CN118624567A - A method for constructing water quality hyperspectral inversion for low-power shore-based automatic monitoring system - Google Patents
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Abstract
本发明公开了一种低功耗岸基站自动监测系统的水质高光谱反演构建方法，包括以下步骤：步骤一：获取待测水体的高光谱反射率数据；步骤二：采集所述待测水体的同步表层水质样本，构建水质高光谱反射率数据集；步骤三：基于所述水质高光谱反射率数据集，构建最优水质智能反演模型；步骤四：基于所述待测水体高光谱反射率数据和所述最优水质智能反演模型，得到实时水质数据，实现岸基站高光谱自动水质监测，本发明的低功耗岸基站自动监测系统定时采集实现了无人值守的连续水体观测，占地面积小，无需固定，降低了设备维护难度。
The present invention discloses a method for constructing water quality hyperspectral inversion of a low-power shore base station automatic monitoring system, comprising the following steps: step one: obtaining hyperspectral reflectance data of a water body to be tested; step two: collecting synchronous surface water quality samples of the water body to be tested, and constructing a water quality hyperspectral reflectance data set; step three: constructing an optimal water quality intelligent inversion model based on the water quality hyperspectral reflectance data set; step four: obtaining real-time water quality data based on the hyperspectral reflectance data of the water body to be tested and the optimal water quality intelligent inversion model, and realizing hyperspectral automatic water quality monitoring of a shore base station. The low-power shore base station automatic monitoring system of the present invention realizes unattended continuous water body observation through regular acquisition, occupies a small area, does not need to be fixed, and reduces the difficulty of equipment maintenance.
Description
技术领域Technical Field
本发明属于水质监测技术领域，具体为一种低功耗岸基站自动监测系统的水质高光谱反演构建方法。The present invention belongs to the technical field of water quality monitoring, and specifically relates to a water quality hyperspectral inversion construction method for a low-power shore base station automatic monitoring system.
背景技术Background Art
快速准确获取水质数据，可以追踪水质变化并及时发现异常情况,如水污染、水质恶化等，准确数据和实时警报可帮助监管部门采取及时措施,减少水资源污染风险，在协助水质改进、定位污染源头、提升水质治理效率等方面具有积极的作用，有助于评估污染源和监控治理效果，为水环境管理提供依据，促进对水质的科学管理与治理，在水资源保护方面发挥着关键作用。Rapid and accurate acquisition of water quality data can track changes in water quality and promptly detect abnormal situations, such as water pollution and water quality deterioration. Accurate data and real-time alerts can help regulatory authorities take timely measures to reduce the risk of water resource pollution. It plays a positive role in assisting water quality improvement, locating pollution sources, and improving water quality management efficiency. It helps to assess pollution sources and monitor management effects, provide a basis for water environment management, and promote scientific management and governance of water quality. It plays a key role in water resource protection.
当前水质监测包括实验室分析、化学分析法在线监测、卫星遥感监测、无人机高光谱监测、水下测量仪监测等，但上述监测方法在不同应用领域均存在缺点。化学分析法在线监测是当前水质检测应用广泛的方法，尤其在固定站应用较多，该方法通过自动控制泵阀进行定时采样，将样品抽取至仪器检测池中，仪器可自动抽取待测指标需要的反应试剂至检测池中，经过预处理和化学反应，利用朗伯比尔定律，采用光度法，根据仪器内置校正曲线计算出待测水样的待测指标，化学分析法是实验室人工分析的自动化实现，依据国家标准测定，数据准确、可靠。Current water quality monitoring includes laboratory analysis, online monitoring by chemical analysis, satellite remote sensing monitoring, drone hyperspectral monitoring, underwater measuring instrument monitoring, etc. However, the above monitoring methods have shortcomings in different application fields. Online monitoring by chemical analysis is a widely used method for water quality testing, especially in fixed stations. This method uses automatic control of pump valves for timed sampling and extracts samples into the instrument detection pool. The instrument can automatically extract the reaction reagents required for the indicators to be tested into the detection pool. After pretreatment and chemical reaction, Lambert-Beer's law is used, and the photometric method is used to calculate the indicators of the water sample to be tested according to the built-in calibration curve of the instrument. The chemical analysis method is the automated realization of manual analysis in the laboratory, and is measured according to national standards. The data is accurate and reliable.
河流、湖泊、水库等流域受气象、水文和污染物自身性质等多种因素的共同影响，在空间和时间上具有差异性。传统基于实验室分析的人工采样具有成本高、费时费力、时空离散和时效性差等缺点，很难及时捕捉一些人为干扰强烈、水情复杂的水质快速变化过程。基于化学分析法的在线监测，具有检测周期长、一台仪器只能检测一个参数、维护成本高、需要试剂反应，废液可能引起二次污染等缺点。卫星遥感因具有大面积、周期性和经济高效等优势，已经被广泛用于透明度、叶绿素、总氮、总磷等水质监测中，但有限的时间分辨率和云雨天气使其在解决长期、连续水质监测问题上仍力不从心。无人机高光谱遥感因灵活机动弥补了中小型水体水质监测不足，但受限于续航时间和云雨天气而难以实现连续观测。随着技术的发展，水下高频探头和多参数水质测量仪可以开展水质连续高频观测，但监测精度低、价格昂贵、维修困难，易附着污染等缺点限制了其大范围应用。Rivers, lakes, reservoirs and other watersheds are affected by multiple factors such as meteorology, hydrology and the properties of pollutants themselves, and have differences in space and time. Traditional manual sampling based on laboratory analysis has the disadvantages of high cost, time-consuming and labor-intensive, spatial and temporal discreteness and poor timeliness. It is difficult to capture some rapid changes in water quality with strong human interference and complex water conditions in a timely manner. Online monitoring based on chemical analysis has the disadvantages of long detection cycle, one instrument can only detect one parameter, high maintenance cost, reagent reaction is required, and waste liquid may cause secondary pollution. Satellite remote sensing has been widely used in water quality monitoring such as transparency, chlorophyll, total nitrogen, and total phosphorus due to its advantages of large area, periodicity and economic efficiency, but its limited time resolution and cloudy and rainy weather make it still unable to solve the problem of long-term and continuous water quality monitoring. UAV hyperspectral remote sensing makes up for the lack of water quality monitoring of small and medium-sized water bodies due to its flexibility and mobility, but it is difficult to achieve continuous observation due to the limitation of endurance time and cloudy and rainy weather. With the development of technology, underwater high-frequency probes and multi-parameter water quality measuring instruments can carry out continuous high-frequency observation of water quality, but their shortcomings such as low monitoring accuracy, high price, difficult maintenance, and easy adhesion of pollution limit their large-scale application.
因此，现有的水质监测手段在数据采集频率、精确性、时效性和代表性滞后于水环境管理与决策部门的需求，尤其是一些突发性、大范围的污水排放和跨区域污染事件不能被及时捕捉。Therefore, the existing water quality monitoring methods lag behind the needs of water environment management and decision-making departments in terms of data collection frequency, accuracy, timeliness and representativeness, especially some sudden, large-scale sewage discharges and cross-regional pollution incidents cannot be captured in a timely manner.
因此，迫切需要提出一种适用于多种水质、多种天气的实时水质监测方法，弥补现有观测手段的不足，为诊断水质污染、成因机制分析及科学化防控提供助力。Therefore, there is an urgent need to propose a real-time water quality monitoring method that is suitable for various water qualities and various weather conditions, to make up for the shortcomings of existing observation methods and to provide assistance for diagnosing water pollution, analyzing the cause mechanism and scientific prevention and control.
发明内容Summary of the invention
针对上述情况，为克服现有技术的缺陷，本发明提供一种低功耗岸基站自动监测系统的水质高光谱反演构建方法，有效的解决了背景技术提出的问题。In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a water quality hyperspectral inversion construction method for a low-power shore base station automatic monitoring system, which effectively solves the problems raised by the background technology.
为实现上述目的，本发明提供如下技术方案：一种低功耗岸基站自动监测系统的水质高光谱反演构建方法，包括以下步骤：To achieve the above object, the present invention provides the following technical solution: a method for constructing a water quality hyperspectral inversion system of a low-power shore base station automatic monitoring system, comprising the following steps:
步骤一：获取待测水体的高光谱反射率数据；Step 1: Obtain the hyperspectral reflectance data of the water body to be tested;
步骤二：采集所述待测水体的同步表层水质样本，构建水质高光谱反射率数据集；Step 2: Collect synchronous surface water quality samples of the water body to be tested and construct a water quality hyperspectral reflectance data set;
步骤三：基于所述水质高光谱反射率数据集，构建最优水质智能反演模型；Step 3: constructing an optimal water quality intelligent inversion model based on the water quality hyperspectral reflectance dataset;
步骤四：基于所述待测水体高光谱反射率数据和所述最优水质智能反演模型，得到实时水质数据，实现岸基站高光谱自动水质监测。Step 4: Based on the hyperspectral reflectance data of the water body to be tested and the optimal water quality intelligent inversion model, real-time water quality data is obtained to realize hyperspectral automatic water quality monitoring at the shore base station.
优选的，步骤一中，所述水体高光谱反射率数据采用光谱采集系统采集，光谱采集系统包括：高光谱仪、光源、测量装置、系统上位机、智能移动终端、物联网平台电脑端或手机端、云服务器端。Preferably, in step one, the water body hyperspectral reflectance data is collected using a spectral acquisition system, and the spectral acquisition system includes: a hyperspectrometer, a light source, a measuring device, a system host computer, an intelligent mobile terminal, an Internet of Things platform computer or mobile phone, and a cloud server.
优选的，所述高光谱仪和光源与系统上位机连接，系统上位机与智能移动终端通过网线连接，智能移动终端与物联网平台通过路由器网络连接，云服务器端与智能移动终端网络连接，自动监测系统由充电电池和太阳能供电。Preferably, the hyperspectrometer and light source are connected to the system host computer, the system host computer is connected to the smart mobile terminal via a network cable, the smart mobile terminal is connected to the Internet of Things platform via a router network, the cloud server is connected to the smart mobile terminal network, and the automatic monitoring system is powered by rechargeable batteries and solar energy.
优选的，所述高光谱反射率数据包括：不同类型水体、不同水质状况水体和不同天气条件的水体高光谱反射率数据。Preferably, the hyperspectral reflectance data includes: hyperspectral reflectance data of water bodies of different types, water bodies of different water quality conditions and water bodies under different weather conditions.
优选的，所述不同类型水体包括：不同营养水平和不同清澈程度的内陆水体和海水水体；Preferably, the different types of water bodies include: inland water bodies and seawater bodies with different nutrient levels and different clarity;
所述不同水质状况水体包括：不同藻华爆发情况和浑浊程度的水体；The water bodies with different water quality conditions include: water bodies with different algal bloom conditions and turbidity levels;
所述不同天气条件包括：晴天、多云、阴天和小雨。The different weather conditions include: sunny, cloudy, overcast and light rain.
优选的，步骤二中，所述最优水质智能反演模型的构建过程为：Preferably, in step 2, the construction process of the optimal water quality intelligent inversion model is:
基于所述水质高光谱反射率数据集，采用多种机器学习算法，构建最优水质智能反演模型；Based on the water quality hyperspectral reflectance dataset, a variety of machine learning algorithms are used to build an optimal water quality intelligent inversion model;
其中，多种机器学习算法包括偏最小二乘、回归模型算法、神经网络算法。Among them, various machine learning algorithms include partial least squares, regression model algorithms, and neural network algorithms.
优选的，将所述最优水质智能反演模型写入云服务器端应用软件中，可在云服务器端、物联网平台和系统上位机上显示实时测量结果；Preferably, the optimal water quality intelligent inversion model is written into the cloud server application software, and the real-time measurement results can be displayed on the cloud server, the Internet of Things platform and the system host computer;
云服务器端应用软件设置高光谱仪信息、自动监测系统信息，具备高光谱反射率数据采集和存储功能、计算功能、实时测量数据展示功能；The cloud server application software sets the hyperspectral instrument information and automatic monitoring system information, and has the functions of hyperspectral reflectance data collection and storage, calculation, and real-time measurement data display;
系统上位机具备系统流程控制、高光谱仪参数控制、光源控制、实时测量数据展示功能；The system host computer has the functions of system process control, hyperspectrometer parameter control, light source control, and real-time measurement data display;
物联网平台具备系统供电断电设置、系统启停周期和开关设置、高光谱仪参数控制、光源控制、实时测量数据展示功能、存储功能和下载功能。The IoT platform has system power supply and power-off settings, system start and stop cycles and switch settings, hyperspectrometer parameter control, light source control, real-time measurement data display, storage and download functions.
优选的，步骤四中，所述实时水质参数包括：浊度、总氮、总磷、氨氮、叶绿素、透明度、悬浮物、高锰酸盐指数、富营养化指数。Preferably, in step 4, the real-time water quality parameters include: turbidity, total nitrogen, total phosphorus, ammonia nitrogen, chlorophyll, transparency, suspended matter, permanganate index, and eutrophication index.
优选的，还包括对所述高光谱反射率数据和所述待测水样进行预处理；Preferably, the method further includes preprocessing the hyperspectral reflectance data and the water sample to be tested;
其中，高光谱反射率数据的预处理包括：非水体光谱剔除、异常水体光谱剔除、光谱数据滤波、光谱数据归一化、特征波段筛选；Among them, the preprocessing of hyperspectral reflectance data includes: non-water body spectrum removal, abnormal water body spectrum removal, spectral data filtering, spectral data normalization, and characteristic band screening;
待测水样的预处理包括：抽滤、样品低温保存、实验室人工测定和水样数据异常值剔除。The pretreatment of the water samples to be tested includes: filtration, sample low-temperature storage, laboratory manual measurement and elimination of abnormal values in water sample data.
优选的，当水质指标超过特定阈值时，物联网平台会通过改变显示颜色和弹出窗口进行警报提醒；其中，特定阈值为国家水质评价标准或者自定义阈值。Preferably, when the water quality index exceeds a specific threshold, the Internet of Things platform will issue an alarm by changing the display color and popping up a window; wherein the specific threshold is a national water quality evaluation standard or a custom threshold.
与现有技术相比，本发明的有益效果是：Compared with the prior art, the present invention has the following beneficial effects:
1、相比已有的化学光度法在线监测系统，本发明的低功耗岸基站自动监测系统定时采集实现了无人值守的连续水体观测，占地面积小，无需固定，降低了设备维护难度；并可将检测多个水质参数检测周期缩短至10分钟，检测周期可自由设定；同时合理化的低功耗设计，大大降低了运行成本；相比传统的地物光谱仪，搭载于岸基站自动监测系统上的高光谱仪小巧精致、性能优越、分辨率高，可有效提高水质参数反演的精度；1. Compared with the existing chemical photometry online monitoring system, the low-power shore base station automatic monitoring system of the present invention realizes unattended continuous water body observation through timed acquisition, occupies a small area, does not need to be fixed, and reduces the difficulty of equipment maintenance; and can shorten the detection cycle of multiple water quality parameters to 10 minutes, and the detection cycle can be freely set; at the same time, the rational low-power design greatly reduces the operating cost; compared with the traditional ground object spectrometer, the high-spectrometer carried on the shore base station automatic monitoring system is small and exquisite, has superior performance and high resolution, and can effectively improve the accuracy of water quality parameter inversion;
2、相比于无人机遥感，本发明提出的低功耗岸基站自动监测系统水质高光谱监测方法是近水面采集水体高光谱反射率数据，不仅增加了数据信噪比而且基本不受大气和气溶胶影响，因此无需要进行大气校正，可弥补卫星和无人机遥感阴雨天气的监测不足；2. Compared with UAV remote sensing, the water quality hyperspectral monitoring method of the low-power shore base station automatic monitoring system proposed in the present invention collects water body hyperspectral reflectance data near the water surface, which not only increases the data signal-to-noise ratio but is also basically unaffected by the atmosphere and aerosols. Therefore, there is no need for atmospheric correction, which can make up for the lack of monitoring of cloudy and rainy weather by satellite and UAV remote sensing;
3、本发明的高光谱仪与光源装置、测量装置、系统上位机、智能移动终端、物联网平台、云服务器端，可实现远程控制系统启停、系统参数，大大提升了水质监测智能化水平；3. The hyperspectrometer and light source device, measuring device, system host computer, intelligent mobile terminal, Internet of Things platform, and cloud server of the present invention can realize remote control of system start and stop and system parameters, greatly improving the intelligent level of water quality monitoring;
4、相比传统的水下探头设备，本发明反演水质参数信息无需直接接触水体，不仅减小了能耗、因风浪造成的设备损耗以及设备维护的难度，而且减小了因生物附着污染造成的数据误差。4. Compared with traditional underwater probe equipment, the present invention does not need to directly contact the water body to invert water quality parameter information, which not only reduces energy consumption, equipment loss caused by wind and waves, and the difficulty of equipment maintenance, but also reduces data errors caused by biological attachment pollution.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
附图用来提供对本发明的进一步理解，并且构成说明书的一部分，与本发明的实施例一起用于解释本发明，并不构成对本发明的限制。The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.
在附图中：In the attached picture:
图1为本发明的一种低功耗岸基站自动监测系统的水质高光谱反演构建方法流程示意图；FIG1 is a schematic flow chart of a method for constructing a water quality hyperspectral inversion system for a low-power shore-based automatic monitoring system according to the present invention;
图2为本发明低功耗岸基站自动监测系统正视示意图；FIG2 is a schematic front view of a low-power shore-based base station automatic monitoring system according to the present invention;
具体实施方式DETAILED DESCRIPTION
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例；基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
实施例一，由图1给出，本发明涉及一种低功耗岸基站自动监测系统的水质高光谱反演构建方法，包括以下步骤：Embodiment 1, as shown in FIG1 , the present invention relates to a method for constructing a water quality hyperspectral inversion system of a low-power shore base station automatic monitoring system, comprising the following steps:
步骤一：获取待测水体的高光谱反射率数据；Step 1: Obtain the hyperspectral reflectance data of the water body to be tested;
步骤二：采集所述待测水体的同步表层水质样本，构建水质高光谱反射率数据集；Step 2: Collect synchronous surface water quality samples of the water body to be tested and construct a water quality hyperspectral reflectance data set;
步骤三：基于所述水质高光谱反射率数据集，构建最优水质智能反演模型；Step 3: constructing an optimal water quality intelligent inversion model based on the water quality hyperspectral reflectance dataset;
步骤四：基于所述待测水体高光谱反射率数据和所述最优水质智能反演模型，得到实时水质数据，实现岸基站高光谱自动水质监测。Step 4: Based on the hyperspectral reflectance data of the water body to be tested and the optimal water quality intelligent inversion model, real-time water quality data is obtained to realize hyperspectral automatic water quality monitoring at the shore base station.
步骤一中，所述水体高光谱反射率数据采用光谱采集系统采集，光谱采集系统包括：高光谱仪、光源、测量装置、系统上位机、智能移动终端、物联网平台电脑端或手机端、云服务器端。In step one, the water body hyperspectral reflectance data is collected using a spectral acquisition system, which includes: a hyperspectrometer, a light source, a measuring device, a system host computer, an intelligent mobile terminal, an Internet of Things platform computer or mobile phone, and a cloud server.
所述高光谱仪和光源与系统上位机连接，系统上位机与智能移动终端通过网线连接，智能移动终端与物联网平台通过路由器网络连接，云服务器端与智能移动终端网络连接，自动监测系统由充电电池和太阳能供电。The hyperspectrometer and light source are connected to the system host computer, the system host computer is connected to the intelligent mobile terminal through a network cable, the intelligent mobile terminal is connected to the Internet of Things platform through a router network, the cloud server is connected to the intelligent mobile terminal network, and the automatic monitoring system is powered by rechargeable batteries and solar energy.
所述高光谱反射率数据包括：不同类型水体、不同水质状况水体和不同天气条件的水体高光谱反射率数据。The hyperspectral reflectance data include: hyperspectral reflectance data of water bodies of different types, water bodies of different water quality conditions and water bodies of different weather conditions.
所述不同类型水体包括：不同营养水平和不同清澈程度的内陆水体和海水水体；The different types of water bodies include: inland water bodies and marine water bodies with different nutrient levels and different clarity;
所述不同水质状况水体包括：不同藻华爆发情况和浑浊程度的水体；The water bodies with different water quality conditions include: water bodies with different algal bloom conditions and turbidity levels;
所述不同天气条件包括：晴天、多云、阴天和小雨。The different weather conditions include: sunny, cloudy, overcast and light rain.
步骤二中，所述最优水质智能反演模型的构建过程为：In step 2, the construction process of the optimal water quality intelligent inversion model is:
基于所述水质高光谱反射率数据集，采用多种机器学习算法，构建最优水质智能反演模型；Based on the water quality hyperspectral reflectance dataset, a variety of machine learning algorithms are used to build an optimal water quality intelligent inversion model;
其中，多种机器学习算法包括偏最小二乘、回归模型算法、神经网络算法。Among them, various machine learning algorithms include partial least squares, regression model algorithms, and neural network algorithms.
将所述最优水质智能反演模型写入云服务器端应用软件中，可在云服务器端、物联网平台和系统上位机上显示实时测量结果；The optimal water quality intelligent inversion model is written into the cloud server application software, and the real-time measurement results can be displayed on the cloud server, the Internet of Things platform and the system host computer;
云服务器端应用软件设置高光谱仪信息、自动监测系统信息，具备高光谱反射率数据采集和存储功能、计算功能、实时测量数据展示功能；The cloud server application software sets the hyperspectral instrument information and automatic monitoring system information, and has the functions of hyperspectral reflectance data collection and storage, calculation, and real-time measurement data display;
系统上位机具备系统流程控制、高光谱仪参数控制、光源控制、实时测量数据展示功能；The system host computer has the functions of system process control, hyperspectrometer parameter control, light source control, and real-time measurement data display;
物联网平台具备系统供电断电设置、系统启停周期和开关设置、高光谱仪参数控制、光源控制、实时测量数据展示功能、存储功能和下载功能。The IoT platform has system power supply and power-off settings, system start and stop cycles and switch settings, hyperspectrometer parameter control, light source control, real-time measurement data display, storage and download functions.
步骤四中，所述实时水质参数包括：浊度、总氮、总磷、氨氮、叶绿素、透明度、悬浮物、高锰酸盐指数、富营养化指数。In step 4, the real-time water quality parameters include: turbidity, total nitrogen, total phosphorus, ammonia nitrogen, chlorophyll, transparency, suspended matter, permanganate index, and eutrophication index.
还包括对所述高光谱反射率数据和所述待测水样进行预处理；It also includes preprocessing the hyperspectral reflectance data and the water sample to be tested;
其中，高光谱反射率数据的预处理包括：非水体光谱剔除、异常水体光谱剔除、光谱数据滤波、光谱数据归一化、特征波段筛选；Among them, the preprocessing of hyperspectral reflectance data includes: non-water body spectrum removal, abnormal water body spectrum removal, spectral data filtering, spectral data normalization, and characteristic band screening;
待测水样的预处理包括：抽滤、样品低温保存、实验室人工测定和水样数据异常值剔除。The pretreatment of the water samples to be tested includes: filtration, sample low-temperature storage, laboratory manual measurement and elimination of abnormal values in water sample data.
当水质指标超过特定阈值时，物联网平台会通过改变显示颜色和弹出窗口进行警报提醒；其中，特定阈值为国家水质评价标准或者自定义阈值。When the water quality index exceeds a specific threshold, the IoT platform will issue an alarm by changing the display color and popping up a window; the specific threshold is the national water quality evaluation standard or a custom threshold.
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
尽管已经示出和描述了本发明的实施例，对于本领域的普通技术人员而言，可以理解在不脱离本发明的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由所附权利要求及其等同物限定。Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
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