Nursing bed and smart home management system

Abstract

The invention relates to the technical field of smart home, and provides a nursing bed and a smart home management system.The nursing bed comprises a nursing bed body, and the nursing bed body comprises a bed board and a smart screen; the bed board is provided with a plurality of independent supporting parts, and a pose sensor is arranged in each supporting part and used for collecting real-time pose data of the body part of a user; the intelligent screen is fixedly installed on a guardrail of the nursing bed body and is in wireless connection with the intelligent household equipment. The system comprises a signal receiving module, an instruction determining module and an equipment control module. The signal receiving module is used for receiving pose sensor data; the instruction determination module is used for converting the attitude data into a first control instruction according to a preset mapping rule; the device control module is used for controlling at least one smart home device in a linkage mode according to the first control instruction, and the same device executes differentiated actions under different posture data.

Description

Technical Field

[0001] This invention relates to the field of smart home control technology, and in particular to a nursing bed and a smart home management system. Background Technology

[0002] Nursing beds are beds with multiple nursing functions designed according to the bedridden living habits and treatment needs of patients or the elderly.

[0003] With the development of smart home technology, some nursing beds are equipped with the function of controlling smart home devices, providing convenience for patients and the elderly who need to stay in bed.

[0004] However, current smart home devices mainly rely on voice control or touch screen control. But for patients or the elderly, due to limited mobility, unclear speech, reduced range of motion, or tremors, it is difficult to perform precise button presses or touch screen swipes. Most smart home devices have complex control interfaces and confusing operation steps, making them unsuitable for the elderly or patients.

[0005] Therefore, how to automatically execute different smart home device control commands based on the posture of elderly people or patients in nursing beds is a problem that needs to be solved. Summary of the Invention

[0006] This application proposes a nursing bed and a smart home management system, which uses a posture sensor integrated into the nursing bed to collect posture data and convert the posture data into differentiated control commands; the same smart home device can perform different actions (adjustment of light brightness, opening and closing of curtains) according to different posture data (lying angle, pressure distribution changes).

[0007] To achieve the above objectives, this application provides the following technical solution: In a first aspect, this application proposes a nursing bed, said nursing bed comprising: The nursing bed body includes a bed board and a smart screen; The bed board is equipped with multiple independent support components, each of which has a built-in posture sensor to collect real-time posture data of the user's body parts. The smart screen is fixedly installed on the guardrail of the nursing bed and wirelessly connected to smart home devices.

[0008] In conjunction with the first aspect, the pose sensor includes a six-axis pose sensor and a pressure sensor; When a user is on the bed, the angle and acceleration are detected by a six-axis attitude sensor, and the pressure distribution data is collected by a pressure sensor.

[0009] Secondly, this application proposes a smart home system applicable to the aforementioned nursing bed, the system comprising: a signal receiving module, an instruction determining module, and a device control module; The signal receiving module is used to receive data from the pose sensor; The instruction determination module is used to convert attitude data into first control instructions according to preset mapping rules; The device control module is used to control at least one smart home device in conjunction with the first control command, and the same device performs differentiated actions under different posture data.

[0010] In conjunction with the second aspect, the receiving of pose sensor data includes: Step S1: Perform CRC check on the received data packet. If the check fails, send a retransmission request to the sensor and increment the retransmission count by 1. If the retransmission count exceeds 3, mark the sensor as abnormal and proceed to step S5. Step S2: After the verification is passed, parse the attitude data in the data packet, including angle value, pressure value and sampling time; Step S3: Compare the angle value with the preset physical limit range. If it exceeds the range, it is determined to be invalid data and step S5 is executed. Step S4: Calculate the change between two adjacent sampling data. If the change exceeds the preset maximum allowable fluctuation value, it is determined to be a sudden change data and the data is smoothed. Step S5: Package the processed valid data, invalid data markers, or sensor abnormal status information and send them to the instruction determination module.

[0011] In conjunction with the second aspect, the generation of the first control command includes: Step S10: Receive the raw attitude data transmitted by the pose sensor, timestamp the data and sort it according to the acquisition order; Step S20: Extract the feature parameters from each set of attitude data, including the attitude change amplitude, duration, and rate of change; Step S30: Compare the feature parameters with multiple preset threshold ranges; wherein, If the feature parameter falls within a certain threshold range, proceed to step S40; If the data does not fall within any threshold range, return to step S10 to receive data again; Step S40: Call the preset condition judgment rule to determine whether the current feature parameters meet the single attitude trigger or combined attitude trigger conditions; wherein, single attitude trigger means that the feature parameters of a single support component meet the threshold, and combined attitude trigger means that the feature parameters of at least two support components meet the corresponding threshold at the same time. Step S50: If the single attitude triggering condition is met, the corresponding basic control command is directly matched; if the combined attitude triggering condition is met, a logical AND operation is performed to combine multiple basic control commands into a composite control command. Step S60: Output the generated basic control command or composite control command as the first control command to the device control module.

[0012] In conjunction with the second aspect, the generation of the first control command further includes: The acquisition period is preset, and the pose sensing data and corresponding control commands within the acquisition period are determined to generate the first mapping sample set; The first mapping sample set is divided into subsets according to time periods to generate multiple sample subsets; Set a rule threshold. When a certain pose data and corresponding instruction exist in the sample subset, and both meet the rule threshold within the acquisition period, the corresponding pose feature is added to the custom rule library.

[0013] In conjunction with the second aspect, the generation of the first control command further includes: Step 51: Receive user posture data collected by the posture sensor, and extract posture feature vectors from the data, including the pressure distribution ratio and relative angle relationship of each support component; Step 52: Compare the pose feature vector with the pre-stored multi-user feature template library and calculate the vector similarity; Step 53: Determine whether the highest similarity value exceeds the preset threshold. If yes, proceed to step 54; otherwise, mark as an unknown user and execute the default control rules. Step 54: Retrieve the user's personalized control rule set, which contains the user's unique gesture-command mapping relationship; Step 55: Following the process of steps 51-54, repeat the user identification process on the subsequently received attitude data. If the identification results are consistent for three consecutive times, maintain the current rule set; if inconsistent results occur, repeat steps 52-54 to verify the identity.

[0014] In conjunction with the second aspect, controlling at least one smart home device includes: Step S100: Receive the first control command and parse the device type, action type, and parameter requirements in the command; Step S200: Query the current device status list to determine whether the target device is online and controllable; if the device is offline, generate a device offline prompt message and terminate the process; Step S300: If the device is online, further determine whether the current operating parameters of the device are consistent with the instruction requirements; if they are consistent, no action needs to be performed; if they are inconsistent, proceed to step S400. Step S400: Perform the corresponding parameter adjustment steps according to the action type: For switching actions: directly send an on / off signal; If it is an adjustment action: calculate the difference between the current parameter and the target parameter, send adjustment signals in stages according to the preset step size, wait 500ms after each signal is sent, and detect the real-time parameters fed back by the equipment until the target parameter is reached; Step S500: Receive feedback on the execution result from the device. If the feedback indicates successful execution, record the execution time and update the device status list. If the feedback indicates failed execution, resend the control signal. The number of retries shall not exceed N, where N < 3.

[0015] In conjunction with the second aspect, the same device performs differentiated actions under different posture data, including: Step 101: Receive attitude data of each support component in real time and calculate the attitude change frequency per unit time; Step 102: Determine whether the frequency of change exceeds the preset abnormal threshold. If so, proceed to step 103. Step 103: Extract the posture data sequence within this time period and perform similarity matching with the preset posture templates for bed fall risk and spastic posture templates; Step 104: If the similarity exceeds the first preset value, it is determined to be a high-risk abnormal posture, and step 105 is executed; if the similarity is between the second preset value and the first preset value, it is determined to be medium-risk, and step 106 is executed; if it is lower than the second preset value, it is determined to be normal fluctuation, and step 1 is returned; wherein, the second preset value is lower than the first preset value. Step 105: Generate an emergency alarm command to control the alarm device to activate the audible and visual alarm and send a warning message containing attitude data to the preset contact person; Step 106: Generate a reminder command, control the bedside lamp to flash, and display a query message on the smart screen. If no cancellation command is received from the user within the preset time, the process will be upgraded to high-risk handling.

[0016] In conjunction with the second aspect, the same device performing differentiated actions under different posture data further includes: Receive the first control command and parse the master device type and associated device type in the command; Query the current status of the master device. If the master device is in a powered-off state, first send a start command and wait for the master device to respond that it has started. After startup, control commands are sent to associated devices in a preset order, with a preset interval between each transmission to avoid network congestion. The system receives real-time feedback on the execution progress of each device. If a related device times out, it records the device as having failed to coordinate and continues to control other devices. After all devices are controlled, a collaborative result report is generated, which includes the number of successful devices, the IDs of failed devices, and the execution time, and is stored in the local log.

[0017] The beneficial effects of this invention are: This application uses a posture sensor to collect user posture data and combines it with AI algorithms to automatically identify the user, addressing the difficulty elderly people face in operating smart home devices and the unfriendly nature of traditional manual systems. Furthermore, to address the inability of existing systems to automatically adjust control commands based on changes in the elderly person's posture, this application compares posture feature vectors with a pre-stored multi-user feature template library to achieve accurate user identification and personalized control. For the elderly person's control needs for different smart home devices, this application also automatically adjusts control commands based on user identity.

[0018] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0021] In the attached diagram: Figure 1 This is a diagram illustrating the composition of a nursing bed according to an embodiment of the present invention; Figure 2 This is a diagram illustrating the composition of a smart home system according to an embodiment of the present invention; Figure 3 This is a diagram illustrating the first instruction implementation of a smart home system according to an embodiment of the present invention.

[0022] 1 is the nursing bed body, 2 is the bed board, 3 is the independent support component, 4 is the guardrail, 5 is the pressure sensor, 6 is the six-axis attitude sensor, and 7 is the smart screen. Detailed Implementation

[0023] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0024] Example 1: See Figure 1 A nursing bed, the nursing bed comprising: The nursing bed body 1 includes a bed board 2 and a smart screen 7; The bed board 2 is equipped with multiple independent support components 3, each of which has a built-in posture sensor to collect real-time posture data of the user's body parts; The smart screen 7 is fixedly installed on the guardrail 4 of the nursing bed body 1 and wirelessly connected to smart home devices.

[0025] In this application, the nursing bed body 1 integrates a bed board 2 to support the user and a smart screen 7 to enable intelligent interaction and control smart home devices. The bed board 2 provides basic support functions to meet the user's needs for lying down, getting up, and other postures; the smart screen 7 serves as a human-computer interaction interface, replacing traditional physical buttons or remote controls and simplifying the operation process. Multiple independent support components 3 are installed on the bed board 2. Each support component 3 can individually adjust its height or angle according to the user's body position. Each support component has a built-in posture sensor that detects angle, acceleration, and pressure distribution to capture the postural characteristics of different parts of the user's body. The independent support components 3 enhance the flexibility of body position adjustment, adapting to different user body shapes and rehabilitation needs. The smart screen 7 is installed at the guardrail 4 for easy and intuitive operation when the user is lying down or sitting. It establishes a connection with smart home devices via wireless communication protocols to achieve data transmission and command issuance. Wireless communication protocols include Wi-Fi, Bluetooth, and ZigBee; smart home devices include lights, curtains, and air conditioning.

[0026] Example 2: The pose sensor includes a six-axis pose sensor 6 and a pressure sensor 5; When a user is on the bed board 2, the angle and acceleration are detected by the six-axis attitude sensor 6, and the pressure distribution data is collected by the pressure sensor 5.

[0027] In this application, the posture sensor employs a combination of a six-axis posture sensor 6 and a pressure sensor 5 for monitoring. The six-axis posture sensor 6 integrates a three-axis accelerometer and a three-axis gyroscope, capturing dynamic posture characteristics by detecting the angle changes and linear acceleration of the object in three-dimensional space. The pressure sensor 5 consists of multiple distributed pressure sensing units, capturing static contact characteristics and determining the body position and pressure center point by detecting the pressure values ​​and distribution in different areas of the bed board 2. The presence of a user on the bed board 2 is determined by the initial pressure signal from the pressure sensor 5, and then the coordinated acquisition of the six-axis posture sensor 6 and the pressure sensor 5 array is initiated: the six-axis sensor outputs angle and acceleration data in real time; the pressure sensor 5 array synchronously outputs the pressure values ​​of each sensing unit, forming a pressure distribution matrix.

[0028] In one embodiment, as the user gets up, the back angle increases while the pressure distribution on the buttocks moves towards the foot of the bed. This dynamic posture characteristic indicates that the user is getting up, at which point the lights are turned on.

[0029] Example 3: See Figure 2 This application proposes a smart home system applicable to the aforementioned nursing bed, the system comprising: a signal receiving module, an instruction determining module, and a device control module; The signal receiving module is used to receive data from the pose sensor; In this application, the signal receiving module is responsible for data input, the instruction determination module is responsible for logic processing, and the device control module is responsible for execution output. The modules communicate with each other through an internal data interface.

[0030] The signal receiving module receives raw data collected by the distributed posture sensors on the nursing bed board 2 in real time through wired or wireless communication protocols, performs preliminary verification on the data, and then transmits it to the instruction determination module.

[0031] The instruction determination module is used to convert attitude data into first control instructions according to preset mapping rules; The instruction determination module has a built-in preset mapping rule base, which contains the correspondence between posture data features and control instructions. The module extracts key features of the posture data through algorithms, matches them with the rule base, and generates specific control instructions; for example, turning on the bedroom light to 50% brightness.

[0032] The device control module is used to control at least one smart home device in conjunction with the first control command, and the same device performs differentiated actions under different posture data.

[0033] Based on the device identifier, action parameters, and linkage logic in the first control command, control signals are sent to the target device via the smart home protocol. For the same device, different actions are controlled to be performed according to the differentiated parameters in the command.

[0034] Example 4: See Figure 3 Receive pose sensor data, including: Step S1: Perform CRC check on the received data packet. If the check fails, send a retransmission request to the sensor and increment the retransmission count by 1. If the retransmission count exceeds 3, mark the sensor as abnormal and proceed to step S5. In step S1, the CRC check calculates a checksum on the data packet and compares it with the checksum from the sending end to determine whether errors occurred during data transmission due to interference, packet loss, or other reasons. If the check fails, the system sends a retransmission request to the sensor, accumulating the number of retransmissions. If the retransmission fails more than three times, it is determined that there is a sensor hardware failure or a communication link abnormality, and the system is marked as an abnormal state. The CRC check quickly identifies transmission errors, preventing erroneous data from entering subsequent processing flows. The CRC check is a cyclic redundancy check.

[0035] Step S2: After the verification is passed, parse the attitude data in the data packet, including angle value, pressure value and sampling time; In step S2, the data packets that pass the CRC check are parsed to extract key information from preset fields: angle value, pressure value, and sampling time, and converted into a structured data format recognizable by the system. The angle value is the tilt angle output by the six-axis sensor; the pressure value is the pressure data of each unit of the pressure sensor 5 during actual implementation. The sampling time is the timestamp of data acquisition.

[0036] Step S3: Compare the angle value with the preset physical limit range. If it exceeds the range, it is determined to be invalid data and step S5 is executed. In step S3, a preset physical limit range for the angle value is defined, and the parsed angle value is compared with this range. If the value exceeds the range, it is determined to be an invalid value caused by sensor malfunction or data interference. In this application, the physical limit range is the maximum tilt angle of the human back that can move, from 0° to 80°. Sensor malfunctions include problems such as sensor installation tilt.

[0037] Step S4: Calculate the change between two adjacent sampling data. If the change exceeds the preset maximum allowable fluctuation value, it is determined to be a sudden change data and the data is smoothed. In step S4, the change between the current sampled data and the data at the previous moment is calculated and compared with the preset maximum allowable fluctuation value. If the value exceeds the threshold, it is determined to be abrupt data, and the data is smoothed using a moving average or low-pass filtering algorithm. The change includes the difference in angle values ​​and the rate of change in pressure values; abrupt data includes sensor impacts and sudden user actions; the smoothing process replaces the abrupt value with the average value of the previous and subsequent moments.

[0038] Step S5: Package the processed valid data, invalid data markers, or sensor abnormal status information and send them to the instruction determination module.

[0039] In step S5, the data after verification, parsing, range filtering, and smoothing are classified and packaged: valid data indicates angle / pressure values ​​that have passed all verifications; invalid data is marked to indicate data that exceeds the physical range; sensor abnormal status indicates sensor IDs that failed to retransmit, which are transmitted to the instruction determination module through a standardized interface.

[0040] Example 5: The generation of the first control command includes: Step S10: Receive the raw attitude data transmitted by the pose sensor, timestamp the data and sort it according to the acquisition order; In step S10, after receiving the raw data transmitted by the sensor, namely the angle and pressure values, a unique timestamp is added to each data point, and the data is sorted according to the physical time sequence of data acquisition to form a time-series data queue.

[0041] Step S20: Extract the feature parameters from each set of attitude data, including the attitude change amplitude, duration, and rate of change; In step S20, key features are calculated from the time-series data: the magnitude of change, which in actual implementation represents the difference between the current data and the initial data, such as the change in angle from 0° to 30°; the duration, which in actual implementation represents the time it takes for the posture to change from the beginning to the point of stabilization, such as a rolling motion lasting 2 seconds; and the rate of change, which in actual implementation represents the magnitude change per unit time, such as an angle change of 15° per second.

[0042] Step S30: Compare the feature parameters with multiple preset threshold ranges; wherein, If the feature parameter falls within a certain threshold range, proceed to step S40; If the data does not fall within any threshold range, return to step S10 to receive data again; In step S30, the system has multiple built-in threshold ranges, and the extracted feature parameters are compared with each set of thresholds one by one. Only data that falls completely within a certain threshold range is considered a valid action and proceeds to the next step; otherwise, it is considered an invalid action, and data is received again. Invalid actions indicate sensor noise or unintentional micro-movements.

[0043] Step S40: Call the preset condition judgment rule to determine whether the current feature parameters meet the single attitude trigger or combined attitude trigger conditions; wherein, single attitude trigger means that the feature parameters of a single support component meet the threshold, and combined attitude trigger means that the feature parameters of at least two support components meet the corresponding threshold at the same time. In step S40, the conditional judgment rule defines two triggering modes: Single trigger means that only the angle change of the back support component meets the standing threshold; A combined trigger indicates that a change in back angle and a shift in hip pressure simultaneously meet the threshold for getting out of bed.

[0044] The system then matches the corresponding rules and determines the trigger type based on the support components to which the feature parameters belong, namely the back and leg sensors.

[0045] Step S50: If the single attitude triggering condition is met, the corresponding basic control command is directly matched; if the combined attitude triggering condition is met, a logical AND operation is performed to combine multiple basic control commands into a composite control command. In step S50, the basic control command is a preset single device action; the composite control command combines multiple basic commands through logical AND operation, and the basic commands include turning on the lights and turning on the air conditioner combined into a wake-up scene command. The corresponding command library is called according to the trigger type: a single trigger matches a single basic command, and a combined trigger merges multiple basic commands according to rules.

[0046] Step S60: Output the generated basic control command or composite control command as the first control command to the device control module.

[0047] In step S60, the basic / composite instructions are encapsulated into a standardized format and transmitted to the device control module through an internal interface to trigger subsequent physical actions of the smart home devices. The standardized format includes the device ID, action parameters, and execution order.

[0048] Example 6: The generation of the first control command further includes: The acquisition period is preset, and the pose sensing data and corresponding control commands within the acquisition period are determined to generate the first mapping sample set; In this application, a fixed time period is preset, and the raw data collected by the posture sensor is recorded synchronously within the period. The raw data represents angles and pressure values, etc. The control commands executed by the device represent turning on the lights and adjusting the mattress firmness. This forms a one-to-one mapping sample pair of posture data and control commands, which is combined into the first mapping sample set.

[0049] The first mapping sample set is divided into subsets according to time periods to generate multiple sample subsets; In this application, the first mapping sample set is divided into multiple independent subsets according to the time period characteristics of the user's usage scenario. Each subset corresponds to the attitude data and command mapping relationship for a specific time period.

[0050] Set a rule threshold. When a certain pose data and corresponding instruction exist in the sample subset, and both meet the rule threshold within the acquisition period, the corresponding pose feature is added to the custom rule library.

[0051] In this application, a preset rule threshold is used to perform statistical analysis on a subset of samples from each time period. If a set of posture data-command mappings continuously meets the threshold condition within a period, the feature parameters of that posture data are automatically added to a custom rule library. The feature parameters represent the pressure distribution characteristics and angle variation range when lying on one's side.

[0052] Example 7: The generation of the first control command further includes: Step 51: Receive user posture data collected by the posture sensor, and extract posture feature vectors from the data, including the pressure distribution ratio and relative angle relationship of each support component; In step 51, the user's contact with the nursing bed is collected by the posture sensors, namely: pressure sensor 5 and six-axis posture sensor 6. Two types of core features are extracted from them: pressure distribution ratio, which represents the proportion of pressure values ​​of each support component such as back, buttocks and legs; and relative angle relationship, which represents the difference in tilt angle of each bed board 2 component, such as the angle difference between the back and leg bed boards 2. These are combined to form a multi-dimensional posture feature vector.

[0053] Step 52: Compare the pose feature vector with the pre-stored multi-user feature template library and calculate the vector similarity; In step 52, the system has a built-in multi-user feature template library. Each user in the multi-user feature template library corresponds to a set of standard feature vectors. Through the vector similarity algorithm, the cosine similarity and Euclidean distance are selected. The matching degree between the current feature vector and each set of vectors in the template library is calculated, and the similarity scores are output and sorted. In this process, the user's posture information is the input information. There is comparison information of different risk postures in the template library. The similarity is determined by comparing the two.

[0054] Step 53: Determine whether the highest similarity value exceeds the preset threshold. If yes, proceed to step 54; otherwise, mark as an unknown user and execute the default control rules. In step 53, a preset similarity threshold is set, and the highest similarity score is compared with the threshold: if it exceeds the threshold, it is determined to be a known user.

[0055] Step 54: Retrieve the user's personalized control rule set, which contains the user's unique gesture-command mapping relationship; In step 54, each known user is associated with an independent set of personalized control rules, storing their unique posture features-control command mapping. The corresponding rule set is retrieved based on the recognition result as the basis for command generation.

[0056] Step 55: Following the process of steps 51-54, repeat the user identification process on the subsequently received posture data. If the identification results are consistent for three consecutive times, maintain the current rule set; if inconsistent results occur, re-execute steps 52-54 to confirm the identity. In this application, the user's identity information and other privacy data are collected by obtaining user authorization through a built-in protocol signing method when the nursing bed of this application is started for the first time or when the nursing bed of this application is re-registered and started.

[0057] In step 55, the system continuously performs identity verification on the newly received posture data and sets up a verification mechanism that confirms the identity of the user after three consecutive verifications: if the three consecutive verification results are for the same user, the current rule set is maintained; if the verification result is inconsistent with the current user, for example, if the user is changed midway, the comparison process is immediately re-executed, i.e., steps 52-54, confirming the identity of the new user and switching the rule set.

[0058] Example 8: The control of at least one smart home device includes: Step S100: Receive the first control command and parse the device type, action type, and parameter requirements in the command; In step S100, the system receives the first control instruction output by the instruction generation module, extracts key information such as device type, action type, and parameter requirements through the syntax parser, and converts it into a standardized instruction format that the device can recognize.

[0059] Step S200: Query the current device status list to determine whether the target device is online and controllable; if the device is offline, generate a device offline prompt message and terminate the process; In step S200, the system maintains a real-time updated device status list, which includes online status, fault codes, battery level, etc. Upon receiving an instruction, the system immediately queries the status of the target device: subsequent operations are allowed only if the device is online and not locked by a fault; otherwise, a device offline / fault message is generated and the process is terminated.

[0060] Step S300: If the device is online, further determine whether the current operating parameters of the device are consistent with the instruction requirements; if they are consistent, no action needs to be performed; if they are inconsistent, proceed to step S400. In step S300, the real-time parameters in the device status list are retrieved and compared with the parameters required by the instruction: if the parameters match, no operation is required and the process ends directly; if they do not match, parameter adjustment is triggered.

[0061] Step S400: Perform the corresponding parameter adjustment steps according to the action type: For switching actions: directly send an on / off signal; If it is an adjustment action: calculate the difference between the current parameter and the target parameter, send adjustment signals in stages according to the preset step size, wait 500ms after each signal is sent, and detect the real-time parameters fed back by the equipment until the target parameter is reached; In step S400, switching actions involve sending a binary on / off signal to the device without the need for intermediate parameters; adjustment actions involve calculating the parameter difference, sending adjustment signals in stages according to a preset step size, with each step interval of 500ms, receiving parameter changes from the device in real time, and dynamically correcting the next adjustment amount until the target value is reached.

[0062] Step S500: Receive feedback on the execution result from the device. If the feedback indicates successful execution, record the execution time and update the device status list. If the feedback indicates failed execution, resend the control signal. The number of retries shall not exceed N, where N < 3.

[0063] In step S500, after the device executes the instruction, it returns a "success / failure" status code: if successful, the status list is updated; if unsuccessful, a retry mechanism is triggered, with a maximum of 2 retries (N=2). If it still fails, the process is terminated and a fault log is recorded.

[0064] Example 9: The same device performs differentiated actions under different posture data, including: Step 101: Receive attitude data of each support component in real time and calculate the attitude change frequency per unit time; In step 101, sensor data from supporting components such as mattresses and seats is continuously received; the sensor data includes pressure distribution and angle changes; then, the frequency of change is calculated by counting the number of changes in posture data per unit time through a sliding time window. The number of changes includes the number of pressure value fluctuations and the frequency of angle adjustments.

[0065] Step 102: Determine whether the frequency of change exceeds the preset abnormal threshold. If so, proceed to step 103. In step 102, an abnormal frequency threshold is preset, and the frequency value calculated in real time is compared with the threshold: if it exceeds the threshold, it is judged as "suspected abnormal action" and triggers further risk assessment; if it does not exceed the threshold, it is judged as normal physiological activity and monitoring continues.

[0066] Step 103: Extract the posture data sequence within this time period and perform similarity matching with the preset posture templates for bed fall risk and spastic posture templates; In step 103, the complete data sequence for the abnormal frequency period is extracted and compared with the pre-stored bed fall risk template and spasticity template to calculate the sequence similarity. The bed fall risk template is a template composed of features such as the pressure shift characteristics of the body moving towards the edge of the bed and the leg suspension angle. The spasticity template represents the frequency characteristics of the trunk's periodic left-right swaying and the pattern of sudden increases and decreases in pressure values.

[0067] Step 104: If the similarity exceeds the first preset value, it is determined to be a high-risk abnormal posture, and step 105 is executed; If the similarity is between the second preset value and the first preset value, it is determined to be of medium risk, and step 106 is executed; If the value is lower than the second preset value, it is determined to be a normal fluctuation, and the process returns to step 1; wherein the second preset value is lower than the first preset value. In step 104, this application sets two levels of similarity thresholds. High risk is defined as a risk value >80%, indicating a high degree of match between the data sequence and the template. For example, a fall from bed is observed. Medium risk is defined as a similarity threshold between 50% and 80%, where only partial features match, such as a patient's body moving towards the edge of the bed but not reaching the critical angle for a fall. Normal fluctuations are defined as a similarity <50%, indicating low similarity and accidental actions, such as a brief shift caused by a user reaching for a water cup.

[0068] Step 105: Generate an emergency alarm command to control the alarm device to activate the audible and visual alarm and send a warning message containing attitude data to the preset contact person; In step 105, after a high-risk assessment, the system immediately generates an emergency command: Local alarm: triggers the bedside audible and visual alarm; Remote notification: sends a warning message to a preset contact via SMS / APP, including the risk type, real-time posture data, and time of occurrence.

[0069] Step 106: Generate a reminder command, control the bedside lamp to flash, and display an inquiry message on the smart screen 7. If no cancellation command is received from the user within the preset time, the process will be upgraded to high-risk handling.

[0070] In step 106, after the medium risk assessment, the system executes a mild reminder-user confirmation process: Local reminder: The bedside lamp flashes at a low frequency, and the smart screen 7 displays "Do you need help?"; Timeout escalation: If the user does not cancel the reminder via voice / touchscreen within 10 seconds, it will automatically escalate to high risk and execute the emergency alarm process in step 105.

[0071] Example 10: The same device performing differentiated actions under different attitude data also includes: Receive the first control command and parse the master device type and associated device type in the command; In this application, after receiving control commands, the system extracts the type information of the master device and associated devices through semantic parsing to clarify the master-slave relationship between the devices.

[0072] Query the current status of the master device. If the master device is in a powered-off state, first send a start command and wait for the master device to respond that it has started. In this application, the status of the main device is queried in real time: if the main device is not started, a start command is sent first, and after the device feedback signal confirms that the main device is ready, subsequent operations are performed; if the main device has been started, the associated device control process is directly entered.

[0073] After startup, control commands are sent to associated devices in a preset order, with a preset interval between each transmission to avoid network congestion. In this application, the control sequence of associated devices is preset, and the instruction sending interval is fixed, forming a control flow of serial transmission and interval buffering, so as to avoid multiple device instructions occupying network bandwidth at the same time.

[0074] The system receives real-time feedback on the execution progress of each device. If a related device times out, it records the device as having failed to coordinate and continues to control other devices. In this application, an execution timeout threshold is set for each associated device, and the execution / success / failure status returned by the device is monitored in real time: if no feedback is received after the timeout, that is, the device is offline or has a communication failure, it is immediately marked as a collaboration failure, and the device is skipped to continue controlling subsequent devices without interrupting the overall process.

[0075] After all devices are controlled, a collaborative result report is generated, which includes the number of successful devices, the IDs of failed devices, and the execution time, and is stored in the local log.

[0076] In this application, after the collaborative process is completed, the system automatically summarizes the data: counts the number of successful / failed devices, records the unique identifier of the failed device, calculates the total time taken from the start of the master device to the completion of the last device, generates a structured report and stores it in a local log file.

[0077] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A nursing bed, characterized in that, The nursing bed includes: The nursing bed body includes a bed board and a smart screen; The bed board is equipped with multiple independent support components, each of which has a built-in posture sensor to collect real-time posture data of the user's body parts. The smart screen is fixedly installed on the guardrail of the nursing bed and wirelessly connected to smart home devices.

2. The nursing bed as described in claim 1, characterized in that, The pose sensor includes a six-axis pose sensor and a pressure sensor; When a user is on the bed, the angle and acceleration are detected by a six-axis attitude sensor, and the pressure distribution data is collected by a pressure sensor.

3. A smart home system applicable to a nursing bed as described in any one of claims 1 or 2, characterized in that, The system includes: a signal receiving module, an instruction determining module, and a device control module; The signal receiving module is used to receive data from the pose sensor; The instruction determination module is used to convert attitude data into first control instructions according to preset mapping rules; The device control module is used to control at least one smart home device in conjunction with the first control command, and the same device performs differentiated actions under different posture data.

4. A smart home system as described in claim 3, characterized in that, The received pose sensor data includes: Step S1: Perform CRC check on the received data packet. If the check fails, send a retransmission request to the sensor and increment the retransmission count by 1. If the retransmission count exceeds 3, mark the sensor as abnormal and proceed to step S5. Step S2: After the verification is passed, parse the attitude data in the data packet, including angle value, pressure value and sampling time; Step S3: Compare the angle value with the preset physical limit range. If it exceeds the range, it is determined to be invalid data and step S5 is executed. Step S4: Calculate the change between two adjacent sampling data. If the change exceeds the preset maximum allowable fluctuation value, it is determined to be a sudden change data and the data is smoothed. Step S5: Package the processed valid data, invalid data markers, or sensor abnormal status information and send them to the instruction determination module.

5. A smart home system as described in claim 3, characterized in that, The generation of the first control command includes: Step S10: Receive the raw attitude data transmitted by the pose sensor, timestamp the data and sort it according to the acquisition order; Step S20: Extract the feature parameters from each set of attitude data, including the attitude change amplitude, duration, and rate of change; Step S30: Compare the feature parameters with multiple preset threshold ranges; wherein, If the feature parameter falls within a certain threshold range, proceed to step S40; If the data does not fall within any threshold range, return to step S10 to receive data again; Step S40: Call the preset condition judgment rule to determine whether the current feature parameters meet the single attitude trigger or combined attitude trigger conditions; wherein, single attitude trigger means that the feature parameters of a single support component meet the threshold, and combined attitude trigger means that the feature parameters of at least two support components meet the corresponding threshold at the same time. Step S50: If the single attitude triggering condition is met, the corresponding basic control command is directly matched; if the combined attitude triggering condition is met, a logical AND operation is performed to combine multiple basic control commands into a composite control command. Step S60: Output the generated basic control command or composite control command as the first control command to the device control module.

6. A smart home system as described in claim 3, characterized in that, The generation of the first control command further includes: The acquisition period is preset, and the pose sensing data and corresponding control commands within the acquisition period are determined to generate the first mapping sample set; The first mapping sample set is divided into subsets according to time periods to generate multiple sample subsets; Set a rule threshold. When a certain pose data and corresponding instruction exist in the sample subset, and both meet the rule threshold within the acquisition period, the corresponding pose feature is added to the custom rule library.

7. A smart home system as described in claim 3, characterized in that, The generation of the first control command further includes: Step 51: Receive user posture data collected by the posture sensor, and extract posture feature vectors from the data, including the pressure distribution ratio and relative angle relationship of each support component; Step 52: Compare the pose feature vector with the pre-stored multi-user feature template library and calculate the vector similarity; Step 53: Determine whether the highest similarity value exceeds the preset threshold. If yes, proceed to step 54; otherwise, mark as an unknown user and execute the default control rules. Step 54: Retrieve the user's personalized control rule set, which contains the user's unique gesture-command mapping relationship; Step 55: Following the process of steps 51-54, repeat the user identification process on the subsequently received attitude data. If the identification results are consistent for three consecutive times, maintain the current rule set; if inconsistent results occur, repeat steps 52-54 to verify the identity.

8. A smart home system as described in claim 3, characterized in that, The control of at least one smart home device includes: Step S100: Receive the first control command and parse the device type, action type, and parameter requirements in the command; Step S200: Query the current device status list to determine whether the target device is online and controllable; if the device is offline, generate a device offline prompt message and terminate the process; Step S300: If the device is online, further determine whether the current operating parameters of the device are consistent with the instruction requirements; if they are consistent, no action needs to be performed; if they are inconsistent, proceed to step S400. Step S400: Perform the corresponding parameter adjustment steps according to the action type: For switching actions: directly send an on / off signal; If it is an adjustment action: calculate the difference between the current parameter and the target parameter, send adjustment signals in stages according to the preset step size, wait 500ms after each signal is sent, and detect the real-time parameters fed back by the equipment until the target parameter is reached; Step S500: Receive feedback on the execution result from the device. If the feedback indicates successful execution, record the execution time and update the device status list. If the feedback indicates failed execution, resend the control signal. The number of retries shall not exceed N, where N < 3.

9. A smart home system as described in claim 3, characterized in that, The same device performs differentiated actions under different posture data, including: Step 101: Receive attitude data of each support component in real time and calculate the attitude change frequency per unit time; Step 102: Determine whether the frequency of change exceeds the preset abnormal threshold. If so, proceed to step 103. Step 103: Extract the posture data sequence within this time period and perform similarity matching with the preset posture templates for bed fall risk and spastic posture templates; Step 104: If the similarity exceeds the first preset value, it is determined to be a high-risk abnormal posture, and step 105 is executed; if the similarity is between the second preset value and the first preset value, it is determined to be medium-risk, and step 106 is executed; if it is lower than the second preset value, it is determined to be normal fluctuation, and step 1 is returned; wherein, the second preset value is lower than the first preset value. Step 105: Generate an emergency alarm command to control the alarm device to activate the audible and visual alarm and send a warning message containing attitude data to the preset contact person; Step 106: Generate a reminder command, control the bedside lamp to flash, and display a query message on the smart screen. If no cancellation command is received from the user within the preset time, the process will be upgraded to high-risk handling.

10. A smart home system as described in claim 3, characterized in that, The same device performing differentiated actions under different attitude data also includes: Receive the first control command and parse the master device type and associated device type in the command; Query the current status of the master device. If the master device is in a powered-off state, first send a start command and wait for the master device to respond that it has started. After startup, control commands are sent to associated devices in a preset order, with a preset interval between each transmission to avoid network congestion. The system receives real-time feedback on the execution progress of each device. If a related device times out, it records the device as having failed to coordinate and continues to control other devices. After all devices are controlled, a collaborative result report is generated, which includes the number of successful devices, the IDs of failed devices, and the execution time, and is stored in the local log.

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