Newborn nursing intelligent call reminding system

Abstract

The invention relates to the technical field of neonatal care, in particular to an intelligent call reminding system for neonatal care. The method comprises the steps that a data acquisition unit acquires physiological data and environmental data of a newborn in real time; the data monitoring unit monitors potential abnormal health conditions in real time by using a chaotic feature detection algorithm based on the physiological data; the emotion judgment unit dynamically optimizes an emotion judgment model based on environmental data in combination with a Boltzmann machine algorithm to obtain an emotion state demand; the data transmission management unit transmits all data to the intelligent early warning notification unit in real time and stores physiological data and environmental data; the intelligent early warning notification unit carries out real-time early warning through a visual screen and a family member terminal according to the health abnormal condition and emotional state requirements. According to the intelligent call reminding system for newborn nursing, dual intelligent early warning on physiological monitoring and emotion judgment is realized through health abnormality early warning of chaotic feature detection and intelligent emotion judgment of a Boltzmann machine algorithm.

Description

Technical Field

[0001] The invention relates to the technical field of neonatal care, in particular to an intelligent call reminder system for neonatal care. Background Art

[0002] An intelligent call reminder system for neonatal care is designed to timely identify abnormal health conditions of neonates and accurately judge the emotional needs of neonates. Through the combination of chaotic feature detection and Boltzmann machine algorithm, it controls the warning level and notification method, and realizes accurate monitoring of early health abnormalities and personalized care reminders.

[0003] Existing neonatal care call reminder systems usually have difficulty identifying complex physiological data fluctuations and nonlinear emotional states. In addition, since the emotions of neonates are closely related to their physical health, and urine humidity can also affect the subtle emotions of neonates, it is impossible to fully and accurately judge the emotions of neonates, and there are frequent false alarms in minor abnormal situations. Therefore, an intelligent neonatal care call reminder system is designed. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent call reminder system for neonatal care to solve the problem raised in the above background technology that the emotions of the newborn are highly correlated with the physical health, and the urine humidity will also affect the micro-emotions of the newborn, which will lead to the inability to accurately judge the emotions of the newborn under comprehensive conditions and frequent false alarms in minor abnormal situations.

[0005] To achieve the above object, the present invention provides a newborn care intelligent call reminder system, comprising: A data acquisition unit, wherein the data acquisition unit acquires physiological data and environmental data of the newborn in real time through sensors; It also includes a data monitoring unit, which monitors and analyzes the health status of the newborn in real time based on the physiological data, and uses a chaotic feature detection algorithm to monitor potential abnormal health conditions in real time, and transmits the abnormal health conditions to the intelligent early warning notification unit through the data transmission management unit for early warning; It also includes an emotion judgment unit, which dynamically optimizes the emotion judgment model based on environmental data combined with the Boltzmann machine algorithm to obtain the emotion state requirements, and transmits the emotion state requirements to the intelligent early warning notification unit through the data transmission management unit for early warning; It also includes a data transmission management unit, which transmits all monitoring data to the intelligent early warning notification unit in real time and uses a database to store the physiological data and environmental data of the newborn; It also includes an intelligent early warning notification unit, which provides real-time early warnings through a visual screen and a family member's terminal according to abnormal health conditions and emotional state requirements.

[0006] As a further improvement of the technical solution, the data acquisition unit includes a data acquisition module and a data transmission module; The data acquisition module is used to collect physiological data and environmental data of the newborn using sensors; the physiological data include body temperature, heart rate and blood oxygen saturation; the environmental data include room temperature, room humidity, crying and urine humidity; The data transmission module is used to transmit the collected physiological data and environmental data of the newborn to the data monitoring unit and the emotion judgment unit through wireless communication.

[0007] As a further improvement of the technical solution, the data monitoring unit includes a health analysis module and a result transmission module; The health analysis module preliminarily determines the health status of the newborn based on the physiological data, and regularly uses the chaotic feature detection algorithm to calculate the Lyapunov exponent of the newborn's physiological data to monitor potential health abnormalities in real time; The result transmission module is used to transmit the initially determined health status of the newborn, the Lyapunov index of physiological data, and potential abnormal health conditions to the intelligent early warning notification unit in real time through the data transmission management unit for early warning.

[0008] As a further improvement of the technical solution, the health analysis module preliminarily determines the health status of the newborn based on the physiological data, and regularly uses the chaotic feature detection algorithm to calculate the Lyapunov exponent of the newborn's physiological data to monitor potential health abnormalities in real time. The specific method is as follows: S2.1. Obtain physiological data of newborns, including: body temperature , heart rate and blood oxygen saturation ; S2.2. Initial monitoring of the health status of newborns: like , abnormal body temperature; like , abnormal heart rate; like , abnormal blood oxygen; S2.3. Based on body temperature, heart rate and blood oxygen saturation data, the chaotic feature detection algorithm is used to calculate the comprehensive Lyapunov exponent of the newborn's physiological data and generate newborn health warning information.

[0009] As a further improvement of the present technical solution, in S2.3, based on the body temperature, heart rate and blood oxygen saturation data, a chaotic feature detection algorithm is used to calculate the comprehensive Lyapunov exponent of the neonatal physiological data to generate neonatal health warning information. The specific method steps are as follows: S2.3.1. Body temperature , heart rate and blood oxygen saturation The data were normalized. Constructing the normalized body temperature , heart rate and blood oxygen saturation The time series are: body temperature time series , heart rate time series And blood oxygen saturation time series ; S2.3.2. Construction of comprehensive time series : ; in, is a comprehensive time series; is the weight of the body temperature time series; is the weight of the heart rate time series; is the weight of the blood oxygen saturation time series; and ; For time; S2.3.3. Based on comprehensive time series , calculate the synthetic time series perturbation : ; in, is the comprehensive time series perturbation; for Composite time series of time; S2.3.4. Calculate the comprehensive Lyapunov exponent using comprehensive time series perturbations : ; in, is the comprehensive Lyapunov exponent; is the serial number; For time The comprehensive time series perturbation of ; For time The comprehensive time series perturbation of ; S2.3.5 If , and generate newborn health warning information.

[0010] As a further improvement of the present technical solution, the emotion judgment unit includes an emotion analysis module, an emotion optimization decision module and an emotion result transmission module; The emotion analysis module is used to analyze the room temperature, humidity, crying and urine humidity to preliminarily determine the emotional state of the newborn; The emotion optimization decision module uses the Boltzmann machine algorithm, takes crying and urine humidity data as input parameters, and takes room temperature and room humidity as auxiliary conditions to construct an energy function, calculate the probability distribution of emotional states, and dynamically generate emotional state requirements; The emotional result transmission module is used to transmit the emotional state of the newborn and the optimized emotional state requirements to the intelligent early warning notification unit through the data transmission management unit for early warning.

[0011] As a further improvement of the technical solution, the emotion analysis module is used to comprehensively analyze the room temperature, humidity, crying and urine humidity to preliminarily judge the emotional state of the newborn. The specific method steps are as follows: S3.1.1. Obtain environmental data, including: room temperature , Room humidity , crying and urine humidity ; S3.1.2. Preliminary assessment of the newborn's emotional state: like , temperature discomfort; like , humidity is not suitable; like , mild demand; like , moderate demand; like , high demand, needing quiet or a diaper change; S3.1.3. Transmit the emotional state of the newborn to the emotional result transmission module.

[0012] As a further improvement of this technical solution, the emotion optimization decision module uses the Boltzmann machine algorithm to convert crying sounds into and urine humidity The room temperature is taken as input parameter. and room humidity As auxiliary condition to construct energy function , calculate the probability distribution of emotional states, and dynamically generate emotional state requirements. The specific steps are as follows: S3.2.1. Crying and urine humidity The room temperature is taken as input parameter. and room humidity As auxiliary condition to construct energy function : ; ; in, , and is a constant; is the nonlinear influence term of crying intensity; It is the influence item of urine humidity; For room temperature and room humidity The influence function of S3.2.2. According to the energy function Compute the probability distribution of emotional states: ; in, is the Boltzmann temperature parameter; For the Kinds of emotional state energy function; The relative probability of judging the current emotional state as correct; S3.2.3. Select the emotional state with the maximum relative probability as the current optimal emotional demand.

[0013] As a further improvement of the technical solution, the data transmission management unit includes a data storage module and an information transmission module; The data storage module uses a database to store the physiological data, environmental data, abnormal health conditions and emotional state requirements of the newborn; The information transmission module is used to transmit physiological data, environmental data, abnormal health conditions and emotional state requirements to the intelligent early warning notification unit through wireless transmission technology.

[0014] As a further improvement of the technical solution, the intelligent warning notification unit includes a warning classification module and a notification visualization module; The warning classification module receives abnormal health conditions and emotional state requirements from the data transmission management unit, and automatically determines the warning level according to the urgency of the abnormal health conditions and emotional state requirements, and triggers corresponding reminder methods by classification. The specific method is as follows: S5.1, low-level warning: triggered when the data monitoring unit does not detect anomalies and the emotion judgment unit detects mild demand; S5.2, Intermediate warning: triggered when the data monitoring unit detects neonatal health warning information and the emotion judgment unit detects moderate demand; S5.3, advanced warning: triggered when the data monitoring unit detects newborn health warning information and the emotion judgment unit detects high demand; The notification visualization module is used to display physiological data, environmental data, abnormal health conditions and emotional state requirements in real time on the monitoring screen and family terminal.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the newborn care intelligent call reminder system, the comprehensive Lyapunov exponent analysis based on the chaotic feature detection algorithm can monitor the nonlinear fluctuations of the newborn's body temperature, heart rate and blood oxygen saturation in real time, identify potential health abnormalities, and issue health warnings in advance.

[0016] 2. In the newborn care intelligent call reminder system, the emotion judgment model optimized by the Boltzmann machine algorithm can realize accurate judgment of emotional needs based on crying and urine wetness, and dynamically generate the optimal emotional needs judgment by combining room temperature and humidity as auxiliary conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; The meaning of each number in the figure is: 1. Data collection unit; 2. Data monitoring unit; 3. Emotion judgment unit; 4. Data transmission management unit; 5. Intelligent early warning notification unit. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, a newborn care intelligent call reminder system is provided, comprising: A data acquisition unit 1, wherein the data acquisition unit 1 collects physiological data and environmental data of the newborn in real time through sensors; In this embodiment, the data acquisition unit 1 includes a data acquisition module and a data transmission module; The data acquisition module is used to collect physiological data and environmental data of the newborn using sensors; the physiological data include body temperature, heart rate and blood oxygen saturation; the environmental data include room temperature, room humidity, crying and urine humidity; The data transmission module is used to transmit the collected physiological data and environmental data of the newborn to the data monitoring unit 2 and the emotion judgment unit 3 through wireless communication.

[0020] It also includes a data monitoring unit 2, which monitors and analyzes the health status of the newborn in real time based on physiological data, and uses a chaotic feature detection algorithm to monitor potential abnormal health conditions in real time, and transmits the abnormal health conditions to the intelligent early warning notification unit 5 through the data transmission management unit 4 for early warning; In this embodiment, the data monitoring unit 2 includes a health analysis module and a result transmission module; The health analysis module preliminarily determines the health status of the newborn based on the physiological data, and regularly uses the chaotic feature detection algorithm to calculate the Lyapunov exponent of the newborn's physiological data to monitor potential health abnormalities in real time; The result transmission module is used to transmit the initially determined health status of the newborn, the Lyapunov index of physiological data, and potential health abnormalities to the intelligent early warning notification unit 5 in real time through the data transmission management unit 4 for early warning.

[0021] The health analysis module preliminarily determines the health status of the newborn based on the physiological data, and regularly uses the chaotic feature detection algorithm to calculate the Lyapunov exponent of the newborn's physiological data to monitor potential health abnormalities in real time. The specific method is as follows: S2.1. Obtain physiological data of newborns, including: body temperature , heart rate and blood oxygen saturation ; S2.2. Initial monitoring of the health status of newborns: like , abnormal body temperature; like , abnormal heart rate; like , abnormal blood oxygen; S2.3. Based on body temperature, heart rate and blood oxygen saturation data, the chaotic feature detection algorithm is used to calculate the comprehensive Lyapunov exponent of the newborn's physiological data and generate newborn health warning information.

[0022] In this embodiment, chaos feature detection is used to analyze the complex fluctuation patterns in body temperature, heart rate and blood oxygen saturation data to identify health risks; chaos feature detection algorithms are used to analyze the complexity and nonlinear fluctuations of newborn physiological data. By calculating the Lyapunov exponent chaos feature, it is found that seemingly random physiological data may indicate abnormalities. When the data is detected to enter a chaotic state, the system will issue an early warning to avoid the situation from getting worse.

[0023] The Lyapunov index is used to analyze the sensitivity and nonlinear characteristics of time series data. This index can be used to determine whether the physiological data of newborns is in a chaotic state rather than a healthy and orderly state; In S2.3, based on the body temperature, heart rate and blood oxygen saturation data, a chaotic feature detection algorithm is used to calculate the comprehensive Lyapunov exponent of the neonatal physiological data to generate neonatal health warning information. The specific method steps are as follows: S2.3.1. Body temperature , heart rate and blood oxygen saturation The data were normalized. Constructing the normalized body temperature , heart rate and blood oxygen saturation The time series are: body temperature time series , heart rate time series And blood oxygen saturation time series ; S2.3.2. Construction of comprehensive time series : ; in, is a comprehensive time series; is the weight of the body temperature time series; is the weight of the heart rate time series; is the weight of the blood oxygen saturation time series; and ; For time; S2.3.3. Based on comprehensive time series , calculate the synthetic time series perturbation : ; in, is the comprehensive time series perturbation; for Composite time series of time; S2.3.4. Calculate the comprehensive Lyapunov exponent using comprehensive time series perturbations : ; in, is the comprehensive Lyapunov exponent; is the serial number; For time The comprehensive time series perturbation of ; For time The comprehensive time series perturbation of ; S2.3.5 If , and generate newborn health warning information.

[0024] In this embodiment, the weight of the body temperature time series , the weight of the heart rate time series and the weight of the blood oxygen saturation time series , based on medical research or clinical experience, the weights in this embodiment are set as: ; ; ; Body temperature, heart rate and blood oxygen saturation are key indicators of neonatal health. Each data reflects a specific physiological state, but their changing trends are usually related. Calculating the Lyapunov index of each physiological parameter separately may cause false alarms due to short-term fluctuations in individual data. The combined Lyapunov index can be used as a measure of comprehensive volatility to capture the dynamic changes in the overall health status of the newborn.

[0025] It also includes an emotion judgment unit 3, which dynamically optimizes the emotion judgment model based on environmental data combined with the Boltzmann machine algorithm to obtain the emotion state requirements, and transmits the emotion state requirements to the intelligent warning notification unit 5 through the data transmission management unit 4 for warning; In this embodiment, the Boltzmann machine is an energy model, and the introduction of this model can improve the processing ability of multivariate complex data of emotional changes; for example, when urine wetness and crying appear synchronously, the Boltzmann machine can automatically determine the optimal alarm strategy to ensure the best nursing response; In this embodiment, the emotion judgment unit 3 includes an emotion analysis module, an emotion optimization decision module and an emotion result transmission module; The emotion analysis module is used to analyze the room temperature, humidity, crying and urine humidity to preliminarily determine the emotional state of the newborn; The emotion optimization decision module uses the Boltzmann machine algorithm, takes crying and urine humidity data as input parameters, and takes room temperature and room humidity as auxiliary conditions to construct an energy function, calculate the probability distribution of emotional states, and dynamically generate emotional state requirements; The emotional result transmission module is used to transmit the emotional state of the newborn and the optimized emotional state requirements to the intelligent early warning notification unit 5 through the data transmission management unit 4 for early warning.

[0026] The emotion analysis module is used to comprehensively analyze the room temperature, humidity, crying and urine humidity to preliminarily judge the emotional state of the newborn. The specific method steps are as follows: S3.1.1. Obtain environmental data, including: room temperature , Room humidity , crying and urine humidity ; S3.1.2. Preliminary assessment of the newborn's emotional state: like , temperature discomfort; like , humidity is not suitable; like , mild demand; like , moderate demand; like , high demand, needing quiet or a diaper change; S3.1.3. Transmit the emotional state of the newborn to the emotional result transmission module.

[0027] The emotion optimization decision module uses the Boltzmann machine algorithm to convert crying sounds into and urine humidity The room temperature is taken as input parameter. and room humidity As auxiliary conditions to construct energy function , calculate the probability distribution of emotional states, and dynamically generate emotional state requirements. The specific steps are as follows: S3.2.1. Crying and urine humidity The room temperature is taken as input parameter. and room humidity As auxiliary conditions to construct energy function : ; ; in, , and is a constant, , , ; is the nonlinear influence term of crying intensity; It is the influence item of urine humidity; For room temperature and room humidity The influence function of S3.2.2. According to the energy function Compute the probability distribution of emotional states: ; in, is the Boltzmann temperature parameter; For the Kind of emotional state energy function; The relative probability of judging the current emotional state as correct; S3.2.3. Select the emotional state with the maximum relative probability as the current optimal emotional demand.

[0028] It also includes a data transmission management unit 4, which transmits all monitoring data to the intelligent early warning notification unit 5 in real time, and uses a database to store the physiological data and environmental data of the newborn; In this embodiment, the data transmission management unit 4 includes a data storage module and an information transmission module; The data storage module uses a database to store the physiological data, environmental data, abnormal health conditions and emotional state requirements of the newborn; The information transmission module is used to transmit physiological data, environmental data, abnormal health conditions and emotional state requirements to the intelligent early warning notification unit 5 through wireless transmission technology.

[0029] It also includes an intelligent early warning notification unit 5, which provides real-time early warning through a visual screen and a family member terminal according to abnormal health conditions and emotional state requirements; In this embodiment, the intelligent warning notification unit 5 includes a warning classification module and a notification visualization module; The warning classification module receives abnormal health conditions and emotional state requirements from the data transmission management unit 4, and automatically determines the warning level according to the urgency of the abnormal health conditions and emotional state requirements, and triggers the corresponding reminder method by classification. The specific method is as follows: S5.1, low-level warning: triggered when the data monitoring unit 2 detects no abnormality and the emotion judgment unit 3 detects a mild demand; S5.2, intermediate warning: triggered when the data monitoring unit 2 detects the neonatal health warning information and the emotion judgment unit 3 detects the moderate demand; S5.3, high-level warning: triggered when the data monitoring unit 2 detects the health warning information of the newborn and the emotion judgment unit 3 detects the high demand; The notification visualization module is used to display physiological data, environmental data, abnormal health conditions and emotional state requirements in real time on the monitoring screen and family terminal.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A newborn care intelligent call reminder system, characterized in that: include: A data acquisition unit (1), wherein the data acquisition unit (1) acquires physiological data and environmental data of the newborn in real time through sensors; A data monitoring unit (2), wherein the data monitoring unit (2) monitors and analyzes the health status of the newborn in real time based on physiological data, and uses a chaotic feature detection algorithm to monitor potential abnormal health conditions in real time, and transmits the abnormal health conditions to the intelligent early warning notification unit (5) through the data transmission management unit (4) for early warning; An emotion judgment unit (3), wherein the emotion judgment unit (3) dynamically optimizes the emotion judgment model based on environmental data combined with the Boltzmann machine algorithm to obtain an emotion state requirement, and transmits the emotion state requirement to the intelligent early warning notification unit (5) through the data transmission management unit (4) for early warning; A data transmission management unit (4), wherein the data transmission management unit (4) transmits all monitoring data to the intelligent early warning notification unit (5) in real time, and uses a database to store physiological data and environmental data of the newborn; An intelligent early warning notification unit (5), wherein the intelligent early warning notification unit (5) issues a real-time early warning via a visual screen and a family member terminal according to abnormal health conditions and emotional state requirements.

2. The newborn care intelligent call reminder system according to claim 1, characterized in that: The data acquisition unit (1) comprises a data acquisition module and a data transmission module; The data acquisition module is used to collect physiological data and environmental data of the newborn using sensors; the physiological data include body temperature, heart rate and blood oxygen saturation; the environmental data include room temperature, room humidity, crying and urine humidity; The data transmission module is used to transmit the collected physiological data and environmental data of the newborn to the data monitoring unit (2) and the emotion judgment unit (3) through wireless communication.

3. The newborn care intelligent call reminder system according to claim 1, characterized in that: The data monitoring unit (2) comprises a health analysis module and a result transmission module; The health analysis module preliminarily determines the health status of the newborn based on the physiological data, and regularly uses the chaotic feature detection algorithm to calculate the Lyapunov exponent of the newborn's physiological data to monitor potential health abnormalities in real time; The result transmission module is used to transmit the initially determined health status of the newborn, the Lyapunov index of physiological data, and potential health abnormalities in real time to the intelligent early warning notification unit (5) via the data transmission management unit (4) for early warning.

4. The newborn care intelligent call reminder system according to claim 3, characterized in that: The health analysis module preliminarily determines the health status of the newborn based on the physiological data, and regularly uses the chaotic feature detection algorithm to calculate the Lyapunov exponent of the newborn's physiological data to monitor potential health abnormalities in real time. The specific method is as follows: S2.

1. Obtain physiological data of newborns, including: body temperature , heart rate and blood oxygen saturation ; S2.

2. Initial monitoring of the health status of newborns: like , abnormal body temperature; like , abnormal heart rate; like , abnormal blood oxygen; S2.

3. Based on body temperature, heart rate and blood oxygen saturation data, the chaotic feature detection algorithm is used to calculate the comprehensive Lyapunov exponent of the newborn's physiological data and generate newborn health warning information.

5. The newborn care intelligent call reminder system according to claim 4, characterized in that: In S2.3, based on the body temperature, heart rate and blood oxygen saturation data, a chaotic feature detection algorithm is used to calculate the comprehensive Lyapunov exponent of the neonatal physiological data to generate neonatal health warning information. The specific method steps are as follows: S2.3.

1. Body temperature , heart rate and blood oxygen saturation The data were normalized. Constructing the normalized body temperature , heart rate and blood oxygen saturation The time series are: body temperature time series , heart rate time series And blood oxygen saturation time series ; S2.3.

2. Construction of comprehensive time series : ； in, is a comprehensive time series; is the weight of the body temperature time series; is the weight of the heart rate time series; is the weight of the blood oxygen saturation time series; and ; For time; S2.3.

3. Based on comprehensive time series , calculate the synthetic time series perturbation : ； in, is the comprehensive time series perturbation; for Composite time series of time; S2.3.

4. Calculate the comprehensive Lyapunov exponent using comprehensive time series perturbations : ； in, is the comprehensive Lyapunov exponent; is the serial number; For time The comprehensive time series perturbation of ; For time The comprehensive time series perturbation of ; S2.3.5 If , and generate newborn health warning information.

6. The newborn care intelligent call reminder system according to claim 1, characterized in that: The emotion judgment unit (3) includes an emotion analysis module, an emotion optimization decision module and an emotion result transmission module; The emotion analysis module is used to analyze the room temperature, humidity, crying and urine humidity to preliminarily determine the emotional state of the newborn; The emotion optimization decision module uses the Boltzmann machine algorithm, takes crying and urine humidity data as input parameters, and takes room temperature and room humidity as auxiliary conditions to construct an energy function, calculate the probability distribution of emotional states, and dynamically generate emotional state requirements; The emotional result transmission module is used to transmit the emotional state of the newborn and the optimized emotional state requirements to the intelligent early warning notification unit (5) through the data transmission management unit (4) for early warning.

7. The newborn care intelligent call reminder system according to claim 6, characterized in that: The emotion analysis module is used to comprehensively analyze the room temperature, humidity, crying and urine humidity to preliminarily judge the emotional state of the newborn. The specific method steps are as follows: S3.1.

1. Obtain environmental data, including: room temperature , Room humidity , crying and urine humidity ; S3.1.

2. Preliminary assessment of the newborn's emotional state: like , temperature discomfort; like , humidity is not suitable; like , mild demand; like , moderate demand; like , high demand, needing quiet or a diaper change; S3.1.

3. Transmit the emotional state of the newborn to the emotional result transmission module.

8. The newborn care intelligent call reminder system according to claim 7, characterized in that: The emotion optimization decision module uses the Boltzmann machine algorithm to convert crying sounds into and urine humidity The room temperature is taken as input parameter. and room humidity As auxiliary condition to construct energy function , calculate the probability distribution of emotional states, and dynamically generate emotional state requirements. The specific steps are as follows: S3.2.

1. Crying and urine humidity The room temperature is taken as input parameter. and room humidity As auxiliary condition to construct energy function : ； ； in, , and is a constant; is the nonlinear influence term of crying intensity; It is the influence item of urine humidity; For room temperature and room humidity The influence function of S3.2.

2. According to the energy function Compute the probability distribution of emotional states: ； in, is the Boltzmann temperature parameter; For the Kinds of emotional state energy function; The relative probability of judging the current emotional state as correct; S3.2.

3. Select the emotional state with the maximum relative probability as the current optimal emotional demand.

9. The newborn care intelligent call reminder system according to claim 1, characterized in that: The data transmission management unit (4) comprises a data storage module and an information transmission module; The data storage module uses a database to store the physiological data, environmental data, abnormal health conditions and emotional state requirements of the newborn; The information transmission module is used to transmit physiological data, environmental data, abnormal health conditions and emotional state requirements to the intelligent early warning notification unit (5) through wireless transmission technology.

10. The newborn care intelligent call reminder system according to claim 1, characterized in that: The intelligent warning notification unit (5) comprises a warning classification module and a notification visualization module; The warning classification module receives abnormal health conditions and emotional state requirements from the data transmission management unit (4), and automatically determines the warning level according to the urgency of the abnormal health conditions and emotional state requirements, and triggers corresponding reminder methods by classification. The specific method is as follows: S5.1, low-level warning: triggered when the data monitoring unit (2) detects no abnormality and the emotion judgment unit (3) detects a mild demand; S5.2, intermediate warning: triggered when the data monitoring unit (2) detects the neonatal health warning information and the emotion judgment unit (3) detects the moderate demand; S5.3, high-level warning: triggered when the data monitoring unit (2) detects newborn health warning information and the emotion judgment unit (3) detects high demand; The notification visualization module is used to display physiological data, environmental data, abnormal health conditions and emotional state requirements in real time on the monitoring screen and family terminal.

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