Intelligent sickbed control method and system, computer and storage medium

Abstract

The invention provides an intelligent sickbed control method and system, a computer and a storage medium. The method comprises the following steps: collecting bedridden data of a patient in real time based on a sensor at the bottom of a mattress; dynamically determining distribution range data of the pressure distribution data and the body movement data according to the data distribution characteristics; calculating according to a preset triangle membership function to obtain input fuzzy values of distribution range data of the body movement data and the pressure distribution data; based on a fuzzy control rule, according to the input fuzzy value, obtaining an output fuzzy value of the mattress hardness; and performing defuzzification processing on the output fuzzy value based on a gravity center method to calculate a target adjustment value of the mattress hardness, and adjusting the mattress hardness to the target adjustment value through a pneumatic system. Intelligent adjustment of the hardness of the mattress is achieved, and the requirements of different patients for body types and postures are met.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to an intelligent bed control method, system, computer and storage medium. Background Art

[0002] Traditional hospital beds have some limitations in the patient care process, such as the need to manually adjust the bed position, different requirements for the softness of the bed for each patient, and even the inability to adapt to the bed well during hospitalization, and lack of intelligence and high personalization. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a smart bed control method, system, computer and storage medium, aiming to solve the technical problems in the prior art of insufficient intelligence and high personalization of separate beds.

[0004] In order to achieve the above object, in a first aspect, the present invention provides: a smart bed control method, comprising the following steps: The sensor at the bottom of the mattress collects the patient's bed data in real time, wherein the bed data includes the patient's body movement data and pressure distribution data; The mean and standard deviation of each bed rest data are calculated based on the statistical learning method, and the distribution range data of the pressure distribution data and the body movement data are dynamically determined according to the data distribution characteristics; By using a fuzzy logic control algorithm and according to a preset triangle membership function, the input fuzzy value of the distribution range data of the body movement data and the pressure distribution data is calculated; Obtaining an output fuzzy value of the mattress hardness according to the input fuzzy value based on a fuzzy control rule; The output fuzzy value is defuzzified based on the center of gravity method to calculate the target adjustment value of the mattress hardness, and the mattress hardness is adjusted to the target adjustment value through the pneumatic system.

[0005] According to one aspect of the above technical solution, the step of dynamically determining the distribution range data of the pressure distribution data and the body motion data according to the data distribution characteristics specifically includes: Based on the following calculation expression, the distribution range data is calculated: ; ; ; ; ; ; In the formula, , and are the distribution range data, mean and standard deviation of the pressure distribution data, , and are the distribution range data, mean value and standard deviation of body motion data respectively, n represents the number of pressure distribution data and body motion data collected in bed, and are the i-th pressure distribution data in the bed resting data and the i-th body motion data in the bed resting data, respectively, and k is the confidence interval parameter; Based on the newly added data points collected in real time, the mean and standard deviation of the bed rest data are updated to obtain the mean update value and the standard deviation update value, so as to dynamically update the distribution range data.

[0006] According to one aspect of the above technical solution, the method further includes: The abnormal values ​​in the bed data are screened out according to the standard deviation, and if the pressure distribution data satisfies any of the following expressions, the pressure distribution data is determined to be abnormal data: ; If the body motion data satisfies any of the following expressions, the body motion data is determined to be abnormal data: .

[0007] According to one aspect of the above technical solution, the calculation expressions of the mean update value and the standard deviation update value are: ; ; ; ; In the formula, and are the standard deviation update value and mean update value of the pressure distribution data, and are the standard deviation update value and mean update value of body motion data, New data points for pressure distribution data, New data points for body motion data.

[0008] According to one aspect of the above technical solution, the fuzzy control rule is: When the pressure distribution data is within the first parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the third hardness level; When the pressure distribution data is within the first parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the second hardness level; When the pressure distribution data is within the second parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the first hardness level; When the pressure distribution data is within the second parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the second hardness level; When the pressure distribution data is within the third parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the first hardness level; When the pressure distribution data is within the third parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the third hardness level, wherein the parameter ranges of the pressure distribution data are, from low to high, the first parameter range, the second parameter range, and the third parameter range; the parameter ranges of the body movement data are, from low to high, the fourth range parameter and the fifth range parameter; and the mattress hardness outputs are, from soft to hard, the first hardness level, the second hardness level, and the third hardness level.

[0009] According to one aspect of the above technical solution, the calculation expression of the triangular membership function of the pressure distribution data is: ; ; ; In the formula, Indicates the low index membership of the pressure distribution data, Indicates the middle index membership of pressure distribution data, It represents the high index membership of the pressure distribution data, and P is the normalized value of the pressure distribution data; The calculation expression of the triangular membership function of the body motion data is: ; ; In the formula, Indicates the low index membership of body motion data, It represents the high index membership of body motion data, and M is the normalized value of body motion data; The calculation expression of the triangular membership function of the mattress hardness is: ; ; ; In the formula, Indicates the low index membership of the mattress hardness, Indicates the middle index membership of the mattress hardness, It represents the high index membership of mattress hardness, and y is the normalized value of mattress hardness.

[0010] In a second aspect, the present invention provides an intelligent bed control system, comprising: A data module, used to collect the patient's bed data in real time based on the sensor at the bottom of the mattress, wherein the bed data includes the patient's body movement data and pressure distribution data; The distribution module is used to calculate the mean and standard deviation of each bed rest data based on the statistical learning method, and dynamically determine the distribution range data of the pressure distribution data and the body movement data according to the data distribution characteristics; A fuzzy input module is used to obtain input fuzzy values ​​of distribution range data of body motion data and pressure distribution data through fuzzy logic control algorithm and according to a preset triangle membership function calculation; A fuzzy output module, used for obtaining an output fuzzy value of the mattress hardness according to the input fuzzy value based on a fuzzy control rule; The adjustment module is used to perform defuzzification processing on the output fuzzy value based on the center of gravity method to calculate the target adjustment value of the mattress hardness, and adjust the mattress hardness to the target adjustment value through the pneumatic system.

[0011] According to one aspect of the above technical solution, the distribution module is specifically used to calculate the distribution range data based on the following calculation expression: ; ; ; ; ; ; In the formula, , and are the distribution range data, mean and standard deviation of the pressure distribution data, , and are the distribution range data, mean value and standard deviation of body motion data respectively, n represents the number of pressure distribution data and body motion data collected in bed, and are the i-th pressure distribution data in the bed resting data and the i-th body motion data in the bed resting data, respectively, and k is the confidence interval parameter; Based on the newly added data points collected in real time, the mean and standard deviation of the bed rest data are updated to obtain the mean update value and the standard deviation update value, so as to dynamically update the distribution range data.

[0012] According to one aspect of the above technical solution, the system further includes: The abnormal data screening module is used to screen out abnormal values ​​in the bed data according to the standard deviation, and if the pressure distribution data satisfies any of the following expressions, the pressure distribution data is determined to be abnormal data: ; If the body motion data satisfies any of the following expressions, the body motion data is determined to be abnormal data: .

[0013] In a third aspect, the present invention further provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the intelligent bed control method as described in the above technical solution is implemented.

[0014] In a fourth aspect, the present invention further provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the intelligent bed control method as described in the above technical solution is implemented.

[0015] Compared with the prior art, the beneficial effects of the present invention are: dynamically determining the value range of pressure distribution and body motion variables through statistical learning methods, combining fuzzy logic control algorithms to control the motor and pneumatic system to adjust the hardness of the mattress, softening the mattress when the pressure is high to disperse the pressure and reduce the risk of pressure sores; enhancing support when the body moves frequently, realizing intelligent adjustment of the hardness of the mattress to adapt to the body shape and posture requirements of different patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the intelligent bed control method in the first embodiment of the present invention; Figure 2 A three-dimensional mapping relationship diagram between pressure distribution data, body movement data and mattress hardness in the first embodiment of the present invention; Figure 3 This is a structural block diagram of the intelligent bed control system in the second embodiment of the present invention; Figure 4 is a schematic diagram of the hardware structure of a computer in a third embodiment of the present invention; The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0017] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0020] Embodiment 1 See also Figure 1 , which is a flow chart of the intelligent bed control method in the first embodiment of the present invention. As shown in the figure, the method includes the following steps: Step S100, based on the sensor at the bottom of the mattress, the patient's bed data is collected in real time, and the bed data includes the patient's body movement data and pressure distribution data. The above sensors include pressure sensors, acceleration sensors and temperature sensors. The pressure sensor determines the patient's position and body pressure distribution by measuring the change in pressure, and the acceleration sensor is used to detect the patient's body movement and posture changes. The temperature sensor is used to monitor the temperature of the mattress to prevent overheating.

[0021] Step S200, the mean and standard deviation of each bed data are calculated based on the statistical learning method, and the distribution range data of the pressure distribution data and the body movement data are dynamically determined according to the data distribution characteristics. Pressure distribution and body movement data are key input variables for adjusting the hardness of the mattress. In order to adapt to the individual differences of different patients, the value range is dynamically determined by the statistical learning method, and combined with the fuzzy control rules, the hardness of the mattress can be adjusted more accurately.

[0022] Preferably, in this embodiment, the step of dynamically determining the distribution range data of the pressure distribution data and the body motion data according to the data distribution characteristics specifically includes: Based on the following calculation expression, the distribution range data is calculated: ; ; ; ; ; ; In the formula, , and are the distribution range data, mean and standard deviation of the pressure distribution data, , and are the distribution range data, mean value and standard deviation of body motion data respectively, n represents the number of pressure distribution data and body motion data collected in bed, and are respectively the i-th pressure distribution data in the bed-ridden data and the i-th body motion data in the bed-ridden data, and k is a confidence interval parameter. Preferably, in this embodiment, the value of k is 2, indicating a 95% confidence interval.

[0023] Based on the newly added data points collected in real time, the mean and standard deviation of the bed rest data are updated to obtain the mean update value and the standard deviation update value, so as to dynamically update the distribution range data.

[0024] Furthermore, in this embodiment, the calculation expressions of the mean update value and the standard deviation update value are: ; ; ; ; In the formula, and are the standard deviation update value and mean update value of the pressure distribution data, and are the standard deviation update value and mean update value of body motion data, New data points for pressure distribution data, New data points for body motion data.

[0025] Step S300, using a fuzzy logic control algorithm and according to a preset triangle membership function, the input fuzzy value of the distribution range data of the body movement data and the pressure distribution data is calculated.

[0026] Specifically, in this embodiment, the calculation expression of the triangular membership function of the pressure distribution data is: ; ; ; In the formula, Indicates the low index membership of the pressure distribution data, Indicates the middle index membership of pressure distribution data, represents the high index membership of the pressure distribution data, and P is the normalized value of the pressure distribution data. The above normalized value is obtained by mapping the actual collected value to a standardized range of 0-100 through sensor calibration, so as to facilitate the processing of fuzzy rules and the dynamic adjustment of the mattress hardness.

[0027] Specifically, in this embodiment, the calculation expression of the triangular membership function of the body motion data is: ; ; In the formula, Indicates the low index membership of body motion data, It represents the high index membership of body motion data, and M is the normalized value of body motion data.

[0028] The calculation expression of the triangular membership function of the mattress hardness is: ; ; ; In the formula, Indicates the low index membership of the mattress hardness, Indicates the middle index membership of the mattress hardness, It represents the high index membership of mattress hardness, and y is the normalized value of mattress hardness.

[0029] Preferably, in this embodiment, the method further includes: The abnormal values ​​in the bed data are screened out according to the standard deviation, and if the pressure distribution data satisfies any of the following expressions, the pressure distribution data is determined to be abnormal data: ; If the body motion data satisfies any of the following expressions, the body motion data is determined to be abnormal data: .

[0030] Step S400, obtaining an output fuzzy value of the hardness of the mattress according to the input fuzzy value based on a fuzzy control rule.

[0031] Specifically, in this embodiment, the fuzzy control rule is: When the pressure distribution data is within the first parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the third hardness level; When the pressure distribution data is within the first parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the second hardness level; When the pressure distribution data is within the second parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the first hardness level; When the pressure distribution data is within the second parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the second hardness level; When the pressure distribution data is within the third parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the first hardness level; When the pressure distribution data is within the third parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the third hardness level, wherein the parameter ranges of the pressure distribution data are, from low to high, the first parameter range, the second parameter range, and the third parameter range; the parameter ranges of the body movement data are, from low to high, the fourth range parameter and the fifth range parameter; and the mattress hardness outputs are, from soft to hard, the first hardness level, the second hardness level, and the third hardness level.

[0032] Preferably, in this embodiment, fuzzy control rules are formulated based on medical experience and statistical learning methods, as shown in Table 1: Table 1

[0033] As shown in Table 1, the preferred values ​​of the first parameter range, the second parameter range, and the third parameter range are respectively: (0-40), (40-70), and (70-100); the preferred values ​​of the fourth range parameter and the fifth range parameter are respectively: (0-40), and (40-100); the preferred values ​​of the first hardness grade, the second hardness grade, and the third hardness grade are respectively: (0-40), (40-60), and (80-100).

[0034] Step S500, defuzzify the output fuzzy value based on the center of gravity method to calculate the target adjustment value of the mattress hardness, and adjust the mattress hardness to the target adjustment value through the pneumatic system. These sensing devices transmit the acquired data to the control system for analysis and processing through the connection with the control system. The control system can judge the patient's needs and posture based on these data, and intelligently adjust the height, angle and hardness of the mattress to adapt to the patient's needs and physical characteristics, and provide optimal comfort and care. Figure 2 The three-dimensional mapping relationship between pressure distribution data and body movement data and mattress hardness is shown. When the pressure is low and the body movement is low, the system will set the bed harder. When the pressure distribution data is high and the body movement data is low, the system will set the bed softer.

[0035] Preferably, in this embodiment, the calculation formula for defuzzifying the output fuzzy value based on the centroid method is as follows: ; Wherein, E is the target adjustment value, is the sum of the degrees of membership of each hardness grade.

[0036] In summary, the intelligent bed control method in the above-mentioned embodiment of the present invention dynamically determines the value range of pressure distribution and body movement variables through statistical learning methods, and controls the motor and pneumatic system to adjust the hardness of the mattress in combination with the fuzzy logic control algorithm. When the pressure is high, the mattress is softened to disperse the pressure and reduce the risk of pressure sores; when the body moves frequently, the support is enhanced, and the hardness of the mattress is intelligently adjusted to adapt to the body shape and posture requirements of different patients.

[0037] Embodiment 2 The second embodiment of the present application also provides an intelligent bed control system, which is used to implement the embodiments and preferred implementations, and will not be repeated here. As used below, the terms "module", "unit", "subunit", etc. can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0038] like Figure 3 As shown, the system includes: a data module 100 , a distribution module 200 , a fuzzy input module 300 , a fuzzy output module 400 and an adjustment module 500 .

[0039] The data module 100 is used to collect the patient's bed data in real time based on the sensor at the bottom of the mattress, and the bed data includes the patient's body movement data and pressure distribution data; The distribution module 200 is used to calculate the mean and standard deviation of each bed-resting data based on a statistical learning method, and dynamically determine the distribution range data of the pressure distribution data and the body movement data according to the data distribution characteristics; The fuzzy input module 300 is used to obtain the input fuzzy value of the distribution range data of the body movement data and the pressure distribution data through the fuzzy logic control algorithm and according to the preset triangle membership function calculation; The fuzzy output module 400 is used to obtain an output fuzzy value of the mattress hardness according to the input fuzzy value based on the fuzzy control rule; The adjustment module 500 is used to perform defuzzification processing on the output fuzzy value based on the center of gravity method to calculate the target adjustment value of the mattress hardness, and adjust the mattress hardness to the target adjustment value through the pneumatic system.

[0040] Preferably, in this embodiment, the distribution module 200 is specifically used to calculate and obtain the distribution range data based on the following calculation expression: ; ; ; ; ; ; In the formula, , and are the distribution range data, mean and standard deviation of the pressure distribution data, , and are the distribution range data, mean value and standard deviation of body motion data respectively, n represents the number of pressure distribution data and body motion data collected in bed, and are the i-th pressure distribution data in the bed resting data and the i-th body motion data in the bed resting data, respectively, and k is the confidence interval parameter; Based on the newly added data points collected in real time, the mean and standard deviation of the bed rest data are updated to obtain the mean update value and the standard deviation update value, so as to dynamically update the distribution range data.

[0041] Preferably, in this embodiment, the system further includes: The abnormal data screening module is used to screen out abnormal values ​​in the bed data according to the standard deviation, and if the pressure distribution data satisfies any of the following expressions, the pressure distribution data is determined to be abnormal data: ; If the body motion data satisfies any of the following expressions, the body motion data is determined to be abnormal data: .

[0042] In some application scenarios of this embodiment, a pressure distribution map on the mattress can be generated by multiple pressure sensors and acceleration sensors. The pressure distribution in different areas can be displayed. Medical staff can understand the patient's pressure distribution based on these visual images, identify potential high-pressure points and their duration, understand the patient's sleep state, and monitor the patient's physical condition in time. At the same time, medical staff can also send control instructions through remote devices to adjust the state and settings of the mattress on the basis of remote monitoring. The control instructions will be transmitted to the control system through the network, and then the execution component will adjust the height, angle and hardness of the mattress accordingly. Once the execution component adjusts the state of the mattress, the control system will send real-time feedback information back to the medical staff's device. This may include the current state of the mattress, whether the parameter setting is successful, and the response to the operation. Medical staff can obtain the adjustment effect of the mattress, as well as the feedback and state of the patient in real time.

[0043] Furthermore, the control system can be equipped with a user interface for patients or medical staff to interact with and set up the bed. The user interface can include a touch screen, buttons, remote controls, etc., so that users can select preset modes, adjust the position of the mattress, etc. according to their needs. The preset modes include static mode, eating mode, sleeping mode, recuperation mode, etc., and patients can select the appropriate mode according to their needs.

[0044] Preferably, in this embodiment, the above system includes a vital sign monitoring and analysis device, including a sensor data acquisition module, a signal conditioning and amplification module, a data conversion and processing module, a physiological index monitoring module, an early warning and alarm module, a data storage and tracing module and a remote monitoring and sharing module; Among them, the sensor data acquisition module includes a heart rate sensor and an infrared non-contact body temperature measurement device, which transmits physiological signals to the signal conditioning and amplification circuit. At the same time, the infrared non-contact body temperature measurement device periodically measures the temperature change of the human body surface through infrared radiation, thereby obtaining a body temperature signal; Signal conditioning and amplification module Physiological signals collected by sensors usually need to be conditioned and amplified to ensure signal quality and accuracy; After signal conditioning and amplification, the data conversion and processing module converts the physiological signal into a digital signal and transmits it to the vital sign monitoring and analysis system. The system processes and analyzes the digital signal through algorithms and data processing methods, extracts features and trends, and obtains more information about the patient's physiological condition; The physiological index monitoring module can monitor and display various physiological indexes of patients in real time through data processing and analysis, such as heart rate, blood pressure, respiratory rate, body temperature, etc. These indexes can be displayed in the form of numbers, curves or charts on a central monitoring station with a support screen or medical staff; The alarm and early warning module monitors whether the patient's physiological indicators are abnormal according to the set parameters and thresholds. When the physiological indicators exceed the set normal range, the system will automatically trigger an alarm to remind medical staff to intervene and deal with the problem; The data storage and tracing module can store the monitored physiological index data in a local database or cloud platform so that medical staff can trace and review the data and conduct a comprehensive analysis and evaluation of the patient's health status; The remote monitoring and sharing module supports remote monitoring functions for certain vital signs monitoring and analysis systems, which can transmit the patient's physiological indicator data to the medical staff's mobile device or central monitoring station to achieve remote real-time monitoring and alarm notification. At the same time, medical staff can also share data with other medical team members through the system to achieve collaboration and sharing. Through the above working process, the vital signs monitoring and analysis system on the bed can monitor and analyze the patient's physiological indicators in real time, and provide accurate health status assessment to medical staff, helping them to take corresponding measures and decisions in time, and provide personalized medical care.

[0045] It should be noted that each module can be a functional module or a program module, and can be implemented by software or hardware. For modules implemented by hardware, each module can be located in the same processor; or each module can be located in different processors in any combination.

[0046] Embodiment 3 A third embodiment of the present application provides a computer, which may include a processor 81 and a memory 82 storing computer program commands.

[0047] Specifically, the processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0048] Among them, the memory 82 may include a large-capacity memory for data or commands. By way of example and not limitation, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 82 may be inside or outside the data processing device. In a specific embodiment, the memory 82 is a non-volatile memory. In a specific embodiment, the memory 82 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0049] The memory 82 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program commands executed by the processor 81 .

[0050] The processor 81 implements any one of the intelligent bed control methods in the above embodiments by reading and executing computer program commands stored in the memory 82 .

[0051] In some embodiments, the computer may further include a communication interface 83 and a bus 80. Figure 4 As shown, the processor 81, the memory 82, and the communication interface 83 are connected via a bus 80 and communicate with each other.

[0052] The communication interface 83 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication interface 83 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0053] The bus 80 includes hardware, software or both, and couples the components of the computer to each other. The bus 80 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 80 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 80 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0054] Embodiment 4 The fourth embodiment of the present application provides a readable storage medium having computer program commands stored thereon; when the computer program commands are executed by a processor, any one of the intelligent bed control methods in the above embodiments is implemented.

[0055] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0056] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A smart bed control method, characterized in that: The following steps are involved: The sensor at the bottom of the mattress collects the patient's bed data in real time, wherein the bed data includes the patient's body movement data and pressure distribution data; The mean and standard deviation of each bed rest data are calculated based on the statistical learning method, and the distribution range data of the pressure distribution data and the body movement data are dynamically determined according to the data distribution characteristics; By using a fuzzy logic control algorithm and according to a preset triangle membership function, the input fuzzy value of the distribution range data of the body movement data and the pressure distribution data is calculated; Obtaining an output fuzzy value of the mattress hardness according to the input fuzzy value based on a fuzzy control rule; The output fuzzy value is defuzzified based on the center of gravity method to calculate the target adjustment value of the mattress hardness, and the mattress hardness is adjusted to the target adjustment value through the pneumatic system.

2. The intelligent bed control method according to claim 1, characterized in that: The step of dynamically determining the distribution range data of the pressure distribution data and the body motion data according to the data distribution characteristics specifically includes: Based on the following calculation expression, the distribution range data is calculated: ； ； ； ； ； ； In the formula, , and are the distribution range data, mean and standard deviation of the pressure distribution data, , and are the distribution range data, mean value and standard deviation of body motion data respectively, n represents the number of pressure distribution data and body motion data collected in bed, and are the i-th pressure distribution data in the bed resting data and the i-th body motion data in the bed resting data, respectively, and k is the confidence interval parameter; Based on the newly added data points collected in real time, the mean and standard deviation of the bed rest data are updated to obtain the mean update value and the standard deviation update value, so as to dynamically update the distribution range data.

3. The intelligent bed control method according to claim 2, characterized in that: The method further comprises: The abnormal values ​​in the bed data are screened out according to the standard deviation, and if the pressure distribution data satisfies any of the following expressions, the pressure distribution data is determined to be abnormal data: ； If the body motion data satisfies any of the following expressions, the body motion data is determined to be abnormal data: 。 4. The intelligent bed control method according to claim 2, characterized in that: The calculation expressions of the mean update value and the standard deviation update value are: ； ； ； ； In the formula, and are the standard deviation update value and mean update value of the pressure distribution data, and are the standard deviation update value and mean update value of body motion data, New data points for pressure distribution data, New data points for body motion data.

5. The intelligent bed control method according to claim 1, characterized in that: The fuzzy control rules are: When the pressure distribution data is within the first parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the third hardness level; When the pressure distribution data is within the first parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the second hardness level; When the pressure distribution data is within the second parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the first hardness level; When the pressure distribution data is within the second parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the second hardness level; When the pressure distribution data is within the third parameter range and the body movement data is within the fourth parameter range, the mattress hardness output is the first hardness level; When the pressure distribution data is within the third parameter range and the body movement data is within the fifth parameter range, the mattress hardness output is the third hardness level, wherein the parameter ranges of the pressure distribution data are, from low to high, the first parameter range, the second parameter range, and the third parameter range; the parameter ranges of the body movement data are, from low to high, the fourth range parameter and the fifth range parameter; and the mattress hardness outputs are, from soft to hard, the first hardness level, the second hardness level, and the third hardness level.

6. The intelligent bed control method according to claim 1, characterized in that: The calculation expression of the triangular membership function of the pressure distribution data is: ； ； ； In the formula, Indicates the low index membership of the pressure distribution data, Indicates the middle index membership of pressure distribution data, It represents the high index membership of the pressure distribution data, and P is the normalized value of the pressure distribution data; The calculation expression of the triangular membership function of the body motion data is: ； ； In the formula, Indicates the low index membership of body motion data, It represents the high index membership of body motion data, and M is the normalized value of body motion data; The calculation expression of the triangular membership function of the mattress hardness is: ； ； ； In the formula, Indicates the low index membership of the mattress hardness, Indicates the middle index membership of the mattress hardness, It represents the high index membership of mattress hardness, and y is the normalized value of mattress hardness.

7. An intelligent bed control system, characterized in that: include: A data module, used to collect the patient's bed data in real time based on the sensor at the bottom of the mattress, wherein the bed data includes the patient's body movement data and pressure distribution data; The distribution module is used to calculate the mean and standard deviation of each bed rest data based on the statistical learning method, and dynamically determine the distribution range data of the pressure distribution data and the body movement data according to the data distribution characteristics; A fuzzy input module is used to obtain input fuzzy values ​​of distribution range data of body motion data and pressure distribution data through fuzzy logic control algorithm and according to a preset triangle membership function calculation; A fuzzy output module, used for obtaining an output fuzzy value of the mattress hardness according to the input fuzzy value based on a fuzzy control rule; The adjustment module is used to perform defuzzification processing on the output fuzzy value based on the center of gravity method to calculate the target adjustment value of the mattress hardness, and adjust the mattress hardness to the target adjustment value through the pneumatic system.

8. The intelligent bed control system according to claim 7, characterized in that: The fuzzy input module is specifically used for: Based on the following calculation expression, the distribution range data is calculated: ； ； ； ； ； ； In the formula, , and are the distribution range data, mean and standard deviation of the pressure distribution data, , and are the distribution range data, mean value and standard deviation of body motion data respectively, n represents the number of pressure distribution data and body motion data collected in bed, and are the i-th pressure distribution data in the bed resting data and the i-th body motion data in the bed resting data, respectively, and k is the confidence interval parameter; Based on the newly added data points collected in real time, the mean and standard deviation of the bed rest data are updated to obtain the mean update value and the standard deviation update value, so as to dynamically update the distribution range data.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the intelligent bed control method according to any one of claims 1 to 6 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the intelligent bed control method as described in any one of claims 1 to 6 is implemented.

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