Medical equipment monitoring method and device based on Internet of Things, and medical equipment

Abstract

The embodiment of the invention provides a medical equipment monitoring method and device based on the Internet of Things and medical equipment. The method comprises the following steps: acquiring operation data of the medical equipment; obtaining internal component failure data and contact material failure data of the medical equipment; performing fusion according to the operation data, the contact material failure data and the internal component failure data to obtain fusion probability distribution mean value data and standard deviation and variance data; calculating the failure age limit of the medical equipment through the probability distribution mean value data, the standard deviation data and the variance data; generating multiple types of alarm information according to the failure age limit, and uploading the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected with the Internet of Things terminal; and controlling the Internet of Things terminal to obtain the operation image, and when the operation image comprises a human face, outputting the multiple types of alarm information, thereby accurately calculating the time limit of the failure of the medical equipment, improving the stability of the medical equipment, reducing the fault occurrence rate of the equipment, reminding a user to replace the equipment in time, and ensuring the safety of the equipment.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a medical equipment monitoring method based on the Internet of Things, a medical equipment monitoring device based on the Internet of Things, a medical equipment and a computer-readable storage medium. Background Art

[0002] Some existing medical devices are in direct contact with patients, and their quality and performance are directly related to the life safety and health of patients. In terms of production, strict testing can be carried out to ensure that medical devices comply with relevant standards and regulations during design, manufacturing, packaging, transportation and use, and reduce medical accidents caused by device failure or quality problems. During use, sensor technology, wireless communication technology and cloud computing technology can be used to monitor the working status and performance of the equipment in real time to ensure its normal operation and provide the best medical services. However, the failure of medical equipment is a very serious matter for patients, especially emergency patients, so how to monitor the failure time limit of medical equipment is an urgent problem to be solved in current technology. Summary of the invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a medical device monitoring method based on the Internet of Things, a medical device monitoring device based on the Internet of Things, a medical device and a computer-readable storage medium that overcome the above problems or at least partially solve the above problems.

[0004] In order to solve the above problems, an embodiment of the present invention discloses a medical device monitoring method based on the Internet of Things, wherein the medical device includes a medical device in contact with a patient; the method includes:

[0005] Obtaining operating data of the medical device;

[0006] Obtaining failure data of internal components and contact materials of the medical device;

[0007] According to the operation data, contact material failure data and internal component failure data, fusion is performed to obtain fusion probability distribution mean data and standard deviation and variance data;

[0008] The expiration date of the medical device is calculated by using the probability distribution mean data, standard deviation and variance data;

[0009] Generate multiple types of alarm information according to the expiration period, and upload the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal;

[0010] The Internet of Things terminal is controlled to obtain an operation image, and when the operation image includes a face, the multiple types of alarm information are output.

[0011] Preferably, the operating data includes respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters; the operating data of the medical device obtained includes:

[0012] The respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters of the medical device are obtained.

[0013] Preferably, the internal component failure data includes active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters; the internal component failure data and contact material failure data of the medical device obtained include:

[0014] Active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters of the medical device are obtained.

[0015] Preferably, the operation data, contact material failure data and internal component failure data are fused to obtain fused probability distribution mean data and standard deviation and variance data, including:

[0016] Combine and filter the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters to obtain specific filtered data;

[0017] Amplifying and calibrating the specific filtering data, contact material failure data, and internal component failure data to obtain calibration data;

[0018] The mean, standard deviation and variance of the calibration data are calculated to obtain the mean data, standard deviation and variance data of the fused probability distribution.

[0019] Preferably, the expiration date of the medical device is calculated by using the probability distribution mean data and standard deviation and variance data, including:

[0020] Convert the probability distribution mean data into the mean vector for each category;

[0021] The covariance matrix of each category is obtained by calculating the mean vector;

[0022] The failure age of medical equipment is obtained based on the preset failure probability distribution model and covariance matrix, standard deviation and variance data.

[0023] Preferably, obtaining the failure age of the medical device according to a preset failure probability distribution model and standard deviation and variance data includes:

[0024] Calculate the equipment adjustment factor according to the contact material failure data;

[0025] The equipment adjustment coefficient is applied to the preset failure probability distribution model, and the failure age of the medical equipment is obtained according to the preset failure probability distribution model and the standard deviation and variance data.

[0026] The embodiment of the present invention discloses a medical device monitoring device based on the Internet of Things, wherein the medical device includes a medical device in contact with a patient; the device includes:

[0027] A first acquisition module, used to acquire the operation data of the medical device;

[0028] A second acquisition module is used to acquire the internal component failure data and contact material failure data of the medical device;

[0029] A fusion module, used to fuse the operation data, contact material failure data and internal component failure data to obtain fusion probability distribution mean data and standard deviation and variance data;

[0030] A calculation module, used to calculate the expiration date of the medical device through the probability distribution mean data, standard deviation and variance data;

[0031] An alarm information module, used to generate multiple types of alarm information according to the expiration period, and upload the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal;

[0032] The output module is used to control the Internet of Things terminal to obtain an operation image, and when the operation image includes a face, output the multiple types of alarm information.

[0033] Preferably, the operating data includes respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters; the first acquisition module includes:

[0034] The first acquisition submodule is used to acquire the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters of the medical device.

[0035] An embodiment of the present invention discloses a medical device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned medical device monitoring method based on the Internet of Things when executing the computer program.

[0036] An embodiment of the present invention discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned medical device monitoring method based on the Internet of Things are implemented.

[0037] The embodiments of the present invention include the following advantages:

[0038] In an embodiment of the present invention, the medical device includes a medical device that contacts a patient; the medical device monitoring method based on the Internet of Things includes: obtaining operation data of the medical device; obtaining failure data of internal components and contact materials of the medical device; fusing the operation data, contact material failure data and internal component failure data to obtain fused probability distribution mean data and standard deviation and variance data; calculating the expiration date of the medical device through the probability distribution mean data and standard deviation and variance data; generating multiple types of alarm information according to the expiration date, and uploading the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal; controlling the Internet of Things terminal to obtain an operation image, and when the operation image includes a face, outputting the multiple types of alarm information, the time limit of medical device failure can be accurately calculated, the stability of the medical device can be improved, the failure rate of the device can be reduced, and the user can be reminded to replace the device in time, thereby ensuring the safety of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 is a schematic diagram of an embodiment of a medical device monitoring method based on the Internet of Things according to an embodiment of the present invention;

[0041] Figure 2 It is a structural block diagram of an embodiment of a medical equipment monitoring device based on the Internet of Things according to an embodiment of the present invention;

[0042] Figure 3 The internal structure diagram of a medical device according to an embodiment is shown. DETAILED DESCRIPTION

[0043] In order to make the technical problems, technical solutions and beneficial effects solved by the embodiments of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] In a core concept of an embodiment of the present invention, the relevant parameters of the materials of the medical device that directly contacts the patient are taken as one of the variables to construct a failure probability distribution model. In addition, the relevant parameters of multiple internal components of the medical device are calculated to obtain the fused probability distribution mean data and standard deviation and variance data to construct a complete failure probability distribution model. The failure probability distribution model constructed by the probability distribution mean data and standard deviation and variance data is used to obtain the failure age of the medical device. Specifically, the mean vector on each category is calculated, and then the covariance matrix of each category is obtained, so as to calculate the failure age of the medical device. Multiple types of alarm information are generated according to the failure age, and the multiple types of alarm information are uploaded to the Internet of Things server. The time limit of medical device failure can be accurately calculated, the stability of the medical device is improved, the failure rate of the device is reduced, and the user is reminded to replace the device in time, thereby ensuring the safety of the device.

[0045] Reference Figure 1 , shows a schematic diagram of an embodiment of a medical device monitoring method based on the Internet of Things according to an embodiment of the present invention, wherein the medical device includes a medical device in contact with a patient; specifically, the following steps may be included:

[0046] Step S101, obtaining the operation data of the medical device;

[0047] In a system architecture, the Internet of Things server of the embodiment of the present invention can be connected to a variety of medical devices and Internet of Things terminals, and the data of various medical devices and Internet of Things terminals can be uploaded to the Internet of Things server. The method can be applied to the Internet of Things terminal, and the medical devices include ventilators, peripheral blood monitors, electrocardiogram monitors, electroencephalogram monitors, etc. The embodiment of the present invention does not impose too many restrictions on this; the Internet of Things terminal can include various different terminals such as personal computers, laptops, smart phones, smart terminals and portable wearable devices. The embodiment of the present invention does not limit the specific type of the terminal. The operating system of the terminal may include Android, Harmony OS, IOS, Windows Phone, Windows, etc., and the present invention does not impose too many restrictions on this.

[0048] The Internet of Things server described in the embodiment of the present invention is connected to the Internet of Things terminal; the Internet of Things terminal can obtain a monitoring operation image, and when the monitoring operation image includes a face, output the multiple types of alarm information.

[0049] In an embodiment of the present invention, the operating data may include historical data generated by the medical device, which may include respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters; the operating data of the medical device obtained includes:

[0050] The respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters of the medical device are obtained; that is, in the embodiment of the present invention, the operating data refers to multiple different types of data generated during the operation of the medical device, and the embodiment of the present invention does not impose too many restrictions on this.

[0051] Step S102, obtaining the internal component failure data and contact material failure data of the medical device;

[0052] In actual application to the embodiments of the present invention, the internal component failure data refers to the failure time limit and failure rate of the internal components of the medical device, and the internal component failure data may include active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, signal processing and interference suppression component failure parameters and other types of component failure parameters; the active component may include a transistor, an integrated circuit IC, the transistor may include a bipolar transistor (BJT) and a field effect transistor (FET) such as MOSFET, JFET, etc., and the integrated circuit IC includes a microprocessor (MCU), a digital signal processor (DSP), and an operational amplifier; the passive component may include a resistor, a capacitor, and an inductor; the connector may include a plug, a socket, a pin header, a female header, a terminal block, a push button switch, a toggle switch, and a rotary switch; the sensor may include a temperature sensor, a humidity sensor, a light sensor, a relay, and a solenoid valve, etc., and the signal processing and interference suppression component may include a filter, a voltage stabilizer, a transient voltage suppressor, a fuse, and a thermal protector, etc. The embodiments of the present invention do not impose too many restrictions on the types of internal components;

[0053] The contact material failure data refers to the failure time limit and failure rate of the parts of the medical device that are in direct contact with the human body, such as the air bag mask of the ventilator, the blood pressure cuff of the peripheral blood monitor, electrocardiogram monitor, and electroencephalogram monitor, the surface contact electrodes and other parts that are in direct contact with the human body. The embodiments of the present invention do not impose too many restrictions on this.

[0054] In a preferred embodiment of the embodiments of the present invention, the internal component failure data includes active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters; the internal component failure data and contact material failure data of the medical device obtained include: obtaining the active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters of the medical device.

[0055] For example, the internal component failure data and contact material failure data are shown in the following table:

[0056]

[0057] Table 1: Internal component failure data and contact material failure data

[0058] Step S103, fusing the operation data, contact material failure data and internal component failure data to obtain fused probability distribution mean data and standard deviation and variance data;

[0059] In the embodiment of the present invention, the acquired operation data, contact material failure data and internal component failure data can be fused and calculated to obtain fused probability distribution mean data and standard deviation and variance data.

[0060] Specifically applied to the embodiment of the present invention, the operation data, contact material failure data and internal component failure data are fused to obtain the fused probability distribution mean data and standard deviation and variance data, including: combining and filtering the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters to obtain specific filtered data; amplifying and calibrating the specific filtered data with the contact material failure data and the internal component failure data to obtain calibration data; calculating the mean, standard deviation and variance of the calibration data to obtain the fused probability distribution mean data and standard deviation and variance data;

[0061] In the embodiment of the present invention, combined filtering is first performed on the operation data. First, the above-mentioned various operation data, such as respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters are successively converted into frequency domain data, and then filtered by a combined filter. The expression of the combined filter is as follows:

[0062]

[0063] Wherein, F represents the filtered data, f i Indicates the frequency corresponding to the maximum value of the sub-wave amplitude spectrum, cos[-2πf i / (1+k a t)] represents the stretched cosine function, J max represents the maximum value of the frequency curve envelope, t represents the time information, k a represents the first empirical coefficient, fl m Indicates that f i the frequency after stratification according to the threshold range, m represents the number of stratifications according to the threshold range, n=0, 1, 2······i, is a positive integer, representing the number of maximum frequency values;

[0064] In the embodiment of the present invention, the adaptive filter is combined with the wavelet transform filter to filter out the potential difference interference signal, reduce the root mean square error, improve the signal-to-noise ratio, ensure the accuracy of the combined filtering of the operating data, and improve the reliability of the system.

[0065] Furthermore, after obtaining the specific filtering data, the specific filtering data is amplified and calibrated with the contact material failure data and the internal component failure data to obtain calibration data; the mean, standard deviation and variance of the calibration data are calculated to obtain the mean data, standard deviation and variance data of the fused probability distribution; specifically, the contact material failure data is converted into a first calibration coefficient; the internal component failure data is converted into a second calibration coefficient, and the specific filtering data is amplified and calibrated by the first calibration coefficient and the first calibration coefficient to obtain calibration data; the mean, standard deviation and variance of the calibration data are calculated to obtain the mean data, standard deviation and variance data of the fused probability distribution, and the contact material failure data and the internal component failure data are used to calibrate the operating data, that is, the historical operating data is calibrated to ensure the accuracy of the data.

[0066] For example, the average value of X1a to X1e in the above table is calculated to obtain the mean value of internal component failure, and the average value of X2f to X2g in the above table is calculated to obtain the mean value of contact material failure;

[0067] Then, the product of the mean failure value of the internal components and the second empirical coefficient is determined as the first calibration coefficient; the product of the mean failure value of the contact material and the third empirical coefficient is determined as the second calibration coefficient; the above calibration method is only an example of an embodiment of the present invention, and calibration can also be performed in other ways, and the embodiment of the present invention does not impose too many restrictions on this.

[0068] Step S104, calculating the expiration date of the medical device through the probability distribution mean data, standard deviation and variance data;

[0069] Specifically applied to the embodiments of the present invention, after obtaining the probability distribution mean data and standard deviation and variance data, the expiration date of the medical device can be calculated based on the probability distribution mean data and standard deviation and variance data. Specifically, the expiration date of the medical device calculated based on the probability distribution mean data and standard deviation and variance data includes: converting the probability distribution mean data into a mean vector on each category; obtaining the covariance matrix of each category through the mean vector calculation; and obtaining the expiration date of the medical device based on a preset failure probability distribution model and the covariance matrix, standard deviation and variance data.

[0070] In the embodiment of the present invention, the operation data is first divided according to the data type to obtain multiple categories of operation data, such as division according to respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters, and then the mean vector of each category of data is calculated according to the probability distribution mean data;

[0071] It should be noted that the difference between the mean vector and the data in each category is used as a matrix operation to obtain the covariance matrix;

[0072] It should be noted that the expression of the preset failure probability distribution model is as follows:

[0073]

[0074] Among them, Σ z represents the covariance matrix, P(s) represents the comprehensive distribution probability, θ represents the sample data, β represents the equipment adjustment coefficient, ε represents the mean vector, σ 2 represents the variance of the operating data, and σ represents the standard deviation of the operating data. It should be noted that the comprehensive distribution probability refers to the probability of failure of a certain category of components in a certain year. For example, the output of a comprehensive distribution probability may be: the probability of a filter failing in the 5th year is 75%. Sample data refers to multiple types of newly input operating data.

[0075] In a preferred embodiment of the embodiments of the present invention, the failure age of the medical device is obtained according to the preset failure probability distribution model and the standard deviation and variance data, including: calculating the equipment adjustment coefficient β according to the contact material failure data; applying the equipment adjustment coefficient β to the preset failure probability distribution model, and obtaining the failure age of the medical device according to the preset failure probability distribution model and the standard deviation and variance data.

[0076] Step S105: Generate multiple types of alarm information according to the expiration period, and upload the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal;

[0077] In an embodiment of the present invention, after the expiration date is obtained, multiple types of alarm information can be generated according to the expiration date. The alarm information may include the expiration date, component type, contact material type, etc. The embodiment of the present invention does not impose too many restrictions on this, and the multiple types of alarm information are uploaded to the Internet of Things server.

[0078] Step S106: Control the Internet of Things terminal to obtain an operation image, and when the operation image includes a face, output the multiple types of alarm information.

[0079] Further applied to the embodiment of the present invention, the Internet of Things server can control the Internet of Things terminal through instructions to obtain the operation image, and identify whether the operation image includes a face. When a face is included, it means that the user is in front of the Internet of Things terminal, and output the multiple types of alarm information to prompt the user to pay attention to the alarm information.

[0080] In an embodiment of the present invention, the medical device includes a medical device that contacts a patient; the medical device monitoring method based on the Internet of Things includes: obtaining operation data of the medical device; obtaining failure data of internal components and contact materials of the medical device; fusing the operation data, contact material failure data and internal component failure data to obtain fused probability distribution mean data and standard deviation and variance data; calculating the expiration date of the medical device through the probability distribution mean data and standard deviation and variance data; generating multiple types of alarm information according to the expiration date, and uploading the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal; controlling the Internet of Things terminal to obtain an operation image, and when the operation image includes a face, outputting the multiple types of alarm information, the time limit of medical device failure can be accurately calculated, the stability of the medical device can be improved, the failure rate of the device can be reduced, and the user can be reminded to replace the device in time, thereby ensuring the safety of the device.

[0081] It should be noted that, for the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that this embodiment is not limited by the order of the actions described, because according to this embodiment, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by this embodiment.

[0082] Reference Figure 2 , shows a structural block diagram of an embodiment of a medical device monitoring device based on the Internet of Things of this embodiment, wherein the medical device includes a medical device in contact with a patient; specifically, the following modules may be included:

[0083] A first acquisition module 301 is used to acquire the operation data of the medical device;

[0084] The second acquisition module 302 is used to acquire the internal component failure data and contact material failure data of the medical device;

[0085] A fusion module 303 is used to fuse the operation data, contact material failure data and internal component failure data to obtain fusion probability distribution mean data and standard deviation and variance data;

[0086] A calculation module 304 is used to calculate the expiration date of the medical device through the probability distribution mean data and standard deviation and variance data;

[0087] The alarm information module 305 is used to generate multiple types of alarm information according to the expiration period, and upload the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to the Internet of Things terminal;

[0088] The output module 306 is used to control the Internet of Things terminal to obtain an operation image, and when the operation image includes a face, output the multiple types of alarm information.

[0089] Preferably, the operating data includes respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters; the first acquisition module includes:

[0090] The first acquisition submodule is used to acquire the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters of the medical device.

[0091] Preferably, the internal component failure data includes active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters; the second acquisition module includes:

[0092] The second acquisition submodule is used to acquire active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters of the medical device.

[0093] Preferably, the fusion module includes:

[0094] A combined filtering submodule, for combining and filtering the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters to obtain specific filtering data;

[0095] an amplification and calibration submodule, for amplifying and calibrating the specific filtering data with the contact material failure data and the internal component failure data to obtain calibration data;

[0096] The calculation submodule is used to calculate the mean, standard deviation and variance of the calibration data to obtain the mean data, standard deviation and variance data of the fused probability distribution.

[0097] Preferably, the calculation module includes:

[0098] The conversion submodule is used to convert the probability distribution mean data into the mean vector for each category;

[0099] A covariance matrix submodule, used to obtain the covariance matrix of each category through the mean vector calculation;

[0100] The failure age submodule is used to obtain the failure age of medical equipment based on the preset failure probability distribution model and covariance matrix, standard deviation, and variance data.

[0101] Preferably, the expiration date submodule includes:

[0102] An equipment adjustment coefficient obtaining unit, used for calculating the equipment adjustment coefficient according to the contact material failure data;

[0103] The failure age acquisition unit is used to apply the equipment adjustment coefficient to the preset failure probability distribution model, and obtain the failure age of the medical equipment according to the preset failure probability distribution model and standard deviation and variance data.

[0104] Each module in the above-mentioned medical device monitoring system based on the Internet of Things can be implemented in whole or in part through software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the medical device in the form of hardware, or can be stored in the memory in the medical device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0105] The IoT-based medical device monitoring system provided above can be used to execute the IoT-based medical device monitoring method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0106] In one embodiment, a medical device is provided, whose internal structure diagram can be as follows: Figure 3 As shown. The medical device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the medical device is used to provide computing and control capabilities. The memory of the medical device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the medical device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for a medical device monitoring device based on the Internet of Things is implemented. The display screen of the medical device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the medical device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the housing of the medical device, or an external keyboard, touchpad or mouse, etc.

[0107] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the present application scheme, and does not constitute a limitation on the medical device to which the present application scheme is applied. The specific medical device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0108] In one embodiment, a medical device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the above embodiment are implemented:

[0109] Obtaining operating data of the medical device;

[0110] Obtaining failure data of internal components and contact materials of the medical device;

[0111] According to the operation data, contact material failure data and internal component failure data, fusion is performed to obtain fusion probability distribution mean data and standard deviation and variance data;

[0112] The expiration date of the medical device is calculated by using the probability distribution mean data, standard deviation and variance data;

[0113] Generate multiple types of alarm information according to the expiration period, and upload the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal;

[0114] The Internet of Things terminal is controlled to obtain an operation image, and when the operation image includes a face, the multiple types of alarm information are output.

[0115] Preferably, the operating data includes respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters; the operating data of the medical device obtained includes:

[0116] The respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters of the medical device are obtained.

[0117] Preferably, the internal component failure data includes active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters; the internal component failure data and contact material failure data of the medical device obtained include:

[0118] Active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters of the medical device are obtained.

[0119] Preferably, the operation data, contact material failure data and internal component failure data are fused to obtain fused probability distribution mean data and standard deviation and variance data, including:

[0120] Combine and filter the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters to obtain specific filtered data;

[0121] Amplifying and calibrating the specific filtering data, contact material failure data, and internal component failure data to obtain calibration data;

[0122] The mean, standard deviation and variance of the calibration data are calculated to obtain the mean data, standard deviation and variance data of the fused probability distribution.

[0123] Preferably, the expiration date of the medical device is calculated by using the probability distribution mean data and standard deviation and variance data, including:

[0124] Convert the probability distribution mean data into the mean vector for each category;

[0125] The covariance matrix of each category is obtained by calculating the mean vector;

[0126] The failure age of medical equipment is obtained based on the preset failure probability distribution model and covariance matrix, standard deviation and variance data.

[0127] Preferably, obtaining the failure age of the medical device according to a preset failure probability distribution model and standard deviation and variance data includes:

[0128] Calculate the equipment adjustment factor according to the contact material failure data;

[0129] The equipment adjustment coefficient is applied to the preset failure probability distribution model, and the failure age of the medical equipment is obtained according to the preset failure probability distribution model and the standard deviation and variance data.

[0130] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above embodiment are implemented:

[0131] Obtaining operating data of the medical device;

[0132] Obtaining failure data of internal components and contact materials of the medical device;

[0133] According to the operation data, contact material failure data and internal component failure data, fusion is performed to obtain fusion probability distribution mean data and standard deviation and variance data;

[0134] The expiration date of the medical device is calculated by using the probability distribution mean data, standard deviation and variance data;

[0135] Generate multiple types of alarm information according to the expiration period, and upload the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal;

[0136] The Internet of Things terminal is controlled to obtain an operation image, and when the operation image includes a face, the multiple types of alarm information are output.

[0137] Preferably, the operating data includes respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters; the operating data of the medical device obtained includes:

[0138] The respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters of the medical device are obtained.

[0139] Preferably, the internal component failure data includes active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters; the internal component failure data and contact material failure data of the medical device obtained include:

[0140] Active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters of the medical device are obtained.

[0141] Preferably, the operation data, contact material failure data and internal component failure data are fused to obtain fused probability distribution mean data and standard deviation and variance data, including:

[0142] Combine and filter the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters to obtain specific filtered data;

[0143] Amplifying and calibrating the specific filtering data, contact material failure data, and internal component failure data to obtain calibration data;

[0144] The mean, standard deviation and variance of the calibration data are calculated to obtain the mean data, standard deviation and variance data of the fused probability distribution.

[0145] Preferably, the expiration date of the medical device is calculated by using the probability distribution mean data and standard deviation and variance data, including:

[0146] Convert the probability distribution mean data into the mean vector for each category;

[0147] The covariance matrix of each category is obtained by calculating the mean vector;

[0148] The failure age of medical equipment is obtained based on the preset failure probability distribution model and covariance matrix, standard deviation and variance data.

[0149] Preferably, obtaining the failure age of the medical device according to a preset failure probability distribution model and standard deviation and variance data includes:

[0150] Calculate the equipment adjustment factor according to the contact material failure data;

[0151] The equipment adjustment coefficient is applied to the preset failure probability distribution model, and the failure age of the medical equipment is obtained according to the preset failure probability distribution model and the standard deviation and variance data.

[0152] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0153] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0154] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0158] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0159] The above is a detailed introduction to a medical device monitoring method based on the Internet of Things, a medical device monitoring device based on the Internet of Things, a medical device and a computer-readable storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A medical equipment monitoring method based on the Internet of Things, characterized in that: The medical device comprises a medical device in contact with a patient; the method comprises: Obtaining operating data of the medical device; Obtaining failure data of internal components and contact materials of the medical device; According to the operation data, contact material failure data and internal component failure data, fusion is performed to obtain fusion probability distribution mean data and standard deviation and variance data; The expiration date of the medical device is calculated by using the probability distribution mean data, standard deviation and variance data; Generate multiple types of alarm information according to the expiration period, and upload the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal; The Internet of Things terminal is controlled to obtain an operation image, and when the operation image includes a face, the multiple types of alarm information are output.

2. The method for monitoring medical equipment based on the Internet of Things according to claim 1, characterized in that: The operating data includes respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters; the operating data of the medical device obtained includes: The respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters of the medical device are obtained.

3. The method for monitoring medical equipment based on the Internet of Things according to claim 1, characterized in that: The internal component failure data includes active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters; The acquired internal component failure data and contact material failure data of the medical device include: Active component failure parameters, passive component failure parameters, connector failure parameters, sensor failure parameters, and signal processing and interference suppression component failure parameters of the medical device are obtained.

4. The method for monitoring medical equipment based on the Internet of Things according to claim 2, characterized in that: The operation data, contact material failure data and internal component failure data are fused to obtain fused probability distribution mean data and standard deviation and variance data, including: Combine and filter the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters to obtain specific filtered data; Amplifying and calibrating the specific filtering data, contact material failure data, and internal component failure data to obtain calibration data; The mean, standard deviation and variance of the calibration data are calculated to obtain the mean data, standard deviation and variance data of the fused probability distribution.

5. The method for monitoring medical equipment based on the Internet of Things according to claim 2, characterized in that: The expiration date of the medical device is calculated by using the probability distribution mean data, standard deviation, and variance data, including: Convert the probability distribution mean data into the mean vector for each category; The covariance matrix of each category is obtained by calculating the mean vector; The failure age of medical equipment is obtained based on the preset failure probability distribution model and covariance matrix, standard deviation and variance data.

6. The method for monitoring medical equipment based on the Internet of Things according to claim 5, characterized in that: The method of obtaining the failure age of the medical device according to the preset failure probability distribution model and standard deviation and variance data includes: Calculate the equipment adjustment factor according to the contact material failure data; The equipment adjustment coefficient is applied to the preset failure probability distribution model, and the failure age of the medical equipment is obtained according to the preset failure probability distribution model and the standard deviation and variance data.

7. A medical equipment monitoring device based on the Internet of Things, characterized in that: The medical device includes a medical device that contacts a patient; the device includes: A first acquisition module, used to acquire the operation data of the medical device; A second acquisition module is used to acquire the internal component failure data and contact material failure data of the medical device; A fusion module, used to fuse the operation data, contact material failure data and internal component failure data to obtain fusion probability distribution mean data and standard deviation and variance data; A calculation module, used to calculate the expiration date of the medical device through the probability distribution mean data, standard deviation and variance data; An alarm information module, used to generate multiple types of alarm information according to the expiration period, and upload the multiple types of alarm information to an Internet of Things server; wherein the Internet of Things server is connected to an Internet of Things terminal; The output module is used to control the Internet of Things terminal to obtain an operation image, and when the operation image includes a face, output the multiple types of alarm information.

8. The medical equipment monitoring device based on the Internet of Things according to claim 1, characterized in that: The operation data includes respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters; the first acquisition module includes: The first acquisition submodule is used to acquire the respiratory parameters, heart rate parameters, blood pressure parameters, blood oxygen parameters, electrocardiogram parameters and electroencephalogram parameters of the medical device.

9. A medical device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the medical device monitoring method based on the Internet of Things described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the medical device monitoring method based on the Internet of Things described in any one of claims 1 to 6 are implemented.

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