also satisfies n ⁇ p, at which time the superimposed data is a superposition of the eleventh, tenth, ninth and eighth sensor data.
  • FIG. 3 is a flowchart of another method for detecting a substance according to an exemplary embodiment of the present disclosure. As shown in FIG. 3, step 103 includes:
  • step 1035 the newly acquired m data is superimposed to obtain superimposed data.
  • m are positive integers
  • n is the total number of currently recorded sensor data.
  • the initial value of m is 1, m ⁇ n.
  • Step 1036 the signal to noise ratio detection is performed on the superimposed data.
  • step 1039 when m ⁇ m max , step 1035 to step 1036 are performed again, and when m is greater than m max , it is determined that the data condition is not satisfied.
  • the Raman detecting device is a detecting device for the signal-to-noise ratio detection determination
  • the newly acquired m data can be selected for superposition.
  • nth sensor data and the n-1th sensor data are superimposed as superimposed data, that is, the nth sensor data and the n-1th sensor data, and then the signal to noise ratio detection is performed.
  • the loop is terminated until the signal-to-noise ratio is detected, or m ⁇ n and m ⁇ m max are not satisfied.
  • m max can be set by dividing the maximum integration duration by the time interval and rounding up, or can be set by the user.
  • FIG. 4 is a flowchart of another substance detecting method according to an exemplary embodiment of the present disclosure. As shown in FIG. 4, step 104 includes:
  • step 1041 the superimposed data is preprocessed, and the preprocessing includes: denoising, base subtraction, and normalization processing.
  • Step 1042 Perform material identification on the preprocessed superimposed data by using a Raman recognition algorithm.
  • the pre-processed superimposed data is a Raman spectrum that reflects the material properties. Since the Raman spectrum information of different substances is fixed and unique, the Raman spectrum can be analyzed for substance identification.
  • FIG. 5 is a flowchart of still another method for detecting a substance according to an exemplary embodiment of the present disclosure. As shown in FIG. 5, the method further includes:
  • Step 106 When the amount of change between the substance identification result and the last output substance detection result exceeds a preset threshold, outputting the first prompt information, where the first prompt information is used to prompt the user between the substance detection result of the substance to be tested The amount of change is abnormal. And/or, outputting second prompt information, the second prompt information is used to prompt the user to control the moving speed of the Raman detecting device.
  • the composition of the substance to be tested changes at this time, and the amount of change may be a change of a different substance. It may also be a change in the concentration of the same substance.
  • the first prompt information may be output to display the change of the substance to be tested to the user.
  • a second prompt message may also be output, prompting the user to reduce the speed of moving the Raman detecting device (or reducing the moving speed of the mechanical device such as a drone or a robot). Since the density of the output substance recognition result becomes large when the moving speed is lowered, the amount of change between the substance detection results of the adjacent two outputs is correspondingly reduced.
  • the preset threshold may be set by the user or may be a default value.
  • FIG. 6 is a flowchart of still another method for detecting a substance according to an exemplary embodiment of the present disclosure. As shown in FIG. 6, the method further includes:
  • Step 107 When the data condition is not met, the third prompt information is output, and the third prompt information is used to prompt the user that the currently collected sensor data does not satisfy the data condition.
  • n ⁇ p or the superimposed data does not pass the signal to noise ratio detection, or in the method shown in FIG. 3, m>n or m>
  • m max the user can be prompted that the currently collected sensor data cannot obtain an accurate detection result.
  • the present disclosure starts the timing when the Raman detecting device moves and emits the laser according to the prescribed route, records the data collected by the spectrometer sensor according to the preset time interval, and linearly superimposes the collected data, and integrates
  • the time length or the signal-to-noise ratio detection result is a condition for judging the superimposed data, and when the condition is satisfied, the substance to be tested is detected, and the substance recognition result is output.
  • the timing is not interrupted, but the timing and acquisition of the next time interval are continued, and the continuous substance recognition result on the prescribed route can be output. It can avoid the problems of missed detection and uncertainty that may occur in the fixed-point detection method, and has the effect of improving the accuracy and output efficiency of the substance detection.
  • FIG. 7 is a block diagram of a substance detecting apparatus according to an exemplary embodiment of the present disclosure. As shown in FIG. 7, the apparatus 200 includes:
  • the timing module 201 is configured to start timing when the Raman detecting device starts to emit laser light and the Raman detecting device moves according to a preset route.
  • the recording module 202 is configured to record current sensor data of the spectrometer sensor when the timing is detected to reach a preset time interval, and clear the spectrometer sensor, and continue counting by the timing module.
  • the determining module 203 is configured to determine whether the preset data condition is met according to the currently recorded n sensor data.
  • the identification module 204 is configured to perform substance identification according to the n sensor data when the data condition is met.
  • the output module 205 is configured to output a substance recognition result.
  • the step of recording the current sensor data of the spectrometer sensor to the output substance recognition result is recorded again when it is detected that the timing meets the preset time interval.
  • the determining module 203 is configured to:
  • n sensor data It is judged whether the number of n sensor data reaches p.
  • n is the total number of currently recorded sensor data
  • p is the number of specified data
  • the number of specified data is determined according to the ratio of the fixed integration duration to the time interval of the Raman detection device. , the time interval is less than the fixed integration time.
  • the signal to noise ratio detection is performed on the superimposed data.
  • the determining module 203 is configured to:
  • n are positive integers
  • n is the total number of currently recorded sensor data.
  • the initial value of m is 1, m ⁇ n.
  • Signal-to-noise ratio detection is performed on the superimposed data.
  • m max is a preset maximum number of allowed superpositions, and m max is a positive integer.
  • FIG. 8 is a block diagram of another substance detecting apparatus according to an exemplary embodiment of the present disclosure.
  • the identifying module 204 includes:
  • the pre-processing sub-module 2041 is configured to perform pre-processing on the superimposed data, and the pre-processing includes: denoising, base subtraction, and normalization processing.
  • the substance identification sub-module 2042 is configured to perform material identification on the pre-processed superimposed data by using a Raman recognition algorithm.
  • FIG. 9 is a block diagram of still another substance detecting apparatus according to an exemplary embodiment of the present disclosure. As shown in FIG. 9, the apparatus 200 further includes:
  • the prompting module 206 is configured to output first prompt information when the amount of change between the substance identification result and the last detected substance detection result exceeds a preset threshold, and the first prompt information is used to prompt the user to detect the substance of the substance to be tested.
  • the amount of change between the anomalies is abnormal. and / or,
  • the prompting module 206 is further configured to output second prompt information, where the second prompt information is used to prompt the user to control the moving speed of the Raman detecting device.
  • the prompting module 206 is configured to output a third prompt information when the data condition is not met, where the third prompt information is used to prompt the user that the currently collected sensor data does not satisfy the data condition.
  • the present disclosure starts the timing when the Raman detecting device moves and emits the laser according to the prescribed route, and records the data collected by the spectrometer sensor according to the preset time interval, and the collected number is collected. According to the linear superposition, the superimposed data is judged on the condition of the integral duration or the signal-to-noise ratio detection result, and the substance to be tested is detected when the condition is satisfied, and the substance identification result is output. At the same time, the timing is not interrupted, but the timing and acquisition of the next time interval are continued, and the continuous substance recognition result on the prescribed route can be output. It can avoid the problems of missed detection and uncertainty that may occur in the fixed-point detection method, and has the effect of improving the accuracy and output efficiency of the substance detection.
  • FIG. 10 is a block diagram of an electronic device 300, according to an exemplary embodiment.
  • the electronic device 300 can include a processor 301, a memory 302, a multimedia component 303, an input/output (I/O) interface 304, and a communication component 305.
  • the processor 301 is configured to control the overall operation of the electronic device 300 to complete all or part of the steps of the substance detecting method described above.
  • the memory 302 is used to store various types of data to support operations at the electronic device 300, such as may include instructions for any application or method operating on the electronic device 300, as well as application related data, For example, contact data, sent and received messages, pictures, audio, video, and so on.
  • the memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read only memory ( Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read Only Read-Only Memory (ROM), magnetic memory, flash memory, disk or optical disk.
  • the multimedia component 303 can include a screen and audio components.
  • the screen may be, for example, a touch screen, and the audio component is used to output and/or input an audio signal.
  • the audio component can include a microphone for receiving an external audio signal.
  • the received audio signal may be further stored in memory 302 or transmitted via communication component 305.
  • the audio component also includes at least one speaker for outputting an audio signal.
  • the I/O interface 304 provides an interface between the processor 301 and other interface modules, such as a keyboard, a mouse, a button, and the like. These buttons can be virtual buttons or physical buttons.
  • the communication component 305 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 305 can include: Wi-Fi module, Bluetooth module, NFC module.
  • the electronic device 300 may be integrated with one or more application-specific integrated circuits.
  • Application Specific Integrated Circuit ASIC
  • DSP Digital Signal Processor
  • DSPD Digital Signal Processing Device
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • controller microcontroller, microprocessor or other electronic components are implemented to perform the above-mentioned substance detection method.
  • a computer readable storage medium comprising program instructions, such as a memory 302 comprising program instructions executable by processor 301 of electronic device 300 to perform the substance detection described above method.
  • the present disclosure starts the timing when the Raman detecting device moves and emits the laser according to the prescribed route, records the data collected by the spectrometer sensor according to the preset time interval, and linearly superimposes the collected data, and integrates
  • the time length or the signal-to-noise ratio detection result is a condition for judging the superimposed data, and when the condition is satisfied, the substance to be tested is detected, and the substance recognition result is output.
  • the timing is not interrupted, but the timing and acquisition of the next time interval are continued, and the continuous substance recognition result on the prescribed route can be output. It can avoid the problems of missed detection and uncertainty that may occur in the fixed-point detection method, and has the effect of improving the accuracy and output efficiency of the substance detection.

Abstract

一种物质检测方法、装置、存储介质及电子设备，涉及物质检测领域，该方法包括：当拉曼检测设备开始对待测物质发射激光且拉曼检测设备按照预设路线移动时，开始计时（101）。当检测到计时达到预设的时间间隔时，记录光谱仪传感器当前的传感器数据，并将光谱仪传感器清零，继续计时（102）。根据当前已记录的n个传感器数据确定是否满足预设的数据条件（103）。当满足数据条件时，根据n个传感器数据进行物质识别（104）。输出物质识别结果（105）。以及，再次执行当检测到计时满足预设的时间间隔时，记录光谱仪传感器当前的传感器数据至输出物质识别结果的步骤。该方法能够提高物质检测的准确度和输出效率。

Description

物质检测方法、装置、存储介质及电子设备 技术领域
本公开涉及物质检测领域，尤其涉及一种物质检测方法、装置、存储介质及电子设备。
背景技术
拉曼检测设备利用拉曼散射来检测物质的成分，当前在多个领域都有应用，包括专业的石油勘探，药物检测，到生活中常见安全检查，缉毒等场景，同时，随着拉曼检测设备的成本不断下降，在民用市场也有了更多的应用，例如用于农药检测，真假货检测等。
目前的拉曼检测设备的检测方法都是针对一个点的物质检测，即拉曼检测设备发射激光聚焦的位置上，收集到该点上的数据进行检测，针对成分单一的物质，现有的方法能够获得有效的检测结果，但如果需要检测不均匀混合物中物质的成分和分布情况，采用定点采集的方法，不同采集点的选取会得到不同的检测结果，同时会产生漏测和测不准等问题。
发明内容
本公开提供一种物质检测方法、装置、存储介质及电子设备，用以解决定点采集导致测量结果不准确以及部分场景不适用的问题。
为了实现上述目的，根据本公开实施例的第一方面，提供一种物质检测方法，所述方法包括：
当拉曼检测设备开始对待测物质发射激光且所述拉曼检测设备按照预设路线移动时，开始计时；
当检测到计时达到预设的时间间隔时，记录光谱仪传感器当前的传感器数据，并将所述光谱仪传感器清零，继续计时；
根据当前已记录的n个传感器数据确定是否满足预设的数据条件；
当满足所述数据条件时，根据所述n个传感器数据进行物质识别；
输出物质识别结果；以及，
再次执行所述当检测到计时满足预设的时间间隔时，记录光谱仪传感器 当前的传感器数据至所述输出物质识别结果的步骤。
根据本公开实施例的第二方面，提供一种物质检测装置，所述装置包括：
计时模块，用于当拉曼检测设备开始对待测物质发射激光且所述拉曼检测设备按照预设路线移动时，开始计时；
记录模块，用于当检测到计时达到预设的时间间隔时，记录光谱仪传感器当前的传感器数据，并将所述光谱仪传感器清零，由所述计时模块继续计时；
判断模块，用于根据当前已记录的n个传感器数据确定是否满足预设的数据条件；
识别模块，用于当满足所述数据条件时，根据所述n个传感器数据进行物质识别；
输出模块，用于输出物质识别结果；
再次执行所述当检测到计时满足预设的时间间隔时，记录光谱仪传感器当前的传感器数据至所述输出物质识别结果的步骤。
根据本公开实施例的第三方面，提供一种计算机可读存储介质，所述计算机可读存储介质中包括一个或多个程序，所述一个或多个程序用于执行本公开实施例的第一方面所述的方法。
根据本公开实施例的第四方面，提供一种物质检测装置，包括：本公开实施例的第三方面所述的计算机可读存储介质；以及
一个或者多个处理器，用于执行所述计算机可读存储介质中的程序。
通过上述技术方案，本公开通过在拉曼检测设备按照规定路线移动并发射激光时，开始计时，按照预设的时间间隔记录光谱仪传感器采集的数据，对采集的数据及进行线性叠加，并以积分时长或信噪比检测结果为条件对叠加后的数据进行判断，当满足条件时对待测物质进行检测，输出物质识别结果。与此同时，计时并不中断，而是继续下一个时间间隔的计时和采集，能够输出规定路线上连续的物质识别结果。能够避免定点检测方法可能发生的漏测和测不准的问题，具有提高物质检测的准确度和输出效率的效果。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
图1是根据本公开一示例性实施例提供的一种物质检测方法的流程图；
图2是根据本公开一示例性实施例提供的另一种物质检测方法的流程图；
图3是根据本公开一示例性实施例提供的另一种物质检测方法的流程图；
图4是根据本公开一示例性实施例提供的另一种物质检测方法的流程图；
图5是根据本公开一示例性实施例提供的再一种物质检测方法的流程图；
图6是根据本公开一示例性实施例提供的又一种物质检测方法的流程图；
图7是根据本公开一示例性实施例提供的一种物质检测装置的框图；
图8是根据本公开一示例性实施例提供的另一种物质检测装置的框图；
图9是根据本公开一示例性实施例提供的再一种物质检测装置的框图；
图10是根据一示例性实施例示出的一种电子设备的框图。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本公开的一些方面相一致的装置和方法的例子。
在介绍本公开提供的物质检测方法、装置以及存储介质之前，首先对本公开各个实施例所涉及应用场景进行介绍。该应用场景为利用拉曼检测设备按照一定的路线对待测物质进行检测。拉曼检测设备上设置有激光发射器和光谱仪传感器。
图1是根据本公开一示例性实施例提供的一种物质检测方法的流程图，如图1所示，该方法包括：
步骤101，当拉曼检测设备开始对待测物质发射激光且拉曼检测设备按照预设路线移动时，开始计时。
示例的，对待测物质进行检测，先打开拉曼检测设备的激光发射器，激光发射器是持续发射的。再由用户手持拉曼检测设备，或由无人机、机器人等机械装置携带拉曼检测设备按照预设路线移动，并此时开始计时。预设路线可以根据待测物质的形态、体积等特征来设定，也可以由用户根据具体需要来设定，如果待测物质的信息较少，无法确定路线，还可以选择预存的默认路线先进行检测。
步骤102，当检测到计时达到预设的时间间隔时，记录光谱仪传感器当前的传感器数据，并将光谱仪传感器清零，继续计时。
举例来说，光谱仪传感器中采集的数据在每次采集完成后都需要清空，即每达到预设的时间间隔，将光谱仪传感器当前的传感器数据进行存储，再将光谱仪传感器中的数据清空。其中预设的时间间隔可以小于现有检测方法的一般检测时长，以一般检测时长为2-3s，那么可以将预设的时间间隔设置为0.5s。例如，可以将每个0.5s的传感器数据和相应的时刻作为一条记录存储在一张表中，如表1所示，也可以根据记录时刻的先后顺序将传感器数据存储在一个LIFO(英文：Last In First Out，中文：后进先出)表中。
表1
时刻 传感器数据
0.5s 0-0.5s之间的传感器数据
1s 0.5s-1s之间的传感器数据
1.5s 1s-1.5s之间的传感器数据
2s 1.5s-2s之间的传感器数据
…… ……
步骤103，根据当前已记录的n个传感器数据确定是否满足预设的数据条件。
示例的，预设的数据条件可以根据拉曼检测设备分为固定积分时长的积分和自动信噪比检测判定两类。如果采用固定积分时长的积分的方式，则可以选择将固定积分时长内的传感器数据进行叠加，并对叠加后的数据进行信噪比检测，通过信噪比检测则为满足预设的数据条件。如果采用自动信噪比检测判定的方式，则可以逐步将最新的传感器数据进行叠加，并对每一次叠加后的数据进行信噪比检测，通过信噪比检测则为满足预设的数据条件。
步骤104，当满足数据条件时，根据n个传感器数据进行物质识别。
步骤105，输出物质识别结果。
以及，再次执行步骤102至105。
示例的，在步骤102中，记录传感器数据的同时，计时一直在继续，通过不断循环步骤102至步骤105，能够连续地输出预设路线上的物质识别结果，因此相比于定点检测方法中物质识别结果是离散的一个数值，本实施例 中的方法能够将物质识别结果以趋势变化曲线、状态图或数据表等形式显示出来。
需要说明的是，在本实施例中，当满足结束条件或者接收到用户的关闭指令后，可以控制拉曼检测设备关闭激光发射器，终止本次检测。其中结束条件可以是预设路线已经移动完毕或者设定一个检测的关闭时间，根据这个关闭时间来确定是否关闭激光发射器。还可以由用户下发关闭指令，主动关闭激光发射器。
综上所述，本公开通过在拉曼检测设备按照规定路线移动并发射激光时，开始计时，按照预设的时间间隔记录光谱仪传感器采集的数据，对采集的数据及进行线性叠加，并以积分时长或信噪比检测结果为条件对叠加后的数据进行判断，当满足条件时对待测物质进行检测，输出物质识别结果。与此同时，计时并不中断，而是继续下一个时间间隔的计时和采集，能够输出规定路线上连续的物质识别结果。能够避免定点检测方法可能发生的漏测和测不准的问题，具有提高物质检测的准确度和输出效率的效果。
图2是根据本公开一示例性实施例提供的另一种物质检测方法的流程图，如图2所示，步骤103包括：
步骤1031，判断n个传感器数据的个数是否达到p个。其中，n，p为正整数，n为当前已记录的传感器数据的总个数，p为指定数据个数，指定数据个数是根据拉曼检测设备的固定积分时长与时间间隔之比确定的，时间间隔小于固定积分时长。
步骤1032a，当n<p时，确定不满足数据条件。
步骤1032b，当n≥p时，获取最新采集的p个传感器数据的叠加数据。
步骤1033，对叠加数据进行信噪比检测。
步骤1034，当叠加数据通过信噪比检测时，确定满足数据条件。当叠加数据未通过信噪比检测时，确定不满足数据条件。
举例来说，当拉曼检测设备是固定积分时长的检测设备，那么可以根据固定积分时长和时间间隔的比值来确定p，例如，p可以是固定积分时长除以时间间隔，并向上取整，需要说明的是，时间间隔小于固定积分时长。以固定积分时长为2s，时间间隔为0.5s为例，那么p为4，即判断n是否达到4个。当n<4的时候，确定为不满足数据条件，即n个传感器数据对应的时间范围没有达到固定积分时长，拉曼检测设备还不能进行检测。当n≥4的 时候，将最新采集的p个传感器数据进行线性叠加，得到叠加数据。之后，再对叠加数据进行信噪比检测，通过信噪比检测极为满足数据条件，未通过信噪比检测则为不满足数据条件。其中，最新采集的p个传感器数据，为n个传感器数据中记录时间与当前时刻的时间差最小的p个传感器数据。
例如，当前时刻为5s，此时n为10，以0.5s时采集到的传感器数据为第一个，1s时采集到的传感器数据为第二个以此类推到第十个，此时满足n≥p，那么叠加数据为这10个数据中最新的4个传感器数据(第十个、第九个、第八个和第七个)进行叠加的结果。在进行到下一个时间间隔5.5s时，n为11，同样满足n≥p，此时叠加数据为第十一个、第十个、第九个和第八个传感器数据的叠加。
图3是根据本公开一示例性实施例提供的另一种物质检测方法的流程图，如图3所示，步骤103包括：
步骤1035，将最新采集的m个数据进行叠加，得到叠加数据。其中，m，n为正整数，n为当前已记录的传感器数据的总个数，在每个时间间隔中，m的初始值为1，m≤n。
步骤1036，对叠加数据进述信噪比检测。
1037a，当叠加数据通过信噪比检测时，确定满足数据条件。
1037b，当叠加数据未通过信噪比检测时，令m＝m+1，并判断m是否大于n。
1038a，当m>n时，确定不满足数据条件。
1038b，当m≤n时，判断m是否大于mmax，mmax为预设的允许叠加的最大个数，mmax为正整数。
1039，当m≤mmax时，再次执行步骤1035至步骤1036，当m大于mmax时，确定不满足数据条件。
举例来说，当拉曼检测设备是信噪比检测判定的检测设备，那么可以选择最新采集的m个数据进行叠加。m的初始值为1，即第一次，取n个传感器数据中最新的一个作为叠加数据，即当前刚刚记录的传感器数据，进行信噪比检测。如果信噪比检测通过，则确定满足数据条件，若不通过，则令m＝m+1，即m为2，此时如果满足m≤n且m≤mmax，则取n个传感器数据中最新的两个进行叠加作为叠加数据，即第n个传感器数据和第n-1个传感器数据，再进行信噪比检测。以此类推，直到通过信噪比检测，或者不满足 m≤n且m≤mmax才结束循环。其中，mmax可以由最大积分时长除以时间间隔，并向上取整来设置，也可以由用户来设置。
图4是根据本公开一示例性实施例提供的另一种物质检测方法的流程图，如图4所示，步骤104包括：
步骤1041，对叠加数据进行预处理，预处理包括：去噪声、基底扣除和归一化处理。
步骤1042，利用拉曼识别算法对预处理后的叠加数据进行物质识别。
示例的，经过预处理后的叠加数据，即是能够反映出物质特性的拉曼光谱，因为不同物质的拉曼光谱信息固定且唯一，所以能够通过分析拉曼光谱来进行物质识别。
图5是根据本公开一示例性实施例提供的再一种物质检测方法的流程图，如图5所示，该方法还包括：
步骤106，当物质识别结果与上一次输出的物质检测结果之间的变化量超过预设阈值时，输出第一提示信息，第一提示信息用于提示用户待测物质的物质检测结果之间的变化量异常。和/或，输出第二提示信息，第二提示信息用于提示用户控制拉曼检测设备的移动速度。
举例来说，如果当前时刻的物质识别结果与上一次输出的物质检测结果之间的变化量过大，则说明待测物质的成分此时发生了变化，其中变化量可以是不同物质的变化，也可以是同一物质浓度的变化，此时可以输出第一提示信息，向用户显示待测物质的变化。同时，还可以输出第二提示信息，提示用户降低移动拉曼检测设备的速度(或降低无人机或机器人等机械装置的移动速度)。因为当降低移动速度后，输出物质识别结果的密度变大，相应的降低相邻两次输出的物质检测结果之间的变化量。其中预设阈值可以由用户设定，也可以是默认值。
图6是根据本公开一示例性实施例提供的又一种物质检测方法的流程图，如图6所示，该方法还包括：
步骤107，当不满足所述数据条件时，输出第三提示信息，第三提示信息用于提示用户当前采集的传感器数据不满足数据条件。
示例的，当不满足所述数据条件时，即在图2所示方法中n<p或叠加数据未通过信噪比检测的情况下，或在图3所示方法中m>n或m>mmax的情况下，可以提示用户当前采集的传感器数据无法得到准确的检测结果。
综上所述，本公开通过在拉曼检测设备按照规定路线移动并发射激光时，开始计时，按照预设的时间间隔记录光谱仪传感器采集的数据，对采集的数据及进行线性叠加，并以积分时长或信噪比检测结果为条件对叠加后的数据进行判断，当满足条件时对待测物质进行检测，输出物质识别结果。与此同时，计时并不中断，而是继续下一个时间间隔的计时和采集，能够输出规定路线上连续的物质识别结果。能够避免定点检测方法可能发生的漏测和测不准的问题，具有提高物质检测的准确度和输出效率的效果。
图7是根据本公开一示例性实施例提供的一种物质检测装置的框图，如图7所示，该装置200包括：
计时模块201，用于当拉曼检测设备开始对待测物质发射激光且所述拉曼检测设备按照预设路线移动时，开始计时。
记录模块202，用于当检测到计时达到预设的时间间隔时，记录光谱仪传感器当前的传感器数据，并将所述光谱仪传感器清零，由所述计时模块继续计时。
判断模块203，用于根据当前已记录的n个传感器数据确定是否满足预设的数据条件。
识别模块204，用于当满足所述数据条件时，根据所述n个传感器数据进行物质识别。
输出模块205，用于输出物质识别结果。
再次执行当检测到计时满足预设的时间间隔时，记录光谱仪传感器当前的传感器数据至输出物质识别结果的步骤。
可选的，判断模块203用于：
判断n个传感器数据的个数是否达到p个。其中，n，p为正整数，n为当前已记录的传感器数据的总个数，p为指定数据个数，指定数据个数是根据拉曼检测设备的固定积分时长与时间间隔之比确定的，时间间隔小于固定积分时长。
当n<p时，确定不满足数据条件。
当n≥p时，获取最新采集的p个传感器数据的叠加数据。
对叠加数据进行所述信噪比检测。
当叠加数据通过信噪比检测时，确定满足数据条件。当叠加数据未通过信噪比检测时，确定不满足数据条件。
可选的，判断模块203用于：
将最新采集的m个数据进行叠加，得到叠加数据。其中，m，n为正整数，n为当前已记录的传感器数据的总个数，在每个时间间隔中，m的初始值为1，m≤n。
对叠加数据进行信噪比检测。
当叠加数据通过信噪比检测时，确定满足数据条件。
当叠加数据未通过信噪比检测时，令m＝m+1，并判断m是否大于n。
当m>n时，确定不满足数据条件。
当m≤n时，判断m是否大于mmax，mmax为预设的允许叠加的最大个数，mmax为正整数。
当m≤mmax时，再次执行将最新采集的m个数据进行叠加至对叠加数据进行信噪比检测的步骤，当m大于mmax时，确定不满足数据条件。
图8是根据本公开一示例性实施例提供的另一种物质检测装置的框图，如图8所示，识别模块204包括：
预处理子模块2041，用于对叠加数据进行预处理，预处理包括：去噪声、基底扣除和归一化处理。
物质识别子模块2042，用于利用拉曼识别算法对预处理后的叠加数据进行物质识别。
图9是根据本公开一示例性实施例提供的再一种物质检测装置的框图，如图9所示，该装置200还包括：
提示模块206，用于当物质识别结果与上一次输出的物质检测结果之间的变化量超过预设阈值时，输出第一提示信息，第一提示信息用于提示用户待测物质的物质检测结果之间的变化量异常。和/或，
提示模块206，还用于输出第二提示信息，第二提示信息用于提示用户控制拉曼检测设备的移动速度。
可选的，提示模块206，用于当不满足数据条件时，输出第三提示信息，第三提示信息用于提示用户当前采集的传感器数据不满足数据条件。
其中，上述各个模块所实现功能的具体说明已经在上述方法实施例中进行了详细描述，此处不再赘述。
综上所述，本公开通过在拉曼检测设备按照规定路线移动并发射激光时，开始计时，按照预设的时间间隔记录光谱仪传感器采集的数据，对采集的数 据及进行线性叠加，并以积分时长或信噪比检测结果为条件对叠加后的数据进行判断，当满足条件时对待测物质进行检测，输出物质识别结果。与此同时，计时并不中断，而是继续下一个时间间隔的计时和采集，能够输出规定路线上连续的物质识别结果。能够避免定点检测方法可能发生的漏测和测不准的问题，具有提高物质检测的准确度和输出效率的效果。
图10是根据一示例性实施例示出的一种电子设备300的框图。如图10所示，该电子设备300可以包括：处理器301，存储器302，多媒体组件303，输入/输出(I/O)接口304，以及通信组件305。
其中，处理器301用于控制该电子设备300的整体操作，以完成上述的物质检测方法中的全部或部分步骤。存储器302用于存储各种类型的数据以支持在该电子设备300的操作，这些数据例如可以包括用于在该电子设备300上操作的任何应用程序或方法的指令，以及应用程序相关的数据，例如联系人数据、收发的消息、图片、音频、视频等等。该存储器302可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，例如静态随机存取存储器(Static Random Access Memory，简称SRAM)，电可擦除可编程只读存储器(Electrically Erasable Programmable Read-Only Memory，简称EEPROM)，可擦除可编程只读存储器(Erasable Programmable Read-Only Memory，简称EPROM)，可编程只读存储器(Programmable Read-Only Memory，简称PROM)，只读存储器(Read-Only Memory，简称ROM)，磁存储器，快闪存储器，磁盘或光盘。多媒体组件303可以包括屏幕和音频组件。其中屏幕例如可以是触摸屏，音频组件用于输出和/或输入音频信号。例如，音频组件可以包括一个麦克风，麦克风用于接收外部音频信号。所接收的音频信号可以被进一步存储在存储器302或通过通信组件305发送。音频组件还包括至少一个扬声器，用于输出音频信号。I/O接口304为处理器301和其他接口模块之间提供接口，上述其他接口模块可以是键盘，鼠标，按钮等。这些按钮可以是虚拟按钮或者实体按钮。通信组件305用于该电子设备300与其他设备之间进行有线或无线通信。无线通信，例如Wi-Fi，蓝牙，近场通信(Near Field Communication，简称NFC)，2G、3G或4G，或它们中的一种或几种的组合，因此相应的该通信组件305可以包括：Wi-Fi模块，蓝牙模块，NFC模块。
在一示例性实施例中，电子设备300可以被一个或多个应用专用集成电 路(Application Specific Integrated Circuit，简称ASIC)、数字信号处理器(Digital Signal Processor，简称DSP)、数字信号处理设备(Digital Signal Processing Device，简称DSPD)、可编程逻辑器件(Programmable Logic Device，简称PLD)、现场可编程门阵列(Field Programmable Gate Array，简称FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述的物质检测方法。
在另一示例性实施例中，还提供了一种包括程序指令的计算机可读存储介质，例如包括程序指令的存储器302，上述程序指令可由电子设备300的处理器301执行以完成上述的物质检测方法。
综上所述，本公开通过在拉曼检测设备按照规定路线移动并发射激光时，开始计时，按照预设的时间间隔记录光谱仪传感器采集的数据，对采集的数据及进行线性叠加，并以积分时长或信噪比检测结果为条件对叠加后的数据进行判断，当满足条件时对待测物质进行检测，输出物质识别结果。与此同时，计时并不中断，而是继续下一个时间间隔的计时和采集，能够输出规定路线上连续的物质识别结果。能够避免定点检测方法可能发生的漏测和测不准的问题，具有提高物质检测的准确度和输出效率的效果。
以上结合附图详细描述了本公开的优选实施方式，但是，本公开并不限于上述实施方式中的具体细节，在本公开的技术构思范围内，可以对本公开的技术方案进行多种简单变型，这些简单变型均属于本公开的保护范围。
另外需要说明的是，在上述具体实施方式中所描述的各个具体技术特征，在不矛盾的情况下，可以通过任何合适的方式进行组合，为了避免不必要的重复，本公开对各种可能的组合方式不再另行说明。
此外，本公开的各种不同的实施方式之间也可以进行任意组合，只要其不违背本公开的思想，其同样应当视为本公开所公开的内容。

Claims (14)

  1. 一种物质检测方法，其特征在于，所述方法包括：
    当拉曼检测设备开始对待测物质发射激光且所述拉曼检测设备按照预设路线移动时，开始计时；
    当检测到计时达到预设的时间间隔时，记录光谱仪传感器当前的传感器数据，并将所述光谱仪传感器清零，继续计时；
    根据当前已记录的n个传感器数据确定是否满足预设的数据条件；
    当满足所述数据条件时，根据所述n个传感器数据进行物质识别；
    输出物质识别结果；以及，
    再次执行所述当检测到计时满足预设的时间间隔时，记录光谱仪传感器当前的传感器数据至所述输出物质识别结果的步骤。
  2. 根据权利要求1所述的方法，其特征在于，所述根据当前已记录的n个传感器数据确定是否满足预设的数据条件，包括：
    判断所述n个传感器数据的个数是否达到p个；其中，n，p为正整数，n为当前已记录的传感器数据的总个数，p为指定数据个数，所述指定数据个数是根据所述拉曼检测设备的固定积分时长与所述时间间隔之比确定的，所述时间间隔小于所述固定积分时长；
    当所述n<p时，确定不满足所述数据条件；
    当所述n≥p时，获取最新采集的p个传感器数据的叠加数据；
    对所述叠加数据进行所述信噪比检测；
    当所述叠加数据通过所述信噪比检测时，确定满足所述数据条件；当所述叠加数据未通过所述信噪比检测时，确定不满足所述数据条件。
  3. 根据权利要求1所述的方法，其特征在于，所述根据当前已记录的n个传感器数据确定是否满足预设的数据条件，包括：
    将最新采集的m个数据进行叠加，得到叠加数据；其中，m，n为正整数，n为当前已记录的传感器数据的总个数，m的初始值为1，m≤n；
    对所述叠加数据进行所述信噪比检测；
    当所述叠加数据通过所述信噪比检测时，确定满足所述数据条件；
    当所述叠加数据未通过所述信噪比检测时，令m＝m+1，并判断m是否大于n；
    当m>n时，确定不满足所述数据条件；
    当m≤n时，判断m是否大于mmax，mmax为预设的允许叠加的最大个数，mmax为正整数；
    当m≤mmax时，再次执行所述将最新采集的m个数据进行叠加至所述对所述叠加数据进行所述信噪比检测的步骤，当m大于mmax时，确定不满足所述数据条件。
  4. 根据权利要求2或3所述的方法，其特征在于，所述当满足所述数据条件时，根据所述n个传感器数据进行物质识别，包括：
    对所述叠加数据进行预处理，所述预处理包括：去噪声、基底扣除和归一化处理；
    利用拉曼识别算法对预处理后的所述叠加数据进行物质识别。
  5. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    当所述物质识别结果与上一次输出的物质检测结果之间的变化量超过预设阈值时，输出第一提示信息，所述第一提示信息用于提示用户所述待测物质的物质检测结果之间的变化量异常；和/或；
    输出第二提示信息，所述第二提示信息用于提示用户控制所述拉曼检测设备的移动速度。
  6. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    当不满足所述数据条件时，输出第三提示信息，所述第三提示信息用于提示用户当前采集的传感器数据不满足所述数据条件。
  7. 一种物质检测装置，其特征在于，所述装置包括：
    计时模块，用于当拉曼检测设备开始对待测物质发射激光且所述拉曼检测设备按照预设路线移动时，开始计时；
    记录模块，用于当检测到计时达到预设的时间间隔时，记录光谱仪传感器当前的传感器数据，并将所述光谱仪传感器清零，由所述计时模块继续计 时；
    判断模块，用于根据当前已记录的n个传感器数据确定是否满足预设的数据条件；
    识别模块，用于当满足所述数据条件时，根据所述n个传感器数据进行物质识别；
    输出模块，用于输出物质识别结果；
    再次执行所述当检测到计时满足预设的时间间隔时，记录光谱仪传感器当前的传感器数据至所述输出物质识别结果的步骤。
  8. 根据权利要求7所述的装置，其特征在于，所述判断模块用于：
    判断所述n个传感器数据的个数是否达到p个；其中，n，p为正整数，n为当前已记录的传感器数据的总个数，p为指定数据个数，所述指定数据个数是根据所述拉曼检测设备的固定积分时长与所述时间间隔之比确定的，所述时间间隔小于所述固定积分时长；
    当所述n<p时，确定不满足所述数据条件；
    当所述n≥p时，获取最新采集的p个传感器数据的叠加数据；
    对所述叠加数据进行所述信噪比检测；
    当所述叠加数据通过所述信噪比检测时，确定满足所述数据条件；当所述叠加数据未通过所述信噪比检测时，确定不满足所述数据条件。
  9. 根据权利要求7所述的装置，其特征在于，所述判断模块用于：
    将最新采集的m个数据进行叠加，得到叠加数据；其中，m，n为正整数，n为当前已记录的传感器数据的总个数，m的初始值为1，m≤n；
    对所述叠加数据进行所述信噪比检测；
    当所述叠加数据通过所述信噪比检测时，确定满足所述数据条件；
    当所述叠加数据未通过所述信噪比检测时，令m＝m+1，并判断m是否大于n；
    当m>n时，确定不满足所述数据条件；
    当m≤n时，判断m是否大于mmax，mmax为预设的允许叠加的最大个数，mmax为正整数；
    当m≤mmax时，再次执行所述将最新采集的m个数据进行叠加至所述 对所述叠加数据进行所述信噪比检测的步骤，当m大于mmax时，确定不满足所述数据条件。
  10. 根据权利要求8或9所述的装置，其特征在于，所述识别模块包括：
    预处理子模块，用于对所述叠加数据进行预处理，所述预处理包括：去噪声、基底扣除和归一化处理；
    物质识别子模块，用于利用拉曼识别算法对预处理后的所述叠加数据进行物质识别。
  11. 根据权利要求7所述的装置，其特征在于，所述装置还包括：
    提示模块，用于当所述物质识别结果与上一次输出的物质检测结果之间的变化量超过预设阈值时，输出第一提示信息，所述第一提示信息用于提示用户所述待测物质的物质检测结果之间的变化量异常；和/或；
    所述提示模块，还用于输出第二提示信息，所述第二提示信息用于提示用户控制所述拉曼检测设备的移动速度。
  12. 根据权利要求7所述的装置，其特征在于，所述装置还包括：
    提示模块，用于当不满足所述数据条件时，输出第三提示信息，所述第三提示信息用于提示用户当前采集的传感器数据不满足所述数据条件。
  13. 一种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中包括一个或多个程序，所述一个或多个程序用于执行权利要求1至6中任一项所述的方法。
  14. 一种电子设备，其特征在于，包括：
    权利要求13中所述的计算机可读存储介质；以及
    一个或者多个处理器，用于执行所述计算机可读存储介质中的程序。
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Families Citing this family (3)

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Publication number Priority date Publication date Assignee Title
WO2019036941A1 (zh) * 2017-08-23 2019-02-28 深圳前海达闼云端智能科技有限公司 物质检测方法、装置、存储介质及电子设备
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2103926A2 (en) * 2008-03-18 2009-09-23 ITT Manufacturing Enterprises, Inc. Dual pulse single event raman spectroscopy
US20100128263A1 (en) * 2008-11-27 2010-05-27 Nanophoton Corp. Optical microscope and spectrum measuring method
CN101925806A (zh) * 2008-01-25 2010-12-22 瑞尼斯豪公司 行扫描拉曼光谱仪
CN103760146A (zh) * 2014-01-03 2014-04-30 中国农业大学 一种粉状物的非接触成分检测系统和方法
CN104076023A (zh) * 2014-07-22 2014-10-01 中国人民解放军第三军医大学第一附属医院 体液拉曼光谱检测装置
JP2016180733A (ja) * 2015-03-25 2016-10-13 日本分光株式会社 顕微分光装置
CN206270250U (zh) * 2016-11-22 2017-06-20 山东格物光电科技有限公司 使用单模激光的手持式拉曼检测仪
CN107995948A (zh) * 2017-08-23 2018-05-04 深圳前海达闼云端智能科技有限公司 物质检测方法、装置、存储介质及电子设备

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104749156B (zh) * 2013-12-27 2017-08-29 同方威视技术股份有限公司 拉曼光谱检测方法
CN106770151B (zh) * 2016-11-01 2019-10-25 北京华泰诺安技术有限公司 一种快速检测拉曼光谱信号的方法及装置

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101925806A (zh) * 2008-01-25 2010-12-22 瑞尼斯豪公司 行扫描拉曼光谱仪
EP2103926A2 (en) * 2008-03-18 2009-09-23 ITT Manufacturing Enterprises, Inc. Dual pulse single event raman spectroscopy
US20100128263A1 (en) * 2008-11-27 2010-05-27 Nanophoton Corp. Optical microscope and spectrum measuring method
CN103760146A (zh) * 2014-01-03 2014-04-30 中国农业大学 一种粉状物的非接触成分检测系统和方法
CN104076023A (zh) * 2014-07-22 2014-10-01 中国人民解放军第三军医大学第一附属医院 体液拉曼光谱检测装置
JP2016180733A (ja) * 2015-03-25 2016-10-13 日本分光株式会社 顕微分光装置
CN206270250U (zh) * 2016-11-22 2017-06-20 山东格物光电科技有限公司 使用单模激光的手持式拉曼检测仪
CN107995948A (zh) * 2017-08-23 2018-05-04 深圳前海达闼云端智能科技有限公司 物质检测方法、装置、存储介质及电子设备

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