Intelligent ward management method and system based on Internet of Things

Abstract

The invention relates to the technical field of medical information smart wards, in particular to a smart ward management method and system based on the Internet of Things. Environmental parameters in a smart ward and physiological parameters of a patient in the smart ward are obtained; performing comprehensive analysis according to the environmental parameters and the physiological parameters to obtain corresponding analysis results; and according to an analysis result, a control instruction used for adjusting environment equipment in the smart ward is generated and / or corresponding guidance information is sent to the management end, so that the ward environment and the patient state are combined, and integrity and collaboration are realized. Intelligent management and real-time monitoring and response of the ward are realized through the control instruction and / or guidance information, the timeliness of medical services is improved, dynamic allocation of medical resources is facilitated, the resource utilization efficiency is improved, the operation process is greatly simplified, and the use experience of medical staff and patients is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical information smart ward, and particularly relates to a smart ward management method and system based on Internet of Things. BACKGROUND

[0002] With the continuous progress of medical technology and the increasing demand of patients for medical services, the traditional ward management mode has been difficult to meet the needs of modern medicine. The traditional ward management mainly relies on manual operation, and there are problems such as untimely information transmission, unreasonable resource allocation, and incomplete patient state monitoring. Although there are some smart ward management systems on the market at present, most of these systems have single function, mainly focusing on environmental monitoring or patient physiological parameter monitoring, and lack of integrity and collaboration. SUMMARY

[0003] Therefore, the present application aims to provide a smart ward management method and system based on Internet of Things to solve the problems mentioned in the background.

[0004] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0005] The smart ward management method based on Internet of Things comprises the following steps:

[0006] S1, acquiring the environmental parameters in the smart ward and the physiological parameters of the patients in the smart ward;

[0007] S2, comprehensively analyzing the environmental parameters and the physiological parameters to obtain corresponding analysis results;

[0008] S3, generating control instructions for adjusting the environmental equipment in the smart ward and / or sending corresponding guidance information to the management end according to the analysis results.

[0009] Further, step S1 further comprises:

[0010] According to the acquired environmental parameters in the smart ward, the environmental parameter observation value is calculated by using the following formula, and the environmental parameter observation value is taken as the environmental parameter in step S2.

[0011] yt=c+∑ i＝1 pφiyt-i+∑ i＝1 qθi∈t-i+∈t

[0012] Wherein, yt represents the observation value of the time series at time point t; c represents the constant term; φi is the autoregressive coefficient, representing the influence of the observation value of the past p time points on the current value; yt-i represents the observation value of the time series at time point t-i; θi is the moving average coefficient, representing the influence of the error term of the past q time points on the current value; ∈t-i represents the error term of the time series at time point t-i; ∈t represents the error term of the time series at time point t; p is the autoregressive order; q is the moving average order.

[0013] Further, step S1 further comprises:

[0014] According to the physiological parameters of the patient in the smart ward obtained, and using the following formula, the physiological parameter output value is calculated, and the physiological parameter output value is taken as the physiological parameter in step S2.

[0015] f(x)=sign(∑ i＝1n αiyiK(xi,x)+b)

[0016] Wherein, f(x) represents the output value of the classification decision function; sign is a sign function; ∑ i＝1n is the sum symbol, representing the sum of all support vectors for i=1 to n; αi is the solution of the support vector machine optimization problem; yi is the true class label of the i-th support vector; K(xi, x) is a kernel function; xi is the i-th support vector; x represents the input sample to be classified; b represents the bias term; n represents the total number of support vectors.

[0017] Further, the environmental parameters include temperature, humidity, illumination and air quality; and the physiological parameters include heart rate, blood pressure and blood oxygen saturation.

[0018] Further, the environmental equipment includes one or more of an air conditioner, a lighting lamp and an exhaust fan.

[0019] The Internet of Things smart ward management system based on the processor and the memory, the memory has a program or instructions stored, the program or instructions are executed by the processor to realize the following steps:

[0020] S1, obtaining the environmental parameters in the smart ward and the physiological parameters of the patient in the smart ward;

[0021] S2, comprehensively analyzing the environmental parameters and the physiological parameters to obtain corresponding analysis results;

[0022] S3, generating a control instruction for adjusting the environmental equipment in the smart ward and / or sending corresponding guidance information to the management end according to the analysis results.

[0023] Further, the program or instructions executed by the processor further realize the following steps:

[0024] According to the acquired environmental parameters in the smart ward, and using the following formula, the environmental parameter observation value is calculated, and the environmental parameter observation value is taken as the environmental parameter in step S2.

[0025] yt=c+∑ i＝1 pφiyt-i+∑ i＝1 qθi∈t-i+∈t

[0026] Wherein, yt represents the observation value of time series at time point t; c represents the constant term; φi is the autoregressive coefficient, which represents the influence of the observation value of the past p time points on the current value; yt-i represents the observation value of time series at time point t-i; θi is the moving average coefficient, which represents the influence of the error term of the past q time points on the current value; ∈t-i represents the error term of time series at time point t-i; ∈t represents the error term of time series at time point t; p is the autoregressive order; q is the moving average order.

[0027] Further, the program or instruction is executed by the processor to further implement the following steps:

[0028] According to the acquired physiological parameters of the patient in the smart ward, and using the following formula, the physiological parameter output value is calculated, and the physiological parameter output value is taken as the physiological parameter in step S2.

[0029] f(x)=sign(∑ i＝1n αiyiK(xi,x)+b)

[0030] Wherein, f(x) represents the output value of the classification decision function; sign is a sign function; ∑ i＝1n is the sum symbol, which represents the summation of all support vectors i=1 to n; αi is the solution of the support vector machine optimization problem; yi is the true class label of the i-th support vector; K(xi, x) is a kernel function; xi is the i-th support vector; x represents the input sample to be classified; b represents the bias term; n represents the total number of support vectors.

[0031] Further, the program or instruction is executed by the processor to further implement the following steps: the environmental parameters include temperature, humidity, illumination and air quality; the physiological parameters include heart rate, blood pressure and blood oxygen saturation.

[0032] Further, the program or instruction is executed by the processor to further implement the following steps: the environmental equipment includes one or more of air conditioner, lighting lamp and exhaust fan.

[0033] The beneficial effects of the present application are:

[0034] The IoT-based smart ward management method and system provided by the present invention obtains environmental parameters within the smart ward and the physiological parameters of the patients within the smart ward; performs a comprehensive analysis based on the environmental and physiological parameters to obtain corresponding analysis results; and then, based on the analysis results, generates control instructions for adjusting the environmental equipment within the smart ward and / or sends corresponding guidance information to the management end, thereby combining the ward environment and the patient status to achieve integrity and coordination. Furthermore, through control instructions and / or guidance information, intelligent ward management, real-time monitoring, and response are achieved, improving the timeliness of medical services, facilitating the dynamic allocation of medical resources, and improving resource utilization efficiency. This greatly simplifies the operating process and enhances the user experience of medical staff and patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Shown is a flowchart of the steps of the IoT-based smart ward management method of the present invention;

[0036] Figure 2 Shown is a structural block diagram of the Internet of Things-based smart ward management system of the present invention;

[0037] Description of Figure Numbers:

[0038] 1-Processor; 2-Memory. DETAILED DESCRIPTION

[0039] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0040] like Figure 1 As shown, the IoT-based smart ward management method provided by the present invention includes the following steps:

[0041] S1. Obtain environmental parameters in the smart ward and physiological parameters of patients in the smart ward;

[0042] S2. Conduct comprehensive analysis based on environmental parameters and physiological parameters to obtain corresponding analysis results;

[0043] S3. Based on the analysis results, generate control instructions for adjusting the environmental equipment in the smart ward and / or send corresponding guidance information to the management end.

[0044] From the above description, it can be seen that the present invention has the following beneficial effects:

[0045] The application provides a smart ward management method based on the Internet of Things, which comprises the following steps: acquiring environmental parameters in a smart ward and physiological parameters of patients in the smart ward; comprehensively analyzing the environmental parameters and the physiological parameters to obtain corresponding analysis results; and generating control instructions for adjusting environmental equipment in the smart ward and / or sending corresponding guidance information to a management terminal according to the analysis results, so that the ward environment and the patient state are combined to realize integrity and synergy. The control instructions and / or the guidance information realize intelligent management, real-time monitoring and response of the ward, improve the timeliness of medical services, facilitate dynamic allocation of medical resources, improve resource utilization efficiency, greatly simplify the operation process, and improve the use experience of medical staff and patients.

[0046] Further, step S1 further comprises:

[0047] According to the acquired environmental parameters in the smart ward, the environmental parameter observation value is calculated by using the following formula, and the environmental parameter observation value is taken as the environmental parameter in step S2.

[0048] yt=c+∑ i＝1 φiyt-i+∑ i＝1 θi∈t-i+∈t

[0049] Wherein, yt represents the observation value of the time series at time point t; c represents a constant term; φi is an autoregressive coefficient, representing the influence of the observation values of the past p time points on the current value; yt-i represents the observation value of the time series at time point t-i; θi is a moving average coefficient, representing the influence of the error terms of the past q time points on the current value; ∈t-i represents the error term of the time series at time point t-i; ∈t represents the error term of the time series at time point t; p is the autoregressive order; q is the moving average order.

[0050] From the above description, it can be seen that through the above formula, the environmental parameters can be predicted for a certain period of time, that is, the prediction can be made in advance, which is convenient for intelligent management of the ward.

[0051] Further, step S1 further comprises:

[0052] According to the acquired physiological parameters of patients in the smart ward, the physiological parameter output value is calculated by using the following formula, and the physiological parameter output value is taken as the physiological parameter in step S2.

[0053] f(x)=sign(∑ i＝1n αiyiK(xi,x)+b)

[0054] Wherein, f(x) represents the output value of the classification decision function; sign is a sign function; ∑ i＝1nfor the summation sign, represents the summation over all support vectors i = 1 to n; alpha i is the solution of the support vector machine optimization problem; y i is the true class label of the i th support vector; K (x i, x) is the kernel function; x i is the i th support vector; x represents the input sample to be classified; b represents the bias term; and n represents the total number of support vectors.

[0055] From the above description, it can be seen that through the above formula, the physiological parameters can be predicted for a certain time, that is, the prediction can be made in advance, which is convenient for intelligent management of the ward.

[0056] Further, the environmental parameters include temperature, humidity, illumination and air quality; and the physiological parameters include heart rate, blood pressure and blood oxygen saturation.

[0057] From the above description, it can be seen that the use of the above diversified parameters can improve the prediction accuracy and make the intelligent management of the ward more comprehensive.

[0058] Further, the environmental equipment includes one or more of an air conditioner, a lighting lamp and an exhaust fan.

[0059] From the above description, it can be seen that the air conditioner can adjust the temperature and humidity in the ward, the lighting lamp can adjust the illumination in the ward, and the exhaust fan can adjust the air quality in the ward.

[0060] Referring to Figure 2 The application provides a smart ward management system based on Internet of Things, which comprises a processor 1 and a memory 2.

[0061] S1, acquiring environmental parameters in the smart ward and physiological parameters of patients in the smart ward;

[0062] S2, comprehensively analyzing the environmental parameters and the physiological parameters to obtain corresponding analysis results;

[0063] S3, generating a control instruction for adjusting environmental equipment in the smart ward and / or sending corresponding guidance information to a management end according to the analysis results.

[0064] From the above description, the application has the following beneficial effects:

[0065] The application provides a smart ward management system based on the Internet of Things, which obtains environmental parameters in a smart ward and physiological parameters of patients in the smart ward; comprehensively analyzes the environmental parameters and the physiological parameters to obtain corresponding analysis results; generates control instructions for adjusting environmental equipment in the smart ward and / or sends corresponding guidance information to a management terminal according to the analysis results, so that the ward environment and the patient state are combined to realize integrity and synergy. The control instructions and / or the guidance information realize intelligent management, real-time monitoring and response of the ward, improve the timeliness of medical services, facilitate dynamic allocation of medical resources, improve resource utilization efficiency, greatly simplify the operation process, and improve the use experience of medical staff and patients.

[0066] Further, the program or instruction is executed by the processor to further implement the following steps:

[0067] According to the obtained environmental parameters in the smart ward, an environmental parameter observation value is calculated by using the following formula, and the environmental parameter observation value is taken as the environmental parameter in step S2.

[0068] yt=c+∑ i＝1 φiyt-i+∑ i＝1 θi∈t-i+∈t

[0069] yt represents the observation value of the time sequence at time point t; c represents a constant term; φi is an autoregressive coefficient, representing the influence of the observation values of the past p time points on the current value; yt-i represents the observation value of the time sequence at time point t-i; θi is a moving average coefficient, representing the influence of the error terms of the past q time points on the current value; ∈t-i represents the error term of the time sequence at time point t-i; ∈t represents the error term of the time sequence at time point t; p is the autoregressive order; and q is the moving average order.

[0070] From the above description, it can be known that the environmental parameters can be predicted for a certain period of time by using the above formula, that is, a preliminary judgment can be made in advance, which is convenient for intelligent management of the ward.

[0071] Further, the program or instruction is executed by the processor to further implement the following steps:

[0072] According to the obtained physiological parameters of the patients in the smart ward, a physiological parameter output value is calculated by using the following formula, and the physiological parameter output value is taken as the physiological parameter in step S2.

[0073] f(x)=sign(∑ i＝1n αiyiK(xi,x)+b)

[0074] f(x) represents the output value of the classification decision function; sign is a sign function; ∑i＝1n is the sum symbol, represents the sum of all support vectors for i = 1 to n; ai is the solution of the support vector machine optimization problem; yi is the true class label of the ith support vector; K(xi, x) is the kernel function; xi is the ith support vector; x represents the input sample to be classified; b represents the bias term; and n represents the total number of support vectors.

[0075] From the above description, it can be seen that through the above formula, the physiological parameters can be predicted for a certain period of time, that is, the prediction can be made in advance, which is convenient for intelligent management of the ward.

[0076] Further, the program or instructions are executed by the processor to further implement the following steps: the environmental parameters include temperature, humidity, illumination and air quality; and the physiological parameters include heart rate, blood pressure and blood oxygen saturation.

[0077] From the above description, it can be seen that the use of the above diversified parameters can improve the prediction accuracy and make the intelligent management of the ward more comprehensive.

[0078] Further, the program or instructions are executed by the processor to further implement the following steps: the environmental equipment includes one or more of an air conditioner, a lighting lamp and an exhaust fan.

[0079] From the above description, it can be seen that the air conditioner can adjust the temperature and humidity in the ward, the lighting lamp can adjust the illumination in the ward, and the exhaust fan can adjust the air quality in the ward.

[0080] The following preferred embodiments or application embodiments are listed to help those skilled in the art better understand the technical content of the present application and the technical contribution made by the present application over the prior art:

[0081] Preferred embodiment one:

[0082] As shown in Figure 1 The Internet of Things-based intelligent ward management method provided by the present application comprises the following steps:

[0083] S1, obtaining the environmental parameters in the intelligent ward and the physiological parameters of the patients in the intelligent ward; in the present embodiment, the environmental parameters include temperature, humidity, illumination and air quality; and the physiological parameters include heart rate, blood pressure and blood oxygen saturation.

[0084] Step S1 further comprises:

[0085] According to the obtained environmental parameters in the intelligent ward, the environmental parameter observation value is calculated by using the following formula, and the environmental parameter observation value is used as the environmental parameter in step S2.

[0086] yt = c + ∑ i＝1 pφiyt-i+∑i＝1 qt-i ∈ t-i + ∈t

[0087] where yt represents the observation of the time series at time point t; role: this is the target value that the model is trying to predict, representing the time series value at the current time.

[0088] c represents the constant term; role: represents the long-term mean or baseline value of the time series.

[0089] φi is the autoregressive coefficient, representing the influence of the past p observation values on the current value; role: used to describe the autocorrelation of the time series, i.e. the relationship between the current value yt and the past values yt-1, yt-2, …, yt-p.

[0090] yt-i represents the observation of the time series at time point t-i; role: represents the past p observation values, used to predict the current value yt.

[0091] θi is the moving average coefficient, representing the influence of the past q error terms on the current value; role: used to describe the error correlation of the time series, i.e. the relationship between the current value yt and the past errors ∈t-1, ∈t-2, …, ∈t-q.

[0092] ∈t-i represents the error term of the time series at time point t-i; role: represents the past q prediction errors, used to adjust the prediction of the current value yt.

[0093] ∈t represents the error term of the time series at time point t; role: represents the random noise or unpredictable part at the current time, usually assumed to be white noise with mean 0 and constant variance.

[0094] p is the autoregressive order; role: represents the number of past observation values used in the model, i.e. yt-1, yt-2, …, yt-p.

[0095] q is the moving average order; role: represents the number of past error terms used in the model, i.e. ∈t-1, ∈t-2, …, ∈t-q.

[0096] In the above manner, the time series analysis (such as ARIMA model) is used to predict the change of environmental parameters in the future period of time, and the ward environment is adjusted in advance.

[0097] Random noise (∈t): represents the unpredictable random fluctuations at the current time.

[0098] In this embodiment, for example, the detection value of PH2.5 in the ward is detected, and yt is analyzed and output dynamically adjusted according to the value of PH2.5 in the past period of time to predict the next time point, and the ward environment value is adjusted according to the predicted value and trend to control the air conditioner and air purifier, and the like; for another example, the oxygen density in the ward can be predicted to give nursing suggestions for different patients to give oxygen suggestions in different time periods.

[0099] Step S1 further includes:

[0100] According to the physiological parameters of the patient in the smart ward obtained, and using the following formula, the physiological parameter output value is calculated, and the physiological parameter output value is taken as the physiological parameter in step S2;

[0101] f(x) = sign(∑ i＝1n αiyiK(xi, x) + b)

[0102] Where f(x) represents the output value of the classification decision function; function: represents the predicted category of the input sample x, and the output value is +1 or -1, representing two classification results (such as normal or abnormal patient state).

[0103] sign is a sign function; function: converts the value in the parentheses into a classification result, and if the value in the parentheses is ≥0, f(x) = +1. If the value in the parentheses is <0, f(x) = -1.

[0104] ∑ i＝1n is a summation symbol, indicating the summation of all support vectors i = 1 to n; function: calculates the contribution of all support vectors to the classification result.

[0105] αi is the solution of the support vector machine optimization problem; function: represents the weight of the ith support vector, reflecting the importance of the support vector to the classification decision. Value range: 0 ≤ αi ≤ C, where C is a regularization parameter.

[0106] yi is the true class label of the ith support vector; function: represents the class of the support vector, taking the value of +1 or -1.

[0107] K(xi, x) is a kernel function; function: used to calculate the similarity between the input sample x and the ith support vector xi. The kernel function maps the data to a high-dimensional space, so that the non-linear classification problem becomes linearly separable in the high-dimensional space.

[0108] xi is the ith support vector; function: represents the sample point in the training set that plays a key role in the classification decision.

[0109] x represents the input sample to be classified; function: represents the patient state data (such as physiological parameters) that needs to be evaluated.

[0110] b represents a bias term; function: adjust the position of the classification hyperplane, so that the classification result is more accurate.

[0111] n represents the total number of support vectors. Function: represent the number of support vectors participating in the classification decision.

[0112] In the above manner, the physiological parameters of the patient are classified and evaluated using a machine learning algorithm (such as a support vector machine SVM), to determine whether the patient's condition is normal.

[0113] In this embodiment, the system monitors and collects patient vital sign information at a time point, including but not limited to height, weight, respiration, blood pressure, blood oxygen, heart rate, grip strength, and other patient-related information. Each sample is x in the formula, then weighted calculation is performed, and finally the patient's recovery degree is obtained through the algorithm, so as to help medical staff determine whether to increase or reduce nursing investment.

[0114] S2, comprehensive analysis is performed according to the environmental parameters and the physiological parameters, and a corresponding analysis result is obtained;

[0115] S3, according to the analysis result, a control instruction for adjusting the environmental equipment in the smart ward is generated and / or corresponding guidance information is sent to the management end. The environmental equipment includes one or more of an air conditioner, a lighting lamp and an exhaust fan. The air conditioner can adjust the temperature and humidity in the ward, the lighting lamp can adjust the illumination in the ward, and the exhaust fan can adjust the air quality in the ward.

[0116] The smart ward management method based on the Internet of Things provided by the application obtains the environmental parameters in the smart ward and the physiological parameters of the patients in the smart ward; comprehensive analysis is performed according to the environmental parameters and the physiological parameters, and a corresponding analysis result is obtained; then, according to the analysis result, a control instruction for adjusting the environmental equipment in the smart ward is generated and / or corresponding guidance information is sent to the management end, so that the ward environment and the patient state are combined to realize integrity and synergy. And through the control instruction and / or guidance information, the intelligent management, real-time monitoring and response of the ward are realized, the timeliness of medical services is improved, the dynamic allocation of medical resources is beneficial, the resource utilization efficiency is improved, the operation process is greatly simplified, and the use experience of medical staff and patients is improved.

[0117] On the basis of the above, a resource optimization allocation algorithm is additionally provided, a linear programming model is used to optimize the allocation of medical resources, and the utilization of resources is maximized. The specific algorithm is as follows:

[0118] Formula: Maximize Z = ∑ i＝1n CiXi

[0119] Z: Objective value that needs to be maximized. Ci: Coefficient of the ith decision variable Xi (such as single environment, etc.). Xi: The ith decision variable (such as illness, etc.). n: Total number of decision variables.

[0120] To achieve the above management method, a sensor data acquisition module, a data processing and analysis module, a control instruction execution module, and a user interaction module are configured, as follows:

[0121] Sensor data acquisition module: used for collecting data obtained by environmental sensors and physiological parameter sensors, and transmitting the data to the data processing and analysis module through a hardware interface.

[0122] Data processing and analysis module: used for receiving data transmitted by the sensor data acquisition module, calling corresponding algorithms for analysis, generating control instructions and warning information, and sending them to the control instruction execution module and the user interaction module, respectively.

[0123] Control instruction execution module: receives control instructions sent by the control instruction execution module, sends them to the corresponding control devices through the control interface, and adjusts the ward environment.

[0124] User interaction module: receives the analysis results and warning information of the data processing and analysis module, and displays them to medical staff and patients through mobile devices or fixed terminals.

[0125] The hardware interfaces used in this scheme include sensor interfaces, control interfaces, and communication interfaces.

[0126] Sensor interface: used for connecting various environmental sensors and physiological parameter sensors, supporting multiple communication protocols (such as I2C, SPI, UART, etc.).

[0127] Control interface: used for connecting control devices in the ward (such as air conditioners, lights, curtains, etc.), supporting wireless communication (such as ZigBee, Wi-Fi, Bluetooth, etc.).

[0128] Communication interface: used for data transmission between modules within the system, supporting wired (such as Ethernet) and wireless (such as Wi-Fi, 4G / 5G) communication.

[0129] The software interfaces used in this scheme include data receiving interfaces, data processing interfaces, and control instruction interfaces.

[0130] Data receiving interface: used for receiving data from sensors, supporting multiple data formats (such as JSON, XML, etc.).

[0131] Data processing interface: used for calling algorithms and analysis functions of the data processing module.

[0132] Control instruction interface: for sending control instructions to control devices, supporting remote control and local control.

[0133] Preferred embodiment two:

[0134] Referring to Figure 2 The application provides a smart ward management system based on Internet of Things, which comprises a processor 1 and a memory 2, the memory 2 stores programs or instructions, and the programs or instructions are executed by the processor 1 to realize the following steps:

[0135] S1, acquiring environmental parameters in the smart ward and physiological parameters of patients in the smart ward;

[0136] S2, comprehensively analyzing the environmental parameters and the physiological parameters to obtain corresponding analysis results;

[0137] S3, generating control instructions for adjusting environmental equipment in the smart ward and / or sending corresponding guidance information to a management end according to the analysis results.

[0138] Further, the programs or instructions executed by the processor further realize the following steps:

[0139] According to the acquired environmental parameters in the smart ward, the environmental parameter observation value is calculated by using the following formula, and the environmental parameter observation value is used as the environmental parameter in step S2.

[0140] yt=c+∑ i＝1 pφiyt-i+∑ i＝1 qθi∈t-i+∈t

[0141] Wherein, yt represents the observation value of the time series at time point t; c represents a constant term; φi is an autoregressive coefficient, representing the influence of the observation values of the past p time points on the current value; yt-i represents the observation value of the time series at time point t-i; θi is a moving average coefficient, representing the influence of the error terms of the past q time points on the current value; ∈t-i represents the error term of the time series at time point t-i; ∈t represents the error term of the time series at time point t; p is the autoregressive order; q is the moving average order.

[0142] Further, the programs or instructions executed by the processor further realize the following steps:

[0143] According to the acquired physiological parameters of patients in the smart ward, the physiological parameter output value is calculated by using the following formula, and the physiological parameter output value is used as the physiological parameter in step S2.

[0144] f(x)=sign(∑ i＝1n αiyiK(xi,x)+b)

[0145] Where f(x) represents the output value of the classification decision function; sign is the sign function; ∑ i＝1n is the summation symbol, indicating the summation of all support vectors from i = 1 to n; αi is the solution to the support vector machine optimization problem; yi is the true category label of the i-th support vector; K(xi, x) is the kernel function; xi is the i-th support vector; x represents the input sample to be classified; b represents the bias term; and n represents the total number of support vectors.

[0146] Furthermore, when the program or instruction is executed by the processor, the following steps are also implemented: the environmental parameters include temperature, humidity, light and air quality; the physiological parameters include heart rate, blood pressure and blood oxygen saturation.

[0147] Furthermore, when the program or instruction is executed by the processor, the following steps are also implemented: the environmental equipment includes one or more of an air conditioner, a lighting lamp, and an exhaust fan.

[0148] The present invention has been described with reference to the above embodiments and accompanying drawings. However, the above embodiments are merely exemplary embodiments of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the present invention. On the contrary, modifications and equivalents falling within the spirit and scope of the claims are intended to be within the scope of the present invention.

Claims

1. The method for managing smart wards based on the Internet of Things is characterized by: The following steps are involved: S1. Obtain environmental parameters in the smart ward and physiological parameters of patients in the smart ward; S2. Conduct comprehensive analysis based on environmental parameters and physiological parameters to obtain corresponding analysis results; S3. Based on the analysis results, generate control instructions for adjusting the environmental equipment in the smart ward and / or send corresponding guidance information to the management end.

2. The method for managing smart wards based on the Internet of Things according to claim 1, characterized in that: Step S1 further includes: Based on the acquired environmental parameters in the smart ward, the following formula is used to calculate the observed environmental parameter value, which is used as the environmental parameter in step S2; Among them, yt represents the observed value of the time series at time point t; c represents the constant term; φi is the autoregressive coefficient, which represents the impact of the observed value at the past p time points on the current value; yt-i represents the observed value of the time series at time point ti; θi is the sliding average coefficient, which represents the impact of the error term at the past q time points on the current value; ∈ti represents the error term of the time series at time point ti; ∈t represents the error term of the time series at time point t; p is the autoregressive order; q is the sliding average order.

3. The method for managing smart wards based on the Internet of Things according to claim 1, characterized in that: Step S1 further includes: Based on the acquired physiological parameters of the patient in the smart ward, the following formula is used to calculate the physiological parameter output value, and the physiological parameter output value is used as the physiological parameter in step S2; Where f(x) represents the output value of the classification decision function; sign is the sign function; ∑ i＝1n is the summation symbol, indicating the summation of all support vectors from i = 1 to n; αi is the solution to the support vector machine optimization problem; yi is the true category label of the i-th support vector; K(xi, x) is the kernel function; xi is the i-th support vector; x represents the input sample to be classified; b represents the bias term; and n represents the total number of support vectors.

4. The method for managing smart wards based on the Internet of Things according to claim 1, characterized in that: The environmental parameters include temperature, humidity, light and air quality; the physiological parameters include heart rate, blood pressure and blood oxygen saturation.

5. The method for managing smart wards based on the Internet of Things according to claim 1, characterized in that: The environmental equipment includes one or more of an air conditioner, a lighting lamp and an exhaust fan.

6. Based on the Internet of Things smart ward management system, it is characterized by: The system comprises a processor and a memory, wherein the memory stores a program or instruction, and when the program or instruction is executed by the processor, the following steps are implemented: S1. Obtain environmental parameters in the smart ward and physiological parameters of patients in the smart ward; S2. Conduct comprehensive analysis based on environmental parameters and physiological parameters to obtain corresponding analysis results; S3. Based on the analysis results, generate control instructions for adjusting the environmental equipment in the smart ward and / or send corresponding guidance information to the management end.

7. The IoT-based smart ward management system according to claim 6 is characterized in that: When the program or instruction is executed by the processor, the following steps are further implemented: Based on the acquired environmental parameters in the smart ward, the following formula is used to calculate the observed environmental parameter value, which is used as the environmental parameter in step S2; yt=c+∑ i＝1 pφiyt-i+∑ i＝1 qθi∈t-i+∈t Among them, yt represents the observed value of the time series at time point t; c represents the constant term; φi is the autoregressive coefficient, which represents the impact of the observed value at the past p time points on the current value; yt-i represents the observed value of the time series at time point ti; θi is the sliding average coefficient, which represents the impact of the error term at the past q time points on the current value; ∈ti represents the error term of the time series at time point ti; ∈t represents the error term of the time series at time point t; p is the autoregressive order; q is the sliding average order.

8. The IoT-based smart ward management system according to claim 6 is characterized in that: When the program or instruction is executed by the processor, the following steps are further implemented: Based on the acquired physiological parameters of the patient in the smart ward, the following formula is used to calculate the physiological parameter output value, and the physiological parameter output value is used as the physiological parameter in step S2; f(x)=sign(∑ i＝1n αiyiK(xi,x)+b) Where f(x) represents the output value of the classification decision function; sign is the sign function; ∑ i＝1n is the summation symbol, indicating the summation of all support vectors from i = 1 to n; αi is the solution to the support vector machine optimization problem; yi is the true category label of the i-th support vector; K(xi, x) is the kernel function; xi is the i-th support vector; x represents the input sample to be classified; b represents the bias term; and n represents the total number of support vectors.

9. The IoT-based smart ward management system according to claim 6, characterized in that: When the program or instruction is executed by the processor, the following steps are further implemented: the environmental parameters include temperature, humidity, light and air quality; the physiological parameters include heart rate, blood pressure and blood oxygen saturation.

10. The IoT-based smart ward management system according to claim 6, characterized in that: When the program or instruction is executed by the processor, the following steps are further implemented: the environmental equipment includes one or more of an air conditioner, a lighting lamp, and an exhaust fan.

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