技术领域Technical Field
本申请涉及智能防护技术领域，特别涉及一种基于深度学习的无创人体核心温度预测方法及系统。The present application relates to the field of intelligent protection technology, and in particular to a non-invasive human core temperature prediction method and system based on deep learning.
背景技术Background Art
连续监测人体核心温度对于评估剧烈工作期间，人体的热应激反应至关重要。尽管个人生理监测技术已经进步到可以借助多参数传感器系统在多种环境中进行长时间数据采集，但精确测量核心体温依然面临挑战。Continuous monitoring of human core temperature is essential for assessing the body's heat stress response during strenuous work. Although personal physiological monitoring technology has advanced to the point where multi-parameter sensor systems can be used to collect data over a long period of time in a variety of environments, accurately measuring core body temperature remains a challenge.
目前，利用肺动脉温度进行的医疗级核心温度测量仅限于临床使用。而传统上在实验室采用的直肠和食管探针方法，对于需要动态监测的环境显得不够使用。相比之下，使用核心温度胶囊在实际作业环境中具有更高的有效性和实用性，但这种方法仍存在一定局限：首先，因为医疗禁忌，核心温度胶囊不是所有人都适合使用；其次，在饮用过冷或过热的液体后，其测量结果可能不准确。Currently, medical-grade core temperature measurement using pulmonary artery temperature is limited to clinical use. The rectal and esophageal probe methods traditionally used in laboratories are not sufficient for environments that require dynamic monitoring. In contrast, the use of core temperature capsules has higher effectiveness and practicality in actual working environments, but this method still has certain limitations: first, due to medical contraindications, core temperature capsules are not suitable for everyone; second, the measurement results may be inaccurate after drinking too cold or too hot liquids.
因而，亟需提供一种针对上述现有技术不足的技术方案。Therefore, there is an urgent need to provide a technical solution to the above-mentioned deficiencies in the prior art.
发明内容Summary of the invention
本申请的目的在于提供一种基于深度学习的无创人体核心温度预测方法及系统，以解决或缓解上述现有技术中存在的问题。The purpose of this application is to provide a non-invasive human core temperature prediction method and system based on deep learning to solve or alleviate the problems existing in the above-mentioned prior art.
为了实现上述目的，本申请提供如下技术方案：In order to achieve the above objectives, this application provides the following technical solutions:
本申请提供一种基于深度学习的无创人体核心温度预测方法，包括：步骤S101、基于人工环境实验舱在模拟环境下进行暖体假人实验，构建人体热反应数据集，以反演所述人体热反应数据集中的皮肤模拟温度、模拟心率和核心模拟温度之间的回归模型；步骤S102、基于所述回归模型，通过深度学习算法自动调整卡尔曼滤波器中的模型参数；步骤S103、基于模型参数调整后的卡尔曼滤波器，对实时采集的目标个体的实测数据进行优化矫正，得到所述目标个体的实测矫正数据；其中，所述实测数据包括皮肤温度和心率数据，所述实测矫正数据包括皮肤温度矫正数据和心率矫正数据；步骤S104、根据所述目标个体的心率矫正数据和皮肤温度矫正数据，基于长时序预测网络的核心温度预测模型，对所述目标个体的核心温度进行预测。The present application provides a non-invasive human core temperature prediction method based on deep learning, including: step S101, based on an artificial environment test chamber, conducting a warm manikin experiment in a simulated environment, constructing a human thermal response data set to invert the regression model between the skin simulated temperature, simulated heart rate and core simulated temperature in the human thermal response data set; step S102, based on the regression model, automatically adjusting the model parameters in the Kalman filter through a deep learning algorithm; step S103, based on the Kalman filter after the model parameters are adjusted, optimizing and correcting the measured data of the target individual collected in real time to obtain the measured corrected data of the target individual; wherein the measured data includes skin temperature and heart rate data, and the measured corrected data includes skin temperature correction data and heart rate correction data; step S104, according to the heart rate correction data and skin temperature correction data of the target individual, based on the core temperature prediction model of the long time series prediction network, predicting the core temperature of the target individual.
优选的，步骤S101中，根据所述人体热反应数据集，反演所述核心模拟温度与所述模拟心率、所述皮肤模拟温度、所述环境模拟参数之间的回归模型：Preferably, in step S101, based on the human body thermal response data set, a regression model between the core simulated temperature and the simulated heart rate, the skin simulated temperature, and the environmental simulation parameters is inverted:
式中，为所述核心模拟温度，为所述皮肤模拟温度，为所述模拟心率，为人体的模拟新陈代谢率；为所述环境模拟参数；均为反演系数。In the formula, is the simulated temperature of the core, A simulated temperature for the skin, is the simulated heart rate, is the simulated metabolic rate of the human body; Simulating parameters for the environment; are inversion coefficients.
优选的，步骤S102中，所述基于所述回归模型，通过深度学习算法自动调整卡尔曼滤波器中的参数，包括：基于所述回归模型，构建所述卡尔曼滤波器的样本数据集；其中，所述样本数据集中包括：皮肤样本温度、样本心率和核心样本温度；根据所述样本数据集，基于深度学习算法，对卡尔曼滤波函数通过最小化预测误差的方法，自动调整所述卡尔曼滤波器中的模型参数。Preferably, in step S102, the parameters in the Kalman filter are automatically adjusted based on the regression model through a deep learning algorithm, including: constructing a sample data set of the Kalman filter based on the regression model; wherein the sample data set includes: skin sample temperature, sample heart rate and core sample temperature; according to the sample data set, based on a deep learning algorithm, the model parameters in the Kalman filter are automatically adjusted by minimizing the prediction error of the Kalman filter function.
优选的，步骤S103中，按照公式：Preferably, in step S103, according to the formula:
确定所述目标个体的实测矫正数据；Determine the measured correction data of the target individual ;
式中，为所述目标个体在当前时刻的先验状态估计，为状态转移矩阵，为所述目标个体在时刻的后验状态估计，为控制矩阵，为控制输入；为所述目标个体在当前时刻的后验状态估计，为卡尔曼增益，为当前时刻的实测数据，为观测模型矩阵；为后验估计协方差矩阵，为先验估计协方差矩阵；为观测噪声的协方差矩阵。In the formula, The target individual at the current moment The prior state estimate of is the state transfer matrix, For the target individual at time The posterior state estimate of is the control matrix, is the control input; The target individual at the current moment The posterior state estimate of is the Kalman gain, For the current moment The measured data, is the observation model matrix; is the posterior estimated covariance matrix, is the a priori estimated covariance matrix; is the covariance matrix of the observation noise.
优选的，步骤S104中，通过嵌入层将所述实测矫正数据转化为嵌入向量，作为所述核心温度预测模型得输入特征序列，并基于ProbSparse自注意力机制，计算所述输入特征序列的注意力权重，以预测所述目标个体的核心温度。Preferably, in step S104, the measured corrected data is converted into an embedding vector through an embedding layer as an input feature sequence for the core temperature prediction model, and based on the ProbSparse self-attention mechanism, the attention weight of the input feature sequence is calculated to predict the core temperature of the target individual.
优选的，所述通过嵌入层将所述实测矫正数据转化为嵌入向量，包括：对所述实测矫正数据进行数据清洗；对所述数据清洗后数据进行特征选择，并对特征选择的数据进行编码转换，得到数值型编码数据；基于所述长时序预测网络的嵌入层，初始化嵌入矩阵，将所述编码数据转化为所述嵌入向量。Preferably, the method of converting the measured corrected data into an embedded vector through an embedding layer includes: performing data cleaning on the measured corrected data; performing feature selection on the cleaned data, and performing encoding conversion on the feature selected data to obtain numerical encoded data; initializing an embedding matrix based on the embedding layer of the long time series prediction network, and converting the encoded data into the embedded vector.
优选的，所述对特征选择的数据进行编码转换，包括：基于独热编码或标签编码，对特征选择的数据进行编码转换。Preferably, the encoding conversion of the feature selected data includes: encoding conversion of the feature selected data based on one-hot encoding or label encoding.
优选的，步骤S104中，按照公式：Preferably, in step S104, according to the formula:
计算所述输入特征序列的注意力权重；式中，为所述输入特征序列的查询矩阵，为与所述查询矩阵相匹配的键矩阵，为所述输入特征序列的值矩阵，为所述输入特征序列的维度；其中，根据所述输入特征序列，基于稀疏性测量构建所述查询矩阵。Calculate the attention weight of the input feature sequence ; In the formula, is the query matrix of the input feature sequence, is the key matrix that matches the query matrix, is the value matrix of the input feature sequence, is the dimension of the input feature sequence; wherein, according to the input feature sequence, the query matrix is constructed based on the sparsity measurement .
优选的，所述根据所述输入特征序列，基于稀疏性测量构建所述查询矩阵包括，按照公式：Preferably, constructing the query matrix based on the sparsity measurement according to the input feature sequence includes, according to the formula:
确定所述查询矩阵中的第个查询向量的稀疏性测量；Determine the first query vector Sparsity measurement of ;
其中，所述查询向量通过对第个所述输入特征序列进行线性变换得到；为所述键矩阵中的向量数量，，均为正整数；为所述键矩阵中第个向量。Wherein, the query vector Through the The input feature sequence is linearly transformed to obtain; For the bond matrix The number of vectors in , , All are positive integers; For the bond matrix Middle vectors.
本申请实施例还提供一种基于深度学习的无创人体核心温度预测系统，包括：模拟回归单元，配置为基于人工环境实验舱在模拟环境下进行暖体家人实验，构建人体热反应数据集，以反演所述人体热反应数据集中的皮肤模拟温度、模拟心率和核心模拟温度之间的回归模型；The embodiment of the present application also provides a non-invasive human core temperature prediction system based on deep learning, including: a simulation regression unit, configured to conduct a warm body family experiment in a simulated environment based on an artificial environment experimental cabin, to construct a human thermal response data set, to invert a regression model between the skin simulation temperature, the simulated heart rate and the core simulation temperature in the human thermal response data set;
滤波器参数调整单元，配置为基于所述回归模型，通过深度学习算法自动调整卡尔曼滤波器中的模型参数；A filter parameter adjustment unit, configured to automatically adjust model parameters in the Kalman filter through a deep learning algorithm based on the regression model;
目标数据矫正单元，配置为基于模型参数调整后的卡尔曼滤波器，对实时采集的目标个体的实测数据进行优化矫正，得到所述目标个体的实测矫正数据；其中，所述实测数据包括皮肤温度和心率数据，所述实测矫正数据包括皮肤温度矫正数据和心率矫正数据；The target data correction unit is configured to optimize and correct the measured data of the target individual collected in real time based on the Kalman filter after the model parameters are adjusted, so as to obtain the measured corrected data of the target individual; wherein the measured data includes skin temperature and heart rate data, and the measured corrected data includes skin temperature corrected data and heart rate corrected data;
核心温度预测单元，配置为根据所述目标个体的心率矫正数据和皮肤温度矫正数据，基于长时序预测网络的核心温度预测模型，对所述目标个体的核心温度进行预测。The core temperature prediction unit is configured to predict the core temperature of the target individual based on the heart rate correction data and skin temperature correction data of the target individual and the core temperature prediction model of the long time series prediction network.
有益效果Beneficial Effects
本申请实施例提供的基于深度学习的无创人体核心温度预测方法中，首先，基于人工环境实验舱在模拟环境下进行暖体假人实验，构建人体热反应数据集，以反演人体热反应数据集中的皮肤模拟温度、模拟心率和核心模拟温度之间的回归模型；并基于回归模型，通过深度学习算法自动调整卡尔曼滤波器中的模型参数；然后，基于模型参数调整后的卡尔曼滤波器，对实时采集的目标个体的实测数据进行优化矫正，并根据优化矫正得到的目标个体的实测矫正数据，基于长时序预测网络的核心温度预测模型，对目标个体的核心温度进行预测。In the non-invasive human core temperature prediction method based on deep learning provided in the embodiment of the present application, first, a warm manikin experiment is conducted in a simulated environment based on an artificial environment test chamber to construct a human thermal response data set to invert the regression model between the skin simulated temperature, simulated heart rate and core simulated temperature in the human thermal response data set; and based on the regression model, the model parameters in the Kalman filter are automatically adjusted through a deep learning algorithm; then, based on the Kalman filter after the model parameters are adjusted, the measured data of the target individual collected in real time are optimized and corrected, and based on the measured corrected data of the target individual obtained by the optimized correction, the core temperature prediction model of the long time series prediction network is used to predict the core temperature of the target individual.
籍以，通过在人工环境实验舱内的模拟实验，收集覆盖不同生理状态、环境条件下的人体的生理模拟数据，据此建立皮肤温度、心率、核心温度之间的回归模型，并通过回归模型计算能够全面覆盖不同年龄、性别、环境等条件下的样本数据集，以根据样本数据集通过深度学习算法对卡尔曼滤波器的模型参数进行自动调整，使模型参数调整后的卡尔曼滤波器具有更广泛的适用性和更高的精确度；借助具有广泛适用性和更高精确度的卡尔曼滤波器对目标个体的实测皮肤温度和实测心率数据进行矫正，并通过矫正后的皮肤温度和心率数据，基于长时序预测网格对目标个体的核心温度进行预测，有效避免获取人体核心温度时对人体的侵入式测量，实现无需直接侵入人体而通过人体的皮肤温度和心率即可精确预测获取人体核心温度，降低人体核心温度测量时的难度，以支撑人体热应激预警响应和辅助决策。Therefore, through simulation experiments in artificial environment test cabins, physiological simulation data covering human bodies under different physiological states and environmental conditions are collected, and a regression model between skin temperature, heart rate and core temperature is established accordingly. The regression model is used to calculate a sample data set that can comprehensively cover different ages, genders, environments and other conditions, so that the model parameters of the Kalman filter are automatically adjusted through a deep learning algorithm according to the sample data set, so that the Kalman filter after the model parameters are adjusted has wider applicability and higher accuracy; the measured skin temperature and measured heart rate data of the target individual are corrected with the help of a Kalman filter with wide applicability and higher accuracy, and the core temperature of the target individual is predicted based on the long time series prediction grid through the corrected skin temperature and heart rate data, effectively avoiding invasive measurement of the human body when obtaining the human core temperature, and realizing accurate prediction of the human core temperature through the human skin temperature and heart rate without directly invading the human body, thereby reducing the difficulty of measuring the human core temperature, so as to support human heat stress early warning response and auxiliary decision-making.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
构成本申请的一部分的说明书附图用来提供对本申请的进一步理解，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。其中：The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. Among them:
图1为根据本申请的一些实施例提供的一种基于深度学习的无创人体核心温度预测方法的流程示意图；FIG1 is a flow chart of a non-invasive human core temperature prediction method based on deep learning according to some embodiments of the present application;
图2为根据本申请的一些实施例提供的基于深度学习的无创人体核心温度预测方法的技术路线图；FIG2 is a technical roadmap of a non-invasive human core temperature prediction method based on deep learning according to some embodiments of the present application;
图3为根据本申请的一些实施例提供的长时序预测网络Informer的模型结构示意图；FIG3 is a schematic diagram of a model structure of a long time series prediction network Informer according to some embodiments of the present application;
图4为根据本申请的一些实施例提供的核心温度预测的对比示意图；FIG4 is a comparative schematic diagram of core temperature prediction provided according to some embodiments of the present application;
图5为根据本申请的一些实施例提供的基于深度学习的无创人体核心温度预测系统的结构示意图。FIG5 is a schematic diagram of the structure of a non-invasive human core temperature prediction system based on deep learning according to some embodiments of the present application.
具体实施方式DETAILED DESCRIPTION
下面将参考附图并结合实施例来详细说明本申请。各个示例通过本申请的解释的方式提供而非限制本申请。实际上，本领域的技术人员将清楚，在不脱离本申请的范围或精神的情况下，可在本申请中进行修改和变型。例如，示为或描述为一个实施例的一部分的特征可用于另一个实施例，以产生又一个实施例。基于本发明实施例中的实施例，本领域普通技术人员所获得的所有其他实施例，都应当属于本发明实施例保护的范围。The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as a part of an embodiment may be used in another embodiment to produce yet another embodiment. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the embodiments of the present invention should belong to the scope of protection of the embodiments of the present invention.
目前，对人体核心温度的测量（例如肺动脉温度测量、直肠和食管探针法）等均无法避免对人体的侵入，而且受到医疗禁忌或目标个体的主客观因素影响，无法保证测量结果的有效性。基于此，本申请提出了一种基于深度学习的无创人体核心温度预测方法，通过覆盖多种生理和环境条件的数据变异性，并通过深度学习对卡尔曼滤波器进行优化调整，进而，利用卡尔曼滤波器实时更新和矫正目标个体的皮肤温度和心率，使用长时序预测网络对目标个体进行核心温度预测，相较于传统的核心温度测量，无需侵入性操作，提高了人体核心温度测量的安全性和舒适性。At present, the measurement of human core temperature (such as pulmonary artery temperature measurement, rectal and esophageal probe method) cannot avoid intrusion into the human body, and is affected by medical contraindications or subjective and objective factors of the target individual, and the validity of the measurement results cannot be guaranteed. Based on this, this application proposes a non-invasive human core temperature prediction method based on deep learning, which covers the data variability of various physiological and environmental conditions, and optimizes and adjusts the Kalman filter through deep learning. Then, the Kalman filter is used to update and correct the skin temperature and heart rate of the target individual in real time, and the core temperature of the target individual is predicted using a long time series prediction network. Compared with traditional core temperature measurement, no invasive operation is required, which improves the safety and comfort of human core temperature measurement.
如图1至图4所示，该基于深度学习的无创人体核心温度预测方法包括：As shown in FIGS. 1 to 4 , the non-invasive human core temperature prediction method based on deep learning includes:
步骤S101、基于人工环境实验舱在模拟环境下进行暖体假人实验，构建人体热反应数据集，以反演人体热反应数据集中皮肤模拟温度、模拟心率和核心模拟温度之间的回归模型。Step S101: Conduct a warm manikin experiment in a simulated environment based on an artificial environment test chamber to construct a human thermal response data set to invert a regression model between skin simulated temperature, simulated heart rate and core simulated temperature in the human thermal response data set.
本申请中，人工环境实验舱是可以通过人工进行温度、湿度、风速、气体压力等环境条件的模拟调节，通过人工环境实验舱模拟各种不同的环境气候条件，为暖体假人实验提供比较的测试环境。通不过暖体假人是一种模拟人体与环境能量交换的模拟体，通过对暖体假人不同的参数设定，可模拟人体的不同性别、年龄、生理状态等。In this application, the artificial environment test chamber can simulate and adjust the environmental conditions such as temperature, humidity, wind speed, gas pressure, etc. by artificial environment test chamber, and simulate various environmental climate conditions to provide a comparative test environment for the thermal manikin experiment. The thermal manikin is a simulation body that simulates the energy exchange between the human body and the environment. By setting different parameters of the thermal manikin, different genders, ages, physiological states, etc. of the human body can be simulated.
借助于人工环境实验舱和暖体假人实验，模拟不同生理和环境条件，收集覆盖不同环境条件、不同年龄、不同性别、不同生理状态下的生理模拟数据，通过多样化的人口学特征分布，确保模拟数据的多样性和代表性，有效增强模型的广泛适用性和高度准确性。With the help of artificial environment experiment chambers and warm-body manikin experiments, different physiological and environmental conditions are simulated, and physiological simulation data covering different environmental conditions, different ages, different genders, and different physiological states are collected. Through the diverse distribution of demographic characteristics, the diversity and representativeness of the simulation data are ensured, effectively enhancing the wide applicability and high accuracy of the model.
核心温度通常是人体内部的温度，比如胸腔、腹腔、中枢神经的温度，核心温度的维持是保证新陈代谢和生命活动正常进行的必要条件。而皮肤温度则是体表的温度，在日常生活中，通常测量的是体表温度，如腋下、口腔、额头等的温度，这些温度受周围环境和个人因素的影响，并不能总是准确的代表人体的核心温度。心率的变化作为体温变化的指标，比如，运动或外界温度升高时，人体代谢活动增强，产热增多，可能会导致心率升高；反之，在寒冷环境中，为了减少热量散失，心率可能会降低。Core temperature is usually the temperature inside the human body, such as the temperature of the chest, abdomen, and central nervous system. Maintaining core temperature is a necessary condition to ensure the normal metabolism and life activities. Skin temperature is the temperature of the body surface. In daily life, the body surface temperature is usually measured, such as the temperature of the armpit, mouth, forehead, etc. These temperatures are affected by the surrounding environment and personal factors and cannot always accurately represent the core temperature of the human body. Changes in heart rate are an indicator of changes in body temperature. For example, when exercising or the outside temperature rises, the body's metabolic activity increases, heat production increases, and the heart rate may increase; conversely, in a cold environment, in order to reduce heat loss, the heart rate may decrease.
基于此，本申请中，通过人工环境实验舱和暖体假人实验得到覆盖不同生理状态、环境条件下的人体的生理模拟数据后，基于反演计算方法，建立皮肤温度、心率、核心温度之间的回归模型。具体的，根据人体热反应数据集，反演核心模拟温度与模拟心率、皮肤模拟温度、环境模拟参数之间的回归模型。Based on this, in this application, after obtaining physiological simulation data of the human body under different physiological states and environmental conditions through artificial environment test chambers and warm manikin experiments, a regression model between skin temperature, heart rate, and core temperature is established based on the inversion calculation method. Specifically, based on the human body thermal response data set, a regression model between the core simulation temperature and the simulated heart rate, skin simulation temperature, and environmental simulation parameters is inverted.
式中，为核心模拟温度，为皮肤模拟温度，为模拟心率，为模拟新陈代谢率,；为环境模拟参数；均为反演系数。In the formula, is the core simulated temperature, Simulate temperature for the skin, To simulate heart rate, To simulate the metabolic rate, ; is the environmental simulation parameter; are inversion coefficients.
其中，为男性的模拟新陈代谢率，为女性的模拟新陈代谢率。在此，按照公式：in, is the simulated metabolic rate for men, is the simulated metabolic rate of women. Here, according to the formula:
分别确定男性的模拟新陈代谢率和女性的模拟新陈代谢率。籍此，使得构建的回归模型能够覆盖不同年龄、性别、生理状态、环境条件下的人群，具有更广泛的适用性。Determine the simulated metabolic rate for men separately and simulated metabolic rates in women In this way, the constructed regression model can cover people of different ages, genders, physiological states, and environmental conditions, and has a wider applicability.
步骤S102、基于回归模型，通过深度学习算法自动调整卡尔曼滤波器中的模型参数。Step S102: Based on the regression model, the model parameters in the Kalman filter are automatically adjusted through a deep learning algorithm.
本申请中，基于回归模型，构建卡尔曼滤波器的样本数据集。其中，样本数据集包括：皮肤样本温度、样本心率和核心样本温度。籍以，通过在人工环境实验舱内的模拟实验，收集覆盖不同生理状态、环境条件下的人体的生理模拟数据，据此建立皮肤温度、心率、核心温度之间的回归模型，并通过回归模型计算能够全面覆盖不同年龄、性别、环境等条件下的样本数据集，以根据样本数据集通过深度学习算法对卡尔曼滤波器的模型参数进行自动调整。In this application, a sample data set of the Kalman filter is constructed based on the regression model. Among them, the sample data set includes: skin sample temperature, sample heart rate and core sample temperature. Therefore, through simulation experiments in artificial environment test chambers, physiological simulation data covering human bodies under different physiological states and environmental conditions are collected, and a regression model between skin temperature, heart rate and core temperature is established accordingly. The regression model is used to calculate sample data sets that can comprehensively cover conditions of different ages, genders, environments, etc., so as to automatically adjust the model parameters of the Kalman filter through a deep learning algorithm according to the sample data set.
具体的，根据样本数据集，基于深度学习算法，对卡尔曼滤波函数通过最小化预测误差的方法，自动调整卡尔曼滤波器中的模型参数。在此，可以通过最大似然估计、自适应滤波、贝叶斯优化、遗传算法、梯度下降法等对卡尔曼滤波器的模型参数进行优化。Specifically, according to the sample data set, based on the deep learning algorithm, the Kalman filter function is minimized by the prediction error method to automatically adjust the model parameters in the Kalman filter. Here, the model parameters of the Kalman filter can be optimized by maximum likelihood estimation, adaptive filtering, Bayesian optimization, genetic algorithm, gradient descent method, etc.
本申请中，通过机器学习方法，利用神经网络强大的拟合能力，对卡尔曼滤波的系统状态进行递归估计。其中，系统状态的变化通过线性方程进行描述，观测数据中的噪声则通过协方差矩阵进行量化，通过不断的更新过程噪声协方差矩阵Q和观测噪声协方差矩阵R，对卡尔曼滤波器的模型参数进行优化调整，以适应变化的环境条件和不同的个体差异，使模型参数调整后的卡尔曼滤波器具有更广泛的适用性和更高的精确度。In this application, the system state of the Kalman filter is recursively estimated by using a machine learning method and the powerful fitting ability of a neural network. The change of the system state is described by a linear equation, and the noise in the observed data is quantified by a covariance matrix. By continuously updating the process noise covariance matrix Q and the observation noise covariance matrix R, the model parameters of the Kalman filter are optimized and adjusted to adapt to changing environmental conditions and different individual differences, so that the Kalman filter after the model parameters are adjusted has a wider applicability and higher accuracy.
本申请中，在创建卡尔曼滤波器后，定义器损失函数，以衡量卡尔曼滤波器的预测准确性，损失函数采用预测值和真实值之间差的平方和的平均值（即均方误差，）进行衡量。其中，In this application, after creating the Kalman filter, a loss function is defined to measure the prediction accuracy of the Kalman filter. The loss function uses the average value of the sum of squares of the differences between the predicted value and the true value (i.e., the mean square error, ) to measure. Among them,
式中，为样本数据集中的样本数量，，均为正整数；为回归模型计算得到的第个核心样本温度，为由卡尔曼滤波器预测得到的与对应的核心温度预测值。In the formula, is the number of samples in the sample data set, , All are positive integers; The first The core sample temperature, is the value predicted by the Kalman filter and The corresponding core temperature prediction value.
在对卡尔曼滤波器的模型参数进行优化调整过程中，采用minimize函数从scipy.optimize库来执行参数优化任务。具体的， minimize函数将损失函数作为优化目标，通过迭代地调整卡尔曼滤波器的模型参数，寻找使目标函数值最小的参数集合。在此，使用自动求导库（如Python中的NumPy或TensorFlow）来计算函数梯度和嗨森矩阵，以对梯度或函数值的变化进行准确估计。In the process of optimizing the model parameters of the Kalman filter, the minimize function from the scipy.optimize library is used to perform the parameter optimization task. Specifically, the minimize function takes the loss function as the optimization target and iteratively adjusts the model parameters of the Kalman filter to find the parameter set that minimizes the objective function value. Here, an automatic differentiation library (such as NumPy or TensorFlow in Python) is used to calculate the function gradient and the Hirsch matrix to accurately estimate the change in the gradient or function value.
步骤S103、基于模型参数调整后的卡尔曼滤波器，对实时采集的目标个体的实测数据进行优化矫正，得到目标个体的实测矫正数据。Step S103: Based on the Kalman filter after the model parameters are adjusted, the measured data of the target individual collected in real time is optimized and corrected to obtain the measured corrected data of the target individual.
通过深度学习算法对卡尔曼滤波器的模型参数进行优化调整，使调整后的卡尔曼滤波器具有更广泛的适用性和更高的精确度，能够对同年龄、性别、环境等条件下目标个体的实测数据进行针对性的矫正，以避免目标个体的核心温度预测时受到的主客观因素（比如，饮用过冷或过热的液体）的影响，进而提高目标个体的核心温度预测的准确性。The model parameters of the Kalman filter are optimized and adjusted through a deep learning algorithm, making the adjusted Kalman filter more widely applicable and more accurate. It can make targeted corrections to the measured data of target individuals under the same age, gender, environment and other conditions, so as to avoid the influence of subjective and objective factors (such as drinking too cold or too hot liquids) when predicting the core temperature of the target individual, thereby improving the accuracy of the core temperature prediction of the target individual.
通过接触式或非接触式技术对目标个体的皮肤温度进行测量，通过光学传感器或心率监测器对目标个体的心率数据进行测量；然后，将测量得到的目标个体的皮肤温度和心率数据输入模型参数调整后的卡尔曼滤波器，由模型参数调整后的卡尔曼滤波器对输入的目标个体的皮肤温度和心率数据进行有优化矫正，得到目标个体的皮肤温度矫正数据和心率矫正数据。The skin temperature of the target individual is measured by contact or non-contact technology, and the heart rate data of the target individual is measured by an optical sensor or a heart rate monitor; then, the measured skin temperature and heart rate data of the target individual are input into a Kalman filter after the model parameters are adjusted, and the Kalman filter after the model parameters are adjusted performs optimized correction on the input skin temperature and heart rate data of the target individual to obtain skin temperature corrected data and heart rate corrected data of the target individual.
具体的，按照公式：Specifically, according to the formula:
确定目标个体的实测矫正数据；Determine the measured correction data of the target individual ;
式中，为目标个体在当前时刻的先验状态估计，为状态转移矩阵，为目标个体在时刻的后验状态估计，为控制矩阵，为控制输入；为目标个体在当前时刻的后验状态估计，为卡尔曼增益，为当前时刻的实测数据，为观测模型矩阵;为后验估计协方差矩阵，为先验估计协方差矩阵；为观测噪声的协方差矩阵。In the formula, For the target individual at the current moment The prior state estimate of is the state transfer matrix, For the target individual at time The posterior state estimate of is the control matrix, is the control input; For the target individual at the current moment The posterior state estimate of is the Kalman gain, For the current moment The measured data, is the observation model matrix; is the posterior estimated covariance matrix, is the a priori estimated covariance matrix; is the covariance matrix of the observation noise.
在此，模型参数调整后的卡尔曼滤波器根据上一时刻的状态估计来预测当前状态，并利用当前的观测数据，结合先验状态估计，通过卡尔曼增益进行调整，对预测值进行矫正，从而得到后验状态估计，即实测矫正数据。在此过程中，每个时间步重复进行，实现对系统状态的递归估计和更新。Here, the Kalman filter after adjusting the model parameters predicts the current state based on the state estimate of the previous moment, and uses the current observation data, combined with the prior state estimate, to adjust the predicted value through the Kalman gain, thereby obtaining the posterior state estimate, that is, the measured corrected data. In this process, each time step is repeated to achieve recursive estimation and update of the system state.
步骤S104、根据目标个体的心率矫正数据和皮肤温度矫正数据，基于长时序预测网络的核心温度预测模型，对目标个体的核心温度进行预测。Step S104: predicting the core temperature of the target individual based on the core temperature prediction model of the long time series prediction network according to the heart rate correction data and skin temperature correction data of the target individual.
本申请中，构建基于长时序预测模型——Informer的核心温度预测模型。设定核心温度预测模型的预测时间步为0，即不进行核心温度的超前预测，仅预测实时核心温度。在核心温度预测模型训练阶段，采用样本数据集中的数据进行训练，并测试训练模型的预测性能，调节核心温度预测模型的参数。最终，核心温度预测模型的参数设置为：初始迭代次数（epoch）设置为20，耐心度（patience）设置为3，激活函数为GELU，初始学习率为0.00001，损失函数为均方误差（MSE），令牌长度为0，输入序列长度为20，预测序列长度为0，注意力采样数为5，模型维度为512，多头注意力头数为8，编码器层数为2，解码器层数为1。In this application, a core temperature prediction model based on the long time series prediction model - Informer is constructed. The prediction time step of the core temperature prediction model is set to 0, that is, no advance prediction of the core temperature is performed, and only the real-time core temperature is predicted. In the core temperature prediction model training phase, the data in the sample data set is used for training, and the prediction performance of the training model is tested, and the parameters of the core temperature prediction model are adjusted. Finally, the parameters of the core temperature prediction model are set as follows: the initial number of iterations (epoch) is set to 20, the patience is set to 3, the activation function is GELU, the initial learning rate is 0.00001, the loss function is the mean square error (MSE), the token length is 0, the input sequence length is 20, the prediction sequence length is 0, the number of attention samples is 5, the model dimension is 512, the number of multi-head attention heads is 8, the number of encoder layers is 2, and the number of decoder layers is 1.
将获取目标个体的心率矫正数据和皮肤温度矫正数据，作为基于长时序预测网络的核心温度预测模型的输入。在此，通过嵌入层将输入的实测矫正数据转化为嵌入向量，作为核心温度预测模型的输入特征序列。具体的，首先，对输入的实测矫正数据进行数据清洗，比如，缺失值、异常值处理等，以确保输入数据的数据质量；然后，在对数据清洗后的数据进行特征选择，以从输入的数据中选择对预测核心温度有用的特征子集，提高核心温度预测模型的性能，减少计算复杂度。比如，通过相关系数方法、卡方检验方法、主成分分析方法、递归特征消除方法等对数据进行特征选择。The heart rate correction data and skin temperature correction data of the target individual are obtained as the input of the core temperature prediction model based on the long time series prediction network. Here, the input measured correction data is converted into an embedding vector through the embedding layer as the input feature sequence of the core temperature prediction model. Specifically, first, the input measured correction data is cleaned, such as missing values and outliers, to ensure the data quality of the input data; then, feature selection is performed on the cleaned data to select a feature subset from the input data that is useful for predicting the core temperature, thereby improving the performance of the core temperature prediction model and reducing the computational complexity. For example, feature selection is performed on the data through the correlation coefficient method, the chi-square test method, the principal component analysis method, the recursive feature elimination method, etc.
接着，对特征选择的数据进行编码转换，将输入的离散型数据转换为数值型数据。具体的，基于独热编码或标签编码实现对特征选择的数据的编码转换。在此，对特征选择的数据中的分类变量进行是被，并将分类变量中的每个类别创建独热变量，使用独热编码方法转换分类变量，比如使用pandas库的get_dummies函数，或scikit-learn库的OneHotEncoder类等。通过对数据集中的每个分类变量重复上述过程，为每个变量生成一组独热编码列，将转换后的独热编码列与原始数据集（特征选择的数据）中的其他数值型特征进行合并，并在合并数据后，从数据集中删除原始的分类变量列，以避免数据中的多重共线性问题。籍以，通过编码转换，有效确保数据格式适合于长时序预测网络的数据分析。Next, the feature selected data is converted to convert the input discrete data into numerical data. Specifically, the encoding conversion of the feature selected data is implemented based on one-hot encoding or label encoding. Here, the categorical variables in the feature selected data are converted, and each category in the categorical variable is created as a one-hot variable, and the categorical variables are converted using the one-hot encoding method, such as using the get_dummies function of the pandas library, or the OneHotEncoder class of the scikit-learn library. By repeating the above process for each categorical variable in the data set, a set of one-hot encoding columns is generated for each variable, and the converted one-hot encoding columns are merged with other numerical features in the original data set (feature selected data), and after merging the data, the original categorical variable columns are deleted from the data set to avoid multicollinearity problems in the data. Thus, through encoding conversion, the data format is effectively ensured to be suitable for data analysis of long time series prediction networks.
最后，基于长时序预测网络的嵌入层，初始化嵌入矩阵，将编码数据转化为嵌入向量。其中，嵌入矩阵的行对应编码数据中不同特征的数量，嵌入矩阵的列对应编码数据中每个特征的嵌入维度。Finally, based on the embedding layer of the long time series prediction network, the embedding matrix is initialized to convert the encoded data into an embedding vector. The rows of the embedding matrix correspond to the number of different features in the encoded data, and the columns of the embedding matrix correspond to the embedding dimension of each feature in the encoded data.
在将测矫正数据转化为嵌入向量后，作为核心温度预测模型得输入特征序列，并基于ProbSparse自注意力机制，计算输入特征序列的注意力权重，以预测目标个体的核心温度。After converting the measured and corrected data into an embedding vector, it is used as the input feature sequence of the core temperature prediction model, and based on the ProbSparse self-attention mechanism, the attention weight of the input feature sequence is calculated to predict the core temperature of the target individual.
输入特征序列的注意力权重决定了每个输入序列元素（皮肤温度矫正数据和心率矫正数据）对输出元素（核心温度）的贡献大小。在此，基于稀疏性测量构建的查询矩阵（稀疏矩阵）对助理医权重矩阵中非零元素（或较大权重）的分布和比例进行评估。进而，通过稀疏性测量，识别注意力权重中的重要部分，从而提高计算效率。具体的，按照公式：The attention weight of the input feature sequence determines the contribution of each input sequence element (skin temperature corrected data and heart rate corrected data) to the output element (core temperature). Here, the query matrix (sparse matrix) constructed based on the sparsity measurement evaluates the distribution and proportion of non-zero elements (or large weights) in the assistant doctor weight matrix. Furthermore, through the sparsity measurement, the important parts of the attention weight are identified, thereby improving the computational efficiency. Specifically, according to the formula:
确定查询矩阵中的第个查询向量的稀疏性测量。其中，查询向量通过对第个输入特征序列进行线性变换得到；为键矩阵中的向量数量，，均为正整数；为键矩阵中第个向量。Determine the query matrix The query vector Sparsity measurement of . Among them, the query vector Through the The input feature sequence is linearly transformed; is the key matrix The number of vectors in , , All are positive integers; is the key matrix Middle vectors.
在计算注意力权重时，通过输入特征序列的值矩阵、输入特征序列的查询矩阵和与查询矩阵相匹配的键矩阵共同计算输入特征序列的注意力权重。具体的，通过查询矩阵和键矩阵的点积计算确定输入特征序列的注意力分数，如下所示：When calculating the attention weight, the value matrix of the input feature sequence is , query matrix of input feature sequence and the key matrix that matches the query matrix Calculate the attention weight of the input feature sequence together. Specifically, by querying the matrix and key matrix The dot product calculation determines the attention score of the input feature sequence , as shown below:
然后，再通过函数对输入特征序列的注意力分数进行归一化，得到输入特征序列的注意力权重。具体的，按照公式：Then, through The attention score of the function on the input feature sequence Normalize and get the attention weight of the input feature sequence. Specifically, according to the formula:
计算输入特征序列的注意力权重。式中，为输入特征序列的查询矩阵，为与查询矩阵相匹配的键矩阵，为输入特征序列的值矩阵，为输入特征序列的维度。Calculate the attention weight of the input feature sequence In the formula, is the query matrix of the input feature sequence, is the key matrix that matches the query matrix, is the value matrix of the input feature sequence, is the dimension of the input feature sequence.
最有，将注意力权重与对应的值矩阵进行加权求和，输出值即为目标个体的核心温度。籍此，通过矫正后的皮肤温度和心率数据，基于长时序预测网格对目标个体的核心温度进行预测，有效避免获取人体核心温度时对人体的侵入式测量，实现无需直接侵入人体而通过人体的皮肤温度和心率即可精确预测获取人体核心温度，降低人体核心温度测量时的难度，以支撑人体热应激预警响应和辅助决策。Most importantly, the attention weight The corresponding value matrix The weighted sum is performed, and the output value is the core temperature of the target individual. In this way, the core temperature of the target individual is predicted based on the long-term prediction grid through the corrected skin temperature and heart rate data, effectively avoiding invasive measurement of the human body when obtaining the human core temperature, and achieving accurate prediction of the human core temperature through the human skin temperature and heart rate without direct invasion of the human body, reducing the difficulty of measuring the human core temperature, so as to support the human heat stress warning response and auxiliary decision-making.
本申请中，通过深度学习模型对人体的核心温度进行预测，能够综合考虑多种因素的影响，并通过持续学习适应个体差异和环境变化，高效地整合、处理多源生理数据、确保模型的准确性和可靠性，使模型具有在复杂和动态环境下的对核心温度进行预测的能力，从无创测量的皮肤温度和心率等多种生理参数中预测人体的核心温度。同时，通过在人工环境实验舱内的模拟实验，收集覆盖不同生理状态、环境条件下的人体的生理模拟数据，据此建立皮肤温度、心率、核心温度之间的回归模型，并通过回归模型计算能够全面覆盖不同年龄、性别、环境等条件下的样本数据集，以根据样本数据集通过深度学习算法对卡尔曼滤波器的模型参数进行自动调整，使模型参数调整后的卡尔曼滤波器具有更广泛的适用性和更高的精确度。In this application, the core temperature of the human body is predicted by a deep learning model, which can comprehensively consider the influence of multiple factors, and adapt to individual differences and environmental changes through continuous learning, efficiently integrate and process multi-source physiological data, ensure the accuracy and reliability of the model, so that the model has the ability to predict the core temperature in a complex and dynamic environment, and predict the core temperature of the human body from multiple physiological parameters such as skin temperature and heart rate measured non-invasively. At the same time, through simulation experiments in artificial environment test chambers, physiological simulation data covering human bodies under different physiological states and environmental conditions are collected, and a regression model between skin temperature, heart rate, and core temperature is established based on this. The regression model is used to calculate sample data sets that can comprehensively cover different ages, genders, environments, and other conditions, so that the model parameters of the Kalman filter can be automatically adjusted through a deep learning algorithm based on the sample data set, so that the Kalman filter after the model parameters are adjusted has a wider applicability and higher accuracy.
本申请实施例还提供一种基于深度学习的无创人体核心温度预测系统，如图5所示，包括：The embodiment of the present application also provides a non-invasive human core temperature prediction system based on deep learning, as shown in FIG5 , comprising:
模拟回归单元501，配置为基于人工环境实验舱在模拟环境下进行暖体家人实验，构建人体热反应数据集，以反演人体热反应数据集中的皮肤模拟温度、模拟心率和核心模拟温度之间的回归模型；The simulation regression unit 501 is configured to perform a warm body experiment in a simulated environment based on an artificial environment experiment cabin, construct a human thermal response data set, and invert a regression model between the skin simulation temperature, the simulated heart rate, and the core simulation temperature in the human thermal response data set;
滤波器参数调整单元502，配置为基于回归模型，通过深度学习算法自动调整卡尔曼滤波器中的模型参数；A filter parameter adjustment unit 502 is configured to automatically adjust model parameters in the Kalman filter through a deep learning algorithm based on the regression model;
目标数据矫正单元503，配置为基于模型参数调整后的卡尔曼滤波器，对实时采集的目标个体的实测数据进行优化矫正，得到目标个体的实测矫正数据；其中，实测数据包括皮肤温度和心率数据，实测矫正数据包括皮肤温度矫正数据和心率矫正数据；The target data correction unit 503 is configured to optimize and correct the measured data of the target individual collected in real time based on the Kalman filter after the model parameters are adjusted, so as to obtain the measured corrected data of the target individual; wherein the measured data includes skin temperature and heart rate data, and the measured corrected data includes skin temperature corrected data and heart rate corrected data;
核心温度预测单元504，配置为根据目标个体的心率矫正数据和皮肤温度矫正数据，基于长时序预测网络的核心温度预测模型，对目标个体的核心温度进行预测。The core temperature prediction unit 504 is configured to predict the core temperature of the target individual based on the heart rate correction data and skin temperature correction data of the target individual and the core temperature prediction model of the long time series prediction network.
本申请实施例提供的基于深度学习的无创人体核心温度预测系统，能够实现上述任一实施例的基于深度学习的无创人体核心温度预测方法的步骤、流程，并达到相同的技术效果，在此不再一一赘述。The non-invasive human core temperature prediction system based on deep learning provided in the embodiments of the present application can implement the steps and processes of the non-invasive human core temperature prediction method based on deep learning in any of the above embodiments and achieve the same technical effects, which will not be repeated here one by one.
在本发明的描述中，术语“一个实施例”、“一些实施例”、 “示例”、“具体示例”、或“一些示例”等意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。In the description of the present invention, the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.
以上所述仅为本申请的优选实施例，并不用于限制本申请，对于本领域的技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.
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