CN115251852B

CN115251852B - Detection quantification method and system for body temperature regulation function

Info

Publication number: CN115251852B
Application number: CN202211218770.4A
Authority: CN
Inventors: 何将
Original assignee: Anhui Xingchen Zhiyue Technology Co ltd
Current assignee: Anhui Xingchen Zhiyue Technology Co ltd
Priority date: 2022-10-07
Filing date: 2022-10-07
Publication date: 2022-12-30
Anticipated expiration: 2042-10-07
Also published as: CN115251852A

Abstract

The invention provides a detection quantification method of a body temperature regulation function, which comprises the steps of carrying out combined stimulation of temperature and humidity with different intensities on a testee, collecting physiological sign signals of a target part of the testee, and processing the physiological sign signals to obtain a first physiological sign characteristic data set; performing melt coupling feature selection, sequence normalization and melt coupling feature analysis on the first physiological sign feature data set to obtain a first body temperature physiological response curve set, a first body temperature physiological regulation index set, a first body temperature physiological excitation temperature threshold set and a first body temperature physiological excitation humidity threshold set; and analyzing and comparing the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set of the comparison database with the body temperature regulation function to obtain a detection quantitative report or result of the body temperature regulation function state level of the tested person. The invention can detect and quantify the functional state level of the body temperature adjusting function of the testee more comprehensively and accurately.

Description

Detection quantification method and system for body temperature regulation function

Technical Field

The invention relates to the field of detection and quantification of a body temperature regulation function, in particular to a detection and quantification method and system of the body temperature regulation function.

Background

Diabetes, hypoglycemia, hyperthyroidism, congestive heart failure, orthostatic hypotension, as well as infectious diseases, malignancies, neurological disorders, connective tissue diseases or accidental poisoning, etc., all cause or manifest in varying degrees of thermoregulatory dysfunction or disorder, as well as autonomic dysfunction or pathology. And hyperhidrosis, hypohidrosis, anhidrosis or abnormal secretion of local sweat, abnormal sweat components, abnormal thermoregulation, etc. are the most direct physiological manifestations of thermoregulation function and autonomic nerve dysfunction or the manifestations of early symptoms of diseases, and are also important risk factors for clinical early discovery, early prevention, clinical diagnosis and treatment effect evaluation. Taking diabetes as an example, according to statistics, the prevalence rate of diabetes of adults in China is about 12.8%, and the number of diabetes is about 1.3 hundred million; the incidence rate of 'pre-diabetes' with high blood sugar but without the diagnostic standard of diabetes is about 15.5 percent, and if an effective prevention and treatment means is lacked, about 5 to 10 percent of 'pre-diabetes' patients can develop real diabetes every year. Dysregulation of body temperature, dyshidrosis are the most common symptoms of diabetes and "pre-diabetes" combined with autonomic dysfunction or pathology.

Under the condition of healthy body functions, the body temperature regulation of the human body is dynamic, stable, layered and synergistic. In cold environment, the heart rate is reduced, the skin vasoconstriction-blood flow is reduced, the hidden perspiration is reduced, the skin temperature is reduced, and the heat loss of the body is reduced. In a hot environment, as the heat load increases, a series of physiological reactions occur in the human body: heart rate is accelerated, skin vessel expansion-blood flow is increased; when the vascular regulation is insufficient to meet the heat dissipation requirements, the core temperature and skin temperature begin to rise; when the core temperature and skin temperature of human body are raised to a certain height, the sweat gland is activated, and the dominant perspiration is increased to the point of dripping of the profuse sweat.

The existing clinical diagnosis and treatment practices are single and dispersive in technical means, continuous observation and systemic analysis are lacked, particularly, a nervous system-endocrine system-circulatory system cooperative regulation system detection means and a quantitative analysis method are lacked, the functional state level of the body temperature regulation function of a tested patient or a patient is difficult to accurately evaluate, the degree of the disease disorder of related diseases cannot be further judged and recognized, or the treatment and rehabilitation progress cannot be further judged and recognized, and more effective preventive intervention measures and treatment and rehabilitation schemes cannot be provided for the patient.

Therefore, the prior art needs to be improved to improve the accuracy and comprehensiveness of the detection result.

Disclosure of Invention

In view of the above drawbacks and needs of the existing methods, the present invention provides a method for detecting and quantifying thermoregulation function, which can detect and quantify the functional status level of thermoregulation function of a subject or patient more comprehensively and accurately, further judge and identify the degree of disease disorder or treatment and rehabilitation progress of related diseases, and assist in prevention screening, clinical diagnosis and rehabilitation therapy. The invention also provides a detection quantification system of the body temperature regulation function, which is used for realizing the method.

The method specifically comprises the following steps:

a detection and quantification method for a body temperature regulation function comprises the following steps:

according to the design of a body temperature regulation detection scheme, temperature and humidity combined stimulation with different intensity levels is carried out on a testee, physiological sign signals of a plurality of target parts of the testee are acquired, and a first physiological sign signal data set is obtained;

performing signal preprocessing and time frame segmentation on the first physiological sign signal data set, and eliminating abnormal data to obtain a second physiological sign signal data set;

performing signal integration processing, characteristic interval division and signal characteristic analysis on the second physiological sign signal data set to obtain a first physiological sign characteristic data set;

performing fused coupling feature selection, sequence normalization and fused coupling feature analysis on the first physiological sign feature data set to obtain a first body temperature physiological response curve set, a first body temperature physiological regulation index set, a first body temperature physiological excitation temperature threshold set and a first body temperature physiological excitation humidity threshold set;

analyzing and comparing the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set of the tested person with a first body temperature physiological response curve set, a first body temperature physiological regulation index set, a first body temperature physiological excitation temperature threshold set and a first body temperature physiological excitation humidity threshold set of a body temperature regulation function comparison database to obtain a detection quantification report or result of the body temperature regulation function state level of the tested person, wherein the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set of the body temperature regulation function comparison database are the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation humidity threshold set and the first body temperature physiological excitation temperature threshold set of healthy people and body temperature regulation function disorder people of the tested person in the same age group, the same gender and the body temperature regulation function disorder people.

Preferably, the body temperature regulation detection scheme at least comprises a detection mode type, a temperature and humidity intensity sequence, a detection stimulation duration, a detection stimulation interval and a detection stimulation period.

More preferably, the types of detection means include at least environmental stimuli, systemic stimuli, local stimuli.

Preferably, the temperature and humidity intensity sequence is composed of a temperature intensity queue and a humidity intensity queue.

More preferably, the target site includes at least the body surface skin system of the head, neck, torso, extremities.

Preferably, the target part is determined according to the type of the detection mode.

Preferably, the physiological sign signals at least comprise temperature signals, skin electric signals, blood oxygen level dependence BOLD signals, electrocardiosignals and pulse signals, and are acquired by temperature, skin electric and functional near infrared spectrum imaging and/or functional nuclear magnetic resonance imaging, electrocardio and pulse data acquisition equipment to a plurality of target parts.

More preferably, the signal pre-processing comprises at least a/D conversion, resampling, noise reduction, artifact removal, filtering.

Preferably, the time frame segmentation is to perform time alignment and signal data framing interception on different physiological characteristic signals and temperature and humidity signals in the physiological sign signals according to sampling rates of the different physiological characteristic signals in the physiological sign signals and a preset time window length.

Preferably, the signal integration processing is to integrate the second physiological sign signals of multiple parts or multiple channels, the same type and the same temperature and humidity intensity.

More preferably, the integrated processing method at least comprises extracting any one of an average superimposed sequence, a weighted superimposed sequence, a maximum amplitude sequence, a minimum variance sequence, a minimum coefficient of variation sequence and a maximum coefficient of variation sequence.

Preferably, the characteristic interval division at least comprises a baseline state period, an adjustment interval from the current temperature and humidity intensity to the next target temperature and humidity, a constant temperature and humidity interval of the target temperature and humidity, a heart rate pulse-electrocardio pulse observation interval, a sweat secretion-skin electricity observation interval, a blood circulation-BOLD observation interval and a skin temperature-body surface temperature observation interval.

Preferably, the baseline state period refers to that the baseline establishing time is preset for continuous stimulation under the stimulation of the first temperature and humidity intensity, and the tested baseline physiological characteristic signal and characteristic state are obtained.

Preferably, the signal characteristic analysis comprises a temperature signal characteristic, a skin electric signal characteristic, a blood oxygen level dependent BOLD signal characteristic, an electrocardiosignal characteristic and a pulse signal characteristic.

More preferably, the signal characteristics include at least a mean, a root mean square, a maximum, a minimum, a variance, a standard deviation, a coefficient of variation, kurtosis, and skewness.

More preferably, the skin electrical signal characteristics include at least a total level of skin conductance, a signal characteristic of skin conductance response.

More preferably, the blood oxygen level dependent BOLD signal features comprise at least signal features of oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin.

More preferably, the cardiac electrical signal features comprise at least signal features of QRS complex, heart rate and heart rate variability.

More preferably, the decoupling characteristic selection includes at least one or more physiological characteristics of the first physiological characteristic data set as a data source of the decoupling characteristic analysis.

More preferably, the first set of body temperature physiological response curves at least comprises a heart rate pulse response curve, a blood circulation response curve, a sweat secretion response curve and a skin temperature response curve.

More preferably, the first set of body temperature physiology modulation indices at least comprises heart rate pulse modulation index, blood circulation modulation index, sweat secretion modulation index, skin temperature modulation index.

Preferably, the first body temperature physiological excitation temperature threshold set at least comprises a heart rate and pulse excitation temperature threshold, a blood circulation excitation temperature threshold, a sweat secretion excitation temperature threshold and a skin temperature excitation temperature threshold.

Preferably, the first body temperature physiological excitation humidity threshold set at least comprises a heart rate and pulse excitation humidity threshold, a blood circulation excitation humidity threshold, a sweat secretion excitation humidity threshold and a skin temperature excitation humidity threshold.

Preferably, the heart rate pulse response curve and the heart rate pulse modulation index are calculated as follows:

acquiring a baseline state period, a heart rate pulse-electrocardio pulse observation interval, the electrocardiosignal characteristics and the pulse signal characteristics under all temperature and humidity intensities from the first physiological characteristic data set, and selecting through decoupling characteristics to obtain an electrocardio pulse signal characteristic selection set;

extracting signal features in the electrocardio pulse signal feature selection set under the same temperature and humidity intensity to carry out weighting calculation, and generating heart rate pulse response factors under the current temperature and humidity intensity;

calculating heart rate pulse response factors under all temperature and humidity intensities to generate a first response factor sequence;

performing function fitting on the first response factor sequence to generate a heart rate pulse response curve;

performing sequence normalization on the first response factor sequence and the temperature intensity queue and the humidity intensity queue in the temperature and humidity intensity sequence, calculating the incidence relation and the distance characteristic of the first response factor sequence and the temperature and humidity intensity queue, and generating a first relation characteristic data set;

and performing weighted calculation on the first relation characteristic data set to generate a heart rate pulse regulation index.

More preferably, the blood circulation response curve and the blood circulation regulation index are calculated as follows:

acquiring a baseline state period, a blood circulation-BOLD observation interval and the blood oxygen level dependence BOLD signal characteristics under all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through a melt coupling characteristic to obtain a BOLD signal characteristic selection set;

extracting signal features in the BOLD signal feature selection set under the same temperature and humidity intensity to perform weighting calculation, and generating blood circulation response factors under the current temperature and humidity intensity;

calculating to obtain blood circulation response factors under all temperature and humidity intensities, and generating a second response factor sequence;

performing function fitting on the second response factor sequence to generate a blood circulation response curve;

performing sequence normalization on the temperature intensity queue and the humidity intensity queue in the second response factor sequence and the temperature and humidity intensity sequence, calculating the incidence relation and the distance characteristic of the first response factor sequence and the second response factor sequence, and generating a second relation characteristic data set;

and performing weighted calculation on the second relation characteristic data set to generate a blood circulation regulation index.

More preferably, the sweat secretion response curve and the sweat secretion modulation index are calculated as follows:

acquiring the skin electrical signal characteristics under a baseline state period, a sweat secretion-skin electrical observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and performing melt coupling characteristic selection to obtain a skin electrical signal characteristic selection set;

extracting signal features in the skin electric signal feature selection set under different temperature and humidity intensities to perform weighting calculation, and generating sweat secretion response factors under the current temperature and humidity intensities;

calculating sweat secretion response factors under all temperature and humidity intensities to generate a third response factor sequence;

performing function fitting on the third response factor sequence to generate a sweat secretion response curve;

performing sequence normalization on the temperature intensity queue and the humidity intensity queue in the third response factor sequence and the temperature and humidity intensity sequence, calculating the incidence relation and the distance characteristic of the third response factor sequence and the temperature and humidity intensity queue, and generating a third relation characteristic data set;

and performing weighted calculation on the third relation characteristic data set to generate a sweat secretion regulation index.

More preferably, the skin temperature response curve and the skin temperature adjustment index are calculated as follows:

acquiring the temperature signal characteristics in a baseline state period, a skin temperature-body surface temperature observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through decoupling characteristics to obtain a temperature signal characteristic selection set;

extracting signal features in the temperature signal feature selection set under the same temperature and humidity intensity to perform weighting calculation, and generating a skin temperature response factor under the current temperature and humidity intensity;

calculating to obtain skin temperature response factors under all temperature and humidity intensities, and generating a fourth response factor sequence;

performing function fitting on the fourth response factor sequence to generate a skin temperature response curve;

performing sequence normalization on the fourth response factor sequence and a temperature intensity queue and a humidity intensity queue in the temperature and humidity intensity sequence, calculating the incidence relation and the distance characteristic of the fourth response factor sequence and the temperature and humidity intensity queue, and generating a fourth relation characteristic data set;

and performing weighted calculation on the fourth relation characteristic data set to generate a skin temperature regulation index.

Preferably, the heart rate pulse excitation temperature threshold and the heart rate pulse excitation humidity threshold are calculated as follows:

acquiring a baseline state period, a heart rate pulse-electrocardio pulse observation interval, the electrocardiosignal characteristics and the pulse signal characteristics under all temperature and humidity intensities from the first physiological characteristic data set, and selecting through decoupling characteristics to obtain an electrocardio pulse signal excitation characteristic selection set;

respectively carrying out sequence normalization on all signal characteristic sequences of the electrocardio-pulse signal excitation characteristic selection set to obtain an electrocardio-pulse signal excitation characteristic data normalization set;

performing linear weighted fusion on all sequences of the electrocardio pulse signal excitation characteristic data normalization set to generate a first excitation characteristic sequence;

performing first-order difference processing on the first excitation characteristic sequence to obtain a first excitation characteristic difference sequence;

extracting the maximum value of the first excitation characteristic difference sequence and an index corresponding to the maximum value to generate a first excitation sequence maximum value and a first excitation sequence maximum value index;

and extracting a temperature value and a humidity value at the index of the maximum value of the first excitation sequence in the temperature and humidity intensity sequence, and respectively generating a heart rate and pulse excitation temperature threshold value and a heart rate and pulse excitation humidity threshold value.

More preferably, the blood circulation excitation temperature threshold and the blood circulation excitation humidity threshold are calculated as follows:

acquiring blood oxygen level dependence BOLD signal characteristics under a baseline state period, a blood circulation-BOLD observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and performing fused coupling characteristic selection to obtain a BOLD signal excitation characteristic selection set;

respectively performing sequence normalization on all signal characteristic sequences of the BOLD signal excitation characteristic selection set to obtain a BOLD signal excitation characteristic data normalization set;

performing linear weighted fusion on all sequences of the BOLD signal excitation characteristic data normalization set to generate a second excitation characteristic sequence;

performing first-order difference processing on the second excitation characteristic sequence to obtain a second excitation characteristic difference sequence;

extracting the maximum value of the second excitation characteristic difference sequence and an index corresponding to the maximum value to generate a second excitation sequence maximum value and a second excitation sequence maximum value index;

and extracting a temperature value and a humidity value at the index of the maximum value of the second excitation sequence in the temperature and humidity intensity sequence, and respectively generating a blood circulation excitation temperature threshold and a blood circulation excitation humidity threshold.

More preferably, the sweat secretion stimulation temperature threshold and the sweat secretion stimulation humidity threshold are calculated as follows:

acquiring the skin electrical signal characteristics under a baseline state period, a sweat secretion-skin electrical observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and performing melt coupling characteristic selection to obtain a skin electrical signal excitation characteristic selection set;

respectively carrying out sequence normalization on all signal characteristic sequences of the skin electric signal excitation characteristic selection set to obtain a skin electric signal excitation characteristic data normalization set;

performing linear weighted fusion on all sequences of the skin electric signal excitation characteristic data normalization set to generate a third excitation characteristic sequence;

performing first-order difference processing on the third excitation characteristic sequence to obtain a third excitation characteristic difference sequence;

extracting the maximum value of the third excitation characteristic difference sequence and an index corresponding to the maximum value to generate a third excitation sequence maximum value and a third excitation sequence maximum value index;

and extracting a temperature value and a humidity value at the index of the maximum value of the third excitation sequence in the temperature and humidity intensity sequence to generate a sweat secretion excitation temperature threshold and a sweat secretion excitation humidity threshold.

More preferably, the calculation method of the skin temperature excitation temperature threshold and the skin temperature excitation humidity threshold is as follows:

acquiring the temperature signal characteristics under a baseline state period, a skin temperature-body surface temperature observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through melt coupling characteristics to obtain a temperature signal characteristic selection set;

respectively carrying out sequence normalization on all signal characteristic sequences of the temperature excitation characteristic selection set to obtain a temperature signal excitation characteristic data normalization set;

performing linear weighted fusion on all sequences of the temperature signal excitation characteristic data normalization set to generate a fourth excitation characteristic sequence;

performing first-order difference processing on the fourth excitation characteristic sequence to obtain a fourth excitation characteristic difference sequence;

extracting the maximum value of the fourth excitation characteristic difference sequence and an index corresponding to the maximum value to generate a fourth excitation sequence maximum value and a fourth excitation sequence maximum value index;

and extracting a temperature value and a humidity value at the index of the maximum value of the fourth excitation sequence in the temperature and humidity intensity sequence, and respectively generating a skin temperature excitation temperature threshold and a skin temperature excitation humidity threshold.

More preferably, the sequence normalization calculation method is as follows:

for a sequence of values

Obtaining the maximum value of the sequence

And minimum value

；

The sequence normalization is calculated as

Wherein the content of the first and second substances,

is a sequence of values

The normalized sequence of (a).

Preferably, the calculation method of the correlation at least comprises a coherence coefficient, a pearson correlation coefficient, an Jackside similarity coefficient, a linear mutual information coefficient and a linear correlation coefficient.

Preferably, the distance feature is calculated as follows:

for a sequence of values

And a numerical sequence

Distance sequence

For each difference of time-point values in two sequences, i.e.

The distance features being a sequence of distances

The numerical characteristics of (1) mainly include mean value, root mean square, maximum value, minimum value, variance, standard deviation, coefficient of variation, kurtosis and skewness.

Preferably, the body temperature regulation function comparison database is established in the following manner:

according to the design of a body temperature regulation detection scheme, temperature and humidity combined stimulation with different intensity levels is implemented on a healthy population test and a body temperature regulation dysfunction person test, physiological sign signals of a plurality of target parts of the test person are acquired, and a first physiological sign signal data set is obtained;

form a comparison database of the body temperature regulation function of the tested healthy population and the tested body temperature regulation function of the body temperature regulation dysfunction patient.

Furthermore, the invention also provides a detection quantification system of the thermoregulation function, which is used for executing the steps of the method. The following are listed:

a detection quantification system for a body temperature regulation function comprises the following functional modules:

and the detection scheme management module is used for setting, managing and executing the body temperature regulation detection scheme and monitoring the personal safety in the process.

The physical sign signal acquisition module is used for acquiring and acquiring physical sign signals of a plurality of tested target parts to obtain a first physical sign signal data set;

the physical sign signal processing module is used for performing signal preprocessing and time frame segmentation on the first physical sign signal data set, eliminating abnormal data and obtaining a second physical sign signal data set;

the sign characteristic analysis module is used for performing signal integration processing, characteristic interval division and signal characteristic analysis on the second physiological sign signal data set to obtain a first physiological sign characteristic data set;

a characteristic melt-coupling analysis module, configured to perform melt-coupling characteristic selection, sequence normalization, and melt-coupling characteristic analysis on the first physiological sign characteristic data set to obtain a first body temperature physiological response curve set, a first body temperature physiological adjustment index set, a first body temperature physiological excitation temperature threshold set, and a first body temperature physiological excitation humidity threshold set;

and the function evaluation reporting module is used for analyzing and comparing the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold value set and the first body temperature physiological excitation humidity threshold value set of the tested body temperature, with the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold value set and the first body temperature physiological excitation humidity threshold value set of the body temperature regulation function comparison database to obtain a detection quantification report or result of the state level of the tested body temperature regulation function.

Preferably, the detection scheme management module includes the following functional units:

the system comprises a scheme management unit, a data processing unit and a data processing unit, wherein the scheme management unit is used for inputting, setting, editing, deleting and other management of a body temperature regulation detection scheme, and the body temperature regulation detection scheme at least comprises a detection mode type, a detection stimulation duration, a detection stimulation interval, a detection stimulation period and a temperature and humidity intensity sequence;

and the execution monitoring unit is used for generating temperature and humidity signals with corresponding temperature and humidity intensities according to the body temperature regulation detection scheme, starting stimulation, starting physiological sign signal acquisition and real-time personal safety monitoring.

Preferably, the sign signal acquisition module includes the following functional units:

the basic information recording unit is used for recording the personal basic information of a tested person, wherein the personal basic information at least comprises name, gender, birth date, age, height, weight, blood pressure, health condition, disease history information and clinical diagnosis and treatment opinions;

the signal communication setting unit is used for connecting physiological sign signal acquisition equipment or a sensor, realizing signal communication and data transmission, and recording acquisition basic parameters, wherein the acquisition basic parameters at least comprise equipment names, manufacturers, sampling rates, channel names and channel numbers;

and the data recording and storing unit is used for recording and storing the temperature and humidity signals and the physiological sign signals and generating the first physiological sign signal data.

Preferably, the sign signal processing module includes the following functional units:

a digital preprocessing unit, configured to perform signal preprocessing on the first physiological sign signal data set;

and the signal time frame segmentation unit is used for performing signal frame segmentation on the first physiological sign signal data set after signal preprocessing according to the starting time point of the temperature and humidity signal, and eliminating abnormal data to obtain the second physiological sign signal data set.

Preferably, the sign feature analysis module includes the following functional units:

the signal integration processing unit is used for integrating the second physiological sign signals of multiple parts or multiple channels, the same type and the same temperature and humidity intensity;

a characteristic interval setting unit, configured to set the baseline state period, the adjustment interval from the current temperature and humidity intensity to the next target temperature and humidity, the constant temperature and humidity interval of the target temperature and humidity, the heart rate-electrocardiogram observation interval, the sweat secretion-skin electric observation interval, the blood circulation-BOLD observation interval, and the skin temperature-body surface temperature observation interval;

and the signal characteristic analysis unit is used for carrying out signal characteristic extraction on the second physiological sign signal after signal integration processing according to the characteristic interval division.

More preferably, the feature fusion analysis module comprises the following functional units:

a characteristic selection setting unit, configured to screen, define, or set a data source for characteristic fusion coupling analysis of the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set, and the first body temperature physiological excitation humidity threshold set from the first physiological sign characteristic data set;

the response adjustment analysis unit is used for calculating the first body temperature physiological response curve set and the first body temperature physiological adjustment index set;

and the excitation threshold value analysis unit is used for calculating the first body temperature physiological excitation temperature threshold value set and the first body temperature physiological excitation humidity threshold value set.

Preferably, the function evaluation reporting module includes the following functional units:

the reference comparison library unit is used for establishing, storing, updating and managing basic information of a healthy population subject and a body temperature regulation dysfunction person subject, and a body temperature regulation function comparison database consisting of the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set;

the comparison and analysis unit is used for generating a radar map, a line trend map and/or a data table from the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set of the current tested object for comparison and analysis of functional level and functional evaluation;

the report output unit is used for generating and outputting a tested body temperature regulation function level detection quantitative report;

and the data storage unit is used for storing all process data and all result data in the detection and quantification process of the thermoregulation function.

The invention also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that the processor implements the steps of the detection and quantification method when executing the computer program.

The invention also provides a computer-readable storage medium, on which a computer program is stored, which is characterized in that the computer program, when being executed by a processor, carries out the steps of the above-mentioned detection and quantification method.

The method provided by the invention can dynamically and systematically detect and quantify the functional state level of the body temperature regulation function of a tested or patient, and can more comprehensively and accurately measure the body temperature regulation function through the extraction and calculation of four dimensional parameters of heart rate pulse regulation, blood circulation regulation, sweat secretion regulation and skin temperature regulation, thereby further judging and identifying the disease obstacle degree or treatment and rehabilitation progress of related diseases, assisting in prevention screening, clinical diagnosis and rehabilitation treatment, and timely and early reminding a tester to treat.

The body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the current tested body temperature are analyzed and compared with the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the healthy tested body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set, and the functional state level of the body temperature regulation function of the current tested body temperature can be obtained, namely whether the body temperature regulation function is normal or abnormal; meanwhile, the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the current tested body temperature regulation dysfunction person are analyzed and compared with the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the tested body temperature regulation dysfunction person (different disorder degrees), so that the disorder severity degree of the current tested body temperature regulation function can be obtained; and finally, analyzing and comparing the currently tested body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set with the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set in the historical detection of the currently tested body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set, and obtaining the rehabilitation progress condition of the currently tested body temperature regulation function and evaluating the treatment intervention effect.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.

FIG. 1 is a basic flowchart of a method for detecting and quantifying a thermoregulatory function according to an embodiment of the present invention;

FIG. 2 is a block diagram of a system for quantifying detection of thermoregulation provided by an embodiment of the present invention;

fig. 3 is a schematic diagram of a temperature and humidity intensity curve provided by an embodiment of the invention;

fig. 4 is a schematic diagram of physiological sign acquisition of biceps brachii according to an embodiment of the present invention.

Detailed Description

In order to more clearly illustrate the object and technical solution of the present invention, the present invention will be further described with reference to the accompanying drawings in the embodiments of the present application. It should be apparent that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. Other embodiments, which can be derived by one of ordinary skill in the art from the embodiments of the present invention without inventive faculty, are within the scope of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.

As shown in fig. 1, a detection and quantification method for a thermoregulation function provided in an embodiment of the present invention includes the following steps:

p001: according to the design of a body temperature regulation detection scheme, performing combined stimulation on environmental temperature and humidity with different intensities on a tested object, acquiring physiological sign signals of a plurality of target parts of the tested object, and generating a physiological sign signal data set;

p002: performing signal preprocessing and time frame segmentation on the physiological sign signal data set, and eliminating abnormal data to obtain a physiological sign signal data matrix;

p003: performing signal integration processing, characteristic interval division and signal characteristic analysis on the physiological sign signal data matrix to obtain a physiological sign characteristic data matrix;

p004: performing fused coupling feature selection, sequence normalization and fused coupling feature analysis on the physiological sign feature data set to obtain a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set under all temperature and humidity intensities;

p005: and comparing the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the healthy subject and the subject with the thermoregulation dysfunction in the same age group and the same sex to evaluate the body temperature regulation functional state, the disorder degree or the rehabilitation progress of the current subject.

In this example, the following comparative cases should be understood: firstly, analyzing and comparing a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set of a current tested body temperature with a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set of a healthy tested body temperature, and obtaining the functional state level of the body temperature regulation function of the current tested body temperature, namely whether the current tested body temperature regulation function is normal or abnormal; secondly, analyzing and comparing the current body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the person with body temperature regulation dysfunction (with different dysfunction degrees) with the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the person with body temperature regulation dysfunction, and obtaining the dysfunction severity degree of the current body temperature regulation function of the person with body temperature regulation dysfunction; and thirdly, analyzing and comparing the currently tested body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set with the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set in historical detection, and obtaining the rehabilitation progress condition of the currently tested body temperature regulation function and evaluating the treatment intervention effect.

In this embodiment, it is worth to be explained that: the physiological function of a normal healthy human body can quickly finish physiological balance adjustment so as to adapt to the change of the temperature and the humidity of the external environment. The physiological regulation indexes of different body temperatures can represent the regulation and adjustment speed of the physiological function of the testee; the physiological excitation temperature threshold and the humidity threshold of different body temperatures directly indicate the physiological tolerance threshold and the physiological function adjustment result of the testee. In practical clinical detection practice and diagnosis and analysis, the temperature and humidity threshold value excited by body temperature physiology is sensitive and easy to identify, a person with body temperature regulation dysfunction (such as a diabetic) can have a very obvious perspiration problem in an environment with the temperature of 25-30 ℃ and the humidity of 40-45%, and a normal healthy person can have an obvious perspiration problem in an environment with the temperature of 28-32 ℃ and the humidity of 45-60%. The body temperature physiological response curve, body temperature physiological regulation index and body temperature physiological excitation temperature and humidity threshold of different layers have obvious distinctive characteristics, so that the characteristic characteristics of different primary diseases and secondary diseases can be further distinguished and identified by the scheme of the invention.

As shown in fig. 2, a detection and quantification system for a thermoregulation function provided by an embodiment of the present invention includes:

the detection scheme management module S100 is used for setting, managing and executing a body temperature regulation detection scheme and monitoring the personal safety in the process; the system comprises the following functional units:

the system comprises a scheme management unit S110, a body temperature regulation detection unit and a body temperature regulation unit, wherein the scheme management unit S110 is used for inputting, setting, editing, deleting and other management of a body temperature regulation detection scheme, and the body temperature regulation detection scheme at least comprises a detection mode type, a detection stimulation duration, a detection stimulation interval, a detection stimulation period and a temperature and humidity intensity sequence;

and the execution monitoring unit S120 is used for generating temperature and humidity signals with corresponding temperature and humidity intensities according to the body temperature regulation detection scheme, starting stimulation, starting physiological sign signal acquisition and real-time personal safety monitoring.

The physical sign signal acquisition module S200 is used for acquiring and obtaining physical sign signals of a plurality of tested target parts to obtain a physical sign signal data set; the system comprises the following functional units:

a basic information recording unit S210 for recording the personal basic information of the tested person, wherein the personal basic information comprises name, sex, birth date, age, height, weight, blood pressure, health condition and disease history information;

the signal communication setting unit S220 is used for connecting physiological sign signal acquisition equipment or a sensor, realizing signal communication and data transmission, recording acquisition basic parameters, wherein the acquisition basic parameters at least comprise equipment names, manufacturers, sampling rates, channel names and channel numbers;

and the data recording and storing unit S230 is used for recording and storing the temperature and humidity signals and the physiological sign signals and generating physiological sign signal data.

The physical sign signal processing module S300 is used for performing signal preprocessing and time frame segmentation on the physical sign signal data set, eliminating abnormal data and obtaining a physical sign signal data matrix; the system comprises the following functional units:

a digital preprocessing unit S310, configured to perform signal preprocessing on the physiological sign signal data set;

and the signal time frame segmentation unit S320 is used for performing signal frame segmentation on the physiological sign signal data set after signal preprocessing according to the starting time point of the temperature and humidity signal, and eliminating abnormal data to obtain a physiological sign signal data matrix.

The sign characteristic analysis module S400 is used for performing signal integration processing, characteristic interval division and signal characteristic analysis on the physiological sign signal data matrix to obtain a physiological sign characteristic data set; the system comprises the following functional units:

the signal integration processing unit S410 is used for integrating physiological sign signal data matrixes of multiple parts or multiple channels, the same type and the same temperature and humidity intensity;

the characteristic interval setting unit S420 is used for setting a baseline state period, an adjustment interval from the current temperature and humidity intensity to the next target temperature and humidity, a constant temperature and humidity interval of the target temperature and humidity, a heart rate-electrocardio observation interval, a sweat secretion-skin electricity observation interval, a blood circulation-BOLD observation interval and a skin temperature-body surface temperature observation interval;

and the signal characteristic analysis unit S430 is configured to perform signal characteristic extraction on the physiological sign signal data matrix after signal integration processing according to characteristic interval division.

The characteristic melt-coupling analysis module S500 is used for performing melt-coupling characteristic selection, sequence normalization and melt-coupling characteristic analysis on the physiological sign characteristic data set to obtain a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set; the system comprises the following functional units:

the characteristic selection setting unit S510 is used for screening, defining or setting data sources of characteristic fusion coupling analysis of a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set from a physiological sign characteristic data set;

a response adjustment analysis unit S520 for calculating a temperature physiological response curve set and a temperature physiological adjustment index set;

and the excitation threshold analysis unit S530 is used for calculating a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set.

The function evaluation reporting module S600 is used for analyzing and comparing the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the tested body with the body temperature regulation function comparison database to obtain a detection quantitative report or result of the state level of the body temperature regulation function of the tested body; the system comprises the following functional units:

the reference comparison library unit S610 is used for establishing, storing, updating and managing a body temperature regulation function comparison database which is composed of a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set and is used for basic information of a healthy population subject and a body temperature regulation dysfunction person subject;

the function comparison and analysis unit S620 is used for generating a radar map, a line trend map and/or a data table from the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the current tested body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set for comparison and analysis of the function level and function evaluation;

a quantitative report output unit S630 for generating and outputting a quantitative report of the detection of the thermoregulation function level of the subject;

and the key data storage unit S640 is used for storing all process data and all result data in the detection and quantification process of the thermoregulation function.

In this embodiment, the key data is stored in the database mysql.

For a more detailed description of the embodiments, the following description will be made in detail for the specific implementation of the method for detecting and quantifying dynamic metabolic functions. It should be noted that the system is used for executing the method described in fig. 1 and the summary of the invention and the corresponding method flows mentioned below.

P001: according to the design of the body temperature regulation detection scheme, the environment temperature and humidity with different intensities are jointly stimulated to the tested object, physiological sign signals of a plurality of target parts of the tested object are acquired and obtained, and a physiological sign signal data set is generated

In this embodiment, the design and setting of the body temperature adjustment detection scheme, including the type of the detection mode, the temperature and humidity intensity sequence, the stimulation detection duration, the stimulation detection interval, and the stimulation detection period, are completed according to the health status of the subject or the condition of the subject complaint.

In this example, the test piece was placed in a quiet and comfortable heat-insulating enclosure of 60 cubic meters (5 meters by 4 meters by 3 meters), and was worn as a thin and uniform piece of simple clothing, and was semi-lying on a hollow-mesh deck chair. In the whole detection process, the tested person is required to be always in a waking state or is enabled to be always in a waking state through the design of neutral audio and video and a comfortable space environment, and meanwhile, the violent change of the emotion caused by unexpected mental activities is avoided.

In this embodiment, the environmental stimulus is selected to the detection mode type, namely, the thermal physiological feedback of the tested object is observed and measured in a mode of adjusting the environmental temperature and humidity. The temperature and humidity of the environment can be regulated by an air conditioner or a heating device, and the physiological adaptation and the body temperature regulation capability of the whole body to be tested can be observed.

In this embodiment, the temperature and humidity intensity sequence THSD is composed of a temperature intensity queue TSD and a humidity intensity queue HSD, and the intensity queue is generated by a monotonically increasing function: temperature intensity queue

Generating, namely dividing the environment temperature into 10 grades by taking 24.0 ℃ as a baseline state, namely TSD {24.0, 26.0, 28.0, 30.0, 32.0, 34.0, 36.0, 38.0, 40.0 and 42.0}; humidity intensity queue

Generating 10 levels of HSD {25.0, 29.0, 33.0, 37.0, 41.0, 45.0, 49.0, 53.0, 57.0, 61.0} with 25% as baseline ambient humidity; as shown in fig. 3, the temperature and humidity intensity curve. In the practical application of the scheme, the body temperature regulation function detection of different crowds is completed according to different set temperature and humidity queue combination forms, and the temperature regulation function detection can be constant temperature but humidity is increased progressively, constant humidity temperature is increased progressively, humidity temperature is increased progressively simultaneously, temperature is increased progressively and humidity is decreased progressively, and the like, so that the requirements of different detection purposes and detection scenes are met.

In this embodiment, the stimulation duration for each temperature and humidity measurement is 6 minutes.

In the embodiment, because the environmental temperature and humidity continuously rise, people need to monitor whether the environmental temperature and humidity reaches the target environmental temperature and humidity in real time, and the required adjustment time can be estimated through the operating power of the air conditioner or the heating equipment and the size of the closed space, in the embodiment, the adjustment time is 1 minute and 30 seconds (taking 2 minutes), and the rest is carried out for 1 minute in each test value, so the detection stimulation interval time is 9 minutes (2 +6+ 1), and the detection stimulation period is also 9 minutes (2 +6+ 1).

It can be understood that in the practical application of the scheme, the temperature and humidity interval of the temperature and humidity intensity can be increased, and the number of intensity levels can be reduced, so that the detection process is accelerated; the temperature interval of the temperature and humidity intensity can be reduced, and the intensity grade number can be increased, so that the detection accuracy is improved.

In this embodiment, a scheme for real-time monitoring of human safety is formulated according to basic physiological examination and inquiry conditions of a subject before detection: the heart rate, the pulse and the body temperature of a tested person are used as detection safety automatic monitoring indexes, and if the heart rate, the pulse and the body temperature exceed preset maximum thresholds, the temperature and humidity intensity is adjusted for adjusting time or the detection is stopped; whether the behavior feedback of the tested expression, action, language and the like is abnormal or not is observed, and if the abnormality exceeds a preset controllable range, the detection is terminated; the system further guarantees the personal safety of the tested person in the detection process in an automatic and artificial active mode.

In this embodiment, the middle of the biceps brachii muscle on the lateral side of the left and right upper arms is selected as a target part, and a temperature signal, a skin electric signal and a blood oxygen level dependent BOLD signal to be tested are acquired through acquisition equipment of temperature, skin electric and functional near infrared spectral imaging, as shown in fig. 4, a physiological sign acquisition schematic diagram of the biceps brachii muscle; selecting wrist radial tests (touching radial artery) of left and right hands as target parts, and acquiring pulse signals of a tested patient by using piezoelectric pulse acquisition equipment; and selecting dynamic electrocardio acquisition equipment to acquire the electrocardiosignals to be tested.

In this embodiment, to avoid data loss caused by various accidents occurring in the acquisition process, all physiological sign signals are acquired over two channels.

In this embodiment, the temperature acquisition device parameters are two channels, and the sampling rate is 128Hz; the parameters of the skin electricity acquisition equipment are double channels, and the sampling rate is 128Hz; the parameters of the functional near infrared spectrum imaging acquisition equipment are two sampling groups consisting of 8 channels (2 light sources S1/S2 and 8 detectors D1-D4/D5-D8, each sampling group consists of 1 light source S and 4 detectors D, the distance SDD between the light source and the detectors is 2.5 cm), and the sampling rate is 10Hz; the parameters of the pulse acquisition equipment are double channels, and the sampling rate is 128Hz; the parameters of the electrocardio acquisition equipment are double leads (a standard lead I and a standard lead II), and the sampling rate is 256Hz. Attention needs to be paid to the misplacement of the electrocardio collecting electrode and the pulse collecting electrode. The selection of the acquisition equipment or sensor, the acquisition target part and the fixing mode requires the acquisition artifact influence caused by a large amount of sweating.

In this embodiment, the physiological signals of the multiple target portions are collected at the same time, and a physiological signal data set is generated.

P002: performing signal preprocessing and time frame segmentation on the physiological sign signal data set, eliminating abnormal data, and obtaining a physiological sign signal data matrix

In this embodiment, the preprocessing of the temperature signal, the skin electrical signal and the pulse signal is mainly a/D data conversion, and 1Hz low-pass filtering is completed by a Hamming window and a zero-phase FIR digital filter.

In this embodiment, the blood oxygen level depends on the BOLD signal is preprocessed mainly by obtaining light intensity and converting the light intensity into Optical Density (OD), removing a bad channel, removing artifact processing and signal correction, wavelet denoising, using a modified beer-lamber law to convert the change of optical density or absorbance into the concentrations of oxygenated hemoglobin HbO2, deoxygenated hemoglobin HbR and total hemoglobin HbT, and by using a Hamming window and a zero-phase FIR digital filter, completing 0.01-0.35Hz band-pass filtering, and extracting the concentration change signals of HbO2, hbR and HbT.

In the embodiment, the preprocessing of the electrocardiosignal is mainly to perform A/D data conversion, and the band-pass filtering of 0.5 to 35Hz is completed through a Hamming window and a zero-phase FIR digital filter.

In this embodiment, the temperature and humidity rise adjustment operation is performed for 2 minutes (the environmental temperature and humidity have reached the target temperature and humidity level) and then is used as the stimulation start time, the temperature and humidity rise adjustment operation is performed for 9 minutes (the stimulation duration is detected) and then is used as the stimulation end time, the time alignment and the framing interception are performed on the physiological sign signal data, and the temperature and humidity intensity of the current temperature and humidity signal is identified; meanwhile, data which cannot be corrected, including excessive body movement, electrode or optode contact looseness and even falling, stimulation trial interruption and the like, are removed by performing data correction on the physiological sign signals; and finally, generating a physiological sign signal data matrix.

P003: performing signal integration processing, characteristic interval division and signal characteristic analysis on the physiological sign signal data matrix to obtain a physiological sign characteristic data matrix

In this embodiment, the physiological sign signals of the same type and the same temperature and humidity intensity at multiple parts or multiple channels of the physiological sign signal data matrix are subjected to average superposition processing to obtain a physiological sign signal data fusion matrix.

In this embodiment, the characteristic intervals are specifically divided into a baseline state period (24 degrees celsius stimulation duration-6 minutes), an adjustment interval from the current temperature and humidity intensity to the next target temperature and humidity (2 minutes after the stimulation operation), a constant temperature and humidity interval of the target temperature and humidity (from 3 minutes to 9 minutes after the stimulation operation), a heart rate pulse-electrocardiographic pulse observation interval (from 3 minutes to 8 minutes after the stimulation operation), a sweat secretion-electrodermal observation interval (from 3 minutes to 8 minutes after the stimulation operation), a blood circulation-BOLD observation interval (from 3 minutes to 8 minutes after the stimulation operation), and a skin temperature-body surface temperature observation interval (from 3 minutes to 8 minutes after the stimulation operation).

In this embodiment, signal feature analysis is performed on all different types of physiological signals of the physiological sign signal data fusion matrix frame by frame, the signal features mainly include an average value, a root mean square, a maximum value, a minimum value, a variance, a standard deviation, a variation coefficient, kurtosis and skewness, a temperature signal feature, a skin electrical signal feature, a blood oxygen level dependence BOLD signal feature, an electrocardio signal feature and a pulse signal feature are obtained, and a physiological sign feature data matrix FM is generated.

In this embodiment, the skin electrical signal characteristics include signal characteristics of a skin conductance total level SC, a skin conductance level SCL, and a skin conductance response SCR; blood oxygen level dependent BOLD signal features include signal features of oxygenated hemoglobin HbO2, deoxygenated hemoglobin HbR, and total hemoglobin HbT; the electrocardiographic signal features include signal features of QRS complex, heart rate and heart rate variability.

P004: performing melt coupling feature selection, sequence normalization and melt coupling feature analysis on the physiological sign feature data set to obtain a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set under all temperature and humidity intensities

In this embodiment, the details of the selection of the decoupling characteristics are as follows:

the electrocardiosignal-heart rate, taking the average value CGM and the variation coefficient CGCV in the heart rate characteristics as a fusion coupling characteristic analysis data source;

taking the average value PUM and the variation coefficient PUCV in the pulse characteristics as a fusion characteristic analysis data source;

BOLD signal-blood flow, taking average value HTM and variation coefficient HTCV in total hemoglobin HbT characteristics as a melt coupling characteristic analysis data source;

skin electrical signal-sweat secretion, taking average value EDM and coefficient of variation EDCV in skin conduction level SCL characteristics as a fusion coupling characteristic analysis data source;

temperature signal-skin temperature, and the mean value TPM and the coefficient of variation TPCV in the temperature characteristics are used as a data source for analyzing the melt coupling characteristics.

In this embodiment, it should be understood that, in an actual application scenario, an average value, a root-mean-square, a maximum value, and a minimum value are mostly used as main data sources for fused-coupling feature analysis, and these indicators have very stable and determined performances in different feature quantitative analyses, and are an identification indicator and a distinctive indicator of a physiological state in different situations; taking variance, standard deviation, coefficient of variation, kurtosis and skewness as auxiliary data sources for the decoupling characteristic analysis; also, in situations involving weighting calculations, the weighting factors for the primary data source should be no less than the weighting factors for the secondary data source.

In this embodiment, the body temperature physiological response curve set includes a heart rate pulse response curve CPRs, a blood circulation response curve BOLDRs, a sweat secretion response curve SWRs, and a skin temperature response curve STRs.

In this embodiment, the set of physiological body temperature regulation indexes includes a heart rate and pulse regulation index CPRI, a blood circulation regulation index BOLDRI, a sweat secretion regulation index SWRI, and a skin temperature regulation index STRI.

In this embodiment, the method for calculating the heart rate pulse response curves CPRs and the heart rate pulse modulation index CPRI includes the following steps:

1) Acquiring a baseline state period, a heart rate pulse-electrocardio pulse observation interval, electrocardiosignal characteristics under all temperature and humidity intensities and the pulse signal characteristics from a physiological sign characteristic data set FM, and selecting through fused coupling characteristics to obtain an electrocardio pulse signal characteristic selection set;

2) Extracting signal features in the electrocardio pulse signal feature selection set under the same temperature and humidity intensity to perform weighting calculation, and generating a heart rate pulse response factor cpf under the current temperature and humidity intensity;

wherein, the weight of the heart rate average value CGM and the weight of the variation coefficient CGCV are respectively 0.6 and 0.1; the pulse average value PUM and the coefficient of variation PUCV are weighted to 0.25 and 0.05 respectively;

3) Calculating to obtain a heart rate pulse response factor cpf under all temperature and humidity intensities, and generating a first response factor sequence cpfs;

4) Performing polynomial function fitting on the first response factor sequence cpfs to generate a heart rate pulse response curve CPRs;

5) Carrying out sequence normalization on a temperature intensity queue TSD and a humidity intensity queue HSD in a first response factor sequence cpfs and a temperature and humidity intensity sequence THSD, calculating a Pearson correlation coefficient r, an average distance dm and a distance variation coefficient dcv of the former response factor sequence and the latter response factor sequence, and generating a first relation characteristic data set r _ cpfs;

6) Performing weighted calculation on the first relation characteristic data set r _ cpfs to generate a heart rate pulse regulation index CPRI;

wherein, the weights of the Pearson correlation coefficient r, the average distance dm and the distance variation coefficient dcv of cpfs and TSD are 0.2, 0.35 and 0.06 respectively; the weighting of the correlation coefficient r, the average distance dm and the distance variation coefficient dcv of cpfs and HSD are respectively 0.1, 0.25 and 0.04.

In this embodiment, the method for calculating the blood circulation response curve BOLDRs and the blood circulation regulation index BOLDRI includes the following steps:

1) Acquiring blood oxygen level dependence BOLD signal characteristics under a baseline state period, a blood circulation-BOLD observation interval and all temperature and humidity intensities from a physiological sign characteristic data set FM, and selecting through a melt coupling characteristic to obtain a BOLD signal characteristic selection set;

2) Extracting signal features in a BOLD signal feature selection set under the same temperature and humidity intensity to perform weighting calculation, and generating a blood circulation response factor boldf under the current temperature and humidity intensity;

the weight coefficients of the total hemoglobin HbT average HTM and the coefficient of variation HTCV were 0.8 and 0.2, respectively.

3) Calculating to obtain a blood circulation response factor boldf under all temperature and humidity intensities, and generating a second response factor sequence boldfs;

4) Performing polynomial function fitting on the second response factor sequence boldfs to generate a blood circulation response curve BOLDRs;

5) Performing sequence normalization on a second response factor sequence boldfs, a temperature intensity queue TSD and a humidity intensity queue HSD in a temperature and humidity intensity sequence THSD, calculating a Pearson correlation coefficient r, an average distance dm and a distance variation coefficient dcv of the first response factor sequence and the humidity intensity queue HSD, and generating a second relation characteristic data set r _ boldfs;

6) Performing weighted calculation on the second relation characteristic data set r _ bold fs to generate a blood circulation regulation index BOLDRI;

wherein, the weighting of the Pearson correlation coefficient r, the average distance dm and the distance variation coefficient dcv of the boldfs and the TSD are respectively 0.2, 0.35 and 0.06; the weighting of the pearson correlation coefficient r, the average distance dm and the distance variation coefficient dcv of the boldfs and HSD are 0.1, 0.25 and 0.04 respectively.

In this example, the sweat secretion response curve SWRs and the sweat secretion regulation index SWRI are calculated as follows:

1) Acquiring the skin electrical signal characteristics in a baseline state period, a sweat secretion-skin electricity observation interval and all temperature and humidity intensities from a physiological sign characteristic data set FM, and selecting through a melt coupling characteristic to obtain a skin electrical signal characteristic selection set;

2) Extracting signal features in skin electrical signal feature selection sets under different temperature and humidity intensities to perform weighting calculation, and generating a sweat secretion response factor edf under the current temperature and humidity intensity;

wherein, the skin conductance level SCL average value EDM and the weight coefficient of the variation coefficient EDCV are 0.8 and 0.2 respectively.

3) Calculating to obtain a sweat secretion response factor edf under all temperature and humidity intensities, and generating a third response factor sequence edfs;

4) Performing polynomial function fitting on the third response factor sequence edfs to generate a sweat secretion response curve SWRs;

5) Performing sequence normalization on a third response factor sequence edfs, a temperature intensity queue TSD and a humidity intensity queue HSD in a temperature and humidity intensity sequence THSD, calculating a Pearson correlation coefficient r, an average distance dm and a distance variation coefficient dcv of the former and the latter, and generating a third relation characteristic data set r _ swfs;

6) Performing weighted calculation on the third relation characteristic data set r _ swfs to generate a sweat secretion regulation index SWRI;

wherein, the weight of the Pearson correlation coefficient r, the average distance dm and the distance variation coefficient dcv of the edfs and the TSD is 0.2, 0.35 and 0.06 respectively; the weighting of the Pearson correlation coefficient r, the average distance dm and the distance variation coefficient dcv of edfs and HSD are 0.1, 0.25 and 0.04 respectively.

In this embodiment, the method for calculating the skin temperature response curves STRs and the skin temperature regulation index STRI includes the following steps:

1) Acquiring the temperature signal characteristics in a baseline state period, a skin temperature-body surface temperature observation interval and all temperature and humidity intensities from a first physiological characteristic data set FM, and selecting through decoupling characteristics to obtain a temperature signal characteristic selection set;

2) Extracting signal features in a temperature signal feature selection set under the same temperature and humidity intensity to perform weighted calculation, and generating a skin temperature response factor under the current temperature and humidity intensity;

wherein, the weight coefficients of the temperature average value TPM and the variation coefficient TPCV are 0.8 and 0.2 respectively.

3) Calculating to obtain a skin temperature response factor tpf under all temperature and humidity intensities, and generating a fourth response factor sequence tpfs;

4) Performing polynomial function fitting on the fourth response factor sequence tpfs to generate a skin temperature response curve STRs;

5) Performing sequence normalization on a fourth response factor sequence tpfs, a temperature intensity queue TSD and a humidity intensity queue HSD in a temperature and humidity intensity sequence THSD, calculating a Pearson correlation coefficient r, an average distance dm and a distance variation coefficient dcv of the fourth response factor sequence tpfs and the temperature and humidity intensity queue HSD, and generating a fourth relation characteristic data set r _ tpfs;

6) Performing weighted calculation on the fourth relation characteristic data set r _ tpfs to generate a skin temperature regulation index STRI;

wherein, the weighting of the Pearson correlation coefficient r, the average distance dm and the distance variation coefficient dcv of tpfs and TSD is 0.2, 0.35 and 0.06 respectively; the weighting of the Pearson correlation coefficient r, the average distance dm and the distance variation coefficient dcv of tpfs and HSD are 0.1, 0.25 and 0.04 respectively.

In this embodiment, the body temperature physiological excitation temperature threshold set includes a heart rate and pulse excitation temperature threshold CPRT, a blood circulation excitation temperature threshold BOLDRT, a sweat secretion excitation temperature threshold SWRT, and a skin temperature excitation temperature threshold STRT.

In this embodiment, the body temperature physiological excitation humidity threshold set includes a heart rate pulse excitation humidity threshold CPRH, a blood circulation excitation humidity threshold BOLDRH, a sweat secretion excitation humidity threshold SWRH, and a skin temperature excitation humidity threshold STRH.

In this embodiment, the method for calculating the heart rate pulse excitation temperature threshold CPRT and the heart rate pulse excitation humidity threshold CPRH includes the following steps:

1) Acquiring a baseline state period, a heart rate pulse-electrocardio pulse observation interval, electrocardiosignal characteristics and pulse signal characteristics under all temperature and humidity intensities from the first physiological sign characteristic data set FM, and performing fused coupling characteristic selection to obtain an electrocardio pulse signal excitation characteristic selection set;

2) Respectively carrying out sequence normalization on all signal characteristic sequences of the electrocardio-pulse signal excitation characteristic selection set to obtain an electrocardio-pulse signal excitation characteristic data normalization set;

3) Carrying out linear weighted fusion on all sequences of the electrocardio pulse signal excitation characteristic data normalization set to generate a first excitation characteristic sequence cpds;

wherein, the weights of the heart rate average value CGM and the variation coefficient CGCV characteristic normalization sequence are 0.6 and 0.1 respectively; the weights of the pulse average value PUM and the coefficient of variation PUCV characteristic normalized sequence are 0.25 and 0.05 respectively;

4) Carrying out first-order difference processing on the first excitation characteristic sequence cpds to obtain a first excitation characteristic difference sequence d _ cpds;

5) Extracting the maximum value of the first excitation characteristic difference sequence d _ cpds and the index corresponding to the maximum value to generate the maximum value max _ cpds of the first excitation sequence and the index idx _ cpds of the maximum value of the first excitation sequence;

6) And extracting a temperature value and a humidity value at a first excitation sequence maximum value index idx _ cpds in the temperature and humidity intensity sequence THSD, and respectively generating a heart rate and pulse excitation temperature threshold value CPRT and a heart rate and pulse excitation humidity threshold value CPRH.

In this embodiment, the method for calculating the blood circulation excitation temperature threshold value boldt and the blood circulation excitation humidity threshold value boldh includes:

1) Acquiring a baseline state period, a blood circulation-BOLD observation interval and blood oxygen level dependence BOLD signal characteristics under all temperature and humidity intensities from a first physiological sign characteristic data set FM, and selecting through a melt coupling characteristic to obtain a BOLD signal excitation characteristic selection set;

2) Respectively carrying out sequence normalization on all signal characteristic sequences of the BOLD signal excitation characteristic selection set to obtain a BOLD signal excitation characteristic data normalization set;

3) Performing linear weighted fusion on all sequences of the BOLD signal excitation characteristic data normalization set to generate a second excitation characteristic sequence BOLD;

wherein, the weight coefficients of the total hemoglobin HbT average value HTM and the variation coefficient HTCV characteristic normalized sequence are 0.8 and 0.2 respectively.

4) Performing first-order difference processing on the second excitation characteristic sequence bold to obtain a second excitation characteristic difference sequence d _ bold;

5) Extracting the maximum value of the second excitation characteristic difference sequence d _ bodies and the index corresponding to the maximum value to generate a second excitation sequence maximum value max _ bodies and a second excitation sequence maximum value index idx _ bodies;

6) And extracting a temperature value and a humidity value at a second excitation sequence maximum value index idx _ bodies in the temperature and humidity intensity sequence THSD, and respectively generating a blood circulation excitation temperature threshold value BOLDRT and a blood circulation excitation humidity threshold value BOLDRH.

In this embodiment, the calculation method of the sweat secretion stimulation temperature threshold SWRT and the sweat secretion stimulation humidity threshold SWRH includes the following steps:

1) Acquiring skin electrical signal characteristics under a baseline state period, a sweat secretion-skin electrical observation interval and all temperature and humidity intensities from a first physiological characteristic data set FM, and performing melt coupling characteristic selection to obtain a skin electrical signal excitation characteristic selection set;

2) Respectively carrying out sequence normalization on all signal characteristic sequences of the skin electric signal excitation characteristic selection set to obtain a skin electric signal excitation characteristic data normalization set;

3) Performing linear weighted fusion on all sequences of the skin electrical signal excitation characteristic data normalization set to generate a third excitation characteristic sequence edds;

wherein, the skin conductance level SCL average value EDM and the weight coefficient of the EDCV characteristic normalization sequence are 0.8 and 0.2 respectively.

4) Performing first-order difference processing on the third excitation characteristic sequence edds to obtain a third excitation characteristic difference sequence d _ edds;

5) Extracting the maximum value of the third excitation characteristic difference sequence d _ edds and an index corresponding to the maximum value to generate a third excitation sequence maximum value max _ edds and a third excitation sequence maximum value index idx _ edds;

6) And extracting a temperature value and a humidity value at a third excitation sequence maximum value index idx _ edds in the temperature and humidity intensity sequence THSD, and respectively generating a sweat secretion excitation temperature threshold value SWRT and a sweat secretion excitation humidity threshold value SWRH.

In this embodiment, the calculation method of the skin temperature excitation temperature threshold STRT and the skin temperature excitation humidity threshold STRH is as follows:

1) Acquiring temperature signal characteristics of a baseline state period, a skin temperature-body surface temperature observation interval and all temperature and humidity intensities from a first physiological characteristic data set FM, and selecting through decoupling characteristics to obtain a temperature signal characteristic selection set;

2) Respectively carrying out sequence normalization on all signal characteristic sequences of the temperature excitation characteristic selection set to obtain a temperature signal excitation characteristic data normalization set;

3) Performing linear weighted fusion on all sequences of the temperature signal excitation characteristic data normalization set to generate a fourth excitation characteristic sequence tpds;

wherein, the weight coefficients of the temperature average value TPM and the variation coefficient TPCV characteristic normalized sequence are 0.8 and 0.2 respectively.

4) Performing first-order difference processing on the fourth excitation characteristic sequence tpds to obtain a fourth excitation characteristic difference sequence d _ tpds;

5) Extracting the maximum value of the fourth excitation characteristic difference sequence d _ tpds and the index corresponding to the maximum value to generate the maximum value max _ tpds of the fourth excitation sequence and the index idx _ tpds of the maximum value of the fourth excitation sequence;

6) And extracting a temperature value and a humidity value at a fourth excitation sequence maximum value index idx _ tpds in the temperature and humidity intensity sequence THSD, and respectively generating a skin temperature excitation temperature threshold STRT and a skin temperature excitation humidity threshold STRH.

In this embodiment, the sequence normalization calculation method is as follows:

for a sequence of values

Obtaining the maximum value of the sequence

And minimum value

；

A sequence normalization calculation formula of

Wherein, the first and the second end of the pipe are connected with each other,

is a sequence of values

The normalized sequence of (a).

In this embodiment, the calculation method of the association relationship includes a coherence coefficient, a pearson correlation coefficient, an jaccard similarity coefficient, a linear mutual information coefficient, and a linear correlation coefficient.

In this embodiment, the formula for calculating the pearson correlation coefficient is as follows

For discrete time-series signals

And

pearson correlation coefficient r:

wherein the content of the first and second substances,

is composed of

The average value of the average value is calculated,

is composed of

Mean value;

in this embodiment, the distance feature calculation method includes:

for a sequence of values

And a numerical sequence

Sequence of distances

For each difference of time-point values in two sequences, i.e.

The distance features being a sequence of distances

Number ofThe value characteristics mainly comprise average value, root mean square, maximum value, minimum value, variance, standard deviation, variation coefficient, kurtosis and skewness.

In this embodiment, the establishment of the thermoregulation function comparison database is as follows:

according to the design of a body temperature regulation detection scheme, temperature and humidity joint stimulation with different intensity levels is carried out on a healthy person group subject and a body temperature regulation dysfunction person group subject, physiological sign signals of a plurality of tested target parts are acquired, and a physiological sign signal data set is generated;

preprocessing and framing the physiological sign signal data set, and eliminating abnormal data to obtain a physiological sign signal data matrix;

performing signal integration processing, characteristic interval division and signal characteristic analysis on the physiological sign signal data matrix to obtain a physiological sign characteristic data matrix;

performing fused coupling feature selection, sequence normalization and fused coupling feature analysis on the physiological sign feature data set to obtain a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set under all the excitation intensities;

In this example, the following comparative cases should be understood: firstly, analyzing and comparing a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set of a current tested body temperature with a body temperature physiological response curve set, a body temperature physiological regulation index set, a body temperature physiological excitation temperature threshold set and a body temperature physiological excitation humidity threshold set of a healthy tested body temperature, and obtaining the functional state level of the body temperature regulation function of the current tested body temperature, namely whether the current tested body temperature regulation function is normal or abnormal; secondly, analyzing and comparing the current body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the person with body temperature regulation dysfunction (with different dysfunction degrees) with the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set of the person with body temperature regulation dysfunction, and obtaining the dysfunction severity degree of the current body temperature regulation function of the person with body temperature regulation dysfunction; thirdly, analyzing and comparing the currently tested body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set with the body temperature physiological response curve set, the body temperature physiological regulation index set, the body temperature physiological excitation temperature threshold set and the body temperature physiological excitation humidity threshold set in historical detection, obtaining the rehabilitation progress condition of the currently tested body temperature regulation function, and evaluating the treatment intervention effect.

The invention also provides a programmable processor of various types (FPGA, ASIC or other integrated circuit) for running a program, wherein the program performs the steps of the above embodiments when running.

The invention also provides corresponding computer equipment, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the steps in the embodiment are realized when the memory executes the program.

Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the scope of the present invention should be determined by the following claims.

Claims

1. A detection and quantification method for a thermoregulation function is characterized by comprising the following steps:

according to the design of a body temperature regulation detection scheme, carrying out temperature and humidity combined stimulation of different intensity levels on a subject, and acquiring physiological sign signals of a plurality of target parts of the subject to obtain a first physiological sign signal data set;

and analyzing and comparing the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set of the tested person with a first body temperature physiological response curve set, a first body temperature physiological regulation index set, a first body temperature physiological excitation temperature threshold set and a first body temperature physiological excitation humidity threshold set of a body temperature regulation function comparison database to obtain a detection quantitative report or result of the body temperature regulation function state level of the tested person.

2. The method of claim 1, wherein: the body temperature regulation detection scheme at least comprises a detection mode type, a temperature and humidity intensity sequence, a detection stimulation duration, a detection stimulation interval and a detection stimulation period.

3. The method of claim 2, wherein: the detection mode types at least comprise environmental stimulation, systemic stimulation and local stimulation.

4. The method of claim 2, wherein: the temperature and humidity intensity sequence is composed of a temperature intensity queue and a humidity intensity queue.

5. The method of claim 1, wherein: the target site includes at least a body surface skin system of head, neck, trunk, and limbs.

6. The method of claim 1 or 5, wherein: and the target part is determined according to the type of the detection mode.

7. The method of claim 1, wherein: the physiological sign signals at least comprise temperature signals, skin electric signals, blood oxygen level dependent BOLD signals, electrocardio signals and pulse signals, and are acquired by temperature, skin electric signals, functional near infrared spectrum imaging and/or functional nuclear magnetic resonance imaging, electrocardio and pulse data acquisition equipment.

8. The method of claim 1, wherein: the signal preprocessing at least comprises A/D conversion, resampling, noise reduction, artifact removal and filtering.

9. The method of claim 1, wherein: the time frame division is to perform time alignment and signal data framing interception on different physiological characteristic signals and temperature and humidity signals in the physiological sign signals according to sampling rates of the different physiological characteristic signals in the physiological sign signals and preset time window lengths.

10. The method of claim 1, wherein: the signal integration processing is to integrate and process the second physiological sign signals of multiple parts or multiple channels, the same type and the same temperature and humidity intensity.

11. The method of claim 10, wherein: the integration processing at least comprises extracting any one sequence of an average superposition sequence, a weighted superposition sequence, a maximum amplitude sequence, a minimum variance sequence, a minimum variation coefficient sequence and a maximum variation coefficient sequence.

12. The method of claim 1, wherein: the characteristic interval division at least comprises a baseline state period, an adjustment interval from the current temperature and humidity intensity to the next target temperature and humidity, a constant temperature and humidity interval of the target temperature and humidity, a heart rate pulse electrocardio-pulse observation interval, a sweat secretion skin electric observation interval, a blood circulation BOLD observation interval and a skin temperature body surface temperature observation interval.

13. The method of claim 12, wherein: the baseline state period refers to that under the stimulation of the first temperature and humidity intensity, the baseline establishing time is preset for continuous stimulation, and a tested baseline physiological characteristic signal and characteristic state are obtained.

14. The method of claim 1, wherein: the signal characteristic analysis comprises a temperature signal characteristic, a skin electric signal characteristic, a blood oxygen level dependence BOLD signal characteristic, an electrocardiosignal characteristic and a pulse signal characteristic.

15. The method of claim 14, wherein: the signal characteristics include at least a mean, a root mean square, a maximum, a minimum, a variance, a standard deviation, a coefficient of variation, a kurtosis, and a skewness.

16. The method of claim 14, wherein: the skin electrical signal characteristics include at least signal characteristics of total skin conductance level, skin conductance response.

17. The method of claim 14, wherein: the blood oxygen level dependent BOLD signal features at least comprise signal features of oxygenated hemoglobin, deoxygenated hemoglobin and total hemoglobin.

18. The method of claim 14, wherein: the electrocardiosignal characteristics at least comprise signal characteristics of a QRS complex, a heart rate and heart rate variability.

19. The method of claim 1, wherein: the decoupling feature selection at least comprises one or more physiological sign features of the first physiological sign feature data set as a data source of the decoupling feature analysis.

20. The method of claim 1, wherein: the first temperature physiological response curve set at least comprises a heart rate pulse response curve, a blood circulation response curve, a sweat secretion response curve and a skin temperature response curve.

21. The method of claim 20, wherein: the first set of body temperature physiological regulation indices includes at least a heart rate pulse regulation index, a blood circulation regulation index, a sweat secretion regulation index, and a skin temperature regulation index.

22. The method of claim 1, wherein: the first body temperature physiological excitation temperature threshold set at least comprises a heart rate and pulse excitation temperature threshold, a blood circulation excitation temperature threshold, a sweat secretion excitation temperature threshold and a skin temperature excitation temperature threshold.

23. The method of claim 22, wherein: the first body temperature physiological excitation humidity threshold set at least comprises a heart rate pulse excitation humidity threshold, a blood circulation excitation humidity threshold, a sweat secretion excitation humidity threshold and a skin temperature excitation humidity threshold.

24. The method of claim 21, wherein: the heart rate pulse response curve and the heart rate pulse modulation index are calculated as follows:

acquiring a baseline state period, a heart rate, pulse, electrocardio-pulse observation interval, electrocardio-signal characteristics and pulse signal characteristics under all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through decoupling characteristics to obtain an electrocardio-pulse signal characteristic selection set;

extracting signal features in the electrocardio pulse signal feature selection set under the same temperature and humidity intensity to perform weighting calculation, and generating heart rate pulse response factors under the current temperature and humidity intensity;

performing sequence normalization on the temperature intensity queue and the humidity intensity queue in the first response factor sequence and the temperature and humidity intensity sequence, calculating the incidence relation and the distance characteristic of the first response factor sequence and the temperature and humidity intensity queue, and generating a first relation characteristic data set;

25. The method of claim 21, wherein: the blood circulation response curve and the blood circulation regulation index are calculated as follows:

acquiring blood oxygen level dependence BOLD signal characteristics under a baseline state period, a blood circulation BOLD observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through fusion coupling characteristics to obtain a BOLD signal characteristic selection set;

performing sequence normalization on a temperature intensity queue and a humidity intensity queue in the second response factor sequence and the temperature and humidity intensity sequence, calculating the incidence relation and the distance characteristic of the first response factor sequence and the second response factor sequence, and generating a second relation characteristic data set;

26. The method of claim 21, wherein: the sweat secretion response curve and the sweat secretion regulation index are calculated as follows:

acquiring skin electrical signal characteristics in a baseline state period, a sweat secretion skin electrical observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through melt coupling characteristics to obtain a skin electrical signal characteristic selection set;

performing sequence normalization on the temperature intensity queue and the humidity intensity queue in the third response factor sequence and the temperature and humidity intensity sequence, calculating the association relationship and the distance characteristics of the third response factor sequence and the temperature and humidity intensity sequence, and generating a third relationship characteristic data set;

27. The method of claim 21, wherein: the skin temperature response curve and the skin temperature regulation index are calculated as follows:

acquiring temperature signal characteristics of a baseline state period, a skin temperature and body surface temperature observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through decoupling characteristics to obtain a temperature signal characteristic selection set;

performing sequence normalization on the temperature intensity queue and the humidity intensity queue in the fourth response factor sequence and the temperature and humidity intensity sequence, calculating the incidence relation and the distance characteristic of the fourth response factor sequence and the temperature and humidity intensity queue, and generating a fourth relation characteristic data set;

28. The method of claim 23, wherein: the method for calculating the heart rate pulse excitation temperature threshold and the heart rate pulse excitation humidity threshold comprises the following steps:

acquiring a baseline state period, a heart rate pulse electrocardio-pulse observation interval, electrocardio-signal characteristics and pulse signal characteristics under all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through decoupling characteristics to obtain an electrocardio-pulse signal excitation characteristic selection set;

respectively carrying out sequence normalization on all signal characteristic sequences of the electrocardio pulse signal excitation characteristic selection set to obtain an electrocardio pulse signal excitation characteristic data normalization set;

29. The method of claim 23, wherein: the method for calculating the blood circulation excitation temperature threshold and the blood circulation excitation humidity threshold comprises the following steps:

acquiring a baseline state period, a blood circulation BOLD observation interval and blood oxygen level dependence BOLD signal characteristics under all temperature and humidity intensities from the first physiological sign characteristic data set, and selecting through a melt coupling characteristic to obtain a BOLD signal excitation characteristic selection set;

respectively carrying out sequence normalization on all signal characteristic sequences of the BOLD signal excitation characteristic selection set to obtain a BOLD signal excitation characteristic data normalization set;

and extracting a temperature value and a humidity value at the index of the maximum value of the second excitation sequence in the temperature and humidity intensity sequence to respectively generate a blood circulation excitation temperature threshold and a blood circulation excitation humidity threshold.

30. The method of claim 23, wherein: the sweat secretion stimulation temperature threshold and the sweat secretion stimulation humidity threshold are calculated as follows:

acquiring skin electrical signal characteristics under a baseline state period, a sweat secretion skin electrical observation interval and all temperature and humidity intensities from the first physiological sign characteristic data set, and performing melt coupling characteristic selection to obtain a skin electrical signal excitation characteristic selection set;

31. The method of claim 23, wherein: the calculation method of the skin temperature excitation temperature threshold and the skin temperature excitation humidity threshold comprises the following steps:

respectively carrying out sequence normalization on all signal characteristic sequences of the temperature signal characteristic selection set to obtain a temperature signal excitation characteristic data normalization set;

and extracting a temperature value and a humidity value at the maximum index of the fourth excitation sequence in the temperature and humidity intensity sequence, and respectively generating a skin temperature excitation temperature threshold and a skin temperature excitation humidity threshold.

32. The method of any one of claims 1 or 24-31, wherein: the sequence normalization calculation method comprises the following steps:

for a numerical sequence X (t), acquiring a maximum value X _ max and a minimum value X _ min of the sequence;

a sequence normalization calculation formula of

Wherein the content of the first and second substances,

is a normalized sequence of the numerical sequence X (t).

33. The method of any one of claims 24-27, wherein: the calculation method of the incidence relation at least comprises a coherence coefficient, a Pearson correlation coefficient, a Jacobs's similarity coefficient, a linear mutual information coefficient and a linear correlation coefficient.

34. The method of any one of claims 24-27, wherein: the distance feature calculation method comprises the following steps: for the sequence of values X (t) and the sequence of values Y (t), the sequence of distances Z (t) is the difference of each time point value in the two sequences, i.e. the difference of the time point values

Z(t)＝X(t)-Y(t)

The distance features are numerical features of the distance sequence Z (t).

35. The method of claim 34, wherein: distance features include mean, root mean square, maximum, minimum, variance, standard deviation, coefficient of variation, kurtosis, and skewness.

36. The method of claim 1, wherein: the establishment mode of the body temperature regulation function comparison database is as follows:

according to the design of a body temperature regulation detection scheme, temperature and humidity combined stimulation with different intensity levels is carried out on a healthy population to be tested and a body temperature regulation dysfunction population to be tested, physiological sign signals of a plurality of target parts of the tested person are collected and obtained, and a first physiological sign signal data set of the healthy population to be tested and the body temperature regulation dysfunction population to be tested in a body temperature regulation function comparison library is obtained;

performing signal preprocessing and time frame segmentation on a first physiological sign signal data set of a healthy population to be tested and a body temperature regulation dysfunction person population to be tested in a body temperature regulation function comparison library, and eliminating abnormal data to obtain a second physiological sign signal data set of the healthy population to be tested and the body temperature regulation dysfunction person population to be tested in the body temperature regulation function comparison library;

performing signal integration processing, characteristic interval division and signal characteristic analysis on a second physiological sign signal data set of a healthy population to be tested and a temperature regulation dysfunction population to be tested in a temperature regulation function comparison library to obtain a first physiological sign characteristic data set of the healthy population to be tested and the temperature regulation dysfunction population to be tested in the temperature regulation function comparison library;

performing melt-coupling feature selection, sequence normalization and melt-coupling feature analysis on a first physiological characteristic data set of a healthy population subject and a body temperature regulation dysfunction population subject in a body temperature regulation function comparison library to obtain a first body temperature physiological response curve set, a first body temperature physiological regulation index set, a first body temperature physiological excitation temperature threshold set and a first body temperature physiological excitation humidity threshold set;

37. The detection and quantification system for the thermoregulation function is characterized by comprising the following functional modules:

the characteristic melt-coupling analysis module is used for carrying out melt-coupling characteristic selection, sequence normalization and melt-coupling characteristic analysis on the first physiological sign characteristic data set to obtain a first body temperature physiological response curve set, a first body temperature physiological regulation index set, a first body temperature physiological excitation temperature threshold set and a first body temperature physiological excitation humidity threshold set;

and the function evaluation reporting module is used for comparing the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set of the tested body temperature physiological response curve set with the body temperature regulation function to the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set of the database for analysis and comparison to obtain a detection quantitative report or result of the state level of the body temperature regulation function of the tested body.

38. The system of claim 37, wherein: the detection scheme management module comprises the following functional units:

39. The system of claim 38, wherein: the sign signal acquisition module comprises the following functional units:

40. The system of claim 37, wherein: the sign signal processing module comprises the following functional units:

the digital preprocessing unit is used for performing signal preprocessing on the first physiological sign signal data set;

and the signal time frame segmentation unit is used for performing signal frame segmentation on the first physiological sign signal data set after signal preprocessing according to the starting time point of the temperature and humidity signals, and eliminating abnormal data to obtain the second physiological sign signal data set.

41. The system of claim 37, wherein: the sign characteristic analysis module comprises the following functional units:

the signal integration processing unit is used for integrating and processing the second physiological sign signals of multiple parts or multiple channels, the same type and the same temperature and humidity intensity;

the characteristic interval setting unit is used for setting a baseline state period, an adjustment interval from the current temperature and humidity intensity to the next target temperature and humidity, a constant temperature and humidity interval of the target temperature and humidity, a heart rate electrocardio observation interval, a sweat secretion skin electric observation interval, a blood circulation BOLD observation interval and a skin temperature body surface temperature observation interval;

and the signal characteristic analysis unit is used for extracting the signal characteristics of the second physiological sign signal after signal integration processing according to the characteristic interval division.

42. The system of claim 37, wherein: the characteristic decoupling analysis module comprises the following functional units:

the characteristic selection setting unit is used for screening, defining or setting data sources of characteristic fusion coupling analysis of the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set from the first physiological sign characteristic data set;

43. The system of claim 37, wherein: the function evaluation reporting module comprises the following functional units:

the reference comparison library unit is used for establishing, storing, updating and managing basic information of a healthy population subject and a temperature regulation dysfunction person subject, and a temperature regulation function comparison database is formed by the first body temperature physiological response curve set, the first body temperature physiological regulation index set, the first body temperature physiological excitation temperature threshold set and the first body temperature physiological excitation humidity threshold set;

44. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor, when executing the computer program, implements the steps of the method according to any of claims 1-36.

45. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 36.