WO2017128848A1

WO2017128848A1 - Method and apparatus for comparation and assessment of temperature-humidity field models based on tumour cell abnomalities

Info

Publication number: WO2017128848A1
Application number: PCT/CN2016/107688
Authority: WO
Inventors: Hong KANG; Hao Cai
Original assignee: Well Diagnostics Technology (International) Corporation
Priority date: 2016-01-28
Filing date: 2016-11-29
Publication date: 2017-08-03
Also published as: CN105740614A

Abstract

A method and apparatus for monitoring a humidity state and a method and apparatus for monitoring a temperature-humidity state are provided. The method for monitoring humidity state includes: determining a humidity rhythm similarity between a humidity sampling model of a monitored object and a humidity reference model (S102); and identifying the humidity state of the monitored object based on the humidity rhythm similarity (S104). The method and apparatus may identify the humidity state and/or the temperature-humidity state of any part of for example, a breast of a woman and thus identify a physical health degree of a person.

Description

METHOD AND APPARATUS FOR COMPARATION AND ASSESSMENT OF TEMPERATURE-HUMIDITY FIELD MODELS BASED ON TUMOUR CELL ABNOMALITIES

FIELD OF THE INVENTION

The invention relates to physiological state monitoring, and particularly to a method and apparatus for monitoring a humidity state and a method and apparatus for monitoring a temperature-humidity state.

BACKGROUND

Breast cancer screening is a cancer prevention measure for symptomless women in order to achieve early detection, early diagnosis and early treatment of breast cancer and finally reduce breast cancer mortality. Popularization of breast cancer screening is a main factor for reducing the breast cancer mortality in Euro-American countries. In Euro-American countries, Mo-targeted X-ray (MG) detection is a main breast cancer screening method. However, MG detection is more suitable for screening of fatty-type breast lesions, but its imaging effect for compact type breast lesions is poor. In addition, during MG detection, some women will inevitably feel pain. Meanwhile, the screened women will suffer some radiation, and too frequent MG detections will surely aggravate its potential side effects.

B-Ultrasound (BUS) detection has advantages such as easy operation, non-invasiveness, and cost-saving. Presently, BUS detection has already become an important measure for breast cancer screening, especially for women having compact type breast. However, the application research on BUS detection is still immature.

Magnetic Resonance Imaging (MRI) detection may serve as a supplemental means for breast cancer screening, especially for women whose results of both MG detection and BUS detection are negative. As MRI has a relatively high spatial resolution and temporal resolution to soft tissues and is not affected by the compact level of breast, it may display breast lesions more clearly. Moreover, MRI detection is sensitive to multi-center and multi-lesion pathological changes. However, MRI detection is expensive and generally used to screen only high-risk population of breast cancer.

SUMMARY OF THE INVENTION

Normal cells of a human body get energy in the process of turning glucose and oxygen into water and carbon dioxide, and the energy may ensure that the normal cells have a relatively constant temperature. Tumour cells of the human body also get energy in the process of turning glucose and oxygen into water and carbon dioxide, while the division of the tumour cells is strong and will consume a lot of glucose and oxygen. Furthermore, the tumour cells usually have abnormal angiogenesis, the blood flow of which is rich and out of control and thus loses normal circadian rhythm.

Energy produced by metabolic activities of the tumour cells causes the temperature of tumour tissues slightly higher than that of normal tissues. According to principles of molecular thermodynamics of tumour cells, there must be heat diffusion from the tumour tissues having a higher temperature to its surrounding issues and up to body surface. Therefore, temperature distribution, i.e. “temperature field， ” is formed at the tumour cells and its surrounding tissues up to the body surface. Meanwhile, the “temperature field” in combination with humidity (sweating) near the body surface will form a distinctive “microclimate” .

In consideration of the above research findings and the above defects of the prior art, the invention provides a method and apparatus for monitoring a humidity state and a method and apparatus for monitoring a temperature-humidity state, so as to identify the temperature state and/or the temperature-humidity state of any part of a human body, for example, a breast of a woman, and evaluate an abnormal extent of an abnormal temperature-humidity field model caused by the tumour cells at the part, for example, the breast of the human body, with reference to a reference model.

An embodiment of the invention provides a method for monitoring a humidity state, comprising: determining a humidity rhythm similarity between a humidity sampling model of a monitored object and a humidity reference model； and identifying the humidity state of the monitored object based on the humidity rhythm similarity.

Another embodiment of the invention provides an apparatus for monitoring a humidity state, comprising: a humidity similarity determining unit configured to determine a humidity rhythm similarity between a humidity sampling model of a monitored object and a humidity reference model； and a humidity state identifying unit configured to identify the humidity state of the monitored object based on the humidity rhythm similarity.

With the method or apparatus for monitoring a humidity state in the above embodiments, a difference degree between the humidity sample model of the monitored object and the humidity reference model may be determined, thereby determining a normal degree of the humidity state of the monitored object.

Still another embodiment of the invention provides a method for monitoring a temperature-humidity state, comprising: determining a rhythm similarity between a temperature-humidity sampling model of a monitored object and a temperature-humidity reference model； and identifying the temperature-humidity state of the monitored object based on the rhythm similarity.

Further still another embodiment of the invention provides an apparatus for monitoring a temperature-humidity state, comprising: a rhythm similarity determining unit configured to determine a rhythm similarity between a temperature-humidity sampling model of a monitored object and a temperature-humidity reference model； and a temperature-humidity state identifying unit configured to identify the temperature-humidity state of the monitored object based on the rhythm similarity.

With the method and apparatus for monitoring a temperature-humidity state in the above embodiments, a rhythm difference degree between the temperature-humidity sample model of the monitored object and the temperature-humidity reference model may be determined, and thus a normal degree of physical indicators such as the temperature and humidity of the monitored object may be determined.

DESCRIPTION OF THE DRAWINGS

Other features, objects and advantages of the invention will become apparent through reading detailed description of non-limiting embodiments in conjunction with accompanying drawings, wherein a same or similar reference number represents a same or similar feature.

Fig. 1 is a structure diagram of an apparatus for monitoring a humidity state according to an embodiment of the invention；

Fig. 2 is a flow diagram of a method for monitoring a humidity state according to an embodiment of the invention；

Fig. 3 is a structure diagram of an apparatus for monitoring a humidity state according to another embodiment of the invention；

Fig. 4 is a structure diagram of an apparatus for monitoring a humidity state according to still another embodiment of the invention；

Fig. 5 is a structure diagram of an apparatus for monitoring a temperature-humidity state according to an embodiment of the invention；

Fig. 6 is a flow diagram of a method for monitoring a temperature-humidity state according to an embodiment of the invention；

Fig. 7 is a distribution diagram of a temperature-humidity sampling model and a temperature-humidity reference model in a temperature-humidity coordinate system according to an embodiment of the invention；

Fig. 8 is a distribution diagram of a temperature-humidity sampling model and a temperature-humidity reference model in a temperature-humidity-time coordinate system according to an embodiment of the invention.

DETAILED DESCRIPTION

Hereinafter, exemplary implementations will be described more comprehensively by referring to the accompany drawings. However, the exemplary implementations may be practiced in various ways, and should not be construed as being limited to the implementations illustrated herein. On the contrary, the implementations are provided to make the invention comprehensive and complete and to convey the conception of the exemplary implementations comprehensively to those skilled in the art. For the sake of clarity, components in the drawings are not necessarily drawn in proportion . In the drawings, same reference numbers represent same or similar structures, so their detailed description will be omitted.

In addition, the features, structures or characteristics as described may be combined in one or more embodiments in any suitable way. In the following description, many specific details are provided for fully understanding the embodiments of the invention. However, those skilled in the art will appreciate that the embodiments of the invention may be practiced without one or more of the specific details or by employing other methods, components, materials and so on. In other cases, well known structures, materials or operations are not illustrated in detail to avoid unnecessarily obscuring the main technical creativity of the invention.

Compared with temperature and/or humidity information of a monitored object at a single time point, the temperature and/or humidity information of the monitored object in a continuous period may reflect more comprehensively a temperature and/or humidity change rhythm of the monitored object, as well as information such as an underlying physical health degree or the course of a disease leading to the temperature change, e.g., early stage, medium stage, or late stage..

Therefore, a method or apparatus according to some embodiments of the invention will identify a humidity state of the monitored object based on a humidity rhythm similarity between a humidity sampling model of the monitored object and a humidity reference model. Hereinafter, the apparatus and method for monitoring a humidity state according to an embodiment of the invention will be described in conjunction with Figs. 1-2.

Fig. 1 is a structure diagram of an apparatus for monitoring a humidity state according to an embodiment of the invention. Fig. 2 is a flow diagram of a method for monitoring a humidity state according to an embodiment of the invention. As shown in Fig. 1, the apparatus 100 for monitoring a humidity state includes a humidity similarity determining unit 102 and a humidity state identifying unit 104. Here, the humidity similarity determining unit 102 is configured to determine a humidity rhythm similarity between a humidity sampling model of a monitored object and a humidity reference model, i.e. to perform step S102； the humidity state identifying unit 104 is configured to identify the humidity state of the monitored object based on the humidity rhythm similarity, i.e. to perform step S104.

Before determining the humidity state of the monitored object, for example, a designated part on a left breast of a specific woman during a designated period of time, for example, AM 00: 00-03: 00, the apparatus 100 for monitoring the humidity state may acquire the humidity sampling model of the designated part during the designated period of time from a humidity model constructing unit (not shown) . In some embodiments, the humidity model constructing unit may be configured to sample humidities of the designated part during any designated period of time by humidity sensors to construct the humidity sampling model of the designated part during the designated period of time. For example, the humidity model constructing unit may be configured to receive humidity sampling values of the designated part from the humidity sensors with a specific interval during the designated period of time so as to construct the humidity sampling model of the designated part during the designated period of time. The person skilled in the art will appreciate that the humidity model constructing unit may be a part of or external to the apparatus 100 for monitoring the humidity state.

In some embodiments, the humidity sampling model may be a sampling sequence including only humidity sampling values, for example, {H₁, H₂, H₃, …, H_N} ； the humidity reference model may be a reference sequence including only humidity reference values, for example, {S₁, S₂, S₃, …, S_M} , wherein M and N are positive integers. In these embodiments, as a time factor, for example, a corresponding relationship in time between elements in two sequences, is not considered, the humidity similarity determining unit 102 may roughly determine a difference between the humidity sampling model and the humidity reference model, such that the humidity state identifying unit 104 may identify an approximate humidity state of the monitored object.

In order to determine the difference between the humidity sampling model and the humidity reference model more accurately and thus identify the humidity state of the monitored object more accurately, in some embodiments, the humidity sampling model may be a sampling sequence in which each sampling point has two attributes, i.e., humidity sampling value and corresponding sampling time, for example, { (H₁, t₁) , (H₂, t₂) , (H₃, t₃) , …, (H_N, t_N) } ； correspondingly, the humidity reference model may be a reference sequence in which each reference point has two attributes, i.e., humidity reference value and corresponding reference time, for example, { (S₁, T₁) , (S₂, T₂) , (S₃, T₃) , …, (S_M, T_M) } , wherein M and N are positive integers. That is, the humidity sampling model may include one or more sampling points associated with the humidity sampling values and the corresponding sampling time, and the humidity reference model may include one or more reference points associated with the humidity reference values and the corresponding reference time. In these embodiments, the humidity similarity determining unit 102 may firstly find out, in the humidity reference model, respective reference points whose reference time is closest to the sampling time in the humidity sampling model or whose correspondence in time is strongest, and then determine the humidity rhythm similarity between the humidity sampling model and a humidity reference sub-model constituted by the reference points that are found out.

In some embodiments, the humidity similarity determining unit 102 may determine a dispersion degree of the humidity sampling model relative to the humidity reference model, which may reflect the humidity rhythm similarity between the humidity sampling model and the humidity reference model. For example, the humidity similarity determining unit 102 may determine the dispersion degree of the humidity sampling model relative to the humidity reference model by calculating a distance, a standard deviation, a mean deviation, or a variance of the humidity sampling model relative to the humidity reference model.

Here, the standard deviation, also referred to as standard difference or experimental standard deviation, may reflect the dispersion degree of a data set. In the case that two data sets have a same mean, their standard deviations are not necessarily the same. Assuming that a data set includes numerical values X₁, X₂, X₃, …, X_N, wherein N is a positive integer, the arithmetic mean of the data set is μ, and the standard deviation of the data set may be calculated by the following expression:

If the standard deviation is relatively large, it means that differences between most numeric values and the arithmetic mean or a desired value of the data set are relatively large； if the standard deviation is relatively small, it means that most numeric values of the data set are close to the arithmetic mean or the desired value of the data set. In some embodiments, the i^th humidity sampling value X_i (i＝ 1, …, N ) in the humidity sampling model of the monitored part may be considered as x_i in the above expression, an element number corresponding to X_i in sampling time in the humidity reference model of the monitored part or the arithmetic mean of one or more element numbers close to X_i in sampling time in the humidity reference model may be considered as the desired value μ, and then the standard deviation of the humidity sampling model relative to the humidity reference model may be calculated. That is to say, the humidity sampling values X_i at different sampling times may correspond to different desired values μ. Therefore, if the method for monitoring a humidity state according to some embodiments of the invention is applied to evaluate the physical health or the risk of a certain disease of the breast based on the dispersion degree of the humidity information of a part on the breast, the method may be considered as a method for diagnosis and treatment of, for example, breast cancer.

The mean deviation and the variance may also reflect the dispersion degree of the data set. In some embodiments, the mean deviation or the variance of the humidity sample model relative to the humidity reference model may be calculated with a processing similar to calculating the standard deviation of the humidity sample model relative to the humidity reference model, which is thus not redundantly described here.

The distance of the humidity sample model relative to the humidity reference model may be an Euclidean distance, i.e. Euclidean metric between them. The Euclidean distance, which is a common distance metric, refers to a real distance between two points or a natural length of a vector in an m-dimension space. The Euclidean distance in a two-dimension or three-dimension space is the real distance between two points.

In some embodiments, the humidity similarity determining unit 102 may determine a cross-correlation degree of the humidity sampling model relative to the humidity reference model or the cross-correlation degree between the humidity sampling model and the humidity reference model, as the humidity rhythm similarity between the humidity sampling model and the humidity reference model. For example, the humidity similarity determining unit 102 may determine the cross-correlation degree between the humidity sampling model and the humidity reference model by calculating a cosine similarity or a cross-correlation function value between the humidity sampling model and the humidity reference model.

The cosine similarity, also referred to as a cosine value of an intersection angle of two vectors, is used to evaluate the similarity of the two vectors. The cross-correlation function value represents a correlation degree between two time sequences. Those skilled in the art should appreciate the way to calculate the cross-correlation degree between two functions or sequences, which is thus not redundantly described herein.

As described above, the humidity state of the monitored object may reflect the physical health state of the monitored object to some extent. For example, the larger the dispersion degree of the humidity sample model of the monitored object relative to the humidity reference model is, the larger the difference degree between the humidity state of the monitored object and a normal humidity state is, or the smaller the similarity therebetween is, and then the higher the possibility for the monitored object to have a disease (e.g., breast cancer) causing body surface humidity/temperature abnormalities is； on the contrary, the larger the similarity of the humidity sampling model of the monitored object relative to the humidity reference model is, the lower the possibility for the monitored object to have the disease causing body surface humidity/temperature abnormalities is. Therefore, in some embodiments, as shown in Fig. 3, the apparatus 300 for monitoring a humidity state further includes a health state determining unit 106 and a health state reporting unit 108 in addition to the humidity similarity determining unit 102 and the humidity state identifying unit 104 described above. Here, the health state determining unit 106 is configured to determine a physical health degree of the monitored object based on the humidity state of the monitored object； and the health state reporting unit 108 is configured to provide the physical health degree to a user. Here, the physical health degree may be for example, a disease risk of the monitored object. With the apparatus 300 for monitoring a humidity state shown in Fig. 3, more intuitive information about the physical health state of the monitored object may be provided to the user.

In some cases, when the temperature of the monitored object is low, the humidity of the monitored object is correspondingly low； when the temperature of the monitored object is high, the humidity of the monitored object is correspondingly high. However, in view of individual differences in sweating of people, i.e., some people being prone to sweating while some people being not prone to sweating, it is possible that the temperature of the monitored object is not higher than the temperature threshold, although the humidity of the monitored object being larger than a humidity threshold very likely indicates that the temperature of the monitored object is higher than a temperature threshold. Similarly, although the temperature of the monitored object being higher than the temperature threshold very likely indicates that the humidity of the monitored object is larger than the humidity threshold, it is also possible that the humidity of the monitored object is not larger than the humidity threshold.

As described above, when temperatures corresponding to part of sub-models in the temperature sampling model of the monitored object are higher than the temperature threshold, the possibility for the monitored object to have a certain disease causing increase of body surface temperature is relatively high； correspondingly, when humidities corresponding to part of sub-models in the humidity sampling model of the monitored object are higher than the humidity threshold, the possibility that the monitored object has a certain disease causing increase of the body surface temperature is relatively high. Moreover, in some cases, the temperatures corresponding to part of sub-models in the temperature sample model of the monitored object are not higher than the temperature threshold while the humidities corresponding to part of sub-models in the humidity sample model of the monitored object are higher than the humidity threshold, or the temperatures corresponding to part of sub-models in the temperature sample model of the monitored object are higher than the temperature threshold while the humidities corresponding to part of sub-models in the humidity sample model of the monitored object are not higher than the humidity threshold, in which cases the monitored object is abnormal to some extent and has a high possibility of having some disease causing body surface humidity/temperature abnormalities.

That is to say, to some extent, sub-models in which the temperature sampling values are higher/lower than the temperature threshold in the temperature sample model and sub-models in which the humidity sampling values are larger/lower than the humidity threshold in the humidity sample model may better reflect physiological indicators of the human body when its metabolism level is higher or lower than a normal metabolic level, which are valuable for determining and monitoring the physical health state of the monitored object. In some embodiments, the humidity threshold and the temperature threshold may be predetermined with respect to different objects to be monitored. For example, for the breast of the human body, the humidity threshold may be in a range of [60, 100] , preferably a relative humidity of 70％, 80％or 90％； the temperature threshold may be in a range of [34℃, 39.5℃] , preferably 35℃, 36℃, 37℃ or 38℃.

Therefore, in order to determine the physical health state of the monitored object more accurately, in some embodiments, the humidity model constructing unit may be configured to sample humidities of the monitored object when the temperature of the monitored object is higher or lower than the temperature threshold so as to construct the humidity sampling model of the monitored object.

In some embodiments, as shown in Fig. 4, the apparatus 400 for monitoring a humidity state may further include a temperature model constructing unit 110, a temperature similarity determining unit 112 and a temperature state identifying unit 114 in addition to the humidity similarity determining unit 102, the humidity state identifying unit 104, the health state determining unit 106 and the health state reporting unit 108. Here, the temperature model constructing unit 110 is configured to, when the humidity of the monitored object is higher or lower than the humidity threshold, sample temperatures of the monitored object to construct the temperature sampling model of the monitored object； the temperature similarity determining unit 112 is configured to determine a temperature rhythm similarity between the temperature sampling model of the monitored object and a temperature reference model； the temperature state identifying unit 114 is configured to identify the temperature state of the monitored object based on the temperature rhythm similarity between the temperature sampling model of the monitored object and the temperature reference model.

In some embodiments, the temperature sampling model may be a sample sequence including only temperature sample values, for example {W₁, W₂, …, W_N} ； the temperature reference model may be a reference sequence including only temperature reference values, for example {R₁, R₂, …, R_M} , wherein M and N are positive integers. In these embodiments, as the time factor (e.g., a corresponding relationship in time between elements in the two sequences) is not considered, the temperature similarity determining unit 112 may roughly determine a difference between the temperature sampling model and the temperature reference model, such that the temperature state identifying unit 114 may identify an approximate temperature state of the monitored object.

In order to determine the difference between the temperature sampling model and the temperature reference model more accurately and thus identify the temperature state of the monitored object more accurately, in some embodiments, the temperature sampling model may be a sampling sequence in which each sampling point has two attributes (i.e., temperature sampling value and corresponding sampling time) , for example, { (W₁, t₁) , (W₂, t₂) , (W₃, t₃) , …, (W_N, t_N) } ； correspondingly, the temperature reference model may be a reference sequence in which each reference point has two attributes (i.e., temperature reference value and corresponding reference time) , for example, { (R₁, T₁) , (R₂, T₂) , (R₃, T₃) , …, (R_M, T_M) } , wherein M and N are positive integers. That is, the temperature sampling model may include one or more sampling points associated with the temperature sampling values and the corresponding sampling times, and the temperature reference model may include one or more reference points associated with the temperature reference values and the corresponding reference times. In these embodiments, the temperature similarity determining unit 112 may firstly find out respective reference points whose reference time is closest to a sampling time in the temperature sample model or whose correspondence in time is strongest in the humidity reference model, and then determine the temperature rhythm similarity between the temperature sampling model and a temperature reference sub-model constituted by the reference points that are found out.

The temperature similarity determining unit 112 may determine the dispersion degree or the cross-correlation degree of the temperature sampling model of the monitored object relative to the temperature reference model, which may reflect the temperature rhythm similarity between the temperature sampling model of the monitored object and the temperature reference model. For example, the temperature similarity determining unit 112 may determine the dispersion degree of the temperature sample model of the monitored object relative to the temperature reference model by calculating the Euclidean distance, the standard deviation, the mean deviation, or the variance of the temperature sampling model of the monitored object relative to the temperature reference model； the temperature similarity determining unit 112 may determine the cross-correlation degree between the temperature sampling model of the monitored object and the temperature reference model by calculating the cosine similarity or the cross-correlation function value of the temperature sampling model of the monitored object relative to the temperature reference model. The processing for determining the temperature rhythm similarity is similar to the above processing for determining the humidity rhythm similarity, which thus is not redundantly described here.

In some embodiments, the health state determining unit 106 may be further configured to determine the physical health degree of the monitored object based on the temperature state of the monitored object. That is to say, the health state determining unit 106 may be configured to determine the physical health degree of the monitored object based on one or both of the humidity state and the temperature state of the monitored object. In this way, the humidity state and the temperature state of the monitored object may be synthetically monitored, and the physical health state of the monitored object may be determined more accurately in consideration of individual differences in sweating and body temperature of people. As a further example, in an example of monitoring a designated part on the breast of the human body, the physical health degree may include the disease risk of the part on the breast.

The humidity state and/or the temperature state of the designated part on the left breast of the specific woman during the designated period of time, for example, AM 00:00-03: 00 when she is in deep sleep, may be monitored by the apparatus for monitoring humidity state in the above described embodiments.

In some of the above embodiments, the humidity reference model and the temperature reference model may be a humidity statistics model and a temperature statistics model with large samples of ipsilateral parts for the same period or period of time, which are obtained by statistically analyzing the humidity sampling values and the temperature sampling values of the designated part on the left breast of a large number of healthy women during the designated period of time.

In consideration of individual differences in sweating and body temperature of people, in some embodiments, the humidity reference model and the temperature reference model may be a humidity sampling model and a temperature sampling model of a part contralateral to the designated part, which is referred to as a healthy part, of the monitored object during the same period or period of time. For example, when the humidity state and/or the temperature state of for example, the designated part on the left breast of the specific woman during the designated period of time, for example, AM 00: 00-03: 00, are monitored by the apparatus for monitoring a humidity state of the above embodiments, the humidity reference model and the temperature reference model of the designated part on the left breast of the specific woman may be constructed by sampling humidities and temperatures of a corresponding part on the right breast of the specific woman during the designated period of time with a sampling interval in [5s, 60s] .

Furthermore, in consideration of differences in sweating and body surface temperature of the same part on symmetric sides of a same person, in some embodiments, the humidity reference model and the temperature reference model may be the humidity sampling model and the temperature sampling model of the designated part in a historical record for a same period. For example, when the humidity state and/or the temperature state of for example, the designated part on the left breast of the specific woman during the designated period of time, for example, AM 00: 00-03: 00, are monitored by the apparatus for monitoring a humidity state of the above embodiments, the humidity reference model and the temperature reference model of the designated part on the left breast of the specific woman may be constructed by the humidity sampling values and the temperature sampling values of the designated part, for example, a ring with a radius of 2-2.3cm around a nipple or a neighboring area at a distance of 4.5cm from the nipple in a direction pointing to the armpit, on the left breast of the specific woman, during the designated time period of ten years, five years, two years, one year, half a year, or three months ago.

Furthermore, some other embodiments of the invention may identify a temperature-humidity state of the monitored object based on a rhythm similarity between a temperature-humidity sampling model of the monitored object and a temperature-humidity reference model by integrating the temperature sampling model and the humidity sampling model of the monitored object. Hereinafter, an apparatus and method for monitoring a temperature-humidity state according to an embodiment of the invention will be described in conjunction with Figs. 5-6.

Fig. 5 is a structure diagram of an apparatus for monitoring a temperature-humidity state according to an embodiment of the invention. Fig. 6 is a flow diagram of a method for monitoring a temperature-humidity state according to an embodiment of the invention. As shown in Fig. 5, the apparatus 500 for monitoring a temperature-humidity state includes a rhythm similarity determining unit 502 and a temperature-humidity state identifying unit 504. Here, the rhythm similarity determining unit 502 is configured to determine a rhythm similarity between a temperature-humidity sampling model of the monitored object and a temperature-humidity reference model, i.e. to perform step S502； the temperature-humidity state identifying unit 504 is configured to identify the temperature-humidity state of the monitored object based on the rhythm similarity between the temperature-humidity sampling model of the monitored object and the temperature-humidity reference model, i.e. to perform step S504.

Before determining the temperature-humidity state of the monitored object, for example, the designated part on the left breast of the specific woman during the designated period, for example AM 00: 00-03: 00, the apparatus 500 for monitoring a temperature-humidity state may acquire the temperature-humidity sampling model of the designated part during the above designated period from a temperature-humidity model constructing unit (not shown) . In some embodiments, the temperature-humidity model constructing unit may be configured to sample temperatures and humidities of any designated part during any designated period with temperature sensors and humidity sensors to construct the temperature-humidity sampling model of the designated part during the designated period. For example, the temperature-humidity model constructing unit may be configured to receive the temperature sampling values and the humidity sampling values of the designated part from the temperature sensors and the humidity sensors with a specific interval during the designated period to construct the temperature-humidity sampling model of the designated part during the designated period. Those skilled in the art should appreciate that the temperature-humidity model constructing unit may be a part of or external to the apparatus for monitoring a temperature-humidity state.

In some embodiments, the temperature-humidity sampling model may be a sampling sequence in which each sampling point has two attributes (i.e., temperature sample value and humidity sample value) , for example { (W₁, H₁) , (W₂, H₂) , (W₃, H₃) , …, (W_N, H_N) } ； the temperature-humidity reference model may be a reference sequence in which each reference point has two attributes (i.e., temperature reference value and humidity reference value) , for example { (R₁, S₁) , (R₂, S₂) , (R₃, S₃) , …, (R_M, S_M) } , wherein M and N are positive integers. That is to say, the temperature-humidity sampling model may include one or more sampling points associated with the temperature sampling values and the humidity sampling values, and the temperature-humidity reference model may include one or more reference points associated with the temperature reference values and the humidity reference values. In these embodiments, as the time factor (e.g., a corresponding relationship between elements in time in the two sequences) is not considered, the rhythm similarity determining unit 502 may roughly determine the difference between the temperature-humidity sampling model and the temperature-humidity reference model, and the temperature-humidity state identifying unit 504 may identify an approximate temperature-humidity state of the monitored object.

In order to determine the difference between the temperature-humidity sampling model and the temperature-humidity reference model more accurately and then to identify the temperature-humidity state of the monitored object more accurately, in some embodiments, the temperature-humidity sampling model may be a sampling sequence in which each sampling point has three attributes (i.e., temperature sample value, humidity sample value and corresponding sampling time) , for example, { (W₁, H₁, t₁) , (W₂, H₂, t₂) , (W₃, H₃, t₃) , …, (W_N, H_N, t_N) } ； the temperature-humidity reference model may be a reference sequence in which each reference point has three attributes (i.e., temperature reference value, humidity reference value and corresponding reference time) , for example, { (R₁, S₁, T₁) , (R₂, S₂, T₂) , (R₃, S₃, T₃) , …, (R_M, S_M, T_M) } , wherein M and N are positive integers. That is to say, the temperature-humidity sample model may include one or more sampling points associated with the temperature sampling values, the humidity sampling values and the corresponding sampling time, and the temperature-humidity reference model may include one or more reference points associated with the temperature reference values, the humidity reference values and the corresponding reference time. In these embodiments, the rhythm similarity determining unit 502 may firstly find out respective reference points whose reference time is the closest to a sampling time in the temperature-humidity sampling model in the temperature-humidity reference model, and then determine the rhythm similarity between the temperature-humidity sampling model and a temperature-humidity reference sub-model constituted by the reference points that are found out.

In some embodiments, the rhythm similarity determining unit 502 may determine the dispersion degree of the temperature-humidity sampling model relative to the temperature-humidity reference model as the rhythm similarity between the temperature-humidity sampling model and the temperature-humidity reference model. For example, the rhythm similarity determining unit 502 may determine the dispersion degree of the temperature-humidity sampling model relative to the temperature-humidity reference model by calculating the distance (e.g., the Euclidean distance) , the standard deviation, the mean deviation or the variance of the temperature-humidity sampling model relative to the temperature-humidity reference model.

For example, the rhythm similarity determining unit 502 may take the i^th sampling point (W_i, H_i) in the temperature-humidity sampling model of a monitored part as X_i, and takes the temperature reference value and the humidity reference value (R_i, S_i) at a reference point corresponding to the sampling point in time in the temperature-humidity reference model of the monitored part as the desired value μ. Or the temperature-humidity reference value (R_i, S_i) of an element or a mean value (RA_i, SA_i) of the temperature-humidity reference values of several elements close to the temperature-humidity sample point (W_i, H_i) in sampling time in the temperature-humidity reference model is taken as the desired value μ, and the standard deviation of the temperature-humidity sampling model relative to the temperature-humidity reference model is calculated by

(i＝1， …, N) . That is to say, different sampling points in the temperature-humidity sample model may correspond to different desired values μ. Furthermore, the processing of calculating the mean deviation or the variance of the temperature-humidity sample model relative to the temperature-humidity reference model by the rhythm similarity determining unit 502 may be similar to the processing of calculating the standard deviation of the temperature-humidity sample model relative to the temperature-humidity reference model, which thus is not redundantly described herein.

In some embodiments, the rhythm similarity determining unit 502 may determine a cross-correlation degree of the temperature-humidity sample model relative to the temperature-humidity reference model as the rhythm similarity between the temperature-humidity sample model and the temperature-humidity reference model. For example, the rhythm similarity determining unit 502 may determine the cross-correlation degree between the temperature-humidity sampling model and the temperature-humidity reference model by calculating the cosine similarity or the cross-correlation function value between the temperature-humidity sampling model and the temperature-humidity reference model. Those skilled in the art should know methods of calculating the cross-correlation value between two functions or sequences, which are not redundantly described herein.

Fig. 7 is a distribution diagram of a temperature-humidity sampling model and a temperature-humidity reference model in a temperature-humidity coordinate system according to an embodiment of the invention. For ease of understanding, distribution of the above models are visually displayed in a coordinate system. As shown in Fig. 7, the sample points in the temperature-humidity sampling model fall within an elliptical area indicated by 701, and the reference points in the temperature-humidity reference model fall within a round area indicated by 702. In some embodiments, the rhythm similarity determining unit 502 may determine a shortest distance between the ellipse elliptical area corresponding to the temperature-humidity sample model and the round area corresponding to the temperature-humidity reference model (if the two areas are separate from each other) or a connection-line distance between barycenters or geometric centers of the two areas so as to reflect the rhythm similarity between the temperature-humidity sampling model and the temperature-humidity reference model. In some embodiments, each sampling point in Area 701 may have its own weight, and each reference point in Area 702 may also have its own weight, wherein the weights may be the same or different. Correspondingly, the distance between the above two areas may be a weighted distance between the sampling points and the reference points included in the two areas.

In some embodiments, when Area 701 represents, for example, a distribution area of the temperature-humidity sampling points of the designated part on the left breast of the specific woman, and Area 702 represents, for example, a distribution area of the temperature-humidity reference points of the designated part on the left breast of most healthy women, the distance between Area 701 and Area 702 actually represents the distance between the temperature-humidity states of the designated part on the left breast of the specific woman and the same part on the left breast of healthy population. The larger the distance is, the more abnormal the designated part on the left breast of the specific woman is, i.e. the left breast of the specific woman is more likely to have a disease causing body temperature abnormalities, for example, breast cancer.

It is insufficient to compare the designated part on the left breast of the specific woman with normal population, because individual differences are large, for example, some people have a relatively high body temperature and are prone to sweating while other people have a relatively low body temperature and are not prone to sweating all the year around. Therefore, in view that the human body is symmetric to some extent in physiologic, in some embodiments, the designated part on the left breast of the specific woman may be compared with a same part on the right breast thereof, i.e. the temperature-humidity states of the designated parts that are substantially symmetric on the left and right breasts are measured respectively.

However, it is still insufficient to utilize a certain symmetry of the human body in physiology, as the symmetry of the human body is relative rather than absolute. Therefore, in some embodiments, historical data of the designated part on the left breast of the specific woman may be taken as the temperature-humidity reference model. For example, the temperature-humidity state of the designated part was measured during a certain period in March, 2008, and the temperature-humidity state of the designated part was measured again during a substantially same period in March, 2009, then measurement result of March, 2008 may be taken as the temperature-humidity reference model, and the measurement result of March, 2009 may be taken as the temperature-humidity sampling model.

It is still defective to determine whether the designated part on the left breast of the specific woman has a disease based on the areas shown in Fig. 7, because for the plurality of sampling points in for example Area 701, the following two different cases are included: the sampling points at the daytime are located at the right-upper part of the ellipse while the sampling points at the night are located at the left-lower part of the ellipse； the sampling points at the daytime are located at the left-lower part of the ellipse while the sampling points at the night are located at the right-upper part of the ellipse. However, the above cases may not be distinguished in the method for monitoring a temperature-humidity state described in conjunction with Fig. 6； in other words, temperature-humidity sampling areas formed in the above two significantly different cases are the same.

In order to overcome the defect, in some embodiments, Fig. 7 may be stretched along the time axis, i.e. respective temperature-humidity sampling sequence is expanded, so that relevant models/sequences may be visually displayed in a three dimensional space. In Fig. 8, sequences A and B represent a temperature-humidity reference model and a temperature-humidity sampling model, respectively. Distances between corresponding points on the two space curves of A and B may be directly calculated, and then the mean distance between the two space curves of A and B may be calculated by weighting and then adding or arithmetically adding the distances. Of course, the distance or dispersion degree between two curves may also be embodied by other measures such as the above described mean deviation or standard deviation. Alternatively, the two temperature-humidity sampling sequences may be processed by interpolation to generate corresponding fitting curves, and then a space area between the two fitting curves may be calculated by integration as the distance between the two curves during a certain period. The distance between the two space curves of A and B may reflect the rhythm similarity between the temperature-humidity reference model and the temperature-humidity sampling model.

The temperature-humidity state of the monitored object may more accurately reflect the physical health state of the monitored object. For example, the larger the dispersion degree of the temperature-humidity sampling model of the monitored object relative to the temperature-humidity reference model is, the higher the possibility for the monitored object to have the disease causing body surface temperature/humidity abnormalities (e.g, breast cancer) is； on the contrary, the higher the similarity of the temperature-humidity sample model of the monitored object relative to the temperature-humidity sample model is, the lower the possibility for the monitored object to have the disease causing body surface humidity/temperature abnormalities is. Therefore, in some embodiments, the apparatus 500 for monitoring a humidity state may further include the health state determining unit and the health state reporting unit described above in conjunction with Figs. 3-4 in addition to the rhythm similarity determining unit 502 and the temperature-humidity state identifying unit 504 described above. In these embodiments, the health state determining unit is configured to determine the physical health degree of the monitored object based on the temperature-humidity state of the monitored object, and the health state reporting unit is configured to provide the physical health degree of the monitored object to the user. With the apparatus for monitoring a temperature-humidity state discussed above, more intuitive information about the physiological health state of the monitored object may be provided for the user, e.g., information prompting the user of an increase or decrease percentage of a risk of having some body temperature-related diseases (e.g., breast cancer) at a local part of the detected breast within a certain period. Furthermore, as described above, temperature sampling values higher/lower than the temperature threshold and the humidity sampling values higher/lower than the humidity threshold may usually better reflect physiological indicators of the human body under a metaboilic level higher or lower than the normal metabolism, so they are valuable for determining the physical health state of the monitored object. Therefore, in some embodiments, the temperature-humidity model constructing unit may acquire the temperature sampling values higher or lower than the temperature threshold and the humidity sampling values higher or lower than the humidity threshold of the monitored object to construct the temperature-humidity sampling model and the temperature-humidity reference model by utilizing these temperature sampling values and humidity sample values.

It should be noted that the invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of conciseness, specific descriptions of known methods and techniques are omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the process of the method according to the invention is not limited to the specific steps as described and illustrated. Those skilled in the art may make various modifications, alterations and additions, or change execution sequences of the steps upon understanding the spirit of the invention.

Those skilled in the art will appreciate that although the method and apparatus for monitoring a humidity state and the method and apparatus for monitoring a temperature-humidity state according to embodiments of the invention are described by taking the breast of woman as an example, the methods and apparatuses according to the embodiments of the invention may also be used for monitoring a humidity state and a temperature-humidity state of parts with a certain symmetric, such as legs, hands, faces, feet, brain, of the human body, and thus facilitating identification of various diseases, for example, early or middle stage tumours or cancers, which may cause temperature abnormalities of the above parts.

The functional blocks illustrated in the above mentioned structure block diagrams may be implemented as hardware, software, firmware or a combination thereof. When implemented by hardware, they may be, for example, electronic circuits, Application Specific Integrated Circuits (ASIC) , suitable firmware, plug-ins and function cards and so on. When implemented by software, elements of the invention are programs or code segments for implementing required tasks to be executed by a processor. The programs or the code segments may be stored in a machine-readable medium or transmitted in a transmission medium or communication link through data signals carried on carriers. The machine-readable medium may include any medium capable of storing or transmitting information. Examples of the machine-readable medium include an electronic circuit, a semiconductor storage, a ROM, a flash memory, an EROM, a flexible disk, a CD-ROM, an optical disk, a hard disk, an optical media, an RF link and so on. The code segments may be downloaded via a computer network such as Internet or intranet.

In other words, the above described apparatus for monitoring a humidity state or a temperature-humidity state may be embodied as an apparatus including the following components: a processor； a memory storing instructions executable by the processor, wherein the processor is operable to implement the above described method for monitoring a humidity state or a temperature-humidity state when executing the instructions.

The invention may also be embodied as other specific implementations without departing from the spirit and nature of the invention. For example, algorithms described in specific embodiments may be modified while the architecture of the system will not depart from the spirit of the invention. Therefore, the present embodiments are construed as exemplary and unrestrictive, scopes of the invention are defined by the appended claims rather than the above descriptions, and all modifications falling within the meaning and equivalence of the claims are included within the scopes of the invention.

Claims

A method for monitoring a humidity state, comprising:

determining a humidity rhythm similarity between a humidity sampling model of a monitored object and a humidity reference model； and

identifying the humidity state of the monitored object based on the humidity rhythm similarity.
The method for monitoring a humidity state according to claim 1, wherein the humidity sampling model comprises at least one sampling point associated with a humidity sampling value and a corresponding sampling time, and the humidity reference model comprises at least one reference point associated with a humidity reference value and a corresponding reference time.
The method for monitoring a humidity state according to claim 1 or 2, wherein the humidity rhythm similarity is a dispersion degree or a cross-correlation degree of the humidity sampling model relative to the humidity reference model.
The method for monitoring a humidity state according to claim 3, wherein the dispersion degree is a distance, a standard deviation, a mean deviation, or a variance of the humidity sampling model relative to the humidity reference model.
The method for monitoring a humidity state according to claim 3, wherein the mutual-correlation degree is a cosine similarity or a cross-correlation function value between the humidity sampling model and the humidity reference model.
The method for monitoring a humidity state according to any of claims 1-5, wherein the monitored object is a designated part of a human body, and the humidity reference model is a humidity sampling model of the designated part in a historical record for a same period, a humidity sampling model of a part contralateral to the designated part for the same period, or a humidity statistics model of large samples of ipsilateral part for the same period.
The method for monitoring a humidity state according to claim 6, before determining the humidity rhythm similarity, further comprising:

when a temperature of the monitored object is higher or lower than a temperature threshold, sampling humidities of the monitored object to construct the humidity sampling model.
The method for monitoring a humidity state according to claim 6, further comprising:

when a humidity of the monitored object is higher or lower than a humidity threshold, sampling temperatures of the monitored object to construct a temperature sampling model of the monitored object；

determining a temperature rhythm similarity between the temperature sampling model and a temperature reference model； and

identifying a temperature state of the monitored object based on the temperature rhythm similarity.
The method for monitoring a humidity state according to claim 8, wherein the temperature sampling model comprises at least one samplng point associated with a temperature sampling value and a corresponding sampling time, and the temperature reference model comprises at least one reference point associated with a temperature reference value and a corresponding reference time.
The method for monitoring a humidity state according to claim 8 or 9, wherein the temperature rhythm similarity is a dispersion degree or a cross-correlation degree of the temperature sample model relative to the temperature reference model.
The method for monitoring a humidity state according to claim 10, wherein the dispersion degree is a distance, a standard deviation, a mean deviation, or a variance of the temperature sampling model relative to the temperature reference model.
The method for monitoring a humidity state according to claim 10, wherein the cross-correlation degree is a cosine similarity or a cross-correlation function value between the temperature sampling model and the temperature reference model.
The method for monitoring a humidity state according to any of claims 8-12, wherein the monitored object is a designated part of a human body, and the temperature reference model is a temperature sampling model of the designated part in a historical record for a same period, a temperature sampling model of a part contralateral to the designated part for the same period, or a humidity statistics model of a large sample ipsilateral part for the same period.
The method for monitoring a humidity state according to claim 13, further comprising:

determining a physical health degree of the monitored object based on the humidity state and/or the temperature state； and

providing the physical health degree to a user.
An apparatus for monitoring a humidity state, comprising:

a humidity similarity determining unit configured to determine a humidity rhythm similarity between a humidity sampling model of a monitored object and a humidity reference model； and

a humidity state identifying unit configured to identify the humidity state of the monitored object based on the humidity rhythm similarity.
The apparatus for monitoring a humidity state according to claim 15, wherein the humidity sampling model comprises at least one sampling point associated with a humidity sampling value and a corresponding sampling time, and the humidity reference model comprises at least one reference point associated with a humidity reference value and a corresponding reference time.
The apparatus for monitoring a humidity state according to claim 15 or 16, wherein the humidity rhythm similarity is a dispersion degree or a cross-correlation degree of the humidity sampling model relative to the humidity reference model.
The apparatus for monitoring a humidity state according to claim 17, wherein the dispersion degree is a distance, a standard deviation, a mean deviation, or a variance of the humidity sampling model relative to the humidity reference model.
The apparatus for monitoring a humidity state according to claim 17, wherein the cross-correlation degree is a cosine similarity or a cross-correlation function value between the humidity sampling model and the humidity reference model.
The apparatus for monitoring a humidity state according to any of claims 15-19, wherein the monitored object is a designated part of a human body, and the humidity reference model is a humidity sampling model of the designated part in a historical record for a same period, a humidity sampling model of a part contralateral to the designated part for the same period, or a humidity statistics model of large samples of ipsilateral part for the same period.
The apparatus for monitoring a humidity state according to claim 20, further comprising:

a humidity model constructing unit configured to, when a temperature of the monitored object is higher or lower than a temperature threshold, sample humidities of the monitored object to construct the humidity sampling model.
The apparatus for monitoring a humidity state according to claim 20, further comprising:

a temperature model constructing unit configured to, when a humidity of the monitored object is higher or lower than a humidity threshold, sample temperatures of the monitored object to construct a temperature sampling model of the monitored object；

a temperature similarity determining unit configured to determine a temperature rhythm similarity between the temperature sampling model and a temperature reference model； and

a temperature state identifying unit configured to identify a temperature state of the monitored object based on the temperature rhythm similarity.
The apparatus for monitoring a humidity state according to claim 22, wherein the temperature sampling model comprises at least one sampling point associated with a temperature sampling value and a corresponding sampling time, and the temperature reference model comprises at least one reference point associated with a temperature reference value and a corresponding reference time.
The apparatus for monitoring a humidity state according to claim 22 or 23, wherein the temperature rhythm similarity is a dispersion degree or a cross-correlation degree of the temperature sampling model relative to the temperature reference model.
The apparatus for monitoring a humidity state according to claim 24, wherein the dispersion degree is a distance, a standard deviation, a mean deviation, or a variance of the temperature sampling model relative to the temperature reference model.
The apparatus for monitoring a humidity state according to claim 24, wherein the cross-correlation degree is a cosine similarity or a cross-correlation function value between the temperature sampling model and the temperature reference model.
The apparatus for monitoring a humidity state according to any of claims 22-26, wherein the monitored object is a designated part of a human body, the temperature reference model is a temperature sampling model of the designated part in a historical record for a same period, a temperature sampling model of a part contralateral to the designated part for the same period, or a temperature statistics model of large samples of ipsilateral part for the same period.
The apparatus for monitoring a humidity state according to claim 27, further comprising:

a health state determining unit configured to determine a physical health degree of the monitored object based on the humidity state and/or the temperature state； and

a health state reporting unit configured to provide the physical health degree to a user.
A method for monitoring a temperature-humidity state, comprising:

determining a rhythm similarity between a temperature-humidity sampling model of a monitored object and a temperature-humidity reference model； and

identifying the temperature-humidity state of the monitored object based on the rhythm similarity.
The method for monitoring a temperature-humidity state according to claim 29, wherein the rhythm similarity is a distance between barycenters or geometric centers of a first area corresponding to the temperature-humidity sampling model and a second area corresponding to the temperature-humidity reference model.
The method for monitoring a temperature-humidity state according to claim 29, wherein the temperature-humidity sampling model comprises at least one sampling point associated with a temperature sampling value, a humidity sampling value and a corresponding sampling time, and the temperature-humidity reference model comprises at least one reference point associated with a temperature reference value, a humidity reference value and a corresponding reference time.
The method for monitoring a temperature-humidity state according to claim 31, wherein the rhythm similarity is a distance between a first space curve corresponding to the temperature-humidity sampling model and a second space curve corresponding to the temperature-humidity reference model.
The method for monitoring a temperature-humidity state according to claim 32, wherein the distance between the first space curve and the second space curve is a mean deviation, a weighted mean deviation, or an area between the first space curve and the second space curve.
The method for monitoring a temperature-humidity state according to claim 29 or 31, wherein the rhythm similarity is a dispersion degree or a cross-correlation degree of the temperature-humidity sampling model relative to the temperature-humidity reference model.
The method for monitoring a temperature-humidity state according to claim 34, wherein the dispersion degree is a distance, a standard deviation, a mean deviation, or a variance of the temperature-humidity sampling model relative to the temperature-humidity reference model.
The method for monitoring a temperature-humidity state according to claim 34, wherein the cross-correlation degree is a cosine similarity or a cross-correlation function value between the temperature-humidity sampling model and the temperature-humidity reference model.
The method for monitoring a temperature-humidity state according to any of claims 29-36, wherein the monitored object is a designated part of a human body, and the temperature-humidity reference model is a temperature-humidity sampling model of the designated part in a historical record for a same period, a temperature-humidity sampling model of a part contralateral to the designated part for the same period, or a temperature-humidity statistics model of large samples of ipsilateral part for the same period.
The method for monitoring a temperature-humidity state according to claim 37, wherein the designated part is a part of a breast.
The method for monitoring a temperature-humidity state according to claim 38, further comprising:

determining a physical health degree of the part of the breast based on the temperature-humidity state； and

providing the physical health degree to a user.
The method for monitoring a temperature-humidity state according to any of claims 29-39, wherein the temperature-humidity reference model and the temperature-humidity sampling model are constructed based on temperature sampling values higher than a temperature threshold and corresponding humidity sampling values and/or humidity sampling values higher than a humidity threshold and corresponding temperature sample values.
An apparatus for monitoring a temperature-humidity state, comprising:

a rhythm similarity determining unit configured to determine a rhythm similarity between a temperature-humidity sampling model of a monitored object and a temperature-humidity reference model； and

a temperature-humidity state identifying unit configured to identify the temperature-humidity state of the monitored object based on the rhythm similarity.
The apparatus for monitoring a temperature-humidity state according to claim 41, wherein the rhythm similarity is a distance between barycenters or geometric centers of a first area corresponding to the temperature-humidity sampling model and a second area corresponding to the temperature-humidity reference model.
The apparatus for monitoring a temperature-humidity state according to claim 41, wherein the temperature-humidity sampling model comprises at least one sampling point associated with a temperature sampling value, a humidity sampling value and a corresponding sampling time, and the temperature-humidity reference model comprises at least one reference point associated with a temperature reference value, a humidity reference value and a corresponding reference time.
The apparatus for monitoring a temperature-humidity state according to claim 43, wherein the rhythm similarity is a distance between a first space curve corresponding to the temperature-humidity sampling model and a second space curve corresponding to the temperature-humidity reference model.
The apparatus for monitoring a temperature-humidity state according to claim 44, wherein the distance between the first space curve and the second space curve is a mean deviation, a weighted mean deviation, or an area between the first space curve and the second space curve.
The apparatus for monitoring a temperature-humidity state according to claim 41 or 43, wherein the rhythm similarity is a dispersion degree or a cross-correlation degree of the temperature-humidity sampling model relative to the temperature-humidity reference model.
The apparatus for monitoring a temperature-humidity state according to claim 46, wherein the dispersion degree is a distance, a standard deviation, a mean deviation, or a variance of the temperature-humidity sampling model relative to the temperature-humidity reference model.
The apparatus for monitoring a temperature-humidity state according to claim 46, wherein the cross-correlation degree is a cosine similarity or a mutual-correlation function value between the temperature-humidity sampling model and the temperature-humidity reference model.
The apparatus for monitoring a temperature-humidity state according to any of claims 41-48, wherein the monitored object is a designated part of a human body, and the temperature-humidity reference model is a temperature-humidity sampling model of the designated part in a historical record for a same period, a temperature-humidity sampling model of a part contralateral to the designated part for the same period, or a temperature-humidity statistics model of large samples of ipsilateral part for the same period.
The apparatus for monitoring a temperature-humidity state according to claim 49, wherein the designated part is a part of a breast.
The apparatus for monitoring a temperature-humidity state according to claim 50, further comprising:

a health state determining unit configured to determine a physical health degree of the part of the breast based on the temperature-humidity state； and

a health state reporting unit configured to provide the physical health degree to a user.
The apparatus for monitoring a temperature-humidity state according to any of claims 41-51, wherein the temperature-humidity reference model and the temperature-humidity sampling model are constructed based on temperature sampling values higher than a temperature threshold and corresponding humidity sampling values and/or humidity sampling values higher than a humidity threshold and corresponding temperature sampling values.
The apparatus for monitoring a temperature-humidity state according to claim 51, wherein the physical health degree comprises a disease risk of the part of the breast.