CN108932483A - A kind of odor identification method and device based on machine olfaction - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及人工智能技术领域,更具体地说,涉及一种基于机器嗅觉的气味识别方法、装置、设备及计算机可读存储介质。The present invention relates to the technical field of artificial intelligence, and more specifically, to a method, device, equipment and computer-readable storage medium for smell recognition based on machine sense of smell.
背景技术Background technique
目前,机器嗅觉技术的应用越来越广泛,在工业气体监测、化工产品质量监测、室内外环境监测、医疗诊断、中药材鉴别等诸多方面,已有大量的应用案例。At present, the application of machine olfactory technology is becoming more and more extensive. There have been a large number of application cases in many aspects such as industrial gas monitoring, chemical product quality monitoring, indoor and outdoor environmental monitoring, medical diagnosis, and identification of Chinese medicinal materials.
在这些应用领域内,被监测的气味多为混合气味,且电子鼻数据库中的气味信息有限,所以必然存在未知气体。并且,现有的气味识别技术在处理传感器的响应信号时,未考虑到未知气味对识别结果的影响,从而导致识别结果误差较大,降低了识别的效率、准确度和气味定量分析的精度。In these application fields, most of the monitored odors are mixed odors, and the odor information in the electronic nose database is limited, so there must be unknown gases. Moreover, the existing odor recognition technology does not take into account the influence of unknown odors on the recognition results when processing the response signal of the sensor, resulting in large errors in the recognition results, reducing the efficiency and accuracy of recognition and the precision of quantitative odor analysis.
因此,如何提高气味识别的效率、准确度以及气味定量分析的精度,是本领域技术人员需要解决的问题。Therefore, how to improve the efficiency and accuracy of odor recognition and the precision of odor quantitative analysis is a problem to be solved by those skilled in the art.
发明内容Contents of the invention
本发明的目的在于提供一种基于机器嗅觉的气味识别方法、装置、设备及计算机可读存储介质,以提高气味识别的效率、准确度以及气味定量分析的精度。The object of the present invention is to provide a method, device, equipment and computer-readable storage medium for odor identification based on machine sense of smell, so as to improve the efficiency and accuracy of odor identification and the precision of odor quantitative analysis.
为实现上述目的,本发明实施例提供了如下技术方案:In order to achieve the above object, the embodiment of the present invention provides the following technical solutions:
一种基于机器嗅觉的气味识别方法,包括:A method for odor recognition based on machine olfaction, comprising:
获取目标混合气味的混合响应信号;Obtain the mixed response signal of the target mixed odor;
判断所述混合响应信号中是否存在未知响应信号;judging whether there is an unknown response signal in the mixed response signal;
若是,则从所述混合响应信号中滤除所述未知响应信号,获得目标响应信号,并将所述目标响应信号与预设数据库中的模板响应信号进行对比,确定所述目标响应信号对应的气味类型和含量。If so, filter out the unknown response signal from the mixed response signal to obtain a target response signal, and compare the target response signal with a template response signal in a preset database to determine the target response signal corresponding to Odor type and content.
其中,所述从所述混合响应信号中滤除所述未知响应信号,获得目标响应信号,并将所述目标响应信号与预设数据库中的模板响应信号进行对比,确定所述目标响应信号对应的气味类型和含量,包括:Wherein, the unknown response signal is filtered out from the mixed response signal to obtain a target response signal, and the target response signal is compared with a template response signal in a preset database to determine that the target response signal corresponds to Types and levels of odors, including:
判断所述混合响应信号是否为线性响应信号;judging whether the mixed response signal is a linear response signal;
若是,则利用线性混合模型中的优化函数滤除所述未知响应信号,获得所述目标响应信号,将所述目标响应信号与预设数据库中的模板响应信号进行对比,并通过乘子法确定所述目标响应信号对应的气味类型和含量;H表示滤波器混合矩阵,z(t)表示未知响应信号,t表示时间变量,x(t-P)表示噪声信号。If so, use the optimization function in the linear mixed model to filter out the unknown response signal to obtain the target response signal, compare the target response signal with the template response signal in the preset database, and determine by the multiplier method The odor type and content corresponding to the target response signal; H represents the filter mixing matrix, z(t) represents the unknown response signal, t represents the time variable, and x(tP) represents the noise signal.
其中,当所述混合响应信号中不存在未知响应信号,且所述混合响应信号为线性响应信号时,还包括:Wherein, when there is no unknown response signal in the mixed response signal, and when the mixed response signal is a linear response signal, it also includes:
利用所述线性混合模型中的优化函数滤除所述未知响应信号,获得所述目标响应信号;Using the optimization function in the linear mixed model to filter out the unknown response signal to obtain the target response signal;
将所述目标响应信号与预设数据库中的模板响应信号进行对比,通过岭回归方法确定所述目标响应信号对应的气味类型和含量。The target response signal is compared with the template response signal in the preset database, and the odor type and content corresponding to the target response signal are determined by a ridge regression method.
其中,当所述混合响应信号为非线性响应信号时,还包括:Wherein, when the mixed response signal is a nonlinear response signal, it also includes:
利用非线性混合模型中的优化函数滤除所述未知响应信号,获得所述目标响应信号;filtering out the unknown response signal by using an optimization function in the nonlinear mixed model to obtain the target response signal;
将所述目标响应信号与预设数据库中的模板响应信号进行对比,并通过混沌遗传算法确定所述目标响应信号对应的气味类型和含量;Comparing the target response signal with the template response signal in the preset database, and determining the odor type and content corresponding to the target response signal through a chaotic genetic algorithm;
其中,所述非线性混合模型中的优化函数为:min||y(t)-g(t)-z(t)||,y(t)表示混合响应信号,g(t)表示非线性响应信号,z(t)表示未知响应信号。Wherein, the optimization function in the nonlinear mixed model is: min||y(t)-g(t)-z(t)||, y(t) represents the mixed response signal, and g(t) represents the nonlinear Response signal, z(t) represents the unknown response signal.
其中,所述非线性混合模型利用多元自适应回归样条法生成,具体的生成步骤为:Wherein, the nonlinear mixed model is generated using a multivariate adaptive regression spline method, and the specific generation steps are:
获取混合气味的实验数据,并对所述实验数据进行环境因素校正;Obtaining experimental data of mixed odors, and correcting the experimental data for environmental factors;
对校正后的实验数据进行划分,得到多个区间,并基于每个区间生成对应的基函数;Divide the corrected experimental data to obtain multiple intervals, and generate corresponding basis functions based on each interval;
采用均分误差法选择精确度最高的基函数,并将所述精确度最高的基函数对应的模型确定为所述非线性混合模型。The basis function with the highest accuracy is selected by using the average error method, and the model corresponding to the basis function with the highest accuracy is determined as the nonlinear mixed model.
其中,所述通过混沌遗传算法确定所述目标响应信号对应的气味类型和含量,包括:Wherein, the determination of the odor type and content corresponding to the target response signal through the chaotic genetic algorithm includes:
基于所述目标响应信号生成所述混沌遗传算法的种群;generating a population of the chaotic genetic algorithm based on the target response signal;
对所述种群中的个体进行个体评价,并对完成个体评价的种群进行选择运算、交叉运算和变异运算,使所述种群进行迭代更新;Carry out individual evaluation on the individuals in the population, and perform selection operation, crossover operation and mutation operation on the population that has completed the individual evaluation, so that the population is iteratively updated;
判断当前进化代数是否大于预设的最大进化代数;Determine whether the current evolution algebra is greater than the preset maximum evolution algebra;
若是,则输出最优解,并依据所述最优解确定所述目标响应信号对应的气味类型和含量。If yes, output the optimal solution, and determine the type and content of the odor corresponding to the target response signal according to the optimal solution.
一种基于机器嗅觉的气味识别装置,包括:An odor recognition device based on machine sense of smell, comprising:
获取模块,用于获取目标混合气味的混合响应信号;Obtaining module, for obtaining the mixed response signal of target mixed smell;
判断模块,用于判断所述混合响应信号中是否存在未知响应信号;A judging module, configured to judge whether there is an unknown response signal in the mixed response signal;
识别模块,用于当所述混合响应信号中存在未知响应信号时,从所述混合响应信号中滤除所述未知响应信号,获得目标响应信号,并将所述目标响应信号与预设数据库中的模板响应信号进行对比,确定所述目标响应信号对应的气味类型和含量。An identification module, configured to filter out the unknown response signal from the mixed response signal when there is an unknown response signal in the mixed response signal, obtain a target response signal, and compare the target response signal with the preset database The template response signals are compared to determine the odor type and content corresponding to the target response signal.
其中,所述识别模块包括:Wherein, the identification module includes:
判断单元,用于判断所述混合响应信号是否为线性响应信号;a judging unit, configured to judge whether the mixed response signal is a linear response signal;
识别单元,用于当所述混合响应信号为线性响应信号时,利用线性混合模型中的优化函数滤除所述未知响应信号,获得所述目标响应信号,将所述目标响应信号与预设数据库中的模板响应信号进行对比,并通过乘子法确定所述目标响应信号对应的气味类型和含量;An identification unit, configured to use an optimization function in a linear mixed model to filter out the unknown response signal when the mixed response signal is a linear response signal to obtain the target response signal, and compare the target response signal with a preset database Compare the template response signals in , and determine the odor type and content corresponding to the target response signal by the multiplier method;
H表示滤波器混合矩阵,z(t)表示未知响应信号,t表示时间变量,x(t-P)表示噪声信号。 H represents the filter mixing matrix, z(t) represents the unknown response signal, t represents the time variable, and x(tP) represents the noise signal.
一种基于机器嗅觉的气味识别设备,包括:An odor recognition device based on machine sense of smell, comprising:
存储器,用于存储计算机程序;memory for storing computer programs;
处理器,用于执行所述计算机程序时实现上述任意一项所述的基于机器嗅觉的气味识别方法的步骤。A processor, configured to implement the steps of any one of the above-mentioned odor recognition methods based on machine sense of smell when executing the computer program.
一种计算机可读存储介质,所述计算机可读存储介质上存储有计算机程序,所述计算机程序被处理器执行时实现上述任意一项所述的基于机器嗅觉的气味识别方法的步骤。A computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of any one of the above-mentioned odor recognition methods based on machine sense of smell are realized.
通过以上方案可知,本发明实施例提供的一种基于机器嗅觉的气味识别方法,包括:获取目标混合气味的混合响应信号;判断所述混合响应信号中是否存在未知响应信号;若是,则从所述混合响应信号中滤除所述未知响应信号,获得目标响应信号,并将所述目标响应信号与预设数据库中的模板响应信号进行对比,确定所述目标响应信号对应的气味类型和含量。From the above schemes, it can be seen that a method for odor recognition based on machine olfaction provided by an embodiment of the present invention includes: acquiring a mixed response signal of a target mixed odor; judging whether there is an unknown response signal in the mixed response signal; The unknown response signal is filtered out from the mixed response signal to obtain the target response signal, and the target response signal is compared with the template response signal in the preset database to determine the odor type and content corresponding to the target response signal.
可见,所述方法在处理混合气味的混合响应信号之前,首先滤除混合响应信号中的未知响应信号,以避免未知气味对识别结果产生影响;同时,将滤除未知响应信号后得到的目标响应信号与预设数据库中的模板响应信号进行对比,从而确定出目标响应信号对应的气味类型和含量,由此提高了识别效率和准确度,也提高了气味定量分析的精度。It can be seen that before processing the mixed response signal of the mixed odor, the method first filters out the unknown response signal in the mixed response signal to avoid the impact of the unknown odor on the recognition result; at the same time, the target response obtained after filtering out the unknown response signal The signal is compared with the template response signal in the preset database to determine the type and content of the odor corresponding to the target response signal, thereby improving the recognition efficiency and accuracy, and also improving the accuracy of odor quantitative analysis.
相应地,本发明实施例提供的一种基于机器嗅觉的气味识别装置、设备及计算机可读存储介质,也同样具有上述技术效果。Correspondingly, the apparatus, equipment and computer-readable storage medium for smell recognition based on machine sense of smell provided by the embodiments of the present invention also have the above-mentioned technical effects.
附图说明Description of drawings
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.
图1为本发明实施例公开的一种基于机器嗅觉的气味识别方法流程图;Fig. 1 is a flow chart of a method for smell recognition based on machine sense of smell disclosed in an embodiment of the present invention;
图2为本发明实施例公开的三种类似气味的响应模式示意图:Fig. 2 is a schematic diagram of the response modes of three similar odors disclosed in the embodiment of the present invention:
图3为本发明实施例公开的另一种基于机器嗅觉的气味识别方法流程图;FIG. 3 is a flow chart of another odor recognition method based on machine sense of smell disclosed in an embodiment of the present invention;
图4为本发明实施例公开的一种基于机器嗅觉的气味识别装置示意图;Fig. 4 is a schematic diagram of an odor recognition device based on machine sense of smell disclosed in an embodiment of the present invention;
图5为本发明实施例公开的一种基于机器嗅觉的气味识别设备示意图。Fig. 5 is a schematic diagram of an odor recognition device based on machine sense of smell disclosed in an embodiment of the present invention.
具体实施方式Detailed ways
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 following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
本发明实施例公开了一种基于机器嗅觉的气味识别方法、装置、设备及计算机可读存储介质,以提高气味识别的效率、准确度以及气味定量分析的精度。The embodiment of the present invention discloses a method, device, equipment and computer-readable storage medium for smell recognition based on machine sense of smell, so as to improve the efficiency and accuracy of smell recognition and the precision of quantitative analysis of smell.
参见图1,本发明实施例提供的一种基于机器嗅觉的气味识别方法,包括:Referring to Fig. 1, a kind of odor recognition method based on machine sense of smell provided by the embodiment of the present invention, comprises:
S101、获取目标混合气味的混合响应信号;S101. Obtain a mixed response signal of the target mixed odor;
S102、判断混合响应信号中是否存在未知响应信号;若是,则执行S103;若否,则执行S104;S102. Determine whether there is an unknown response signal in the mixed response signal; if yes, execute S103; if not, execute S104;
S103、从混合响应信号中滤除未知响应信号,获得目标响应信号,并将目标响应信号与预设数据库中的模板响应信号进行对比,确定目标响应信号对应的气味类型和含量;S103. Filter out the unknown response signal from the mixed response signal to obtain the target response signal, compare the target response signal with the template response signal in the preset database, and determine the odor type and content corresponding to the target response signal;
具体的,由于混合响应信号中存在未知响应信号,为了消除未知响应信号对气味分析过程的影响,需要首先滤除未知响应信号。其中,由于类似气味的不同响应信号,通过同一传感器采集到的响应模式大致相同,故可将未知响应信号采用特定的函数进行表示。Specifically, since there are unknown response signals in the mixed response signals, in order to eliminate the influence of the unknown response signals on the odor analysis process, it is necessary to filter out the unknown response signals first. Among them, since different response signals similar to odors have roughly the same response mode collected by the same sensor, the unknown response signal can be represented by a specific function.
例如:皇帝柑果肉、脐橙果肉和砂糖桔果肉这三种类似气味的响应模式如图2所示,由图2可以看出,同一传感器具有相同的气味响应模式,故存在未知气味(由单一气体成分产生的单一气味或由多种气体成分产生的复合气味)时,不同未知气味在每个传感器上产生的数值大小各不相同,但在每个传感器上的气味响应模式必定相同。基于此,便可以将特定的目标函数进行优化,以生成兼顾未知气味的识别模型。For example: the response patterns of these three similar odors of emperor tangerine pulp, navel orange pulp and shatangju pulp are shown in Figure 2. As can be seen from Figure 2, the same sensor has the same odor response pattern, so there is an unknown odor (generated by a single gas When a single odor produced by a single gas component or a compound odor produced by multiple gas components), different unknown odors have different numerical values on each sensor, but the odor response pattern on each sensor must be the same. Based on this, a specific objective function can be optimized to generate a recognition model that takes into account unknown odors.
S104、将混合响应信号与预设数据库中的模板响应信号进行对比,确定混合响应信号对应的气味类型和含量。S104. Compare the mixed response signal with the template response signal in the preset database, and determine the odor type and content corresponding to the mixed response signal.
具体的,当混合响应信号中不存在未知响应信号时,则可以直接将混合响应信号与预设数据库中的模板响应信号进行对比,从而确定混合响应信号对应的气味类型和含量。Specifically, when there is no unknown response signal in the mixed response signal, the mixed response signal can be directly compared with the template response signal in the preset database, so as to determine the odor type and content corresponding to the mixed response signal.
可见,本实施例提供了一种基于机器嗅觉的气味识别方法,所述方法在处理混合气味的混合响应信号之前,首先滤除混合响应信号中的未知响应信号,以避免未知气味对识别结果产生影响;同时,将滤除未知响应信号后得到的目标响应信号与预设数据库中的模板响应信号进行对比,从而确定出目标响应信号对应的气味类型和含量,由此提高了识别效率和准确度,也提高了气味定量分析的精度。It can be seen that this embodiment provides a method for odor recognition based on machine olfaction. Before processing the mixed response signal of the mixed odor, the method first filters out the unknown response signal in the mixed response signal, so as to avoid the impact of the unknown odor on the recognition result. At the same time, the target response signal obtained after filtering out the unknown response signal is compared with the template response signal in the preset database to determine the type and content of the odor corresponding to the target response signal, thereby improving the recognition efficiency and accuracy , also improves the precision of odor quantitative analysis.
本发明实施例公开了一种基于机器嗅觉的气味识别方法,相对于上一实施例,本实施例对技术方案作了进一步的说明和优化。The embodiment of the present invention discloses an odor recognition method based on machine sense of smell. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution.
参见图3,本发明实施例提供的另一种基于机器嗅觉的气味识别方法,包括:Referring to Fig. 3, another odor recognition method based on machine olfaction provided by an embodiment of the present invention includes:
S301、获取目标混合气味的混合响应信号;S301. Obtain a mixed response signal of the target mixed odor;
S303、判断混合响应信号中是否存在未知响应信号;若是,则执行S303;若否,则执行S307;S303. Determine whether there is an unknown response signal in the mixed response signal; if yes, execute S303; if not, execute S307;
S303、判断混合响应信号是否为线性响应信号;若是,则执行S304;若否,则执行S305;S303. Determine whether the mixed response signal is a linear response signal; if yes, execute S304; if not, execute S305;
S304、利用线性混合模型中的优化函数滤除未知响应信号,获得目标响应信号,将目标响应信号与预设数据库中的模板响应信号进行对比,并通过乘子法确定目标响应信号对应的气味类型和含量;S304. Use the optimization function in the linear mixed model to filter out the unknown response signal, obtain the target response signal, compare the target response signal with the template response signal in the preset database, and determine the odor type corresponding to the target response signal by the multiplier method and content;
滤波器混合矩阵,z(t)表示未知响应信号,t表示时间变量,x(t-P)表示噪声信号。 Filter mixing matrix, z(t) represents the unknown response signal, t represents the time variable, and x(tP) represents the noise signal.
并且,乘子法用于有约束的非线性规划,属于罚函数法,是在约束问题的Lagrange函数中加入相应的惩罚,既能保证迭代点会收敛到约束问题的极小点,更重要的是能保证数值计算的稳定性。Moreover, the multiplier method is used in constrained nonlinear programming, which belongs to the penalty function method. It adds corresponding penalties to the Lagrange function of the constrained problem, which can ensure that the iteration point will converge to the minimum point of the constrained problem, and more importantly It can guarantee the stability of numerical calculation.
需要说明的是,所述线性混合模型中的优化函数可基于下述过程进行获得。线性混合条件下且同时考虑机器嗅觉传感器阵列的稳态响应和暂态响应时,单个传感器对单种气味的响应可以用有限冲激响应滤波器来描述。例如:若存在m个传感器、n个源信号(源气味),则可建立如下卷积混合数学模型: It should be noted that the optimization function in the linear mixed model can be obtained based on the following process. The response of a single sensor to a single odor can be described by a finite impulse response filter under linear mixture conditions while considering both the steady-state and transient responses of a machine olfactory sensor array. For example: if there are m sensors and n source signals (source odors), the following convolutional mixed mathematical model can be established:
其中,y(t)、x(t)、n(t)分别是由m个传感器响应组成的m×1传感器响应列向量、由n个信号源组成的n×1的信号源列向量、由m个传感器的观测噪声组成的m×1的噪声向量,s表示冲激响应向量的长度,H表示滤波器混合矩阵且与时间无关。需要说明的是,在机器嗅觉研究和应用中,都假定x(t)在一次测量过程(约30秒-1分钟)中基本不变。Among them, y(t), x(t), and n(t) are respectively an m×1 sensor response column vector composed of m sensor responses, an n×1 signal source column vector composed of n signal sources, and An m×1 noise vector composed of the observation noise of m sensors, s represents the length of the impulse response vector, and H represents the filter mixing matrix and has nothing to do with time. It should be noted that in the research and application of machine olfaction, it is assumed that x(t) is basically unchanged during a measurement process (about 30 seconds to 1 minute).
由于信号源的含量(或强度)不能为负,故可构造如下目标函数:Since the content (or strength) of the signal source cannot be negative, the following objective function can be constructed:
由上式可看出,其为带约束条件的最小二乘法,可以用Lagrange乘子法解算出信号源向量x(t)。但由于传感器间可能存在共线性(完全相关或高度相关),所以Lagrange乘子法会面临病态求解问题。为克服Lagrange乘子法的局限性,可采用岭回归算法求解。岭回归公式如下:It can be seen from the above formula that it is the least squares method with constraints, and the signal source vector x(t) can be calculated by using the Lagrange multiplier method. However, because there may be collinearity (complete correlation or high correlation) between sensors, the Lagrange multiplier method will face ill-conditioned solution problems. In order to overcome the limitations of the Lagrange multiplier method, the ridge regression algorithm can be used to solve it. The ridge regression formula is as follows:
其中,X为源气味矢量,H为传递矩阵,H*为H的伪逆矩阵,Y为传感器响应矩阵,γ(0≤γ≤1)为正则化参数,可依据交叉验证获得。但是,若其中存在未知气味,由于传感器阵列响应矩阵A或滤波器响应矩阵Hp是针对特定应用场景的n种源气味来进行描述的,不含未知气体的响应信息,所以利用上述最优化目标求解,必然存在比较大的误差。Among them, X is the source odor vector, H is the transfer matrix, H * is the pseudo-inverse matrix of H, Y is the sensor response matrix, and γ (0≤γ≤1) is the regularization parameter, which can be obtained according to cross-validation. However, if there is an unknown odor, since the sensor array response matrix A or the filter response matrix H p is described for n kinds of source odors in a specific application scenario, and does not contain the response information of the unknown gas, the above optimization objective Solving, there must be a relatively large error.
为了解决上述问题,可依据类似气味的不同响应信号,通过同一传感器采集到的响应模式大致相同的原理进行优化,进而得到线性混合模型中的优不具区分度,将舍弃;另外9个传感器的z(t)分量取下述公式之一进行求解。In order to solve the above problems, according to the different response signals of similar odors, the optimization can be carried out through the principle that the response modes collected by the same sensor are roughly the same, and then the optimal value in the linear mixed model can be obtained. If there is no discrimination, it will be discarded; the z(t) components of the other 9 sensors are solved by one of the following formulas.
其中,式(1)为:xi(t)≥0,xi(t)表示噪声信号;式(2)为:z(t)=0;Among them, formula (1) is: x i (t) ≥ 0, x i (t) represents the noise signal; formula (2) is: z (t) = 0;
式(3)包括四种求解模式,其中,式(3-1)为:z(t)=Aeat,式(3-2)为:z(t)=1-Bebt,式(3-3)为:z(t)=Cect,式(3-4)为:z(t)=kt2+lt+m。Formula (3) includes four solution modes, wherein, formula (3-1) is: z(t)=Ae at , formula (3-2) is: z(t)=1-Be bt , formula (3- 3) is: z(t)=Cec ct , formula (3-4) is: z(t)=kt 2 +lt+m.
当所有传感器的z(t)分量等于0,则意味着不存在未知气味。因此,该函数模型可兼顾未知气味存在的情况。When the z(t) components of all sensors are equal to 0, it means that there is no unknown odor. Therefore, this functional model can take into account the presence of unknown odors.
其中,当响应信号线性增大,且逐步达到稳定状态时,采用Aeat求解z(t);当响应信号线性减小,且逐步达到稳定状态时,采用1-Bebt求解z(t);当响应信号为非线性响应信号,且突变至稳定前的状态时,采用Cect求解z(t);当响应信号为非线性响应信号,且突变至稳定后的状态时,采用kt2+lt+m求解z(t)。Among them, when the response signal increases linearly and gradually reaches a steady state, Ae at is used to solve z(t); when the response signal decreases linearly and gradually reaches a steady state, 1-Be bt is used to solve z(t); When the response signal is a nonlinear response signal and changes to a pre-stabilized state, use Ce ct to solve z(t); when the response signal is a nonlinear response signal and changes to a stable state, use kt 2 +lt +m Solve for z(t).
S305、利用非线性混合模型中的优化函数滤除未知响应信号,获得目标响应信号,并执行S306;S305. Use the optimization function in the nonlinear mixed model to filter out the unknown response signal, obtain the target response signal, and execute S306;
S306、将目标响应信号与预设数据库中的模板响应信号进行对比,并通过混沌遗传算法确定目标响应信号对应的气味类型和含量。S306. Compare the target response signal with the template response signal in the preset database, and determine the odor type and content corresponding to the target response signal through a chaotic genetic algorithm.
当混合响应信号为非线性响应信号时,可利用非线性混合模型中的优化函数滤除未知响应信号,获得目标响应信号;其中,非线性混合模型中的优化函数为:min||y(t)-g(t)-z(t)||,y(t)表示混合响应信号,g(t)表示非线性响应信号,z(t)表示未知响应信号。When the mixed response signal is a nonlinear response signal, the optimization function in the nonlinear mixed model can be used to filter out the unknown response signal to obtain the target response signal; where, the optimized function in the nonlinear mixed model is: min||y(t )-g(t)-z(t)||, y(t) represents a mixed response signal, g(t) represents a nonlinear response signal, and z(t) represents an unknown response signal.
S307、将混合响应信号与预设数据库中的模板响应信号进行对比,确定混合响应信号对应的气味类型和含量。S307. Compare the mixed response signal with the template response signal in the preset database, and determine the odor type and content corresponding to the mixed response signal.
在本实施例中,非线性混合模型利用多元自适应回归样条法生成,具体的生成步骤为:In this embodiment, the nonlinear mixed model is generated using the multivariate adaptive regression spline method, and the specific generation steps are:
获取混合气味的实验数据,并对实验数据进行环境因素校正;Obtain the experimental data of mixed odors, and correct the experimental data for environmental factors;
对校正后的实验数据进行划分,得到多个区间,并基于每个区间生成对应的基函数;Divide the corrected experimental data to obtain multiple intervals, and generate corresponding basis functions based on each interval;
采用均分误差法选择精确度最高的基函数,并将精确度最高的基函数对应的模型确定为非线性混合模型。均分误差法的最小结果对应精确度最高的基函数。The basis function with the highest accuracy is selected by the average error method, and the model corresponding to the basis function with the highest accuracy is determined as a nonlinear mixed model. The smallest result of the equipartition error method corresponds to the basis function with the highest accuracy.
其中,该混合气味的实验数据还可以用于生成预设的气味数据库,用于与待识别的混合响应信号进行对比。Wherein, the experimental data of the mixed odor can also be used to generate a preset odor database for comparison with the mixed response signal to be identified.
所述对实验数据进行环境因素校正包括:采用神经网络的方法对实验数据的温度、湿度和大气压力进行补偿。The correction of environmental factors to the experimental data includes: using a neural network method to compensate the temperature, humidity and atmospheric pressure of the experimental data.
例如:在特定应用下,采用三层BP神经网络进行温度和湿度补偿,神经网络的输入为传感器响应、环境温度、环境湿度和大气压力,输出为校正后的传感器响应。如此可完善预设的气味数据库(数据库所存的数据不可能覆盖整个温度,湿度和大气压力范围),通过校正可使数据库中的数据覆盖范围更为广泛。For example, in a specific application, a three-layer BP neural network is used for temperature and humidity compensation. The input of the neural network is the sensor response, ambient temperature, ambient humidity, and atmospheric pressure, and the output is the corrected sensor response. In this way, the preset odor database can be perfected (the data stored in the database cannot cover the entire range of temperature, humidity and atmospheric pressure), and the data coverage in the database can be made wider through correction.
其中,所述基函数一般为线性函数或二次型函数,通过神经网络可对函数中的参数进行修正,以提高基函数的精确度。Wherein, the base function is generally a linear function or a quadratic function, and the parameters in the function can be corrected through the neural network to improve the accuracy of the base function.
在本实施例中,通过混沌遗传算法确定目标响应信号对应的气味类型和含量,包括:In this embodiment, the type and content of the odor corresponding to the target response signal are determined by a chaotic genetic algorithm, including:
基于目标响应信号生成混沌遗传算法的种群;Generate a population of chaotic genetic algorithm based on the target response signal;
对种群中的个体进行个体评价,并对完成个体评价的种群进行选择运算、交叉运算和变异运算,使种群进行迭代更新;Carry out individual evaluation on the individuals in the population, and perform selection operation, crossover operation and mutation operation on the population that has completed the individual evaluation, so that the population can be iteratively updated;
判断当前进化代数是否大于预设的最大进化代数;Determine whether the current evolution algebra is greater than the preset maximum evolution algebra;
若是,则输出最优解,并依据最优解确定目标响应信号对应的气味类型和含量。If so, output the optimal solution, and determine the type and content of the odor corresponding to the target response signal according to the optimal solution.
具体的,采用混沌遗传算法获得的结果可用来估计混合气味的浓度及类型,其中,浓度的估算结果的精确度可达0.01mg/m3,从而提高了气味定量分析的精度。Specifically, the results obtained by using the chaotic genetic algorithm can be used to estimate the concentration and type of the mixed odor, wherein the accuracy of the estimation result of the concentration can reach 0.01 mg/m 3 , thereby improving the accuracy of the quantitative analysis of the odor.
可见,本实施例提供了另一种基于机器嗅觉的气味识别方法,所述方法在处理混合气味的混合响应信号时,首先判断混合响应信号为线性响应信号还是非线性响应信号,当混合响应信号为线性响应信号时,利用线性混合模型中的优化函数滤除未知响应信号,获得目标响应信号,将目标响应信号与预设数据库中的模板响应信号进行对比,并通过乘子法确定目标响应信号对应的气味类型和含量;当混合响应信号为非线性响应信号时,利用非线性混合模型中的优化函数滤除未知响应信号,获得目标响应信号,并将目标响应信号与预设数据库中的模板响应信号进行对比,并通过混沌遗传算法确定目标响应信号对应的气味类型和含量,由此提高了识别效率和准确度,也提高了气味定量分析的精度。It can be seen that this embodiment provides another odor recognition method based on machine olfaction. When the method processes the mixed response signal of mixed odor, it first judges whether the mixed response signal is a linear response signal or a nonlinear response signal. When the mixed response signal When it is a linear response signal, use the optimization function in the linear mixed model to filter out the unknown response signal to obtain the target response signal, compare the target response signal with the template response signal in the preset database, and determine the target response signal through the multiplier method Corresponding odor type and content; when the mixed response signal is a nonlinear response signal, use the optimization function in the nonlinear mixed model to filter out the unknown response signal to obtain the target response signal, and compare the target response signal with the template in the preset database The response signals are compared, and the odor type and content corresponding to the target response signal are determined by the chaotic genetic algorithm, thereby improving the recognition efficiency and accuracy, and also improving the accuracy of odor quantitative analysis.
需要说明的是,当所述混合响应信号中不存在未知响应信号,且所述混合响应信号为线性响应信号时,还包括:It should be noted that when there is no unknown response signal in the mixed response signal, and the mixed response signal is a linear response signal, it also includes:
利用所述线性混合模型中的优化函数滤除所述未知响应信号,获得所述目标响应信号;Using the optimization function in the linear mixed model to filter out the unknown response signal to obtain the target response signal;
将所述目标响应信号与预设数据库中的模板响应信号进行对比,通过岭回归方法确定所述目标响应信号对应的气味类型和含量。The target response signal is compared with the template response signal in the preset database, and the odor type and content corresponding to the target response signal are determined by a ridge regression method.
具体的,当混合响应信号中不存在未知响应信号,且所述混合响应信号为线性响应信号时,可采用上述线性混合模型中的优化函数和岭回归方法确定所述目标响应信号对应的气味类型和含量,以使得求解过程更为简便,识别结果更为精确。其中,由于现有的气味识别方法已能够识别出气味的类型及含量,所以对于如何识别气味的类型及含量,本发明未做详细说明。本发明公开的气味识别方法在处理步骤上更具可操作性,其处理模型也更优化,故获得的识别结果更准确。Specifically, when there is no unknown response signal in the mixed response signal, and the mixed response signal is a linear response signal, the above-mentioned optimization function and ridge regression method in the linear mixed model can be used to determine the odor type corresponding to the target response signal and content to make the solution process easier and the identification results more accurate. Wherein, since the existing odor identification method can already identify the type and content of the odor, how to identify the type and content of the odor is not described in detail in the present invention. The odor identification method disclosed by the invention is more operable in processing steps, and its processing model is also more optimized, so the obtained identification results are more accurate.
基于上述任意实施例,需要说明的是,所述判断混合响应信号中是否存在未知响应信号,即判断获取到的混合响应信号中是否存在预设数据库中不存在的气味,若存在,则说明混合响应信号中存在未知响应信号;若不存在,则说明混合响应信号中不存在未知响应信号,即混合响应中的所有信号在预设数据库中均可以找到。Based on any of the above-mentioned embodiments, it should be noted that the judging whether there is an unknown response signal in the mixed response signal, that is, judging whether there is an odor that does not exist in the preset database in the obtained mixed response signal, and if it exists, it means that the mixed response signal is unknown. There is an unknown response signal in the response signal; if it does not exist, it means that there is no unknown response signal in the mixed response signal, that is, all the signals in the mixed response can be found in the preset database.
下面对本发明实施例提供的一种基于机器嗅觉的气味识别装置进行介绍,下文描述的一种基于机器嗅觉的气味识别装置与上文描述的一种基于机器嗅觉的气味识别方法可以相互参照。The following is an introduction to an odor recognition device based on machine sense of smell provided by an embodiment of the present invention. The device for smell recognition based on machine sense of smell described below and the method for smell recognition based on machine sense of smell described above can be cross-referenced.
参见图4,本发明实施例提供的一种基于机器嗅觉的气味识别装置,包括:Referring to Fig. 4, an odor recognition device based on machine sense of smell provided by an embodiment of the present invention includes:
获取模块401,用于获取目标混合气味的混合响应信号;An acquisition module 401, configured to acquire a mixed response signal of a target mixed odor;
判断模块402,用于判断所述混合响应信号中是否存在未知响应信号;A judging module 402, configured to judge whether there is an unknown response signal in the mixed response signal;
识别模块403,用于当所述混合响应信号中存在未知响应信号时,从所述混合响应信号中滤除所述未知响应信号,获得目标响应信号,并将所述目标响应信号与预设数据库中的模板响应信号进行对比,确定所述目标响应信号对应的气味类型和含量。An identification module 403, configured to filter out the unknown response signal from the mixed response signal when there is an unknown response signal in the mixed response signal, obtain a target response signal, and compare the target response signal with a preset database Compare the template response signals in , and determine the odor type and content corresponding to the target response signal.
其中,所述识别模块包括:Wherein, the identification module includes:
判断单元,用于判断所述混合响应信号是否为线性响应信号;a judging unit, configured to judge whether the mixed response signal is a linear response signal;
识别单元,用于当所述混合响应信号为线性响应信号时,利用线性混合模型中的优化函数滤除所述未知响应信号,获得所述目标响应信号,将所述目标响应信号与预设数据库中的模板响应信号进行对比,并通过乘子法确定所述目标响应信号对应的气味类型和含量;An identification unit, configured to use an optimization function in a linear mixed model to filter out the unknown response signal when the mixed response signal is a linear response signal to obtain the target response signal, and compare the target response signal with a preset database Compare the template response signals in , and determine the odor type and content corresponding to the target response signal by the multiplier method;
H表示滤波器混合矩阵,z(t)表示未知响应信号,t表示时间变量,x(t-P)表示噪声信号。 H represents the filter mixing matrix, z(t) represents the unknown response signal, t represents the time variable, and x(tP) represents the noise signal.
其中,还包括:Among them, also include:
线性处理模块,用于当所述混合响应信号中不存在未知响应信号,且所述混合响应信号为线性响应信号时,利用所述线性混合模型中的优化函数滤除所述未知响应信号,获得所述目标响应信号;将所述目标响应信号与预设数据库中的模板响应信号进行对比,通过岭回归方法确定所述目标响应信号对应的气味类型和含量。A linear processing module, configured to use an optimization function in the linear mixed model to filter out the unknown response signal when there is no unknown response signal in the mixed response signal, and the mixed response signal is a linear response signal, to obtain The target response signal; comparing the target response signal with the template response signal in the preset database, and determining the type and content of the odor corresponding to the target response signal through ridge regression method.
其中,还包括:Among them, also include:
非线性处理模块,用于当所述混合响应信号为非线性响应信号时,利用非线性混合模型中的优化函数滤除所述未知响应信号,获得所述目标响应信号;将所述目标响应信号与预设数据库中的模板响应信号进行对比,并通过混沌遗传算法确定所述目标响应信号对应的气味类型和含量;The nonlinear processing module is used to filter out the unknown response signal by using the optimization function in the nonlinear mixed model to obtain the target response signal when the mixed response signal is a nonlinear response signal; Comparing it with the template response signal in the preset database, and determining the odor type and content corresponding to the target response signal through a chaotic genetic algorithm;
其中,所述非线性混合模型中的优化函数为:min||y(t)-g(t)-z(t)||,y(t)表示混合响应信号,g(t)表示非线性响应信号,z(t)表示未知响应信号。Wherein, the optimization function in the nonlinear mixed model is: min||y(t)-g(t)-z(t)||, y(t) represents the mixed response signal, and g(t) represents the nonlinear Response signal, z(t) represents the unknown response signal.
其中,所述非线性处理模块还包括:Wherein, the nonlinear processing module also includes:
生成单元,用于利用多元自适应回归样条法生成非线性混合模型;generating unit for generating nonlinear mixed models using multivariate adaptive regression splines;
所述生成单元包括:The generating unit includes:
获取子单元,用于获取混合气味的实验数据,并对所述实验数据进行环境因素校正;Acquiring sub-units for obtaining experimental data of mixed odors, and correcting the experimental data for environmental factors;
划分子单元,用于对校正后的实验数据进行划分,得到多个区间,并基于每个区间生成对应的基函数;Divide the subunits, which are used to divide the corrected experimental data to obtain multiple intervals, and generate corresponding basis functions based on each interval;
确定子单元,用于采用均分误差法选择精确度最高的基函数,并将所述精确度最高的基函数对应的模型确定为所述非线性混合模型。A determining subunit is configured to select the basis function with the highest accuracy by using the average error method, and determine the model corresponding to the basis function with the highest accuracy as the nonlinear mixed model.
其中,非线性处理模块具体用于:Among them, the nonlinear processing module is specifically used for:
基于所述目标响应信号生成所述混沌遗传算法的种群;generating a population of the chaotic genetic algorithm based on the target response signal;
对所述种群中的个体进行个体评价,并对完成个体评价的种群进行选择运算、交叉运算和变异运算,使所述种群进行迭代更新;Carry out individual evaluation on the individuals in the population, and perform selection operation, crossover operation and mutation operation on the population that has completed the individual evaluation, so that the population is iteratively updated;
判断当前进化代数是否大于预设的最大进化代数;Determine whether the current evolutionary algebra is greater than the preset maximum evolutionary algebra;
若是,则输出最优解,并依据所述最优解确定所述目标响应信号对应的气味类型和含量。If yes, output the optimal solution, and determine the type and content of the odor corresponding to the target response signal according to the optimal solution.
下面对本发明实施例提供的一种基于机器嗅觉的气味识别设备进行介绍,下文描述的一种基于机器嗅觉的气味识别设备与上文描述的一种基于机器嗅觉的气味识别方法及装置可以相互参照。The following is an introduction to an odor recognition device based on machine sense of smell provided by an embodiment of the present invention. The smell recognition device based on machine sense of smell described below and the method and device for smell recognition based on machine sense of smell described above can be cross-referenced .
参见图5,本发明实施例提供的一种基于机器嗅觉的气味识别设备,包括:Referring to Fig. 5, an embodiment of the present invention provides an odor recognition device based on machine sense of smell, including:
存储器501,用于存储计算机程序;Memory 501, for storing computer programs;
处理器502,用于执行所述计算机程序时实现上述任意实施例所述的基于机器嗅觉的气味识别方法的步骤。The processor 502 is configured to implement the steps of the odor recognition method based on machine sense of smell described in any of the above embodiments when executing the computer program.
下面对本发明实施例提供的一种计算机可读存储介质进行介绍,下文描述的一种计算机可读存储介质与上文描述的一种基于机器嗅觉的气味识别方法、装置及设备可以相互参照。A computer-readable storage medium provided by an embodiment of the present invention is introduced below. The computer-readable storage medium described below and the method, device, and equipment for odor recognition based on machine sense of smell described above can be referred to each other.
一种计算机可读存储介质,所述计算机可读存储介质上存储有计算机程序,所述计算机程序被处理器执行时实现如上述任意实施例所述的基于机器嗅觉的气味识别方法的步骤。A computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the odor recognition method based on machine sense of smell as described in any of the above embodiments are implemented.
本说明书中各个实施例采用递进的方式描述,每个实施例重点说明的都是与其他实施例的不同之处,各个实施例之间相同相似部分互相参见即可。Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other.
对所公开的实施例的上述说明,使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的,本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下,在其它实施例中实现。因此,本发明将不会被限制于本文所示的这些实施例,而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
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