An intelligent children's ward based on child-friendliness

Abstract

The present invention discloses an intelligent children's ward based on child-friendliness. Step S100: Obtain ward data capable of performing health analysis on the ward environment, where the ward data includes primary ward data and secondary ward data; Step S200: Analyze and process the primary impact data and secondary impact data to generate primary evaluation information and secondary evaluation information; Step S300: Perform integrated analysis based on the generated primary evaluation information and secondary evaluation information, and output environmental health evaluation data; Step S400: Obtain children's sleep parameters capable of evaluating the sleep quality of children; Step S500: Process the children's sleep parameters and analyze and output children's sleep quality evaluation data; Step S600: Perform integrated analysis based on the environmental health evaluation data and the children's sleep quality evaluation data to generate children's ward monitoring information. The present invention achieves a more comprehensive evaluation of the ward comfort data for sick children through the intelligent analysis of the obtainable parameters in the ward.

Description

Technical Field

[0001] The present invention relates to the technical field of medical systems, and particularly relates to an intelligent children's ward based on child-friendliness. Background Art

[0002] Children's wards are mainly designed to improve the comfort of sick children. For a sick child, a good or excellent ward environment can relax the child's mood. The existing children's wards create a pleasant treatment environment through warm, colorful, and interesting designs, which helps to reduce the tension and fear of child patients, thereby enhancing their psychological comfort. With the rapid development of technology, the combination of intelligent technology and child-friendly design can provide more personalized and interesting rehabilitation programs to promote the recovery of child patients;

[0003] However, the intelligent level of children's wards in the existing technology is relatively low, making it difficult to truly achieve the linkage analysis of the ward environment and the mood of sick children, thus unable to judge whether the sick children are comfortable inside, leading to misdiagnosis of the mood of children during hospitalization and a decline in the quality of medical services.

[0004] To solve the above-mentioned problems, an intelligent children's ward based on child-friendliness is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent children's ward based on child-friendliness to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] The intelligent children's ward based on child-friendliness includes an intelligent children's ward method, and the intelligent children's ward method specifically includes the following steps:

[0008] Step S100: Obtain ward data capable of performing a health analysis of the ward environment, where the ward data includes ward primary data and ward secondary data;

[0009] Step S200: Analyze and process the primary influence data and secondary influence data to generate primary evaluation information and secondary evaluation information;

[0010] Step S300: Integrate and analyze based on the generated primary evaluation information and secondary evaluation information, and output environmental health evaluation data;

[0011] Step S400: Obtain children's sleep parameters capable of evaluating the sleep quality of children;

[0012] Step S500: Process the children's sleep parameters and analyze and output children's sleep quality evaluation data;

[0013] Step S600: Integrate and analyze the environmental health assessment data and the children's sleep quality assessment data to generate children's ward monitoring information.

[0014] Furthermore, the ward primary data includes the indoor temperature of the ward, the indoor humidity of the ward, and the indoor light intensity of the ward; the indoor temperature of the ward is the real-time monitored temperature value in the children's ward within the specified monitoring time, the indoor humidity of the ward is the real-time monitored humidity value in the children's ward within the specified monitoring time, and the indoor light intensity of the ward is the real-time monitored light intensity value in the children's ward within the specified monitoring time;

[0015] The ward secondary data includes the ward noise ratio and the proportion of ward dust particles. The ward noise ratio is the numerical ratio of the maximum noise generated in the ward within one hour to the standard noise, and the proportion of ward dust particles is the ratio of the value of inhalable dust particles in the air of the ward to the total value of air particles.

[0016] Furthermore, the primary evaluation information includes a primary shallow impact mark and a primary deep impact mark. The processing steps of the primary impact data are as follows:

[0017] Collect the indoor temperature of the ward, the indoor humidity of the ward, and the indoor light intensity of the ward once every minute, collect sixty groups of data within one hour, calculate the mean values of the indoor temperature of the ward, the indoor humidity of the ward, and the indoor light intensity of the ward, and obtain the mean values of the indoor temperature of the ward, the indoor humidity of the ward, and the indoor light intensity of the ward in the previous detection cycle. Perform difference analysis through the two, divide by the corresponding correction parameters of the indoor temperature of the ward, the indoor humidity of the ward, and the indoor light intensity of the ward respectively and add them up, perform a cube root operation on the result to obtain a primary impact analysis coefficient, set a primary impact control parameter, and compare and analyze the primary impact analysis coefficient with the primary impact control parameter; if the primary impact analysis coefficient is less than the primary impact control parameter, generate a primary shallow impact mark; if the primary impact analysis coefficient is greater than or equal to the primary impact control parameter, generate a primary deep impact mark;

[0018] The secondary evaluation information includes a secondary shallow impact mark and a secondary deep impact mark. The processing steps of the secondary impact data are as follows:

[0019] Perform a weight analysis on the ward noise ratio and the proportion of ward dust particles to obtain a secondary impact coefficient, set a secondary impact control parameter, and compare and analyze the secondary impact coefficient with the secondary impact control parameter; if the secondary impact coefficient is less than the secondary impact control parameter, generate a secondary shallow impact mark; if the secondary impact coefficient is greater than or equal to the secondary impact control parameter, generate a secondary deep impact mark.

[0020] Further, the environmental health assessment data includes an excellent environmental health identifier, an abnormal environmental health identifier, and a dangerous environmental health identifier. The analysis logic of the environmental health assessment data is as follows:

[0021] If a primary shallow impact marker and a secondary shallow impact marker are generated simultaneously, an excellent environmental health identifier is output; if a primary shallow impact marker and a secondary deep impact marker or a secondary shallow impact marker and a primary deep impact marker are generated simultaneously, an abnormal environmental health identifier is output; if a primary deep impact marker and a secondary deep impact marker are generated simultaneously, a dangerous environmental health identifier is output.

[0022] Further, the children's sleep parameters include sleep duration and the ratio of deep sleep to shallow sleep. The sleep duration is the total duration of the child in the sleep state, and the ratio of deep sleep to shallow sleep is the ratio of the duration of the child in deep sleep to the duration of the child in shallow sleep.

[0023] Further, the children's sleep quality assessment data includes a normal children's sleep quality identifier and an abnormal children's sleep quality identifier. The generation steps of the children's sleep quality assessment data are as follows:

[0024] Analyze the pair of sleep duration and the ratio of deep sleep to shallow sleep. Preset a standard sleep duration, perform a difference analysis between the sleep duration and the standard sleep duration, divide the result by the standard sleep duration after multiplying by a duration error correction constant, and perform a product operation with the reciprocal of the ratio of deep sleep to shallow sleep to obtain a sleep quality analysis coefficient. Set a sleep quality analysis threshold, and compare and analyze the sleep quality analysis coefficient with the sleep quality analysis threshold;

[0025] If the sleep quality analysis coefficient is less than the sleep quality analysis threshold, generate a normal children's sleep quality identifier; if the sleep quality analysis coefficient is greater than or equal to the sleep quality analysis threshold, generate an abnormal children's sleep quality identifier.

[0026] Further, the children's ward monitoring information includes a good ward label, a ward defect label, and a ward risk label. The generation logic of the children's ward monitoring information is as follows:

[0027] If an excellent environmental health identifier and a normal children's sleep quality identifier are generated simultaneously, generate a good ward label; if an excellent environmental health identifier and an abnormal sleep quality identifier, an abnormal environmental health identifier and a normal sleep quality identifier, or a dangerous environmental health identifier and a normal sleep quality identifier are generated simultaneously, generate a ward defect label; if an abnormal environmental health identifier and an abnormal children's sleep quality identifier or a dangerous environmental health identifier and an abnormal children's sleep quality identifier are generated simultaneously, generate a ward risk label.

[0028] The present invention also provides an intelligent children's ward based on child-friendliness, and also includes an intelligent children's ward system. The intelligent children's ward system includes a ward data collection module, a primary ward data processing module, a secondary ward data processing module, a children's sleep parameter collection module, a children's sleep parameter processing module, and a comprehensive analysis module:

[0029] The ward data collection module is used to obtain ward data capable of performing a health analysis on the ward environment. The ward data includes primary ward data and secondary ward data, and sends the ward data to the primary ward data processing module;

[0030] The primary ward data processing module is used to analyze and process the primary impact data and the secondary impact data, generate primary evaluation information and secondary evaluation information, and send the primary evaluation information and the secondary evaluation information to the secondary ward data processing module;

[0031] The secondary ward data processing module is used to integratively analyze the generated primary evaluation information and secondary evaluation information, output environmental health evaluation data, and send the environmental health evaluation data to the comprehensive analysis module;

[0032] The children's sleep parameter collection module is used to obtain children's sleep parameters capable of evaluating the children's sleep quality, and send the children's sleep parameters to the children's sleep parameter processing module;

[0033] The children's sleep parameter processing module processes the children's sleep parameters, analyzes and outputs children's sleep quality evaluation data, and sends the children's sleep quality evaluation data to the comprehensive analysis module;

[0034] The comprehensive analysis module is used to integratively analyze the environmental health evaluation data and the children's sleep quality evaluation data, and generate children's ward monitoring information.

[0035] Furthermore, a computer server includes a processor and a memory, and is characterized in that a computer program callable by the processor is stored in the memory;

[0036] The processor executes any one of the above-mentioned methods for an intelligent children's ward based on child-friendliness by calling the computer program stored in the memory.

[0037] A computer-readable storage medium stores an erasable computer program thereon; and is characterized in that when the computer program runs on a computer device, the computer device executes any one of the above-mentioned methods for an intelligent children's ward based on child-friendliness.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] Through the intelligent comprehensive analysis of the environmental information in the ward during the day and the sleep quality information of the sick children in the ward at night, the present invention achieves a more comprehensive evaluation of the ward comfort data for the sick children. Based on the finally generated ward monitoring information, it is more conducive for relevant personnel to actively improve the treatment experience of the children, thereby improving the quality of medical services. In addition, by determining the ward detection information, the prevention of risk matters such as falls and abnormal air aspiration of sick children can be greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a flowchart of an intelligent children's ward method for an intelligent children's ward based on child-friendly type of the present invention;

[0042] Figure 2 It is a module schematic diagram of an intelligent children's ward system for an intelligent children's ward based on child-friendly type of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0044] Embodiment 1:

[0045] Please refer to Figure 1 As shown, an intelligent children's ward based on child-friendly type includes an intelligent children's ward method, and the intelligent children's ward method includes the following steps:

[0046] Step S100, obtaining ward data capable of performing a health analysis on the ward environment, where the ward data includes ward primary data and ward secondary data;

[0047] The first-level ward data includes the indoor temperature, indoor humidity, and indoor light intensity of the ward; the indoor temperature of the ward is the real-time monitored temperature value in the children's ward within the specified monitoring time, the indoor humidity of the ward is the real-time monitored humidity value in the children's ward within the specified monitoring time, and the indoor light intensity of the ward is the real-time monitored light intensity value in the children's ward within the specified monitoring time;

[0048] It should be noted that: the indoor temperature of the ward can be detected by temperature sensors installed in the children's ward, including but not limited to digital temperature sensors and infrared temperature sensors. These sensors can be installed at key positions in the children's ward to monitor the ambient temperature in real time; temperature is one of the important factors affecting the comfort and recovery of patients. Real-time monitoring of temperature helps to ensure that the temperature in the children's ward is always maintained within a suitable range, providing a comfortable treatment environment and contributing to the rapid recovery of children.

[0049] The indoor humidity of the ward can be detected by humidity sensors, specifically digital humidity sensors. Humidity has an important impact on the respiratory system and overall comfort of patients. Real-time monitoring of humidity helps to prevent the air from being too dry or too humid, maintaining a suitable humidity range and contributing to the health and comfort of child patients.

[0050] The indoor light intensity of the ward can be collected by photoresistors and photodiodes. These sensors are placed at different positions in the ward to monitor the light level; sufficient natural light is very important for the psychological and physical health of child patients. Real-time monitoring of light intensity helps to ensure that there is enough light in the ward while avoiding excessive light intensity from affecting the patients' rest, and at the same time, the lights in the ward can be adjusted according to the collected light intensity.

[0051] The second-level ward data includes the ward noise ratio and the proportion of dust particles in the ward. The ward noise ratio is the numerical ratio of the maximum noise generated in the ward within one hour to the standard noise, and the proportion of dust particles in the ward is the ratio of the value of inhalable dust particles in the air in the ward to the total value of air particles.

[0052] It should be noted that: the ward noise ratio can be detected by noise sensors or noise meters, and record the values of the maximum noise generated within one hour and the standard noise; the noise level in the ward is one of the key factors for patients' rest and recovery. Monitoring noise can help medical staff understand the possible environmental interference faced by patients, so as to take measures to reduce noise and improve the treatment environment in the ward, which is helpful for patients' rest and recovery.

[0053] The proportion of dust particles in the ward can be monitored by a particulate matter sensor to measure the concentration of particulate matter in the air, distinguish inhalable particulate matter from total particulate matter, calculate the proportion of dust particles in the ward. Particulate matter in the air may have an impact on the respiratory system and overall health of patients. Monitoring the proportion of dust particles helps to understand the air quality in the ward, detect and solve potential air quality problems in advance, thereby reducing adverse effects on patients.

[0054] Step S200: Analyze and process the primary impact data and secondary impact data to generate primary evaluation information and secondary evaluation information;

[0055] The primary evaluation information includes a primary shallow impact mark and a primary deep impact mark. The processing steps for the primary impact data are as follows:

[0056] Collect the indoor temperature, indoor humidity, and indoor light intensity in the ward once every minute for one hour to obtain sixty groups of data. Calculate the average values of the indoor temperature, indoor humidity, and indoor light intensity in the ward, and obtain the average values of the indoor temperature, indoor humidity, and indoor light intensity in the ward for the previous detection period. Conduct a difference analysis between the two, divide by the correction parameters corresponding to the indoor temperature, indoor humidity, and indoor light intensity in the ward respectively and add them up, and perform a cube root operation on the result to obtain the primary impact analysis coefficient α1. The analysis formula for the primary impact analysis coefficient α1 is:

[0057] ;

[0058] where th is the indoor temperature in the ward, sh is the indoor humidity in the ward, zh is the indoor light intensity in the ward, m, n, and b are the correction parameters corresponding to the indoor temperature, indoor humidity, and indoor light intensity in the ward respectively, v is the v-th group of data among the sixty groups of data for the previous detection period, k is the k-th group of data among the sixty groups of data for the current detection period, and c is the error correction constant;

[0059] Set the primary impact control parameter Zjk, and compare and analyze the primary impact analysis coefficient α1 with the primary impact control parameter Zjk;

[0060] If the primary impact analysis coefficient α1 is less than the primary impact control parameter Zjk, generate a primary shallow impact mark;

[0061] If the primary impact analysis coefficient α1 is greater than or equal to the primary impact control parameter Zjk, generate a primary deep impact mark.

[0062] Among them, the primary shallow impact mark indicates that the changes in the indoor temperature, indoor humidity, and indoor light intensity in the ward are relatively small within two hours and tend to be stable. The impact result of the primary deep impact mark is opposite to that of the primary shallow impact mark;

[0063] It should be noted that when night comes, the monitoring is stopped to avoid large changes in the first-level impact analysis coefficient α1 caused by sudden changes in indoor light intensity. The specified monitoring time in different seasons needs to be adjusted according to climate changes.

[0064] The secondary evaluation information includes secondary shallow impact marks and secondary deep impact marks. The processing steps for the secondary impact data are as follows:

[0065] Perform a weight analysis on the ward noise ratio and the proportion of ward dust particles to obtain the secondary impact coefficient α2. The acquisition formula for the secondary impact coefficient α2 is:

[0066] α2 = ln(nr + dpr * ) + Lt

[0067] where nr is the ward noise ratio, dpr is the proportion of ward dust particles, a1 and a2 are the weight coefficients of the ward noise ratio and the proportion of ward dust particles respectively, a1 + a2 = 1.2483, and Lt is a compensation constant, where Lt > 0;

[0068] Set the secondary impact control parameter Ejk, and compare and analyze the secondary impact coefficient α2 with the secondary impact control parameter Ejk;

[0069] If the secondary impact coefficient α2 is less than the secondary impact control parameter Ejk, generate a secondary shallow impact mark;

[0070] If the secondary impact coefficient α2 is greater than or equal to the secondary impact control parameter Ejk, generate a secondary deep impact mark.

[0071] Among them, the secondary shallow impact mark indicates that the noise impact and the impact of inhalable dust are small, and the ward comfort level is high. The impact of the two-layer deep impact mark is opposite to that of the secondary shallow impact mark.

[0072] It should be noted that the specified monitoring time for the secondary impact data is the same as that for the primary impact data.

[0073] Step S300: Integrate and analyze the generated primary evaluation information and secondary evaluation information, and output environmental health assessment data;

[0074] The environmental health assessment data includes environmental health excellent identification, environmental health anomaly identification, and environmental health hazard identification. The analysis logic of the environmental health assessment data is as follows:

[0075] If both a primary shallow impact mark and a secondary shallow impact mark are generated, output an environmental health excellent identification;

[0076] If a first-level shallow impact marker and a second-level deep impact marker or a second-level shallow impact marker and a first-level deep impact marker are generated simultaneously, an environmental health anomaly identifier is output;

[0077] If a first-level deep impact marker and a second-level deep impact marker are generated simultaneously, an environmental health danger identifier is output.

[0078] Among them, the environmental health danger identifier poses a greater risk to the ward environment compared to the environmental health anomaly identifier, and so on.

[0079] Step S400: Obtain children's sleep parameters that can evaluate the sleep quality of children;

[0080] The children's sleep parameters include sleep duration and the ratio value of deep sleep to shallow sleep. The sleep duration is the total duration of a child in the sleep state, and the ratio value of deep sleep to shallow sleep is the ratio of the duration of deep sleep to the duration of shallow sleep of a child.

[0081] It should be noted that: The sleep duration can be obtained by using sleep monitoring devices, including but not limited to smart watches, mattress sensors, sleep monitors, etc. These devices can monitor the activity status of sick children at night to determine the start and end times of sleep.

[0082] The ratio value of deep sleep to shallow sleep can be collected by a sleep monitoring device. The sleep monitoring device can determine the periods of deep sleep and shallow sleep of a child through different sensors (such as heart rate, respiration, body movement, etc.), and then calculate the ratio value of deep sleep to shallow sleep.

[0083] Step S500: Process the children's sleep parameters and analyze and output children's sleep quality evaluation data;

[0084] The children's sleep quality evaluation data includes a children's sleep quality normal identifier and a children's sleep quality anomaly identifier. The generation steps of the children's sleep quality evaluation data are as follows:

[0085] Analyze the sleep duration and the ratio value of deep sleep to shallow sleep. Set the parameter unit of the sleep duration to s, and preset the standard sleep duration. Perform a difference analysis between the sleep duration and the standard sleep duration, divide the result by the standard sleep duration after multiplying by a duration error correction constant, and then perform a product operation with the reciprocal of the ratio value of deep sleep to shallow sleep to obtain a sleep quality analysis coefficient βs. The acquisition formula for the sleep quality analysis coefficient βs is:

[0086] βs =

[0087] Among them, St is the sleep duration, Sst is the standard sleep duration, DSR is the ratio value of deep sleep to shallow sleep, pl is the duration error correction constant, and pl > 0;

[0088] Set the sleep quality analysis threshold βzs, and compare and analyze the sleep quality analysis coefficient βs with the sleep quality analysis threshold βzs;

[0089] If the sleep quality analysis coefficient βs is less than the sleep quality analysis threshold βzs, generate a normal sleep quality label for children;

[0090] If the sleep quality analysis coefficient βs is greater than or equal to the sleep quality analysis threshold βzs, generate an abnormal sleep quality label for children;

[0091] Among them, the abnormal sleep quality label for children indicates that when a sick child is sleeping, there is a determination that the child's sleep comfort is low and the child's recovery mood is abnormal, that is, the relaxation and pleasure degree of the sick child's mood is determined through the natural performance state of sleep.

[0092] Normal sleep quality label for children and abnormal sleep quality label for children

[0093] Step S600: Integrate and analyze the environmental health assessment data and the children's sleep quality assessment data to generate children's ward monitoring information;

[0094] The children's ward monitoring information includes a ward excellent label, a ward defect label, and a ward risk label. The generation logic of the children's ward monitoring information is as follows:

[0095] Generate an excellent environmental health label and a normal sleep quality label for children simultaneously to generate a ward excellent label;

[0096] Generate an excellent environmental health label and an abnormal sleep quality label, an abnormal environmental health label and a normal sleep quality label, or a dangerous environmental health label and a normal sleep quality label simultaneously to generate a ward defect label;

[0097] Generate an abnormal environmental health label and an abnormal sleep quality label for children or a dangerous environmental health label and an abnormal sleep quality label for children simultaneously to generate a ward risk label.

[0098] It should be noted that relevant personnel detect the intelligent children's comfort based on the children's ward detection information, so as to judge the living comfort of the sick children's ward according to the detection information, and then further conduct a detailed analysis to optimize the ward environment and the mental health counseling of the sick children;

[0099] In addition, the children's ward in the present invention can select the ward to be occupied based on the height of the child to avoid slipping or falling when different children use the squat toilet or the sitting toilet, and further, set the height of the bed and the height of the cabinet placement according to the height of the child;

[0100] Secondly, the drinking water outlet temperature in multiple children's wards is a fixed temperature, that is, to prevent children from drinking cold water without permission during hospitalization and getting colds and diarrhea, or accidentally drinking hot water and getting scalded when they are in a hurry to drink water. Further, the bathing temperature set in the children's ward is also a fixed temperature, which is the most suitable bathing temperature for children, and there will be no phenomenon of getting cold or scalded due to bathing.

[0101] Embodiment 2:

[0102] As Figure 2 shown, an intelligent children's ward based on child-friendliness further includes an intelligent children's ward system. The intelligent children's ward system includes a ward data acquisition module, a ward data primary processing module, a ward data secondary processing module, a child sleep parameter acquisition module, a child sleep parameter processing module, and a comprehensive analysis module;

[0103] The ward data acquisition module is used to obtain ward data that can analyze the health of the ward environment. The ward data includes ward primary data and ward secondary data;

[0104] The ward primary data includes the ward indoor temperature, the ward indoor humidity, and the ward indoor light intensity. The ward indoor temperature is the real-time monitored temperature value in the children's ward within the specified monitoring time. The ward indoor humidity is the real-time monitored humidity value in the children's ward within the specified monitoring time. The ward indoor light intensity is the real-time monitored light intensity value in the children's ward within the specified monitoring time;

[0105] The ward secondary data includes the ward noise ratio and the ward dust particle proportion. The ward noise ratio is the numerical ratio of the maximum noise generated in the ward within one hour to the standard noise. The ward dust particle proportion is the ratio of the value of inhalable dust particles in the air in the ward to the total value of air particles.

[0106] The ward data acquisition module sends the ward data to the ward data primary processing module;

[0107] The ward data primary processing module is used to analyze and process the primary impact data and the secondary impact data to generate primary evaluation information and secondary evaluation information;

[0108] The primary evaluation information includes a primary shallow impact mark and a primary deep impact mark. The processing steps of the primary impact data are:

[0109] Collect the indoor temperature, indoor humidity, and indoor light intensity of the ward every minute, collect 60 groups of data within one hour, calculate the average values of the indoor temperature, indoor humidity, and indoor light intensity of the ward, and obtain the average values of the indoor temperature, indoor humidity, and indoor light intensity of the ward in the previous detection period. Conduct a difference analysis between the two, divide by the corresponding correction parameters of the indoor temperature, indoor humidity, and indoor light intensity of the ward respectively and add them up, perform a cube root operation on the result to obtain the first-level impact analysis coefficient α1, set the first-level impact control parameter Zjk, and conduct a comparison analysis between the first-level impact analysis coefficient α1 and the first-level impact control parameter Zjk;

[0110] If the first-level impact analysis coefficient α1 is less than the first-level impact control parameter Zjk, generate a first-level shallow impact mark; if the first-level impact analysis coefficient α1 is greater than or equal to the first-level impact control parameter Zjk, generate a first-level deep impact mark;

[0111] The secondary evaluation information includes a secondary shallow impact mark and a secondary deep impact mark. The processing steps of the secondary impact data are as follows:

[0112] Conduct a weight analysis on the ward noise ratio and the proportion of ward dust particles to obtain the secondary impact coefficient α2, set the secondary impact control parameter Ejk, and conduct a comparison analysis between the secondary impact coefficient α2 and the secondary impact control parameter Ejk;

[0113] If the secondary impact coefficient α2 is less than the secondary impact control parameter Ejk, generate a secondary shallow impact mark; if the secondary impact coefficient α2 is greater than or equal to the secondary impact control parameter Ejk, generate a secondary deep impact mark.

[0114] The ward data preliminary processing module sends the primary evaluation information and the secondary evaluation information to the ward data secondary processing module;

[0115] The ward data secondary processing module is used to integrate and analyze the generated primary evaluation information and secondary evaluation information, and output environmental health assessment data;

[0116] The environmental health assessment data includes an environmental health excellent identifier, an environmental health abnormality identifier, and an environmental health danger identifier. The analysis logic of the environmental health assessment data is as follows:

[0117] If both a primary shallow impact mark and a secondary shallow impact mark are generated, output the environmental health excellent identifier;

[0118] If both a primary shallow impact mark and a secondary deep impact mark or a secondary shallow impact mark and a primary deep impact mark are generated, output the environmental health abnormality identifier;

[0119] If both a first-level deep influence marker and a second-level deep influence marker are generated, an environmental health hazard identification is output.

[0120] Child sleep parameter acquisition module; used to obtain child sleep parameters capable of evaluating the sleep quality of a child, and send the child sleep parameters to the child sleep parameter processing module;

[0121] The child sleep parameters include sleep duration and the ratio value of deep sleep to light sleep. The sleep duration is the total duration of the child in the sleep state, and the ratio value of deep sleep to light sleep is the ratio of the duration of the child in deep sleep to the duration of the child in light sleep.

[0122] The child sleep parameter acquisition module sends the child sleep parameters to the child sleep parameter processing module;

[0123] Child sleep parameter processing module; processes the child sleep parameters and analyzes and outputs child sleep quality evaluation data;

[0124] The child sleep quality evaluation data includes a child sleep quality normal identification and a child sleep quality abnormal identification. The generation steps of the child sleep quality evaluation data are as follows:

[0125] Analyze the pair of sleep duration and the ratio value of deep sleep to light sleep. Set the parameter unit of the sleep duration to s, and preset the standard sleep duration. Perform a difference analysis between the sleep duration and the standard sleep duration, divide the result by the standard sleep duration after multiplying by the duration error correction constant, and perform a product operation with the reciprocal of the ratio value of deep sleep to light sleep to obtain the sleep quality analysis coefficient βs.

[0126] Set the sleep quality analysis threshold βzs, and compare and analyze the sleep quality analysis coefficient βs with the sleep quality analysis threshold βzs;

[0127] If the sleep quality analysis coefficient βs is less than the sleep quality analysis threshold βzs, generate a child sleep quality normal identification; if the sleep quality analysis coefficient βs is greater than or equal to the sleep quality analysis threshold βzs, generate a child sleep quality abnormal identification.

[0128] The child sleep parameter processing module sends the child sleep quality evaluation data to the comprehensive analysis module;

[0129] Comprehensive analysis module; used to integratively analyze the environmental health assessment data and the child sleep quality evaluation data to generate child ward monitoring information;

[0130] The child ward monitoring information includes a ward excellent label, a ward defect label, and a ward risk label. The generation logic of the child ward monitoring information is as follows:

[0131] Generate excellent ward labels with both excellent environmental health labels and normal children's sleep quality labels at the same time; generate defective ward labels with both excellent environmental health labels and abnormal sleep quality labels, abnormal environmental health labels and normal sleep quality labels, or dangerous environmental health labels and normal sleep quality labels at the same time; generate risky ward labels with both abnormal environmental health labels and abnormal children's sleep quality labels or dangerous environmental health labels and abnormal children's sleep quality labels at the same time.

[0132] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by technicians in the field according to the actual situation.

Claims

1. A child-friendly intelligent pediatric ward, including an intelligent pediatric ward method, comprising the following steps: Step S100, obtaining ward data capable of performing health analysis on the ward environment, wherein the ward data includes ward primary data and ward secondary data; Step S200: Analyze and process the primary impact data and the secondary impact data to generate primary evaluation information and secondary evaluation information; The first-level assessment information includes a first-level shallow impact mark and a first-level deep impact mark. The processing steps of the first-level impact data are: Obtain the first-level impact analysis coefficient α1, the analysis formula of the first-level impact analysis coefficient α1 is: ； Among them, th is the indoor temperature of the ward, sh is the indoor humidity of the ward, zh is the indoor light intensity of the ward, m, n, b are the correction parameters corresponding to the indoor temperature of the ward, the indoor humidity of the ward, and the indoor light intensity of the ward, respectively, v is the vth group of sixty groups of data in the previous detection cycle, k is the kth group of sixty groups of data in the current detection cycle, and c is the error correction constant; The secondary evaluation information includes a secondary shallow impact mark and a secondary deep impact mark. The processing steps of the secondary impact data are as follows: The ward noise ratio and the ward dust particle ratio are weighted and analyzed to obtain the secondary impact coefficient α2. The formula for obtaining the secondary impact coefficient α2 is: α2=ln(nr +dpr* )+Lt Among them, nr is the noise ratio of the ward, dpr is the proportion of dust particles in the ward, a1 and a2 are the weight coefficients of the noise ratio of the ward and the proportion of dust particles in the ward, respectively, a1+a2=1.2483, Lt is the compensation constant, Lt is greater than 0; Step S300: Perform integrated analysis based on the generated primary assessment information and secondary assessment information, and output environmental health assessment data; Step S400, obtaining a child's sleep parameter that can be used to evaluate the child's sleep quality; Step S500: Process the child's sleep parameters, analyze and output the child's sleep quality assessment data, including a normal child sleep quality indicator and an abnormal child sleep quality indicator. The steps for generating the child's sleep quality assessment data are as follows: The sleep duration and the sleep depth ratio are analyzed, the parameter unit of the sleep duration is set to s, and the standard sleep duration is preset. The sleep duration and the standard sleep duration are analyzed for difference, and the difference is multiplied by the duration error correction constant and then divided by the standard sleep duration. The result is multiplied by the reciprocal of the depth ratio value to obtain the sleep quality analysis coefficient βs. The sleep quality analysis coefficient βs is obtained by the following formula: βs= ； Among them, St is the sleep duration, Sst is the standard sleep duration, DSR is the ratio of sleep depth to shallowness, pl is the duration error correction constant, and pl is greater than 0; Step S600: Integrate and analyze the environmental health assessment data and the children's sleep quality assessment data to generate children's ward monitoring information.

2. A child-friendly intelligent pediatric ward according to claim 1, characterized in that: The first-level data of the ward includes the indoor temperature of the ward, the indoor humidity of the ward and the indoor light intensity of the ward; the indoor temperature of the ward is the real-time monitored temperature value in the children's ward within the specified monitoring time, the indoor humidity of the ward is the real-time monitored humidity value in the children's ward within the specified monitoring time, and the indoor light intensity of the ward is the real-time monitored light intensity value in the children's ward within the specified monitoring time; The secondary data of the ward include the ward noise ratio and the ward dust particle ratio. The ward noise ratio is the numerical ratio of the maximum noise generated in the ward within one hour to the standard noise. The ward dust particle ratio is the ratio of the value of inhalable dust particles in the air in the ward to the value of total air particles.

3. The child-friendly intelligent pediatric ward according to claim 2, characterized in that: The environmental health assessment data includes an excellent environmental health mark, an abnormal environmental health mark, and a dangerous environmental health mark. The analysis logic of the environmental health assessment data is: If a first-level shallow impact mark and a second-level shallow impact mark are generated at the same time, the environmental health excellent mark will be output; if a first-level shallow impact mark and a second-level deep impact mark or a second-level shallow impact mark and a first-level deep impact mark are generated at the same time, the environmental health abnormal mark will be output; if a first-level deep impact mark and a second-level deep impact mark are generated at the same time, the environmental health hazard mark will be output.

4. The child-friendly intelligent pediatric ward according to claim 3, characterized in that: The children's sleep parameters include sleep duration and sleep depth-shallowness ratio. The sleep duration is the total time the child is in a sleeping state, and the sleep depth-shallowness ratio is the ratio of the time the child is in deep sleep to the time the child is in shallow sleep.

5. The child-friendly intelligent pediatric ward according to claim 4, characterized in that: The children's ward monitoring information includes a ward excellent label, a ward defect label and a ward risk label. The generation logic of the children's ward monitoring information is: At the same time, an excellent environmental health mark and a normal child sleep quality mark are generated to generate an excellent ward label; at the same time, an excellent environmental health mark and an abnormal sleep quality mark, an abnormal environmental health mark and a normal sleep quality mark, or an environmental health risk mark and a normal sleep quality mark are generated to generate a ward defect label; at the same time, an abnormal environmental health mark and a child sleep quality abnormal mark, or an environmental health risk mark and a child sleep quality abnormal mark are generated to generate a ward risk label.

6. The child-friendly intelligent pediatric ward according to claim 5 further comprises an intelligent pediatric ward system, characterized in that: The intelligent children's ward system includes a ward data acquisition module, a ward data primary processing module, a ward data secondary processing module, a children's sleep parameter acquisition module, a children's sleep parameter processing module and a comprehensive analysis module: Ward data acquisition module; used to obtain ward data that can perform health analysis on the ward environment, the ward data includes ward primary data and ward secondary data, and send the ward data to the ward data primary processing module; Ward data primary processing module: used to analyze and process the primary impact data and the secondary impact data, generate primary evaluation information and secondary evaluation information, and send the primary evaluation information and the secondary evaluation information to the ward data secondary processing module; Ward data secondary processing module; It is used to integrate and analyze the generated primary assessment information and secondary assessment information, output the environmental health assessment data and send the environmental health assessment data to the comprehensive analysis module; Children's sleep parameter acquisition module; used to obtain children's sleep parameters that can evaluate children's sleep quality, and send the children's sleep parameters to the children's sleep parameter processing module; Children's sleep parameter processing module: processes children's sleep parameters, analyzes and outputs children's sleep quality assessment data, and sends the children's sleep quality assessment data to the comprehensive analysis module; Comprehensive analysis module; Used to integrate and analyze environmental health assessment data and children's sleep quality assessment data to generate children's ward monitoring information.

7. A computer server, comprising a processor and a memory, characterized in that: The memory stores a computer program that can be called by the processor; The processor executes any one of the child-friendly intelligent children's ward methods described in claims 1-6 by calling the computer program stored in the memory.

8. A computer-readable storage medium having a rewritable computer program stored thereon; characterized in that: When the computer program is run on a computer device, the computer device executes any one of the child-friendly intelligent children's ward methods according to claims 1-6.

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