Intelligent anti-falling control method, controller, system and storage medium

Abstract

The embodiment of the invention provides an intelligent anti-falling control method, a controller, a system and a storage medium, and the method comprises the steps: obtaining the pressure distribution information of a patient, and obtaining the pressure distribution information through a pressure sensing module; under the condition that the pressure distribution information represents that the patient is located at the edge of the bed, getting-off and getting-on demand information of the patient is obtained; under the condition that the getting-off and getting-on demand information represents that the patient needs to get off the bed or get on the bed, determining a gravity center vector of the patient according to the pressure distribution information; acquiring pathological information of the patient; determining a preset gravity center vector of the patient according to the pathological information; determining first adjustment information of a supporting module on the sickbed according to the patient gravity center vector and a preset gravity center vector; adjusting the supporting module according to the first adjusting information to enable the gravity center vector of the patient to be equal to the preset gravity center vector. A patient can be prevented from falling to the ground when getting on and off the bed, and injury caused by falling of the patient is reduced.

Description

Technical Field

[0001] The present invention relates to the field of but not limited to hospital bed control technology, and in particular to an intelligent fall prevention control method, controller, system and storage medium. Background Art

[0002] Hospital beds are commonly used in hospitals, health centers or health service centers.

[0003] When patients get on or off the hospital bed, if they accidentally fall due to fainting, fatigue, etc., they may easily fall to the ground and cause injury to the patients, but the existing beds do not have corresponding protective measures. Summary of the invention

[0004] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0005] The main purpose of the embodiments of the present invention is to propose an intelligent anti-fall control method, controller, system and storage medium, which can prevent patients from falling to the ground when getting in and out of bed, thereby reducing the damage caused to patients by falls.

[0006] In a first aspect, an embodiment of the present invention provides an intelligent fall prevention control method, comprising:

[0007] Acquire pressure distribution information of the patient, wherein the pressure distribution information is acquired through a pressure sensing module, and the pressure distribution information represents the pressure distribution of the patient at various locations on the bed;

[0008] When the pressure distribution information indicates that the patient is located at the edge of the bed, obtaining the patient's need information for getting in and out of bed;

[0009] In the case where the in-bed or out-of-bed demand information indicates that the patient needs to get out of bed or get in bed, determining a patient center of gravity vector according to the pressure distribution information, wherein the patient center of gravity vector indicates a position and orientation of the patient's center of gravity;

[0010] Acquiring pathological information of the patient, wherein the pathological information represents the patient's medical information and / or physiological information acquired by a physiological monitoring module;

[0011] determining a preset center of gravity vector of the patient according to the pathological information;

[0012] Determining first adjustment information of a support module on a hospital bed according to the patient's center of gravity vector and the preset center of gravity vector;

[0013] The support module is adjusted according to the first adjustment information so that the patient's center of gravity vector is equal to the preset center of gravity vector.

[0014] In some optional embodiments, the hospital bed includes a bed frame and a bed board arranged on the bed frame, the bed board is provided with the support module, the support module is provided with the pressure sensing module, and the obtaining of the patient's pressure distribution information includes:

[0015] Acquiring bed pressure data on the bed through the pressure sensing module;

[0016] Obtaining patient pressure data after removing interfering pressure data of the bed pressure data according to preset pressure data, wherein the preset pressure data represents a pre-established range of patient pressure;

[0017] Obtaining a patient pressure distribution area after eliminating an interfering pressure area in the patient pressure data according to a preset pressure distribution model, wherein the preset pressure distribution model represents a pre-established model of patient pressure distribution;

[0018] The patient pressure distribution area and the pressure data corresponding to the patient pressure distribution area are configured as the pressure distribution information of the patient.

[0019] In some optional embodiments, the step of removing the interference pressure area in the patient pressure data according to the preset pressure distribution model to obtain the patient pressure distribution area includes:

[0020] Comparing the pressure distribution area to be processed corresponding to the patient pressure data with the preset pressure distribution model to obtain a patient pressure distribution model, wherein the patient pressure distribution model represents the pressure distribution model with the highest similarity to the pressure distribution area to be processed;

[0021] Establishing a Gaussian distribution of similarity between the patient pressure distribution model and the pressure distribution area to be processed;

[0022] The patient pressure distribution area is obtained by removing the interference pressure area from the Gaussian distribution according to a preset similarity threshold.

[0023] In some optional embodiments, the step of removing the interference pressure region from the Gaussian distribution according to a preset similarity threshold to obtain the patient pressure distribution region includes:

[0024] configuring a first pressure region in the Gaussian distribution whose similarity is less than the similarity threshold as the interference pressure region;

[0025] Obtaining pressure change information within a preset time for a plurality of second pressure regions whose similarity in the Gaussian distribution is greater than or equal to the similarity threshold;

[0026] When the pressure change information indicates that there is at least one second pressure region where no pressure change occurs within a preset time, the second pressure region where no pressure change occurs is configured as the interference pressure region.

[0027] In some optional embodiments, obtaining the patient's in-and-out-of-bed demand information includes:

[0028] When the patient is at the edge of the bed, determining a tendency to get out of bed or get on bed according to the pressure direction indicated by the pressure distribution information;

[0029] Acquiring voice interaction information or key information of the patient according to the getting out of bed tendency or the getting in bed tendency;

[0030] The in-bed requirement information is determined through voice interaction information or key information.

[0031] In some optional embodiments, the pressure sensing module includes a plurality of pressure sensing units, the support module includes a plurality of support units, each of the support units is provided with the pressure sensing unit, and adjusting the support module according to the first adjustment information includes:

[0032] Determine unit adjustment information of one or more support units of the bed according to the first adjustment information;

[0033] Determine a height adjustment curve of the support unit according to the pathological information and the unit adjustment information, wherein the height adjustment curve represents an adjustment rate of the support unit at different support heights;

[0034] The supporting height of the supporting unit is adjusted according to the height adjustment curve so that the center of gravity vector of the patient is equal to the preset center of gravity vector.

[0035] In some optional embodiments, the hospital bed further comprises a leg support module arranged on the first side of the bed frame, the leg support module comprises a lifting unit and a rotating unit, the lifting unit comprises a lifting support plate and a lifting cylinder, the first end of the lifting support plate is rotatably connected to the bed frame, the first end of the lifting cylinder is rotatably connected to the bed frame, the second end of the lifting cylinder is fixedly connected to the second end of the lifting support plate through the lifting cylinder, the lifting cylinder is used to lift the lifting support plate, the rotating unit comprises a fixing device and a rotating cylinder, the fixing device is used to fix the rotating cylinder, the rotating cylinder is fixedly connected to the lifting support plate through a rotating gas rod, and the method further comprises:

[0036] Determine the first adjustment information of the support module and the second adjustment information of the leg support module according to the pathological information, the preset center of gravity vector and the patient's center of gravity vector;

[0037] Controlling the lifting cylinder to lift the lifting support plate according to the second adjustment information, so that the lifting support plate has a preset support plate length after being extended and retracted;

[0038] The rotating cylinder is controlled to push the lifting support plate through the rotating gas rod according to the second adjustment information, so that the lifting support plate and the lifting cylinder have a preset tilt angle after synchronous rotation.

[0039] In a second aspect, an embodiment of the present invention provides a controller, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the intelligent anti-fall control method described in the first aspect when executing the computer program.

[0040] In a third aspect, an embodiment of the present invention provides an intelligent anti-fall control system, including the controller involved in the above second aspect.

[0041] In a fourth aspect, a computer storage medium stores computer executable instructions, wherein the computer executable instructions are used to execute the intelligent fall prevention control method described in the first aspect.

[0042] The beneficial effects of the present invention include: when the present invention obtains an instruction to adjust the patient's center of gravity vector, the patient's pressure distribution information is obtained through a pressure sensing module, and the pressure distribution information represents the pressure distribution of the patient at various locations on the bed; when the pressure distribution information represents that the patient is located at the edge of the bed, the patient's need information for getting in and out of bed is obtained; when the need information for getting in and out of bed represents that the patient needs to get out of bed or get in bed, the patient's center of gravity vector is determined according to the pressure distribution information, and the patient's center of gravity vector represents the position and orientation of the patient's center of gravity; the patient's pathological information is obtained, and the pathological information represents the patient's medical information and / or physiological information obtained through a physiological monitoring module; the patient's preset center of gravity vector is determined according to the pathological information; the first adjustment information of the support module on the bed is determined according to the patient's center of gravity vector and the preset center of gravity vector; the support module is adjusted according to the first adjustment information so that the patient's center of gravity vector is equal to the preset center of gravity vector. By automatically acquiring the pressure data of the patient on the hospital bed, the patient's center of gravity vector is determined, and the first adjustment information of the support module is determined after comparing it with the preset center of gravity vector expected by the pathological information, so that the support module adjusts the corresponding support height to make the patient at the preset center of gravity vector. In the event that the patient accidentally falls, the patient is prevented from falling to the ground and causing injury because he is at the preset center of gravity vector. Therefore, the present application can prevent the patient from falling to the ground when getting on and off the bed, thereby reducing the injuries caused to the patient by falling.

[0043] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flowchart of the steps of an intelligent anti-fall control method provided by an embodiment of the present invention;

[0045] Figure 2 is a structural schematic diagram of a hospital bed provided by an embodiment of the present invention;

[0046] Figure 3 is a schematic diagram of the distribution of support modules provided in an embodiment of the present invention;

[0047] Figure 4 is a schematic diagram of adjusting multiple support units provided by an embodiment of the present invention;

[0048] Figure 5 is a schematic structural diagram of a leg support module provided by an embodiment of the present invention;

[0049] Figure 6 is a schematic diagram of a controller provided by an embodiment of the present invention.

[0050] Reference numerals: controller 1000, processor 1100, memory 1200;

[0051] A bed board 100, a second baffle 110, a bed frame 120, and a first baffle 130;

[0052] Leg support module 200, first rotating shaft 210, first support plate 211, second support plate 212, third support plate 213, connecting rod 214, lifting gas rod 220, lifting cylinder 221, second rotating shaft 222, rotating gas rod 230, rotating cylinder 231, second fixing rod 232, first fixing rod 233;

[0053] A first cylinder 300, a first gas rod 301, a second cylinder 303, a second gas rod 302, a base 310, and a universal wheel 311;

[0054] Support unit 400 , support cylinder 410 , support gas rod 420 , support plate 430 , and pressure sensing unit 440 . DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is 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.

[0056] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims or the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0057] Hospital beds are commonly used in hospitals, health centers or health service centers.

[0058] When patients get on or off the hospital bed, if they accidentally fall due to fainting, fatigue, etc., they may easily fall to the ground and cause injury to the patients, but the existing beds do not have corresponding protective measures.

[0059] In order to solve the above-mentioned problems, the present application provides an intelligent anti-fall control method, controller, system and storage medium.

[0060] In the present application, an intelligent anti-fall control method, controller, system and storage medium are provided, which are described in detail one by one in the following embodiments.

[0061] like Figure 1 As shown, an embodiment of the present invention provides an intelligent fall prevention control method, comprising:

[0062] S100 , obtaining pressure distribution information of a patient, wherein the pressure distribution information is obtained through a pressure sensing module, and the pressure distribution information represents pressure distribution of the patient at various locations on the bed.

[0063] It should be noted that, refer to Figure 2 In the embodiment of the present invention, a pressure sensing module is provided on the bed board 100 of the hospital bed. The pressure sensing module can obtain the corresponding pressure data, and confirm the pressure of the patient on the bed and the contact area between the patient and the bed through the pressure area information in the pressure data. The specific posture and position of the patient can be confirmed according to the size, shape and position of the contact area. The pressure distribution of the patient on the bed is obtained through the pressure sensing modules evenly distributed on the bed. The pressure sensing modules can adopt resistive, capacitive or piezoelectric pressure sensor technology. The pressure sensing modules cover the main areas that the patient's body may contact, such as the edge of the bed and the head of the bed, so as to fully obtain the pressure distribution when the patient gets up and prepares to get out of bed or when getting into bed.

[0064] In some embodiments, the hospital bed includes a bed frame 120 and a bed board 100 arranged on the bed frame 120, the support module is arranged in the bed board 100, and the pressure sensing module is arranged on the support module, and the obtaining of the patient's pressure distribution information includes: obtaining bed pressure data on the hospital bed through the pressure sensing module; obtaining patient pressure data after eliminating interfering pressure data of the bed pressure data according to preset pressure data, and the preset pressure data represents a pre-established range of patient pressure; obtaining a patient pressure distribution area after eliminating interfering pressure areas in the patient pressure data according to a preset pressure distribution model, and the preset pressure distribution model represents a pre-established model of pressure distribution of the patient when getting on and off the bed; configuring the patient pressure distribution area and the pressure data corresponding to the patient pressure distribution area as the patient's pressure distribution information.

[0065] Specifically, refer to Figure 2-3 , the pressure sensing module is arranged on the support module, and the support module is arranged in the bed board 100. When the patient is on the bed, various parts of the body exert pressure on the support module, and the pressure sensing module converts these pressures into bed pressure data in the form of electrical signals. The pressure sensing module can be composed of a plurality of pressure sensors distributed in different areas of the bed, or composed of pressure sensing pads, which can fully sense the pressure of the contact part between the patient and the bed. Specifically, a first baffle 130 is arranged at both ends of the bed frame 120 of the bed, and a second baffle 110 is arranged on the bed board 100. The first baffle 130 and the second baffle 110 prevent the patient from falling while sleeping. When it is determined that the patient needs to get on and off the bed, the second baffle 110 is rotated so that the second baffle 110 rotates in a direction close to one end of the bed frame 120. Specifically, one end of the second baffle 110 is rotatably connected to the bed board 100 through an electric rotating rod. The bed frame 120 is driven by the first cylinder 300 and the second cylinder 303. The first cylinder 300 pushes the bed frame 120 through the first gas rod 301, and the second cylinder 303 pushes the bed frame 120 through the second gas rod 302. The first cylinder 300 and the second cylinder 303 are driven synchronously or asynchronously to achieve lifting or tilting of the bed frame 120, so as to adapt to different needs of patients. Universal wheels 311 are fixedly connected to the base 310 to facilitate the movement of the bed.

[0066] The preset pressure data is predetermined through a large number of clinical studies, experiments and long-term observations of normal human pressure characteristics. It clarifies the range of pressure on various areas of the bed when patients with different body parts, different body shapes and different health conditions normally get in and out of bed. These pressure ranges will be adjusted and improved according to actual conditions to suit different patient groups.

[0067] Elimination of interfering data: Compare the acquired bed pressure data with the preset pressure data one by one. If the pressure data of a certain area exceeds the corresponding preset pressure range, it is considered that the data may be caused by interfering factors, such as the patient placing other items on the bed, or the pressure sensor itself has a fault. At this time, these out-of-range pressure data are eliminated from the bed pressure data to obtain relatively accurate patient pressure data. This can reduce the impact of interfering factors on subsequent analysis and improve the reliability of data.

[0068] The preset pressure distribution model is specially constructed for the pressure distribution of patients when getting in and out of bed. It comprehensively considers factors such as the body's posture, center of gravity transfer, and changes in contact between various body parts and the bed during getting in and out of bed. For example, when getting in bed, patients usually place part of their body (such as legs) on the edge of the bed first, and then gradually transfer their center of gravity to the bed. At this time, the pressure on the legs, buttocks, back and other parts will change in a certain order and pattern; the opposite is true when getting out of bed. Through the simulation and actual measurement of a large number of getting in and out of bed movements, pressure distribution models at different stages are established to reflect the normal pressure distribution of getting in and out of bed.

[0069] The patient pressure data after removing the interfering pressure data is compared with the preset pressure distribution model. If there are areas in the patient pressure data that are obviously inconsistent with the preset pressure distribution model (areas with similarity less than the similarity threshold), that is, the pressure distribution in this area does not conform to the normal pressure change law when getting in and out of bed, then the area can be judged as an interfering pressure area. For example, in the preset model, the leg pressure should gradually decrease when getting out of bed, but the patient pressure data shows that the pressure in a certain area of ​​the leg suddenly increases and is not in line with common sense, then this area may be an interfering pressure area. These interfering pressure areas are removed from the patient pressure data to obtain a more accurate patient pressure distribution area.

[0070] After obtaining the patient's pressure distribution area and the pressure data corresponding to the area, they are integrated and configured to form the patient's pressure distribution information. This information is stored in a specific data structure, such as a table, which records the location information of the pressure distribution area (such as the coordinate range on the bed), the specific value of the pressure data, and possible timestamps. The pressure distribution information configured in this way can clearly and accurately reflect the pressure of each part of the patient's body in contact with the bed during the process of getting in and out of bed, and provide corresponding data for analyzing the patient's getting in and out of bed behavior, judging the patient's physical condition, and making corresponding adjustments to the bed support module.

[0071] In some embodiments, the patient pressure distribution area is obtained after eliminating the interfering pressure area in the patient pressure data according to the preset pressure distribution model, including: comparing the similarity between the pressure distribution area to be processed corresponding to the patient pressure data and the preset pressure distribution model to obtain the patient pressure distribution model, the patient pressure distribution model represents the pressure distribution model with the highest similarity to the pressure distribution area to be processed; establishing a Gaussian distribution of the similarity between the patient pressure distribution model and the pressure distribution area to be processed; and obtaining the patient pressure distribution area after eliminating the interfering pressure area from the Gaussian distribution according to a preset similarity threshold.

[0072] Specifically, the pressure distribution area to be processed corresponding to the patient pressure data is compared with the preset pressure distribution model for similarity. The preset pressure distribution model is a pre-established model of the pressure distribution of the patient when getting in and out of bed, which contains a variety of possible pressure distribution situations, such as the pressure distribution corresponding to different postures of getting in and out of bed, the body center of gravity transfer method, etc. By calculating the similarity between the pressure distribution area to be processed and each preset pressure distribution model (Euclidean distance, cosine similarity, etc. can be used), the preset pressure distribution model with the highest similarity to the pressure distribution area to be processed is found and determined as the patient pressure distribution model. This patient pressure distribution model can most accurately reflect the pressure distribution of the current patient when getting in and out of bed. For example, assuming that there are three preset pressure distribution models A, B, and C, after calculation, the pressure distribution area to be processed has the highest similarity with model B, then model B becomes the patient pressure distribution model; that is, the pressure distribution area of ​​model B has the highest similarity with the pressure distribution area to be processed.

[0073] Gaussian distribution (normal distribution) is a commonly used probability distribution model that can describe the distribution of data around the mean. In this process, the Gaussian distribution of the patient pressure distribution model and the pressure distribution area to be processed is established in order to quantify the distribution characteristics of the similarity between the two.

[0074] Construction method: With similarity as the horizontal axis and the probability of the similarity as the vertical axis, a Gaussian distribution curve is fitted based on the similarity calculation results of the pressure distribution area to be processed and the patient pressure distribution model at different positions and different pressure values. This curve reflects the probability distribution of the similarity between the pressure distribution area to be processed and the patient pressure distribution model in various aspects. For example, in some areas, the similarity between the two is high, then on the Gaussian distribution curve, the probability of the corresponding similarity value will be larger; while in some areas with large differences, the similarity is lower and the corresponding probability is also smaller.

[0075] The preset similarity threshold is a value set based on experience and actual needs, which is used to determine whether a certain part of the pressure distribution area to be processed is an interference pressure area. The setting of this threshold needs to take into account a variety of factors, such as the accuracy of the pressure sensor, the normal fluctuation range of the patient's getting in and out of bed, etc.

[0076] Elimination operation: Analyze the Gaussian distribution according to the preset similarity threshold. For areas with similarity lower than the preset similarity threshold, it is considered that these areas are very different from the patient pressure distribution model, which is likely to be caused by interference factors (such as abnormal movements of patients when getting in and out of bed, the influence of external objects on the pressure sensor, etc.). Therefore, these areas are eliminated from the pressure distribution area to be processed, and finally the patient pressure distribution area is obtained. For example, the preset similarity threshold is 0.6. If the similarity between a part of the area / sub-area of ​​a pressure distribution area to be processed and the corresponding area of ​​the patient pressure distribution model is 0.5, which is less than the threshold, then the part of the area will be identified as an interference pressure area and eliminated. The ability to more accurately eliminate interference pressure areas from patient pressure data and obtain a patient pressure distribution area that can more truly reflect the pressure distribution of patients when getting in and out of bed will help improve the intelligence of hospital beds and the quality of care for patients.

[0077] In some embodiments, the similarity comparison between the pressure distribution area to be processed corresponding to the patient pressure data and the preset pressure distribution model includes a similarity calculation formula:

[0078] Before calculating the similarity, assume that a preset pressure distribution model is P = (p1, p2Λp n ), the pressure distribution area to be processed is Q=(q1,q2Λq n ), where n represents the dimension of the pressure distribution, q n represents the nth sub-region / position in the pressure distribution area to be processed, p n Represents the nth sub-region / position in the preset pressure distribution model. The similarity between the preset pressure distribution model and the pressure distribution region to be processed is:

[0079]

[0080] Among them, SSIM(P,Q) represents the similarity between the preset pressure distribution model and the pressure distribution area to be processed, μ P represents the mean of P, μ Q represents the mean value of Q; σ P represents the variance of P, σ Q represents the variance of Q; σ PQ represents the covariance of P and Q, C1 and C2 represent different constant values; SSIM(P,Q) is between -1 and 1, and the closer it is to 1, the higher the similarity.

[0081] In some embodiments, the patient pressure distribution area is obtained after eliminating the interference pressure area from the Gaussian distribution according to a preset similarity threshold, including: configuring a first pressure area in the Gaussian distribution whose similarity is less than the similarity threshold as the interference pressure area; obtaining pressure change information of multiple second pressure areas in the Gaussian distribution whose similarity is greater than or equal to the similarity threshold within a preset time; and when the pressure change information indicates that there is at least one second pressure area that has not undergone pressure change within a preset time, configuring the second pressure area that has not undergone pressure change as the interference pressure area.

[0082] Specifically, according to the preset similarity threshold, the Gaussian distribution is preliminarily analyzed. In the Gaussian distribution, each pressure area has a corresponding similarity value. Those first pressure areas whose similarity is less than the similarity threshold are directly configured as interference pressure areas. Because these areas have a low similarity with the patient's pressure distribution model, according to the set standards, they do not conform to the normal pressure distribution, and are likely to be caused by external interference factors (such as items placed by the patient on the bed, abnormal fluctuations of the pressure sensor, etc.), so they are first identified as interference pressure areas.

[0083] For multiple second pressure areas in the Gaussian distribution whose similarity is greater than or equal to the similarity threshold, it is necessary to further analyze their pressure changes within the preset time. The preset time can be set according to actual needs and experience, for example, it can be set to 1 minute, 3 minutes, etc. The pressure data of these second pressure areas are continuously monitored through the pressure sensing module to obtain their pressure change information within the preset time. This information includes the change amplitude and change frequency of the pressure value.

[0084] After obtaining the pressure change information, these second pressure areas are judged. If the pressure change information indicates that there is at least one second pressure area where no pressure change occurs within the preset time, then the second pressure area where no pressure change occurs is configured as an interference pressure area. This is because under normal circumstances, the patient's body in the bed will have some slight movements, or the body's physiological activities will also cause pressure fluctuations. Even in a relatively static state, it is unlikely that the pressure will not change at all for a long time. Therefore, when there is no change in pressure in an area within a preset time, it is very likely that the pressure in this area does not come from the patient's body, but the pressure generated by a fixed external object that should not exist, so it is identified as an interference pressure area.

[0085] All parts configured as interference pressure areas are removed from the pressure distribution area to be processed, and the remaining area is the patient pressure distribution area. This patient pressure distribution area can more accurately reflect the pressure distribution of various parts of the patient's body on the bed during getting in and out of bed, and provides more reliable data support for subsequent operations such as adjusting the bed support module based on the pressure distribution information. In this way, the interference pressure areas are gradually removed from the Gaussian distribution, which improves the accuracy and reliability of the patient pressure distribution area, helps to more accurately understand the patient's physical condition and needs, and thus provide better care and support for patients.

[0086] S200: When the pressure distribution information indicates that the patient is located at the edge of the bed, obtain the patient's need information for getting in and out of bed.

[0087] It should be noted that the methods for obtaining the information on the need to get in and out of bed include: Bedside button interaction: Install a button device with clear markings at a suitable location beside the bed, such as the head of the bed, the foot of the bed, or the armrests on both sides. When the pressure distribution information shows that the patient is at the edge of the bed, the system can guide the patient to operate through voice prompts or flashing lights. For example, the buttons are marked with words such as "get out of bed", "get in bed", and "no help needed". The patient presses the corresponding button according to his or her own needs. After the system receives the button signal, it can obtain the patient's need information for getting in and out of bed.

[0088] Voice recognition interaction: The bed is equipped with a voice recognition device. When the patient is detected at the edge of the bed, the system automatically activates the voice recognition function. The system will issue a voice prompt, such as "It is detected that you are at the edge of the bed. Do you need to get out of bed or get in bed?" The patient directly answers commands such as "I want to get out of bed", "I want to get in bed" or "No, thank you" through voice. The voice recognition system recognizes and analyzes the patient's voice to obtain the patient's need to get out of bed and get in bed. In order to improve the accuracy of voice recognition, the system can be trained in a personalized way to adapt to the accents, speech speeds and other characteristics of different patients.

[0089] Gesture recognition interaction: Install cameras or sensors around the bed and use computer vision technology to realize gesture recognition. When the patient is at the edge of the bed, the system prompts the patient to express his needs through gestures, such as extending one hand to get out of bed, extending two hands to get in bed, etc. The camera or sensor captures the patient's gestures, and after image recognition and analysis, determines the needs of getting in and out of bed represented by the patient's gestures, and converts them into information that the system can recognize, so as to adapt to the conditions and needs of different patients, that is, to facilitate patients who cannot speak or have difficulty speaking.

[0090] Interaction with smart bracelets or wearable devices: Patients wear smart bracelets or other wearable devices, which are connected to the bed system. When the patient is detected at the edge of the bed, the bracelet can remind the patient by vibration, flashing lights, etc. The patient selects options such as "get out of bed" and "get in bed" by operating the buttons on the bracelet or touching the screen. The device sends the patient's selection information to the bed system to obtain the information on getting in and out of bed.

[0091] Nurse call system interaction: The bed is equipped with a button or device to call the nurse. When the patient is at the edge of the bed and needs to get in or out of bed, the patient can press the call button. The system at the nurse station will receive the call signal and display the patient's bed position information and pressure distribution information to indicate that the patient is at the edge of the bed. The nurse communicates with the patient through the intercom or goes to the ward to ask about the patient's specific needs, thereby obtaining the patient's information on getting in or out of bed and providing corresponding help in a timely manner.

[0092] In some optional embodiments, obtaining the patient's need information for getting in and out of bed includes: when the patient is located at the edge of the bed, determining the trend of getting out of bed or getting in bed according to the pressure direction indicated by the pressure distribution information; obtaining the patient's voice interaction information or key information according to the trend of getting out of bed or the trend of getting in bed; and determining the need information for getting in and out of bed through the voice interaction information or key information.

[0093] Specifically, when the patient is at the edge of the bed, the pressure distribution information obtained by the pressure sensing module includes the specific direction of the pressure, which can reflect the force direction and movement trend of the patient's body.

[0094] Judging the tendency to get out of bed: If the pressure distribution information shows that the pressure direction of the part of the patient's body in contact with the bed edge is mainly from the bed surface to the outside of the bed, and the pressure value outside the bed edge is relatively large, it is likely that the patient has a tendency to get out of bed. For example, the pressure of the patient's legs is concentrated on the outside near the bed edge, and the pressure direction is outward. Combined with the overall pressure distribution, it can be inferred that the patient may be preparing to get out of bed.

[0095] Judging the tendency to go to bed: On the contrary, if the pressure distribution information shows that the pressure direction is mainly from the outside of the bed to the bed surface, and the pressure value on the inner side of the bed edge is relatively large, then the patient is more likely to have a tendency to go to bed. For example, if the patient first places part of the body (such as the legs) on the bed edge, the pressure distribution will show that the pressure on the inner side of the bed edge increases, and the pressure direction points to the bed surface, which can be judged that the patient has the intention to go to bed.

[0096] After determining the patient's tendency to get out of bed or get in bed, in order to further clarify the patient's specific needs, it is necessary to obtain the patient's voice interaction information or keystroke information.

[0097] Acquisition of voice interaction information: The hospital bed is equipped with a voice interaction device. When the system determines that the patient has a tendency to get out of bed or get in bed, a voice prompt will be automatically triggered. For example, the system will say, "It is detected that you may have the action of [getting out of bed / getting in bed]. Do you need help?" The patient can answer "yes" or "no" by voice, or express his needs in more detail, such as "I need to get out of bed slowly" or "I want to get in bed now". The voice interaction device will capture and record the patient's voice information for subsequent analysis.

[0098] Acquisition of key information: Special key devices are set up at the bedside, bedside and other locations of the bed. These keys are usually clearly marked, such as "Request to get out of bed", "Request to get in bed", "No help needed", etc. When the system determines that the patient is going to get out of bed or get in bed, it will guide the patient to operate the key through flashing lights or voice prompts. The patient presses the corresponding key according to his or her actual needs, and the system will record the key information.

[0099] After obtaining the patient's voice interaction information or key information, the system will analyze and process this information to determine the patient's need to get in and out of bed.

[0100] Analysis of voice interaction information: For voice information, the system will use voice recognition technology to convert the voice content into text information, and then parse the text through natural language processing algorithms. For example, if the system recognizes that the patient says "I want to get out of bed, please help me", it can clearly see that the patient has the need to get out of bed and needs the assistance of medical staff. According to the different voice content, the system can accurately determine the specific needs of the patient, such as whether auxiliary equipment is needed, whether medical staff are needed to monitor, etc.

[0101] Analysis of key information: For key information, the system will determine the needs based on the different keys pressed by the patient. If the patient presses the "get out of bed request" button, the system will record that the patient has the need to get out of bed; if the "go to bed request" button is pressed, it means that the patient has the need to go to bed; if the "no help needed" button is pressed, the system will think that although the patient has the tendency to get out of bed or go to bed, he does not need external assistance and can complete the action by himself.

[0102] By accurately obtaining information on patients' needs for getting in and out of bed, it provides data for subsequent work such as adjusting the bed support module according to patient needs and arranging medical staff to provide corresponding services, which helps improve patients' care quality and medical experience.

[0103] S300. When the in-bed or out-of-bed demand information indicates that the patient needs to get out of bed or get in bed, determine a patient center of gravity vector according to the pressure distribution information, wherein the patient center of gravity vector indicates a position and orientation of the patient's center of gravity.

[0104] It should be noted that the pressure sensing module will collect pressure data from various parts of the patient's bed in real time, and these data constitute pressure distribution information. The pressure distribution information is used to determine the need to get in and out of bed; for example, when the patient is preparing to get out of bed, the pressure on the side of the body close to the edge of the bed will increase relatively, and the pressure distribution will be concentrated toward the edge of the bed; when preparing to get into bed, the pressure distribution will show the characteristics of the body gradually moving from the outside of the bed to the bed surface. By obtaining and analyzing this pressure distribution information, we can have a preliminary understanding of the contact between various parts of the patient's body and the bed and the changes in body posture.

[0105] Calculation of the center of gravity position: The center of gravity position of the patient is calculated based on the pressure distribution information, and a weighted average method can be used. The bed is divided into multiple small areas, each area corresponds to a pressure sensing unit 440, and the pressure value of each area and the position coordinates of the area in the bed coordinate system are obtained. Assume that the bed is divided into N areas, and the pressure value of the i-th (i=1,2…,N) area is P i , the position coordinate is (x i ,y i ,z i ), then the calculation formula for the patient's center of gravity position (X, Y, Z) is:

[0106]

[0107] Among them, X, Y, and Z represent the horizontal coordinate, vertical coordinate, and vertical coordinate of the patient's center of gravity in the bed coordinate system, respectively. Through the above formula, the specific position of the patient's center of gravity on the bed can be calculated according to the pressure value and position coordinates of each area.

[0108] Determination of the direction of the center of gravity: The direction of the center of gravity mainly reflects the movement trend and direction of the patient's body. When determining the direction of the center of gravity, the direction of pressure change in the pressure distribution information and the pressure distribution of various parts of the body are combined. For example, when the patient is preparing to get out of bed, the center of gravity of the body will move toward the edge of the bed, and the pressure distribution will show a gradual change from the middle of the bed to the edge of the bed. At this time, the direction of the center of gravity is roughly from the bed surface to the outside of the bed; when preparing to get into bed, the direction of the center of gravity is from the outside of the bed to the bed surface. In addition, the specific angle of the center of gravity direction can be further determined by analyzing the relative size and change rate of pressure in various parts of the body. For example, if the rate of change of the patient's leg pressure toward the edge of the bed is significantly greater than that of other parts of the body, it means that the center of gravity is moving toward the edge of the bed and is more biased towards the leg direction, thereby more accurately determining the direction of the center of gravity.

[0109] Determine the patient's center of gravity vector: After calculating the center of gravity position and determining the center of gravity direction, the two can be combined to determine the patient's center of gravity vector. The patient's center of gravity vector can be represented by a three-dimensional vector, the starting point of the vector is the calculated center of gravity position coordinates, and the direction of the vector is the determined center of gravity direction. This patient's center of gravity vector can intuitively reflect the position and direction of the patient's center of gravity, and provide data basis for subsequent adjustment of the bed support module according to the patient's center of gravity, providing auxiliary support, etc.

[0110] Through in-depth analysis of pressure distribution information and the use of reasonable calculation methods and judgment basis, the patient's center of gravity vector can be accurately determined, thereby better meeting the patient's needs when getting out of bed or getting in bed and ensuring the patient's safety and comfort.

[0111] S400: Acquire pathological information of a patient, where the pathological information represents the patient's medical information and / or physiological information acquired through a physiological monitoring module.

[0112] Specifically, medical information includes medical records, diagnosis reports, treatment plans, and medication records, etc., which are not limited to specific ones. The physiological information obtained through the physiological monitoring module can reflect the patient's physical function status in real time and provide dynamic disease monitoring data. Common physiological monitoring modules include electrocardiogram monitoring, blood pressure monitoring, blood oxygen saturation monitoring, respiratory monitoring, etc. The specific physiological parameters are determined according to actual needs and are not limited here.

[0113] S500: Determine a preset center of gravity vector of the patient according to the pathological information.

[0114] Specifically, the corresponding preset center of gravity vector is determined according to different pathological information of the patient, so that the patient has better support comfort and is better protected when falling.

[0115] When patients have neurological diseases such as Parkinson's disease and stroke, they may suffer from motor dysfunction and imbalanced muscle strength on one side of their body. Taking stroke as an example, if the right cerebral hemisphere of the patient is damaged and hemiplegia of the left limb occurs, the center of gravity will tend to the relatively strong side of the right limb; when determining the preset center of gravity vector, the influence of factors such as muscle weakness and limited joint movement on the center of gravity position on the affected side should be considered, and the center of gravity vector may need to be appropriately shifted to the healthy side to ensure the stability of the patient when sitting or standing.

[0116] When the patient has musculoskeletal diseases, such as scoliosis and hip dislocation, the body's skeletal structure and force conduction path will be directly changed. For example, in patients with scoliosis, the curvature of the spine will change the weight distribution on the left and right sides of the body, and the center of gravity will shift accordingly. For such patients, the preset center of gravity vector needs to be determined based on the angle and direction of the scoliosis and the compensation of muscles in various parts of the body. The imaging examination data, such as the bending angle measured by spinal X-rays, is needed to accurately calculate the direction and degree of the center of gravity shift.

[0117] Patients with cardiovascular diseases: Severe heart failure and other cardiovascular diseases may cause physical weakness and edema in patients. Edema will increase the weight of the lower limbs and affect the body's center of gravity. When determining the preset center of gravity vector, the degree and distribution of edema should be considered. The center of gravity may need to be appropriately moved down to maintain the body's balance. At the same time, due to the limited heart function and reduced mobility of patients, the changes in the center of gravity under different activity states also need special attention. For example, when sitting up or standing up, the speed and amplitude of the center of gravity transfer are slower and smaller than those of normal people.

[0118] Treatment methods and rehabilitation stage considerations:

[0119] Surgery: If a patient undergoes joint replacement surgery, such as hip replacement, in the early stages of rehabilitation, the patient's center of gravity will change due to pain and limited mobility on the operated limb, as the surgical trauma and muscles have not yet fully recovered. At this stage, the preset center of gravity vector should be adjusted according to the location of the surgical incision, the degree of muscle damage, and the progress of rehabilitation training. As rehabilitation progresses, muscle strength gradually recovers, joint mobility increases, and the center of gravity vector will gradually approach normal.

[0120] Rehabilitation treatment: During the rehabilitation training, the patient's physical function continues to improve, and the preset center of gravity vector also needs to be dynamically adjusted. For example, for patients with muscle atrophy due to long-term bed rest after a fracture, the center of gravity may be unstable due to insufficient muscle strength in the early stages of rehabilitation. As the rehabilitation training progresses, the muscle strength gradually increases, and the center of gravity will gradually stabilize and return to the normal position. According to the evaluation results of the patient's balance ability, muscle strength, joint mobility, etc. at different stages of rehabilitation, the preset center of gravity vector is continuously adjusted to better protect the patient.

[0121] Individual physiological characteristics and environmental factors:

[0122] Age and body structure: Elderly people have reduced muscle mass, bone loss, decreased body flexibility and balance, and a relatively low center of gravity and poor stability. Children and adolescents are in the growth and development stage, and the proportions of various parts of the body and the position of the center of gravity will change with growth. When determining the preset center of gravity vector, the patient's age factor should be fully considered, combined with their body structure characteristics, such as height, weight, limb length ratio, etc., to formulate a reasonable preset value.

[0123] Environmental factors: The patient's environment will also affect the preset center of gravity vector. For example, on a bed with armrests or supports, the patient may use external force to adjust the center of gravity. At this time, the preset center of gravity vector can be appropriately considered to be closer to the support side to improve the patient's stability and safety. If the patient is on a bed without armrests, the preset center of gravity vector needs to be adjusted to a position that allows the patient to maintain balance and minimizes damage to the patient when the patient falls.

[0124] S600: Determine first adjustment information of a support module on a hospital bed according to the patient's center of gravity vector and the preset center of gravity vector.

[0125] Specifically, it is first necessary to clarify the specific numerical values ​​or coordinate representations of the patient's center of gravity vector and the preset center of gravity vector, and calculate the difference vector between the two. This difference vector reflects the deviation in position and orientation between the patient's actual center of gravity and the expected center of gravity. If the difference in the first direction is large, it means that the patient's center of gravity deviates from the preset position in the left and right directions, and the support force or height of the support module in the left and right directions needs to be adjusted to guide the patient's center of gravity back to the preset position. For example, if the patient's center of gravity is biased to the right, it is necessary to increase the height or support force of the support module on the left side of the bed to make the patient's body tilt to the left, thereby adjusting the center of gravity to the preset position.

[0126] Similarly, the difference in the second direction reflects the deviation of the patient's center of gravity in the front-to-back direction. If the patient's center of gravity is forward, it is necessary to increase the height of the support module at the rear of the bed so that the patient's body leans back to adjust the center of gravity. The difference in the third direction is related to the height of the patient's center of gravity. If the patient's center of gravity is too high, it is necessary to appropriately lower the height of the overall support module, or adjust the height distribution of some support modules to lower the patient's center of gravity.

[0127] In addition to the position deviation, the orientation deviation of the center of gravity vector must also be considered. It can be measured by calculating the angle θ between the two vectors. If θ≠0, it means that the orientation of the patient's center of gravity is inconsistent with the preset direction. For example, when the patient's body is twisted or tilted, the orientation of the center of gravity will change. At this time, it is necessary to adjust the position or angle of the support module in a targeted manner according to the specific orientation deviation to correct the patient's body posture so that the center of gravity direction meets the preset requirements; thereby trying to ensure the stability of the patient's center of gravity, prevent falls, and prevent the patient from falling to the ground when a fall occurs, and minimize the damage to the patient.

[0128] Based on the above analysis of the center of gravity position and orientation deviation, the height adjustment amount required for each support unit 400 is determined. Assuming that the support module has M support units 400, for the kth (k = 1, 2 ..., M) support unit 400, its height adjustment amount Δh k The height Δh can be determined based on the influence of the center of gravity deviation on the location of the support unit 400. For example, if the kth support unit 400 is located on the left side of the bed and the center of gravity of the patient is biased to the right, the height Δh that needs to be increased is calculated based on the size and direction of the center of gravity deviation. k , to push the patient's center of gravity to the left.

[0129] For some support units 400 with adjustable angles, angle adjustment information needs to be determined. According to the center of gravity orientation deviation and the position and function of the support unit 400, the angle that each support unit 400 needs to be adjusted is calculated.

[0130] Considering the comfort and safety of the patient during the adjustment process, it is also necessary to determine the time sequence of the adjustment. That is, determine in what order and time interval each support unit 400 is adjusted. Generally speaking, the principle of overall adjustment first, then local adjustment, and major adjustment first, then minor adjustment should be followed. For example, first make preliminary adjustments to the support unit 400 that has a greater impact on the patient's center of gravity, observe the patient's reaction and changes in the center of gravity, and then make fine adjustments to other support units 400, gradually making the patient's center of gravity vector close to the preset center of gravity vector.

[0131] S700. Adjust the support module according to the first adjustment information so that the patient's center of gravity vector is equal to the preset center of gravity vector.

[0132] Specifically, the first adjustment information includes the adjustment parameters required by each support unit 400 in the support module, such as the height adjustment amount, angle adjustment direction and amplitude, etc. First, this information needs to be parsed and converted into executable instructions. According to the parsed first adjustment information, the control system will send corresponding instructions to each support unit 400 of the support module to drive it to adjust. The support unit 400 of the support module is usually equipped with an electric push rod, a hydraulic device or other adjustable mechanism. During the adjustment process of the support module, the pressure sensing module continues to play a role, monitoring the changes in pressure distribution of various parts of the patient's body in real time, and the control system calculates the patient's center of gravity vector in real time based on the pressure data.

[0133] The patient's center of gravity vector calculated in real time is compared with the preset center of gravity vector to analyze the difference between the two. If it is found that the center of gravity vector still deviates from the preset value, the control system will further adjust the adjustment parameters of the support module according to the size and direction of the difference to form a closed-loop feedback adjustment system. For example, if it is found that the patient's center of gravity still deviates from the preset position in the front-to-back direction, the control system will recalculate and adjust the height or angle of the corresponding support unit 400 to gradually narrow the gap between the center of gravity vector and the preset center of gravity vector.

[0134] Dynamic adjustment: According to the results of comparative analysis, the control system will dynamically adjust the adjustment speed and amplitude of the support unit 400. If the difference between the center of gravity vector and the preset center of gravity vector is large, the control system may appropriately speed up the adjustment speed and increase the adjustment amplitude; if the difference is small, the adjustment speed will be slowed down and fine-tuning will be performed to ensure the smoothness and accuracy of the adjustment process and avoid discomfort to the patient caused by excessive adjustment. When the difference between the patient's center of gravity vector and the preset center of gravity vector is reduced to within the set error range, the control system will determine that the adjustment target has been achieved. At this time, the system will suspend the adjustment operation of the support module and continue to monitor the stability of the patient's center of gravity vector.

[0135] In some optional embodiments, the pressure sensing module includes a plurality of pressure sensing units 440, the support module includes a plurality of support units 400, each of the support units 400 is provided with the pressure sensing unit 440, and the adjusting the support module according to the first adjustment information includes: determining the unit adjustment information of one or more of the support units 400 on the bed according to the first adjustment information; determining a height adjustment curve of the support unit 400 according to the pathological information and the unit adjustment information, the height adjustment curve characterizing the adjustment rate of the support unit 400 at different support heights; adjusting the support height of the support unit 400 according to the height adjustment curve so that the patient's center of gravity vector is equal to the preset center of gravity vector.

[0136] Specifically, refer to Figure 3-4The first adjustment information is a general indication of the adjustment of the entire support module. Since the support module is composed of multiple support units 400, and each support unit 400 corresponds to a different part of the patient's body, the first adjustment information needs to be refined to each support unit 400. For example, the first adjustment information may indicate that the pressure distribution of the patient's buttocks area needs to be adjusted, and the buttocks area is supported by multiple support units 400. This requires that the first adjustment information be decomposed into specific adjustment requirements for each support unit 400, i.e., unit adjustment information, based on the position of these support units 400 on the buttocks, the degree of contribution to the buttocks support, and the specific conditions of the patient's body, so as to change the patient's center of gravity vector. The specific support unit 400 includes a support plate 430, a support gas rod 420, and a support cylinder 410. The support cylinder 410 lifts and lowers the support plate 430 through the support gas rod 420. A pressure sensing unit 440 is provided on the support plate 430. The pressure sensing unit 440 can be a plurality of evenly distributed pressure sensors, or a pressure sensing pad, which is not specifically limited. The change of the patient's center of gravity is achieved by cooperating with multiple support units 400.

[0137] The patient's pathological information constrains and guides the adjustment of the support unit 400. Different pathological conditions will lead to different tolerance and adaptability of various parts of the patient's body to pressure and support. For example, for patients with lumbar disc herniation, the adjustment of the lumbar support unit 400 must be extremely cautious to avoid excessive adjustment that will increase lumbar pressure and aggravate the condition. When determining the height adjustment curve, it is necessary to fully consider the patient's disease type, disease severity, treatment stage and other pathological factors.

[0138] The unit adjustment information is combined with the pathological information to comprehensively consider the target height that the support unit 400 needs to reach and the patient's body tolerance and recovery needs. For example, for a patient who is recovering from a leg fracture, the unit adjustment information of the leg support unit 400 may require a gradual increase to reduce the pressure at the fracture site, but according to the pathological information, the adjustment process must be carried out slowly to prevent the fracture site from being dislocated. Through this comprehensive analysis, the adjustment rate of the support unit 400 at different support heights is determined, and then a height adjustment curve is drawn. The curve can be linear or nonlinear, depending on the patient's condition and adjustment needs.

[0139] According to the determined height adjustment curve, the control system will send corresponding instructions to the support unit 400 to drive the support unit 400 to adjust the height. The support unit 400 is usually equipped with adjustable mechanisms such as electric push rods and hydraulic devices. For example, for an electric push rod type support unit 400, the control system will calculate the length that the electric push rod needs to extend or retract at different time points according to the height adjustment curve, and then send an electrical signal to control the rotation of the motor, driving the push rod to achieve precise adjustment of the height of the support unit 400.

[0140] Real-time monitoring and adjustment: During the adjustment process, the pressure sensing unit 440 will monitor the pressure on the support unit 400 and the changes in the patient's center of gravity vector in real time. The control system will compare these real-time data with the preset target. If deviations are found during the adjustment process, such as the patient's center of gravity vector does not approach the preset center of gravity vector as expected, or the pressure of a certain support unit 400 exceeds the safe range, the control system will adjust the height adjustment curve in time and change the adjustment rate or direction of the support unit 400. For example, if it is found that the support unit 400 is raised too fast and causes discomfort to the patient, the control system will slow down the adjustment rate to ensure that the adjustment process is smooth, safe and effective, and ultimately make the patient's center of gravity vector equal to the preset center of gravity vector.

[0141] In some optional embodiments, the hospital bed further comprises a leg support module 200 arranged on the first side of the bed frame 120, the leg support module 200 comprises a lifting unit and a rotating unit, the lifting unit comprises a lifting support plate and a lifting cylinder 221, the first end of the lifting support plate is rotatably connected to the bed frame 120, the first end of the lifting cylinder 221 is rotatably connected to the bed frame 120, the second end of the lifting cylinder 221 is fixedly connected to the second end of the lifting support plate through the lifting cylinder 221, the lifting cylinder 221 is used to lift the lifting support plate, the rotating unit comprises a fixing device and a rotating cylinder 231, the fixing device is used to fix the rotating cylinder Cylinder 231, the rotating cylinder 231 is fixedly connected to the lifting support plate through the rotating gas rod 230, and the method also includes: determining the first adjustment information of the support module and the second adjustment information of the leg support module 200 according to the pathological information, the preset center of gravity vector and the patient's center of gravity vector; controlling the lifting cylinder 221 to lift the lifting support plate according to the second adjustment information, so that the lifting support plate has a preset support plate length after extension and retraction; controlling the rotating cylinder 231 to push the lifting support plate through the rotating gas rod 230 according to the second adjustment information, so that the lifting support plate and the lifting cylinder 221 have a preset tilt angle after synchronous rotation.

[0142] Specifically, pathological information includes key information such as the patient's disease type, severity, and treatment stage, which directly affects the patient's body support needs. The preset center of gravity vector is the ideal center of gravity position and orientation set based on the patient's physical condition and rehabilitation goals, while the patient center of gravity vector reflects the patient's current actual center of gravity state. Through a comprehensive analysis of these three, we can fully understand the gap between the actual state of the patient's body and the ideal state.

[0143] Determine adjustment information: According to the above analysis, determine the first adjustment information of the support module, that is, clarify the height, angle and other aspects of each support unit 400 in the support module to help the patient adjust the center of gravity to the preset position, refer to Figure 3 At the same time, the second adjustment information of the leg support module 200 is determined, referring to Figure 2 and 5 , including the lifting height, telescopic length, and rotation angle of the lifting support plate in the leg support module 200, so that the leg support module 200 can better assist the patient in adjusting the body posture and cooperate with the support module to adjust the patient's center of gravity. For example, for a patient with a leg fracture, according to the pathological information, it may be necessary to raise the lifting support plate of the leg support module 200 to a certain height and adjust it to a suitable tilt angle to reduce the pressure on the fracture site, and at the same time cooperate with the adjustment of the support module to make the patient's center of gravity reach a preset vector.

[0144] The first end of the lifting support plate is rotatably connected to the bed frame 120 to form a rotatable fulcrum, and is specifically rotatably connected via the first rotating shaft 210 of the first hinge. The first end of the lifting cylinder 221 is also rotatably connected to the bed frame 120 (specifically rotatably connected via the second rotating shaft 222 of the second hinge), and the second end is fixedly connected to the second end of the lifting support plate. When the lifting cylinder 221 is working, the pressure change of the internal gas drives the piston to move, thereby realizing the lifting and lowering of the lifting support plate.

[0145] Control process: According to the requirements of the extension and retraction length of the lifting plate in the second adjustment information, the control system sends a command to the lifting cylinder 221. After receiving the command, the lifting cylinder 221 adjusts the internal gas pressure to extend or retract the lifting rod 220 to a corresponding length, thereby driving the lifting plate to rise and fall to reach the preset plate length. For example, if the second adjustment information requires the lifting plate to rise by 10 cm, the lifting cylinder 221 will accurately control the extension of the lifting rod 220 to raise the lifting plate to the specified height to meet the patient's leg support needs.

[0146] The fixing device in the rotating unit is used to fix the rotating cylinder 231 so that it can work stably. The rotating cylinder 231 is fixedly connected to the lifting support plate through the rotating gas rod 230. When the rotating cylinder 231 is working, the rotating gas rod 230 will rotate under the push of the cylinder, thereby driving the lifting support plate to rotate. Among them, the fixing device specifically includes a first fixing rod 233 connected to the bed frame 120 and the base 310, and a second fixing rod 232 connected to the bed frame 120 and the first fixing rod 233 respectively. The first fixing rod 233 and the second fixing rod 232 are in a triangular structure, and the rotating cylinder 231 is fixed to the second fixing rod 232. The lifting pallet includes a first pallet 211, a second pallet 212 and a third pallet 213. The first end of the first pallet 211 is rotatably connected to the bed frame 120 through a first hinge, that is, it is rotated through the first rotating shaft 210 of the first hinge; the second end of the first pallet 211 is slidably connected to the second pallet 212, and the first end of the second pallet 212 is provided with a first buckle, so as to prevent the second pallet 212 from falling off from the first pallet 211; similarly, the third pallet 213 is slidably connected to the second pallet 212, the third pallet 213 slides in the second pallet 212, and the first end of the third pallet 213 is provided with a second buckle, which is used to prevent the third pallet 213 from falling off from the second pallet 212. The second end of the third pallet 213 is fixedly connected to the second end of the lifting gas rod 220 through a connecting rod 214, so that the lifting gas rod 220 is pushed by the lifting cylinder 221, and then the third pallet 213 is lifted and lowered synchronously, so that the lifting pallets have different lengths.

[0147] Control process: Based on the requirements for the tilt angle of the lifting pallet in the second adjustment information, the control system sends an instruction to the rotating cylinder 231. After receiving the instruction, the rotating cylinder 231 pushes the rotating gas rod 230 through the change of the internal gas pressure, so that the lifting pallet rotates according to the preset direction and angle. Since there is a mechanical connection between the lifting pallet and the lifting cylinder 221, the lifting cylinder 221 will also rotate synchronously while the lifting pallet rotates, and finally the lifting pallet reaches the preset tilt angle. For example, if the second adjustment information requires the lifting pallet to rotate 30 degrees, the rotating cylinder 231 will accurately control the rotating gas rod 230 to rotate the corresponding angle, so that the lifting pallet and the lifting cylinder 221 will rotate synchronously to a tilt position of 30 degrees, so as to provide a suitable leg support angle, help the patient adjust the body posture, and adjust the center of gravity.

[0148] The beneficial effects of the present invention include: when the present invention obtains an instruction to adjust the patient's center of gravity vector, the patient's pressure distribution information is obtained through a pressure sensing module, and the pressure distribution information represents the pressure distribution of the patient at various locations on the bed; when the pressure distribution information represents that the patient is located at the edge of the bed, the patient's need information for getting in and out of bed is obtained; when the need information for getting in and out of bed represents that the patient needs to get out of bed or get in bed, the patient's center of gravity vector is determined according to the pressure distribution information, and the patient's center of gravity vector represents the position and orientation of the patient's center of gravity; the patient's pathological information is obtained, and the pathological information represents the patient's medical information and / or physiological information obtained through a physiological monitoring module; the patient's preset center of gravity vector is determined according to the pathological information; the first adjustment information of the support module on the bed is determined according to the patient's center of gravity vector and the preset center of gravity vector; the support module is adjusted according to the first adjustment information so that the patient's center of gravity vector is equal to the preset center of gravity vector. By automatically acquiring the pressure data of the patient on the hospital bed, the patient's center of gravity vector is determined, and the first adjustment information of the support module is determined after comparing it with the preset center of gravity vector expected by the pathological information, so that the support module adjusts the corresponding support height to make the patient at the preset center of gravity vector. In the event that the patient accidentally falls, the patient is prevented from falling to the ground and causing injury because he is at the preset center of gravity vector. Therefore, the present application can prevent the patient from falling to the ground when getting on and off the bed, thereby reducing the injuries caused to the patient by falling.

[0149] like Figure 6 As shown, Figure 6 The structure block diagram of a controller 1000 provided according to an embodiment of the present application is shown. The components of the controller 1000 include but are not limited to a memory 1200 and a processor 1100. The processor 1100 is connected to the memory 1200 via a bus, and the memory 1200 is used to store data.

[0150] The controller 1000 also includes an access device that enables the controller 1000 to communicate via one or more networks. Examples of these networks include a combination of a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a communication network such as the Internet. The access device 340 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a global interconnection for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0151] The controller 1000 may be any type of stationary or mobile electronic device, including a mobile computer or mobile electronic device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable electronic device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary electronic device such as a desktop computer or a PC. The controller 1000 may also be a mobile or stationary server.

[0152] Among them, the processor 1100 is used to execute computer executable instructions of the intelligent anti-fall control method.

[0153] The above is a schematic scheme of a controller of this embodiment. It should be noted that the technical scheme of the controller and the technical scheme of the above-mentioned intelligent anti-fall control method belong to the same concept, and the details not described in detail in the technical scheme of the controller can be referred to the description of the technical scheme of the above-mentioned intelligent anti-fall control method.

[0154] According to an embodiment of the present application, an intelligent anti-fall control system is also provided. The intelligent anti-fall control system includes a hospital bed, in which a controller 1000 is installed, or the hospital bed and the controller 1000 are connected by communication, so that the hospital bed can be adjusted by the controller 1000. It should be noted that the technical solution of the intelligent anti-fall control system and the technical solution of the above-mentioned intelligent anti-fall control method belong to the same concept, and the details not described in detail in the technical solution of the computing device can be referred to the description of the technical solution of the above-mentioned intelligent anti-fall control method.

[0155] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned intelligent anti-fall control method is implemented.

[0156] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0157] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as a processor, such as software executed by a central processing unit, a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0158] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above implementation mode. Technical personnel familiar with the field can also make various equivalent modifications or substitutions under the shared conditions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. An intelligent anti-fall control method, characterized in that: The method comprises: Acquire pressure distribution information of the patient, wherein the pressure distribution information is acquired through a pressure sensing module, and the pressure distribution information represents the pressure distribution of the patient at various locations on the bed; When the pressure distribution information indicates that the patient is located at the edge of the bed, obtaining the patient's need information for getting in and out of bed; In the case where the in-bed or out-of-bed demand information indicates that the patient needs to get out of bed or get in bed, determining a patient center of gravity vector according to the pressure distribution information, wherein the patient center of gravity vector indicates a position and orientation of the patient's center of gravity; Acquiring pathological information of the patient, wherein the pathological information represents the patient's medical information and / or physiological information acquired by a physiological monitoring module; determining a preset center of gravity vector of the patient according to the pathological information; Determining first adjustment information of a support module on a hospital bed according to the patient's center of gravity vector and the preset center of gravity vector; The support module is adjusted according to the first adjustment information so that the patient's center of gravity vector is equal to the preset center of gravity vector.

2. The intelligent anti-fall control method according to claim 1, characterized in that: The hospital bed comprises a bed frame and a bed board arranged on the bed frame, the bed board is provided with the support module, the support module is provided with the pressure sensing module, and the pressure distribution information of the patient is obtained, including: Acquiring bed pressure data on the bed through the pressure sensing module; Obtaining patient pressure data after removing interfering pressure data of the bed pressure data according to preset pressure data, wherein the preset pressure data represents a pre-established range of patient pressure; Obtaining a patient pressure distribution area after eliminating an interfering pressure area in the patient pressure data according to a preset pressure distribution model, wherein the preset pressure distribution model represents a pre-established model of pressure distribution of the patient when getting in and out of bed; The patient pressure distribution area and the pressure data corresponding to the patient pressure distribution area are configured as the pressure distribution information of the patient.

3. The intelligent anti-fall control method according to claim 2, characterized in that: The step of obtaining the patient pressure distribution area after eliminating the interference pressure area in the patient pressure data according to the preset pressure distribution model includes: Comparing the pressure distribution area to be processed corresponding to the patient pressure data with the preset pressure distribution model to obtain a patient pressure distribution model, wherein the patient pressure distribution model represents the pressure distribution model with the highest similarity to the pressure distribution area to be processed; Establishing a Gaussian distribution of similarity between the patient pressure distribution model and the pressure distribution area to be processed; The patient pressure distribution area is obtained by removing the interference pressure area from the Gaussian distribution according to a preset similarity threshold.

4. The intelligent anti-fall control method according to claim 3, characterized in that: The step of removing the interference pressure region from the Gaussian distribution according to a preset similarity threshold to obtain the patient pressure distribution region includes: configuring a first pressure region in the Gaussian distribution whose similarity is less than the similarity threshold as the interference pressure region; Obtaining pressure change information within a preset time for a plurality of second pressure regions whose similarity in the Gaussian distribution is greater than or equal to the similarity threshold; When the pressure change information indicates that there is at least one second pressure region where no pressure change occurs within a preset time, the second pressure region where no pressure change occurs is configured as the interference pressure region.

5. The intelligent anti-fall control method according to claim 1, characterized in that: The step of obtaining the patient's need information for getting in and out of bed includes: When the patient is at the edge of the bed, determining a tendency to get out of bed or get on bed according to the pressure direction indicated by the pressure distribution information; Acquiring voice interaction information or key information of the patient according to the getting out of bed tendency or the getting in bed tendency; The in-bed requirement information is determined through voice interaction information or key information.

6. The intelligent anti-fall control method according to claim 1, characterized in that: The pressure sensing module includes a plurality of pressure sensing units, the support module includes a plurality of support units, each of the support units is provided with the pressure sensing unit, and adjusting the support module according to the first adjustment information includes: Determine unit adjustment information of one or more support units of the bed according to the first adjustment information; Determine a height adjustment curve of the support unit according to the pathological information and the unit adjustment information, wherein the height adjustment curve represents an adjustment rate of the support unit at different support heights; The supporting height of the supporting unit is adjusted according to the height adjustment curve so that the center of gravity vector of the patient is equal to the preset center of gravity vector.

7. The intelligent anti-fall control method according to claim 2, characterized in that: The hospital bed further comprises a leg support module arranged on the first side of the bed frame, the leg support module comprises a lifting unit and a rotating unit, the lifting unit comprises a lifting support plate and a lifting cylinder, the first end of the lifting support plate is rotatably connected to the bed frame, the first end of the lifting cylinder is rotatably connected to the bed frame, the second end of the lifting cylinder is fixedly connected to the second end of the lifting support plate through the lifting cylinder, the lifting cylinder is used to lift the lifting support plate, the rotating unit comprises a fixing device and a rotating cylinder, the fixing device is used to fix the rotating cylinder, the rotating cylinder is fixedly connected to the lifting support plate through a rotating gas rod, and the method further comprises: Determine the first adjustment information of the support module and the second adjustment information of the leg support module according to the pathological information, the preset center of gravity vector and the patient's center of gravity vector; Controlling the lifting cylinder to lift the lifting support plate according to the second adjustment information, so that the lifting support plate has a preset support plate length after being extended and retracted; According to the second adjustment information, the rotating cylinder is controlled to push the lifting support plate through the rotating gas rod, so that the lifting support plate and the lifting cylinder have a preset tilt angle after synchronous rotation.

8. A controller, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the intelligent fall prevention control method according to any one of claims 1 to 7 when executing the computer program.

9. An intelligent anti-fall control system, characterized in that: include: The controller of claim 8.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and the computer executable instructions are used to execute the intelligent anti-fall control method described in any one of claims 1-7.

Images