Environment temperature intelligent monitoring method for pediatric department

Abstract

The invention relates to the technical field of electric digital data processing, in particular to an intelligent environment temperature monitoring method for a pediatric department. The method comprises: acquiring a temperature signal; acquiring an external influence value of the extreme point according to the change of the temperature signal; taking the difference between the extreme point and the next adjacent extreme point as a change degree value, and obtaining the actual skewness according to the distribution condition of the change degree value and the external influence value; according to the size of the actual skewness, a method for adaptively obtaining Gaussian filtering parameters is determined, the Gaussian filtering parameters are obtained to filter the temperature signals, de-noised temperature data are obtained, and environment temperature data of the pediatric department are monitored. According to the invention, the Gaussian filtering parameters are adaptively obtained, the temperature signals are accurately denoised through the Gaussian filtering algorithm, and the environmental temperature data of the department of pediatrics are accurately monitored.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to an intelligent monitoring method for the environmental temperature in the pediatric department. Background Art

[0002] The monitoring of the environmental temperature in the pediatric department is very important. Especially in the neonatal and infant wards, too high or too low temperature may directly or indirectly have a negative impact on the health of infants and young children. Therefore, accurately and real-time monitoring the environmental temperature in the pediatric department is crucial to ensure the safety and health of children. At present, the latest temperature monitoring technology is to remotely monitor the environmental temperature in the pediatric department through the Internet of Things to achieve automatic recording and remote monitoring of temperature data. However, in the actual process of recording temperature data, the temperature data may be affected by noise interference during the acquisition and transmission process, resulting in the monitored and recorded temperature data not matching the actual temperature data. Therefore, during the process of monitoring and recording temperature data, it is necessary to denoise the temperature data.

[0003] In the existing method, the Gaussian filtering algorithm is used to denoise the temperature data. However, in the Gaussian filtering algorithm, it is necessary to artificially set the Gaussian filtering parameters, and the artificially set Gaussian filtering parameters have errors and cannot accurately denoise the noise generated under different conditions. Furthermore, it is impossible to accurately monitor the temperature data of the environment in the pediatric department, resulting in the inability to accurately control the temperature of the environment in the pediatric department. Summary of the Invention

[0004] In order to solve the technical problem that the artificially set Gaussian filtering parameters in the Gaussian filtering algorithm have errors and cannot accurately denoise the noise generated under different conditions, and thus cannot accurately monitor the temperature data of the environment in the pediatric department, the purpose of the present invention is to provide an intelligent monitoring method for the environmental temperature in the pediatric department. The specific technical solution adopted is as follows: The present invention proposes an intelligent monitoring method for the environmental temperature in the pediatric department. The method includes the following steps: Obtain the temperature data at each moment within a preset time period and convert it into a temperature signal; Construct the time sequence interval of each extreme point on the temperature signal, and obtain the external influence value of each extreme point according to the magnitude and change of each temperature data within each time sequence interval, as well as the number of temperature data; Take the amplitude difference between each extreme point on the temperature signal and the next adjacent extreme point as the change degree value, and obtain the actual skewness of the change degree value distribution according to the distribution of each change degree value and the external influence values of the corresponding two extreme points; When the absolute value of the actual skewness is greater than the preset skewness threshold, obtain the change degree value curve according to the magnitude of the change degree value and the number of the same change degree values; decompose the change degree value curve to obtain a preset number of decomposed curves; according to the temperature data and the external influence value between two extreme points corresponding to each change degree value in each decomposed curve, obtain the overall energy of each decomposed curve; according to the overall energy of each decomposed curve and the variance of all corresponding temperature data, obtain the Gaussian filtering parameter; when the absolute value of the actual skewness is less than or equal to the preset skewness threshold, obtain the Gaussian filtering parameter according to the fluctuation of the temperature data on the temperature signal. Filter the temperature signal according to the Gaussian filtering parameter to obtain the denoised temperature data, and monitor the environmental temperature data of the pediatric department.

[0005] Further, the method for constructing the time sequence interval of each extreme point on the temperature signal is as follows: Take the time period composed of all moments between the (i - 1)-th extreme point and the (i + 1)-th extreme point on the temperature signal as the time sequence interval of the i-th extreme point; wherein, the time sequence interval of the i-th extreme point does not include the moments corresponding to the (i - 1)-th extreme point and the (i + 1)-th extreme point.

[0006] Further, the calculation formula of the external influence value is as follows: In the formula, is the external influence value of the i-th extreme point; is the number of temperature data in the time sequence interval of the i-th extreme point; is the derivative of the n-th temperature data in the time sequence interval of the i-th extreme point; is the n-th temperature data in the time sequence interval of the i-th extreme point; is the average value of all temperature data in the time sequence interval of the i-th extreme point; is the absolute value function; tanh is the hyperbolic tangent function; exp is the exponential function with the natural constant e as the base; norm is the normalization function.

[0007] Further, the method for obtaining the actual skewness of the change degree value distribution according to the distribution of each change degree value and the external influence values of the corresponding two extreme points is as follows: Obtain the ratio of the number of occurrences of each change degree value to the total number of change degree values as the distribution probability of each change degree value; Obtain the skewness of the distribution probability as the skewness of the change degree value distribution; Take the average value of the external influence values of the two extreme points corresponding to each change degree value as the correction weight corresponding to the change degree value; Correct the skewness of the distribution of the degree-of-change values according to the corrected weight to obtain the actual skewness of the distribution of the degree-of-change values.

[0008] Further, the calculation formula for the actual skewness of the distribution of the degree-of-change values is: In the formula, is the actual skewness of the distribution of the degree-of-change values; J is the total number of degree-of-change values; is the corrected weight of the j-th degree-of-change value; is the distribution probability of the j-th degree-of-change value; is the mean value of the distribution probabilities of all degree-of-change values; is the standard deviation of the distribution probabilities of all degree-of-change values.

[0009] Further, the method for obtaining the degree-of-change value curve is: Arrange the degree-of-change values from small to large on the horizontal axis, use the number of occurrences of each degree-of-change value on the horizontal axis as the vertical axis, determine the points corresponding to each degree-of-change value in the coordinate system, perform curve fitting, and use the obtained curve as the degree-of-change value curve.

[0010] Further, the method for decomposing the degree-of-change value curve to obtain a preset number of decomposed curves is: Modify the decomposition condition in the independent component analysis algorithm to Gaussianity, and decompose the degree-of-change value curve through the independent component analysis algorithm with the modified decomposition condition to obtain a preset number of decomposed curves.

[0011] Further, the method for obtaining the overall energy of each decomposed curve according to the temperature data and external influence values between two extreme points corresponding to each degree-of-change value in each decomposed curve is: For any degree-of-change value, use the time period formed by the two moments corresponding to the two extreme points corresponding to this degree-of-change value as the target time period; Obtain the energy of the temperature signal segment corresponding to the target time period as the initial energy of the temperature signal segment corresponding to this degree-of-change value; Correct the initial energy according to the corrected weight of this degree-of-change value to obtain the actual energy of the temperature signal segment corresponding to this degree-of-change value; Take the result of accumulating the actual energies of the temperature signal segments corresponding to all degree-of-change values in each decomposed curve as the overall energy of each decomposed curve.

[0012] Further, the calculation formula for the actual energy is: In the formula, is the actual energy of the temperature signal segment corresponding to the k-th degree of change value; is the correction weight of the k-th degree of change value; is the initial moment of the temperature signal segment corresponding to the k-th degree of change value; is the final moment of the temperature signal segment corresponding to the k-th degree of change value; is the temperature data at the t-th moment within the temperature signal segment corresponding to the k-th degree of change value; is the absolute value symbol.

[0013] Furthermore, the method for obtaining the Gaussian filter parameter according to the overall energy of each decomposition curve and the variance of all corresponding temperature data is as follows: The result of accumulating the overall energies of all decomposition curves is used as the first result; The ratio of the overall energy of each decomposition curve to the first result is used as the first weight of the corresponding decomposition curve; The product of the first weight of each decomposition curve and the variance of all corresponding temperature data is used as the participation variance of the corresponding decomposition curve; The result of accumulating the participation variances of all decomposition curves is used as the Gaussian filter parameter.

[0014] The present invention has the following beneficial effects: Construct the time series intervals for each extreme point on the temperature signal. Based on the magnitudes and variations of each temperature data within each time series interval, as well as the quantity of temperature data, obtain the external influence values for each extreme point, determine the degree of interference of each extreme point by external influencing factors, and denoise the temperature signal while considering external influencing factors to reduce over-smoothing of the temperature signal. To accurately denoise the temperature signal, further use the amplitude difference between each extreme point on the temperature signal and the next adjacent extreme point as the change degree value, analyze the complexity of the noise in the temperature signal based on the change degree value, and then obtain the actual skewness of the distribution of the change degree value according to the distribution of each change degree value and the external influence values of the corresponding two extreme points, which can accurately reflect the complexity of the noise in the temperature signal. When the absolute value of the actual skewness is greater than the preset skewness threshold, the complexity of the noise in the temperature signal is relatively large at this time. Then, according to the magnitudes of the change degree values and the quantity of the same change degree values, obtain the change degree value curve, determine the overall distribution of the noise superposition in the temperature signal, decompose the change degree value curve to obtain a preset number of decomposed curves, and then obtain the overall energy of each decomposed curve to distinguish the primary and secondary parts of the noise contained in the temperature signal. Furthermore, adaptively obtain the Gaussian filtering parameters according to the overall energy of each decomposed curve and the variance of all corresponding temperature data to avoid incomplete noise removal in the temperature signal. Then, filter the temperature signal according to the Gaussian filtering parameters to accurately obtain the denoised temperature data, accurately monitor the environmental temperature data in the pediatric department, and ensure the health and safety of children in the pediatric department. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a flowchart showing a method for intelligent monitoring of environmental temperature in a pediatric department provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following provides a detailed description of a method for intelligent monitoring of environmental temperature in a pediatric department proposed according to the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0019] The following specifically describes the specific solution of an intelligent environmental temperature monitoring method for the pediatric department provided by the present invention in conjunction with the accompanying drawings.

[0020] Please refer to Figure 1 , which shows a schematic flowchart of an intelligent environmental temperature monitoring method for the pediatric department provided by an embodiment of the present invention. The method includes the following steps: Step S1: Obtain temperature data at each moment within a preset time period and convert it into a temperature signal.

[0021] Specifically, in the embodiment of the present invention, temperature sensors are used to obtain and record the temperature data at each moment in the pediatric department. During the process of obtaining temperature data, it may be affected by noise data. In order to monitor the temperature data of the pediatric department environment in real time and accurately and ensure that the temperature data of the pediatric department environment remains normal, it is necessary to denoise the temperature data obtained by the temperature sensors. In the embodiment of the present invention, the temperature data at each moment within the first two hours before the current moment is obtained, that is, the preset time period is set to two hours, where the last moment of the preset time period is the current moment. At the same time, the time interval between two adjacent moments is set to half a minute. The implementer can set the size of the preset time period and the time interval between two adjacent moments according to the actual situation, which is not limited herein. In order to accurately and efficiently denoise the temperature data, in the embodiment of the present invention, the temperature data within the preset time period is converted into a temperature signal, where the horizontal axis of the temperature signal is time and the vertical axis is the magnitude of the temperature data.

[0022] The scenario of the embodiment of the present invention is that at least two hours of temperature data in the pediatric department have been recorded by the temperature sensor before the current moment.

[0023] The purpose of the embodiment of the present invention is: In order to accurately monitor the temperature data in the pediatric department and ensure the safety and health of infants and young children, it is necessary to denoise the temperature data through the Gaussian filtering algorithm. Since the parameters in the Gaussian filtering algorithm need to be set manually, it is easy to cause inaccurate noise removal. Therefore, in order to accurately remove the noise in the temperature data, the embodiment of the present invention analyzes the temperature data within the preset time period, judges the complexity of the noise in the temperature data within the preset time period, and then adaptively obtains the Gaussian filtering parameters in the Gaussian filtering algorithm to ensure that the temperature data within the preset time period is accurately denoised, and then accurately monitors the temperature data of the pediatric department environment, and timely adjusts the temperature in the pediatric department to ensure the safety and health of infants and young children.

[0024] Step S2: Construct the time sequence intervals of each extreme point on the temperature signal, and obtain the external influence value of each extreme point according to the magnitude and variation of each temperature data within each time sequence interval, as well as the number of temperature data.

[0025] Specifically, usually the change rate of temperature data in the pediatric department environment is relatively low. For the noise signals superimposed on the temperature signal, the actual interference on the temperature data caused by different local intensities is different. Among them, the external influence factors in the pediatric department environment have a relatively large interference on the temperature data. Therefore, by analyzing the characteristics of the temperature signal itself, the external influence value caused by external influence factors for each extreme point in the temperature signal can be obtained. Among them, the external influence is the interference of other external factors in the pediatric department environment on the measurement accuracy of the temperature sensor. The main external influence factor is caused by unstable power supply in the pediatric department. For example, when other medical electrical devices are connected to the circuit in the pediatric department, it will cause the power supply in the pediatric department to be unstable briefly, and then cause the measurement accuracy of the temperature sensor to deviate. The corresponding temperature signal segment will be characterized by a large degree of change and a short duration. In order to accurately analyze the influence degree of the fluctuation in the temperature signal caused by unstable power supply, the embodiment of the present invention constructs the time sequence intervals of each extreme point on the temperature signal. When the change degree of the temperature data within the time sequence interval is larger and the number of temperature data is smaller, that is, the shorter the time sequence interval, it indicates that the corresponding extreme point is more affected by the external environment. Therefore, according to the magnitude and variation of each temperature data within each time sequence interval, as well as the number of temperature data, the external influence value of each extreme point is obtained. Among them, the larger the external influence value, the greater the interference of the corresponding extreme point by the external influence factor. The specific method for obtaining the external influence value of each extreme point is as follows: (1) Obtain the time sequence interval.

[0026] Preferably, the method for constructing the time sequence interval is: The time period composed of all moments between the (i - 1)-th extreme point and the (i + 1)-th extreme point on the temperature signal is used as the time sequence interval of the i-th extreme point; among them, the time sequence interval of the i-th extreme point does not include the moments corresponding to the (i - 1)-th extreme point and the (i + 1)-th extreme point. If the i-th extreme point is the first extreme point on the temperature signal, then the time sequence interval of the i-th extreme point is continued forward to the initial end point of the temperature signal; if the i-th extreme point is the last extreme point on the temperature signal, then the time sequence interval of the i-th extreme point is continued backward to the end end point of the temperature signal. The implementer can set the size of the time sequence interval according to the actual situation, and no limitation is made here. According to the method for obtaining the time sequence interval of the i-th extreme point, the time sequence intervals of each extreme point on the temperature signal are obtained.

[0027] (2) Obtain the external influence value.

[0028] As an example, taking the i-th extreme point as an example, when the i-th extreme point is affected by factors of unstable power supply, compared with electromagnetic interference noise, at this time, the temperature data is relatively more affected, which is reflected in that the amplitude of the corresponding temperature signal segment fluctuates greatly in the time series interval of the i-th extreme point. The temperature signal corresponding to a normal pediatric department will not show a large fluctuation. Therefore, in the embodiment of the present invention, the variance of the temperature data in the time series interval of the i-th extreme point is obtained as the first variance. When the first variance is larger, it indicates that the i-th extreme point is more affected by external factors. Unstable power supply is the change that occurs when a certain electrical appliance is connected or disconnected from the circuit. Compared with electromagnetic interference noise, the electrical appliances in the pediatric department have a greater impact on the power supply, that is, the fluctuation of the temperature signal caused by unstable power supply is greater than that caused by electromagnetic interference noise. Therefore, in order to highlight the difference between the factors of unstable power supply and electromagnetic interference noise in the temperature signal, in the embodiment of the present invention, the derivative of each temperature data in the time series interval of the i-th extreme point is obtained to adjust the variance of the temperature data in the time series interval of the i-th extreme point. For the temperature data with a larger derivative in the time series interval of the i-th extreme point, the probability of being affected by unstable power supply is greater. At the same time, the duration of unstable power supply is relatively short. Therefore, when the time series interval of the i-th extreme point is shorter, that is, when the number of temperature data in the time series interval of the i-th extreme point is relatively small, it indicates that the change of the i-th extreme point is more likely to be caused by the inaccurate measurement accuracy of the temperature sensor due to unstable circuit power supply. Therefore, according to the first variance, the derivative of each temperature data in the time series interval of the i-th extreme point, and the number of temperature data in the time series interval of the i-th extreme point, the calculation formula for obtaining the external influence value of the i-th extreme point is: In the formula, is the external influence value of the i-th extreme point; is the number of temperature data in the time series interval of the i-th extreme point; is the derivative of the n-th temperature data in the time series interval of the i-th extreme point; is the n-th temperature data in the time series interval of the i-th extreme point; is the mean value of all temperature data in the time series interval of the i-th extreme point; is the absolute value function; tanh is the hyperbolic tangent function; exp is the exponential function with the natural constant e as the base; norm is the normalization function.

[0029] It should be noted that the smaller it is, the shorter the time series interval of the i-th extreme point is, and the more likely the i-th extreme point is affected by factors of unstable power supply, the larger it is, the larger it is; The larger it is, the more unstable the temperature data within the time series interval of the $i$-th extreme point. By performing correction, accurately determine the degree of external influence on the $i$-th extreme point. The larger it is, the more likely the change in temperature data within the time series interval of the $i$-th extreme point is caused by unstable power supply. The larger it is; therefore, The larger it is, the greater the influence of the unstable power supply factor on the fluctuation of the $i$-th extreme point. Among them, The value range of is from 0 to 1. According to the method of obtaining the external influence value of the $i$-th extreme point, obtain the external influence value of each extreme point on the temperature signal.

[0030] Step S3: Take the amplitude difference between each extreme point on the temperature signal and the next adjacent extreme point as the change degree value. According to the distribution of each change degree value and the external influence values of the corresponding two extreme points, obtain the actual skewness of the change degree value distribution.

[0031] Specifically, various medical devices are frequently used in the pediatric department. The electromagnetic interference noise affecting the temperature signal is relatively complex and conforms to a Gaussian distribution. To prevent the Gaussian filtering algorithm from over-smoothening the temperature signal or having too low a denoising intensity, it is necessary to further analyze the temperature signal. By analyzing the complexity of the noise in the temperature signal, the Gaussian filtering parameters can be adaptively obtained to accurately denoise the temperature signal. To accurately analyze the complexity of the noise in the temperature signal, in the embodiments of the present invention, the absolute value of the difference in amplitude between each extreme point on the temperature signal and the next adjacent extreme point is used as the change degree value. For the last extreme point on the temperature signal, the absolute value of the difference in amplitude between the last extreme point and the previous adjacent extreme point is used as the change degree value of the last extreme point. When the types of noise in the temperature signal are fewer, the distribution of the change degree values is more stable; when the types of noise in the temperature signal are more, that is, the noise is more complex, the distribution of the change degree values is more unstable. Therefore, in the embodiments of the present invention, according to the distribution of the change degree values, the distribution probability of each change degree value is obtained, and then the skewness of the distribution probability is obtained. The closer the skewness is to 0, the fewer the types of noise in the temperature signal; the farther the skewness is from 0, the more the types of noise in the temperature signal. Among them, the method for obtaining the skewness is a well-known calculation and will not be elaborated here. In actual situations, the fluctuations in the temperature signal may be caused by unstable power supply. Therefore, the change degree values may also be caused by unstable power supply. To more accurately analyze the complexity of the noise in the temperature signal and reduce the interference of unstable power supply on the detection of the types of noise, in the embodiments of the present invention, the skewness of the distribution probability is corrected according to the external influence value of each extreme point to obtain the actual skewness, which is prepared to represent the complexity of the noise in the temperature signal. Therefore, according to the distribution of each change degree value and the external influence values of the corresponding two extreme points, the actual skewness of the distribution of the change degree values is obtained. Among them, the larger the actual skewness, the more types of electromagnetic interference noise there are in the temperature signal.

[0032] Preferably, the method for obtaining the actual skewness is as follows: Obtain the ratio of the number of occurrences of each change degree value to the total number of change degree values as the distribution probability of each change degree value; obtain the skewness of the distribution probability as the skewness of the distribution of the change degree values; the larger the skewness, the more unstable the distribution probability, indirectly indicating that the types of noise in the temperature signal are more; to reduce the influence of unstable power supply on the skewness, the average value of the external influence values of the two extreme points corresponding to each change degree value is used as the correction weight for the corresponding change degree value; correct the skewness of the distribution of the change degree values according to the correction weight to obtain the actual skewness of the distribution of the change degree values.

[0033] As an example, the calculation formula for obtaining the actual skewness of the distribution of the change degree values is: In the formula, is the actual skewness of the distribution of the change degree values; J is the total number of change degree values; is the corrected weight of the j-th change degree value; is the distribution probability of the j-th change degree value; is the mean of the distribution probabilities of all change degree values; is the standard deviation of the distribution probabilities of all change degree values.

[0034] It should be noted that by for performing correction, making more accurately represent the complexity of the noise in the temperature signal. The larger [[ID=]], the greater the degree of external influence on the two extreme points corresponding to the j-th change degree value, and the smaller the weight of the distribution probability of the j-th change degree value when calculating the skewness. Therefore, through for performing negative correlation processing. The smaller [[ID=]], the smaller the degree to which the j-th change degree value participates in obtaining the skewness. Among them, The smaller [[ID=]], the fewer the types of noise in the temperature signal.

[0035] Step S4: When the absolute value of the actual skewness is greater than the preset skewness threshold, obtain the change degree value curve according to the magnitude of the change degree values and the number of the same change degree values; decompose the change degree value curve to obtain a preset number of decomposed curves; obtain the overall energy of each decomposed curve according to the temperature data and the external influence value between the two extreme points corresponding to each change degree value within each decomposed curve; obtain the Gaussian filtering parameter according to the overall energy of each decomposed curve and the variance of all corresponding temperature data; when the absolute value of the actual skewness is less than or equal to the preset skewness threshold, obtain the Gaussian filtering parameter according to the fluctuation of the temperature data on the temperature signal.

[0036] Specifically, it can be seen from step S3 that the actual skewness represents the superposition complexity of different types of noises in the temperature signal. The greater the actual skewness, the more types of noises in the temperature signal. To perform ideal denoising on the temperature data, in the embodiment of the present invention, the preset skewness threshold is set to 3. The implementer can set the preset skewness threshold according to the actual situation, which is not limited herein. When the absolute value of the actual skewness is greater than the preset skewness threshold, the temperature signal is subject to more electromagnetic interference noises. To avoid over-smoothening of the temperature signal or insufficient denoising intensity, in the embodiment of the present invention, Gaussian filter parameters are adaptively obtained based on the change degree value. Since the electromagnetic interference noise has Gaussianity in the temperature signal, the change degree value is essentially obtained by the superposition of different electromagnetic interference noises and also has Gaussianity when Gaussian noises are superimposed. Therefore, the change degree values are arranged from small to large on the horizontal axis, and the number of occurrences of each change degree value on the horizontal axis is used as the vertical axis to determine the points corresponding to each change degree value in the coordinate system, and curve fitting is performed. The obtained curve is used as the change degree value curve. Among them, curve fitting is a prior art and will not be elaborated herein. The change degree value curve is actually the superposition result of the same change intervals and different change intervals of different electromagnetic interference noises. Therefore, in the embodiment of the present invention, the preset number is set to 2. The implementer can set the size of the preset number according to the actual situation, which is not limited herein. That is, the change degree value curve is decomposed into 2 decomposed curves. The decomposed curve is the curve corresponding to the superposition of electromagnetic interference noises. Therefore, when the change degree value curve is decomposed by the independent component analysis algorithm in the embodiment of the present invention, the decomposition condition in the independent component analysis algorithm needs to be modified from non-Gaussianity to Gaussianity. Among them, the independent component analysis algorithm is a well-known technology and will not be elaborated herein. To adaptively obtain Gaussian filter parameters, and then based on the temperature data and external influence values between two extreme points corresponding to each change degree value in each decomposed curve, the overall energy of each decomposed curve is obtained. The greater the overall energy, the more dominant the noise corresponding to the decomposed curve is.

[0037] Preferably, the method for obtaining the overall energy is as follows: for any degree of change value, the time period formed by the times corresponding to the two extreme points corresponding to the degree of change value is used as the target time period; the energy of the temperature signal segment corresponding to the target time period is obtained as the initial energy of the temperature signal segment corresponding to the degree of change value; since the influence of external factors, i.e., unstable power supply, on the amplitude of the temperature signal is usually greater than the influence of electromagnetic interference noise on the temperature signal, therefore, in order to reduce the interference of external factors, the initial energy is corrected according to the correction weight of the degree of change value to obtain the actual energy of the temperature signal segment corresponding to the degree of change value; the greater the actual energy, the greater the proportion of the degree of change value in participating in obtaining the overall energy of the decomposition curve where it is located; the result of accumulating the actual energies of the temperature signal segments corresponding to all degrees of change values within each decomposition curve is used as the overall energy of each decomposition curve. Among them, the method for obtaining the energy of the signal is a well-known technology and will not be elaborated further.

[0038] As an example, taking the k-th degree of change value as an example, the k-th degree of change value is obtained from the absolute value of the difference between the amplitudes of two adjacent extreme points. The two extreme points corresponding to the k-th degree of change value are respectively taken as the k-th extreme point and the (k + 1)-th extreme point, and the time period formed by the times corresponding to the k-th extreme point and the (k + 1)-th extreme point is the target time period corresponding to the k-th degree of change value. The energy within the temperature signal segment corresponding to the target time period is obtained to determine the proportion of the temperature data within the target time period in the overall temperature signal. The calculation formula for obtaining the actual energy of the temperature signal segment corresponding to the k-th degree of change value is: In the formula, is the actual energy of the temperature signal segment corresponding to the k-th degree of change value; is the correction weight of the k-th degree of change value; is the initial time of the temperature signal segment corresponding to the k-th degree of change value; is the final time of the temperature signal segment corresponding to the k-th degree of change value; is the temperature data at the t-th time within the temperature signal segment corresponding to the k-th degree of change value; is the absolute value symbol.

[0039] It should be noted that is the integral summation of the temperature data within the target time period corresponding to the k-th degree of change value, that is, the initial energy of the temperature signal segment corresponding to the k-th degree of change value. By correcting the interference of external factors is reduced, making more accurate. Among them, The larger it is, the greater the proportion of electromagnetic interference noise in the temperature signal segment corresponding to the k-th degree of change value during the target time period.

[0040] According to the method of obtaining the actual energy of the temperature signal segment corresponding to the k-th degree of change value, obtain the actual energy of the temperature signal segment corresponding to each degree of change value. The result of accumulating the actual energies of the temperature signal segments corresponding to all degrees of change values within each decomposition curve is used as the overall energy of each decomposition curve.

[0041] For temperature signals with less electromagnetic interference noise, relatively ideal denoising effects can be obtained by adjusting the Gaussian filtering parameters in the Gaussian filtering algorithm. Therefore, in the embodiments of the present invention, the variance of all temperature data corresponding to each decomposition curve is obtained as the main part of the Gaussian filtering parameters, and the variance of the temperature data corresponding to each decomposition curve is adjusted according to the proportion of the overall energy of each decomposition curve to adaptively obtain the Gaussian filtering parameters.

[0042] Preferably, the method for obtaining the Gaussian filtering parameters is as follows: The result of accumulating the overall energies of all decomposition curves is used as the first result; the ratio of the overall energy of each decomposition curve to the first result is used as the first weight of the corresponding decomposition curve; the product of the first weight of each decomposition curve and the variance of all temperature data corresponding thereto is used as the participation variance of the corresponding decomposition curve; the result of accumulating the participation variances of all decomposition curves is used as the Gaussian filtering parameters.

[0043] As an example, the calculation formula for obtaining the Gaussian filtering parameters is: In the formula, is the Gaussian filtering parameter; is the overall energy of the first decomposition curve; is the overall energy of the second decomposition curve; is the variance of all temperature data corresponding to the first decomposition curve; is the variance of all temperature data corresponding to the second decomposition curve; is the first result; is the first weight of the first decomposition curve; is the first weight of the second decomposition curve; is the participation variance of the first decomposition curve; is the participation variance of the second decomposition curve.

[0044] It should be noted that the larger it is, the more likely the noise in the first decomposition curve is the main noise in the temperature signal and the more likely it is the main noise in the temperature signal, the greater the contribution rate to obtaining the Gaussian filtering parameters; The smaller it is, the more likely the noise in the second decomposition curve is the secondary noise in the temperature signal, and the smaller its contribution rate to obtaining the Gaussian filtering parameters.

[0045] When the absolute value of the actual skewness is less than or equal to the preset skewness threshold, it indicates that the electromagnetic interference noise superimposed on the temperature signal is relatively small, that is, the complexity of the electromagnetic interference noise is low. Corresponding to the gentle change of the temperature data in the pediatric department, therefore, the variance of all temperature data on the temperature signal is obtained as the Gaussian filtering parameter.

[0046] Step S5: Filter the temperature signal according to the Gaussian filtering parameter to obtain the denoised temperature data, and monitor the environmental temperature data of the pediatric department.

[0047] Among them, the Gaussian filtering parameter is the variance in the Gaussian filtering algorithm. Furthermore, according to the Gaussian filtering parameter, the temperature signal is filtered by the Gaussian filtering algorithm to obtain the denoised temperature data. When the denoised temperature data is within the set temperature range within the preset time period, it indicates that the current temperature data setting in the pediatric department is reasonable; when the denoised temperature data is not within the set temperature range within the preset time period, it indicates that the current temperature data setting in the pediatric department is unreasonable, and the temperature adjustment device will issue an alarm for reminder. The staff needs to adjust the temperature data in the pediatric department in time to ensure that the temperature in the pediatric department is within the set temperature range and ensure the health and safety of infants and young children. Among them, the set temperature range needs to be combined with the actual date, time and temperature requirements. For example, during the noon in summer, the temperature range in the pediatric department environment should be set between 22° and 26° to ensure that the temperature in the pediatric department will not be too hot and avoid excessive sweating of infants and young children; during the night in summer, the temperature range in the pediatric department environment should be set between 18° and 22° to ensure that the temperature in the pediatric department will not be too cold and avoid infants and young children catching cold.

[0048] So far, the present invention is completed.

[0049] In summary, the embodiment of the present invention obtains the temperature signal; according to the change of the temperature signal, obtains the external influence value of the extreme point; takes the difference between the extreme point and the next adjacent extreme point as the change degree value, and obtains the actual skewness according to the distribution of the change degree value and the external influence value; according to the size of the actual skewness, determines the method for adaptively obtaining the Gaussian filtering parameter, obtains the Gaussian filtering parameter to filter the temperature signal, obtains the denoised temperature data, and monitors the environmental temperature data of the pediatric department. The present invention accurately denoises the temperature signal through adaptively obtaining the Gaussian filtering parameter, and accurately monitors the environmental temperature data of the pediatric department.

[0050] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. An intelligent environmental temperature monitoring method for the pediatric department, characterized in that, The method includes the following steps: Obtain temperature data at each moment within a preset time period and convert it into a temperature signal; Construct a time series interval for each extreme point on the temperature signal, and obtain the external influence value of each extreme point according to the magnitude and change of each temperature data within each time series interval, as well as the number of temperature data; Take the amplitude difference between each extreme point on the temperature signal and the next adjacent extreme point as the change degree value, and obtain the actual skewness of the change degree value distribution according to the distribution of each change degree value and the external influence values of the corresponding two extreme points; When the absolute value of the actual skewness is greater than the preset skewness threshold, obtain the change degree value curve according to the magnitude of the change degree value and the number of the same change degree values; decompose the change degree value curve to obtain a preset number of decomposed curves; according to the temperature data and external influence values between the two extreme points corresponding to each change degree value within each decomposed curve, obtain the overall energy of each decomposed curve; according to the overall energy of each decomposed curve and the variance of all corresponding temperature data, obtain the Gaussian filtering parameter; when the absolute value of the actual skewness is less than or equal to the preset skewness threshold, obtain the Gaussian filtering parameter according to the fluctuation of the temperature data on the temperature signal; Filter the temperature signal according to the Gaussian filtering parameter to obtain the denoised temperature data, and monitor the environmental temperature data of the pediatric department.

2. The intelligent environmental temperature monitoring method for the pediatric department according to claim 1, wherein The method for constructing the time series interval for each extreme point on the temperature signal is as follows: Take the time period formed by all moments between the (i - 1)-th extreme point and the (i + 1)-th extreme point on the temperature signal as the time series interval of the i-th extreme point; wherein, the time series interval of the i-th extreme point does not include the moments corresponding to the (i - 1)-th extreme point and the (i + 1)-th extreme point.

3. The intelligent environmental temperature monitoring method for the pediatric department according to claim 1, wherein The calculation formula for the external influence value is: Wherein, is the external influence value of the i-th extreme point; is the number of temperature data within the time series interval of the i-th extreme point; is the derivative of the n-th temperature data within the time series interval of the i-th extreme point; is the n-th temperature data within the time series interval of the i-th extreme point; is the mean value of all temperature data within the time series interval of the i-th extreme point; is the absolute value function; tanh is the hyperbolic tangent function; exp is the exponential function with the natural constant e as the base; norm is the normalization function.

4. The intelligent environmental temperature monitoring method for the pediatric department according to claim 1, wherein The method for obtaining the actual skewness of the change degree value distribution according to the distribution of each change degree value and the external influence values of the corresponding two extreme points is as follows: Obtain the ratio of the number of occurrences of each change degree value to the total number of change degree values as the distribution probability of each change degree value; Obtain the skewness of the distribution probability as the skewness of the change degree value distribution; Take the mean value of the external influence values of the two extreme points corresponding to each change degree value as the correction weight corresponding to the change degree value; Correct the skewness of the change degree value distribution according to the correction weight to obtain the actual skewness of the change degree value distribution.

5. The intelligent environmental temperature monitoring method for the pediatric department according to claim 4, wherein The calculation formula for the actual skewness of the change degree value distribution is: Wherein, is the actual skewness of the distribution of the change degree value; J is the total number of change degree values; is the corrected weight of the j-th change degree value; is the distribution probability of the j-th change degree value; is the mean value of the distribution probabilities of all change degree values; is the standard deviation of the distribution probabilities of all change degree values.

6. The intelligent environmental temperature monitoring method for the pediatric department according to claim 1, wherein, The method for obtaining the change degree value curve is as follows: Arrange the change degree values from small to large on the horizontal axis, take the number of occurrences of each change degree value on the horizontal axis as the vertical axis, determine the points corresponding to each change degree value in the coordinate system, perform curve fitting, and take the obtained curve as the change degree value curve.

7. The intelligent environmental temperature monitoring method for the pediatric department according to claim 1, characterized in that, The method for decomposing the change degree value curve to obtain a preset number of decomposed curves is as follows: Modify the decomposition condition in the independent component analysis algorithm to Gaussianity, and decompose the change degree value curve through the independent component analysis algorithm with the modified decomposition condition to obtain a preset number of decomposed curves.

8. The intelligent environmental temperature monitoring method for the pediatric department according to claim 4, characterized in that, The method for obtaining the overall energy of each decomposition curve based on the temperature data and external influence values between two extreme points corresponding to each degree-of-change value within each decomposition curve is as follows: For any degree-of-change value, the time period formed by the times corresponding to the two extreme points corresponding to this degree-of-change value is used as the target time period; The energy of the temperature signal segment corresponding to the target time period is obtained as the initial energy of the temperature signal segment corresponding to this degree-of-change value; The initial energy is corrected according to the correction weight of this degree-of-change value to obtain the actual energy of the temperature signal segment corresponding to this degree-of-change value; The result of accumulating the actual energies of the temperature signal segments corresponding to all degree-of-change values within each decomposition curve is used as the overall energy of each decomposition curve.

9. The intelligent environmental temperature monitoring method for the pediatric department according to claim 8, characterized in that, The calculation formula for the actual energy is: Wherein, is the actual energy of the temperature signal segment corresponding to the k-th degree of change value; is the correction weight of the k-th degree of change value; is the initial moment of the temperature signal segment corresponding to the k-th degree of change value; is the final moment of the temperature signal segment corresponding to the k-th degree of change value; is the temperature data at the t-th moment within the temperature signal segment corresponding to the k-th degree of change value; is the absolute value symbol.

10. The intelligent environmental temperature monitoring method for the pediatric department according to claim 1, wherein, The method for obtaining the Gaussian filtering parameter based on the overall energy of each decomposition curve and the variance of all corresponding temperature data is as follows: The result of accumulating the overall energies of all decomposition curves is used as the first result; The ratio of the overall energy of each decomposition curve to the first result is obtained as the first weight corresponding to the decomposition curve; The product of the first weight of each decomposition curve and the variance of all corresponding temperature data is used as the participation variance corresponding to the decomposition curve; The result of accumulating the participation variances of all decomposition curves is used as the Gaussian filtering parameter.

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