A pressure ulcer risk perception and prevention control system based on electrical impedance imaging

Abstract

This invention relates to the field of intelligent nursing technology, specifically to a pressure ulcer risk perception and prevention control system based on electrical impedance imaging (EIT). The system includes an EIT signal acquisition module, an impedance trend grading module, a risk area discrimination module, a bed intervention control module, and an intervention feedback adjustment module. This invention achieves precise differentiation of the physiological state of the patient's body surface and superficial tissues through the principle of electrical impedance imaging. It continuously acquires and dynamically integrates impedance signals from multiple regions, accurately maps changes in local tissue perfusion and abnormal stress trends, automatically identifies and classifies high-risk areas, triggers bed intervention in real time, dynamically tracks intervention effects, coordinates the adjustment of intervention parameters, and gradually improves the response logic and intervention rhythm of each region. This completes intelligent risk discrimination and closed-loop adaptive intervention, promoting personalized, proactive, and traceable process data in nursing scenarios, and supporting adaptive responses to complex conditions and changing nursing needs.

Description

Technical Field

[0001] This invention relates to the field of intelligent nursing technology, and in particular to a pressure ulcer risk perception and prevention control system based on electrical impedance imaging. Background Technology

[0002] The field of intelligent nursing primarily focuses on the dynamic monitoring and intelligent intervention of patients' health status through information sensing and automatic control. It encompasses various methods to improve nursing efficiency and safety, including collecting patients' physiological and behavioral data using various sensing devices, conducting risk analysis on the data, and implementing patient positioning, environmental regulation, and remote nursing advice delivery. Traditional pressure ulcer risk management addresses the issue of localized tissue pressure injuries that are common in long-term bedridden patients. This is mainly achieved by placing pressure sensors at high-risk sites, periodically measuring local pressure data, and assessing risk based on pressure magnitude and duration. Common solutions include manually adjusting the patient's position or activating timed turning devices.

[0003] Current technologies rely on fixed-point pressure monitoring and periodic position adjustments, which have limitations in terms of detection range and information availability. They are difficult to identify changes in internal perfusion and early damage trends in deep tissues. Risk zone definition is affected by body type and bed structure. Manual intervention procedures are fixed and inflexible, some warning signs are masked, and individual differences and stage changes are not fully identified. As a result, some risks are difficult to detect and manage in a timely manner, and nursing measures are prone to deviation, affecting the scientific and safe management of pressure ulcers. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a pressure ulcer risk perception and prevention control system based on electrical impedance imaging.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a pressure ulcer risk perception and prevention control system based on electrical impedance imaging, the system comprising:

[0006] The EIT signal acquisition module analyzes the dynamic changes in impedance in each region based on the electrode distribution array of the bed, compares the signal distribution density and conductivity, classifies the fluctuation regions, filters key change segments, calculates the average amplitude and maximum change of the characteristic segments, and obtains the impedance fluctuation characteristic sequence.

[0007] The impedance trend grading module analyzes the average and maximum amplitudes within each time window based on the impedance fluctuation characteristic sequence, compares the fluctuation trends, and summarizes the grading types by combining the mattress conductivity and tissue perfusion status, thus obtaining the impedance trend distribution characteristics of the time period.

[0008] Based on the impedance trend distribution characteristics of the time period, the risk area identification module filters the EIT units involved in impedance abrupt changes, analyzes the correspondence between the numbers and high-pressure risk areas, judges the correlation between the continuous decline of the area and ischemia, and obtains the high-risk area identification results.

[0009] Based on the high-risk area identification results, the bed intervention control module locates high-risk zones, analyzes the required range of motion, optimizes the turning and air pressure intervention parameters, adjusts the range of motion and control configuration, and integrates the parameters into the bed control system to obtain a set of motion intervention parameters.

[0010] The present invention is improved in that the impedance fluctuation feature sequence includes signal time sequence segments, spatial mapping information, and abnormal change index; the time period impedance trend distribution feature includes trend classification label, response curve type, and reference comparison item; the high-risk area identification result includes risk area distribution label, priority intervention identifier, and identification confidence parameter; and the action intervention parameter set includes action trigger command, execution mode type, and parameter configuration number.

[0011] The present invention is improved in that the EIT signal acquisition module includes:

[0012] The electrode array acquisition submodule is based on the electrode distribution array of the bed. It continuously monitors the impedance signal of each acquisition area. Through regional marking and synchronous recording, the impedance acquisition process of each electrode is tracked throughout the entire process to obtain the impedance monitoring dataset.

[0013] The impedance fluctuation classification submodule compares the impedance changes at each spatial point based on the impedance monitoring dataset, filters out areas of continuous fluctuation, classifies areas with key fluctuation amplitudes, and determines the relationship between the conductivity distribution and signal changes in each area to obtain a fluctuation classification distribution set.

[0014] The feature quantity calculation submodule analyzes the impedance signal sequence of the involved region based on the wave classification distribution set, calculates the average amplitude and maximum wave amplitude of each region, optimizes the feature sorting of each region, and obtains the impedance wave feature sequence.

[0015] The present invention is improved in that the impedance trend grading module includes:

[0016] Based on the impedance fluctuation feature sequence, the fluctuation feature extraction submodule identifies the average amplitude and maximum amplitude of the electrode acquisition signal within each time window, compares the fluctuation amplitude of the same electrode in each time period, judges the fluctuation frequency change characteristics, and obtains the amplitude fluctuation frequency characteristics.

[0017] The trend change discrimination submodule, based on the amplitude fluctuation frequency characteristics, compares the direction and gradient of data changes within adjacent time windows to determine the trend continuity and direction consistency during the continuous change of the signal, calculates the fluctuation of the change gradient, and obtains the trend direction change amplitude.

[0018] The distribution feature summarization submodule obtains the impedance spatial trend distribution area based on the trend direction change area, compares the spatial distribution correspondence, judges the consistency between regional response and conductivity change, and obtains the impedance trend distribution characteristics over a period of time.

[0019] The present invention is improved in that the risk area discrimination module includes:

[0020] The impedance change screening submodule analyzes the impedance change curves at each spatial location in the monitoring data based on the impedance trend distribution characteristics of the time period, screens areas where the impedance curve shows a decrease and the amplitude changes are critical, determines whether the area has continuous abnormal fluctuation characteristics, and obtains the impedance change location result.

[0021] Based on the impedance change location results, the electrode distribution analysis submodule analyzes the spatial location of the electrode numbers on the bed surface, determines the overlap relationship between the electrode numbers and the key monitoring area of ​​the sacrococcygeal region, calculates the spatial overlap ratio and distribution density of the two, and obtains the electrode matching parameters of the sacrococcygeal region.

[0022] The risk status ranking submodule calculates the difference between the average impedance fluctuation amplitude and the average conductivity fluctuation amplitude of the target area over multiple time periods based on the sacrococcygeal electrode matching parameters, obtains the risk trend change amplitude, ranks each area, and obtains the high-risk area identification result.

[0023] The present invention is improved in that the bed intervention control module includes:

[0024] Based on the high-risk area identification results, the partition location determination submodule analyzes the area distribution information and electrode number, determines the spatial correspondence of each number in the bed partition, optimizes the mapping method between each risk area and the bed control area, calculates the spatial distribution correspondence, and obtains the high-risk partition location mapping.

[0025] The action requirement calculation submodule extracts the impedance measurement data before and after intervention from the collection points in the area based on the high-risk zone location mapping, counts the number of collection points, selects the zone impedance benchmark, and calculates the action response adjustment amplitude by combining the force area of ​​the support structure and the length of the linkage structure to obtain the regional action level identifier set.

[0026] The control configuration generation submodule optimizes the action mode and execution order corresponding to each partition based on the regional action level identifier set, filters the intervention priority of high-risk areas, and adjusts the action type, amplitude and sequence of the bed control system to obtain the action intervention parameter set.

[0027] The present invention has an improvement, wherein the system further includes:

[0028] Based on the set of action intervention parameters, the intervention feedback adjustment module calculates the rate of impedance change before and after intervention, compares the impedance improvement trend, and combines the rebound of conductivity in the support area to determine the relationship between impedance change and infusion improvement. It then adjusts the action control configuration to obtain the impedance response adjustment range.

[0029] The impedance response adjustment range includes the response change, the feedback adjustment factor, and the effectiveness judgment result.

[0030] The present invention is improved in that the intervention feedback adjustment module includes:

[0031] The impedance change rate calculation submodule analyzes the action type and the impedance signal of the corresponding high-risk area based on the action intervention parameter set, compares the change trend of the impedance signal curve before and after the intervention, determines the direction of change and continuous change characteristics of the signal curve, summarizes the response performance of impedance with the execution of the action, and obtains the impedance response change level.

[0032] The response improvement trend analysis submodule calls the impedance response change level, compares the conductivity change curve of the mattress support area after intervention, judges the trend consistency between the conductivity curve and the impedance curve, filters the change segments with strong curve trend synchronization, summarizes the trend correlation between the two sets of signals, and obtains the consistent characteristics of the perfusion trend.

[0033] Based on the consistent characteristics of the infusion trend, the control configuration adjustment submodule filters the parameter configuration of the current intervention action and the synchronization performance of the conductivity and impedance curves, optimizes the action duration and amplitude allocation, adjusts the execution order to match the trend changes in the monitoring feedback, and obtains the impedance response adjustment amplitude.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0035] In this invention, the physiological state of the patient's body surface and superficial tissues is precisely distinguished through the principle of electrical impedance imaging. Multi-region impedance signals are continuously collected and dynamically integrated to accurately map changes in local tissue perfusion and abnormal stress trends. High-risk areas are automatically identified and classified, bed movement intervention is triggered in real time, the intervention effect is dynamically tracked, intervention parameters are adjusted in a coordinated manner, and the response logic and intervention rhythm of each region are gradually improved. Intelligent risk judgment and closed-loop adaptive intervention are completed, promoting personalization, initiative, and traceability of process data in nursing scenarios, and supporting adaptive response to complex conditions and changing nursing needs. Attached Figure Description

[0036] Figure 1 This is a system flowchart of the present invention;

[0037] Figure 2 This is a flowchart of the EIT signal acquisition module in this invention;

[0038] Figure 3 This is a flowchart of the impedance trend grading module in this invention;

[0039] Figure 4 This is a flowchart of the risk area discrimination module in this invention;

[0040] Figure 5 This is a flowchart of the bed intervention control module in this invention;

[0041] Figure 6 This is a flowchart of the intervention feedback adjustment module in this invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Example

[0044] Please see Figure 1 This invention provides a technical solution: a pressure ulcer risk perception and prevention control system based on electrical impedance imaging, comprising:

[0045] The EIT signal acquisition module is based on the electrode distribution array of the bed. It analyzes the impedance changes in the acquisition area, compares the impedance fluctuations of each electrode during continuous acquisition, judges the distribution density and conductivity changes of the continuous signal, classifies and analyzes the impedance rise and fall phenomena in the fluctuation area, combines the local conductivity characteristics of the area to screen the fluctuation characteristics, calculates the average amplitude and maximum change of the characteristic segment, and obtains the impedance fluctuation characteristic sequence.

[0046] The impedance trend classification module analyzes the average and maximum impedance amplitudes of each time window based on the impedance fluctuation characteristic sequence, compares the continuous changes in impedance trends within each time window, determines the correlation between impedance fluctuations and subcutaneous soft tissue perfusion in the sacrococcygeal region, and summarizes the impedance fluctuation types of each time period by combining the distribution of conductive characteristics in the mattress support area with the changes in impedance response over time periods, thereby obtaining the impedance trend distribution characteristics of each time period.

[0047] The risk area identification module screens the electrode numbers involved in local impedance mutations based on the impedance trend distribution characteristics over time, analyzes the correspondence between the number distribution and the high-pressure risk area in the sacrococcygeal region, determines the correspondence between the area of ​​continuous impedance decline and the subcutaneous tissue ischemia area, classifies the risk status of each area, and sorts the risk status of each partition to obtain the high-risk area identification results.

[0048] Based on the high-risk area identification results, the bed intervention control module determines the location of the high-risk zone on the bed, analyzes the motion range requirements of the corresponding area and the bed linkage structure, optimizes the intervention parameters for turning over and air pressure regulation, adjusts the motion range and control configuration of the high-risk area, and integrates the parameters into the bed control system to obtain the motion intervention parameter set.

[0049] The intervention feedback adjustment module calculates the rate of impedance change before and after intervention in high-risk areas based on the action intervention parameter set, compares the improvement trend of impedance during the action response, and judges the correspondence between impedance change and regional perfusion improvement by combining the conductivity rebound of the mattress support area. If the judgment criteria are not met, the action control configuration is adjusted to obtain the impedance response adjustment range.

[0050] Impedance fluctuation characteristic sequence includes signal time sequence segments, spatial mapping information, and abnormal change index; time period impedance trend distribution characteristics include trend classification labels, response curve type, and reference comparison items; high-risk area identification results include risk area distribution labels, priority intervention indicators, and identification confidence parameters; action intervention parameter set includes action triggering instructions, execution mode type, and parameter configuration number; impedance response adjustment amplitude includes response change amount, feedback adjustment factor, and effectiveness judgment result.

[0051] Local EIT sensing array:

[0052] Deployed in high-risk areas such as the buttocks and back corresponding to the hospital bed;

[0053] Using a 16-32 electrode structure, the local impedance distribution and its changing trend can be obtained in real time.

[0054] Distinguish between the decrease in conductivity caused by static pressure and the increase in impedance restored by injection after overturning.

[0055] The core basis for pressure ulcer risk identification is the rate of change of electrical impedance. By detecting changes in the conductivity of tissue, the tissue perfusion is assessed, and thus the risk of pressure ulcers is determined. The electrical impedance value (Z) is affected by local pressure, time, and tissue condition.

[0056] Electrical impedance time series modeling:

[0057] Settings: Use Indicates the area In time The electrical impedance value at that time;

[0058] Calculate the relative rate of change of electrical reactance:

[0059] ;

[0060] in, It is the electrical impedance value at the starting time point. It is the current impedance value.

[0061] Tissue perfusion change detection:

[0062] To determine the time-impedance relationship of impedance change, if the impedance of a certain local area decreases by more than 10% within 30 minutes (i.e., This indicates that the tissue perfusion status in this area is abnormal, and the area is in a state of ischemia or compression.

[0063] Risk assessment rules:

[0064] For each region, the risk level is determined based on the rate of change and duration of electrical impedance:

[0065] And the duration is less than or equal to 30 minutes: low risk;

[0066] And the duration exceeds 30 minutes: Medium risk;

[0067] And the duration exceeds 30 minutes: High risk.

[0068] Based on indicators such as impedance change rate, regional asymmetry, and prolonged low impedance retention, the tissue is judged to be in a state of "poor perfusion" and an intelligent risk threshold is set (e.g., a local impedance decrease of more than 10% within ≥30 minutes).

[0069] The core of the bed linkage system lies in automatically controlling the bed's movements (such as turning over, raising, and adjusting mattress air pressure) based on the output of the risk identification unit (i.e., risk level) to reduce the risk of pressure sores.

[0070] Bed control formula:

[0071] Automatic rolling control:

[0072] Generate rolling over instructions based on high-risk areas (such as the hips and back), assuming the rolling over angle is... ;

[0073] The target angle for the rolling over movement is The target for control is the opposite side of the patient's body;

[0074] If the risk level is high (risk_level='high'), then automatic recovery will be activated:

[0075] ;

[0076] Air cushion adjustment:

[0077] Based on feedback from the mattress pressure sensors, the air pressure of the mattress air cushion is controlled. Perform local pressure regulation;

[0078] When the pressure in a certain area exceeds a set threshold, the inflation level of the air cushion is increased.

[0079] ;

[0080] in, Initial air pressure, This is the boost pressure value.

[0081] The system sends action commands to the bed control unit, such as automatic side turning, raising the lower limbs, and adjusting the mattress air pressure. After intervention, it continues to monitor impedance to determine whether the intervention is effective, thus forming a closed-loop feedback control.

[0082] Meanwhile, the system optimizes the response strategy based on parameters such as the patient's weight, medical history, and ability to move independently. If the resistance does not improve, it will push suggestions for manual turning or changing the patient's position.

[0083] In the EIT signal acquisition module, the electrode distribution array refers to multiple EIT acquisition units installed on the bed surface (such as high-risk areas like the buttocks and back), distributed in an array, used to simultaneously acquire tissue impedance signals from multiple sites; the acquisition area refers to the specific area covered and monitored on the bed surface where the EIT unit is located, such as the sacrum, coccyx, and back, areas prone to pressure ulcers in long-term bedridden patients; impedance fluctuation refers to the dynamic change of impedance measured in the same acquisition area over time within the acquisition cycle, reflecting the real-time changes in tissue conductivity and perfusion status; distribution density refers to the acquisition impedance signals at different spatial points (i.e., different EIT unit locations). The distribution concentration on the surface of the signal indicates whether the signal changes in a certain area are more concentrated and active; conductivity reflects the tissue's ability to conduct current, and is one of the fundamental physical quantities measured by EIT, indirectly reflecting physiological states such as tissue water and blood perfusion; fluctuation region refers to the spatial area where frequent impedance changes or large amplitudes are detected, which is usually related to long-term pressure and tissue ischemia; local conductivity characteristics refer to the actual conductivity, impedance response, and other electrical properties of the tissue at a specific acquisition point (under a certain EIT unit); average amplitude is the average absolute value of impedance fluctuations, and maximum change is the maximum change in impedance signal in the same area during the acquisition period, used to quantify the strength of signal fluctuations.

[0084] In the impedance trend grading module, the average and maximum impedance amplitudes are the average and maximum absolute values ​​of EIT signal fluctuations within a specific time window (e.g., a few minutes), used to distinguish tissue states in different regions or time periods. Impedance trend refers to the overall direction and shape of impedance signal changes over time, such as continuous rise, fall, or stability, reflecting changes in tissue perfusion and pressure. The correlation of perfusion status refers to whether the impedance trend is intrinsically related to the quality of blood flow perfusion in soft tissues; for example, a continuous decrease in impedance usually indicates poor perfusion or early ischemia. The conductivity distribution refers to the spatial distribution of conductivity in different areas of the mattress at the same time, which helps to identify support uniformity and local abnormal stress points. Impedance response change refers to the change in impedance signal in a certain area within a specific time window in response to external actions, pressure, and other factors. Impedance fluctuation type is a classification of impedance states in different time periods according to the form of impedance signal manifestation (e.g., sharp drop, slow rise).

[0085] In the risk area discrimination module, local impedance mutation refers to a significant and sudden decrease or change in EIT signal in a local area, which usually indicates an increased risk of early pressure ulcers; electrode number refers to the location and signal attribution of each EIT unit, which facilitates a one-to-one correspondence with the spatial location of risk areas; high pressure risk area refers to the area that is subjected to high pressure intensity for a long time and is most prone to pressure ulcers (such as the sacrum, coccyx, and under the scapula), which requires key monitoring; area with continuous impedance decline refers to a local area where the EIT signal shows a downward trend for a long time, which is often related to tissue ischemia and perfusion disorders; correspondence refers to judging whether a certain change (such as continuous impedance decline) coincides with the actual clinical risk (such as subcutaneous tissue ischemia) in space and time; the risk status of each area is based on the analysis to assign risk labels to different monitoring areas, such as high risk, medium risk, or low risk.

[0086] In the bed intervention control module, the bed linkage structure refers to the hardware mechanism on the hospital bed used to perform automatic interventions such as turning over, lifting, and air pressure adjustment, which usually includes motors, airbags, etc.; intervention parameters refer to the intervention action parameters configured for different risk zones, such as the settings for turning angle, air pressure change, and action duration; action amplitude refers to the magnitude of the specific intervention action, such as the range of turning angle and lifting height; control configuration is the comprehensive parameter setting of each intervention action by the bed linkage system, including the action execution sequence, intensity, and frequency; the bed control system is an intelligent bed operation platform that integrates EIT monitoring, risk analysis, and action control.

[0087] In the intervention feedback adjustment module, the impedance change rate before and after the intervention refers to comparing the impedance change rate of the same high-risk area before and after the intervention to evaluate the intervention effect; the impedance improvement trend refers to whether the impedance signal changes from a decreasing or abnormal trend to an increasing or recovering trend after the intervention, reflecting whether tissue perfusion has improved; the conductivity rebound refers to the situation where the regional tissue conductivity recovers from low after the intervention, indicating that local perfusion has recovered; the correspondence with regional perfusion improvement refers to judging whether the impedance and conductivity rebound are consistent with the actual improvement trend of tissue blood flow perfusion; the judgment criteria refer to setting an indicator system for whether the intervention is considered effective (such as impedance change reaching a certain increase, signal trend reversal, etc.); the action control configuration refers to the readjustment or optimization of the intervention action parameters to achieve a better risk intervention closed loop.

[0088] Please see Figure 2 The EIT signal acquisition module includes:

[0089] The electrode array acquisition submodule is based on the electrode distribution array of the bed. It continuously monitors the impedance signal of each acquisition area. Through regional marking and synchronous recording, the impedance acquisition process of each electrode is tracked throughout the entire process to obtain the impedance monitoring dataset.

[0090] Multiple electrodes are installed on the bed surface in a preset array, densely deployed in key areas such as the sacrum, coccyx, and back. For example, 25 electrode nodes are set in 5 rows and 5 columns in the sacrum and coccyx. Each electrode is assigned an independent number. By sequentially switching the excitation and receiving electrode pairs, impedance data is collected for the area where each electrode point is located. The sampling period is set to record the impedance value once every 5 seconds. The entire acquisition period lasts for 5 minutes, and each electrode will generate 60 impedance data points. During the acquisition process, the data sampling timestamp and electrode spatial location information are recorded simultaneously. Different regions are assigned independent identification numbers to support subsequent regional comparison and analysis. For example, when recording the impedance value of electrode number 13 in the central region of the sacrum and coccyx, its location information (e.g., 25 cm horizontally and 15 cm vertically from the edge of the bed), time node (e.g., 30 seconds), acquisition current path information, and corresponding received voltage value are saved simultaneously, thereby constructing a structured impedance monitoring dataset.

[0091] The impedance fluctuation classification submodule compares the impedance changes at various spatial points based on the impedance monitoring dataset, filters out areas of continuous fluctuation, classifies areas with key fluctuation amplitudes, and determines the relationship between the conductivity distribution and signal changes in each area to obtain a fluctuation classification distribution set.

[0092] Impedance change records for each electrode point are extracted one by one. First, the difference between the maximum and minimum impedance values ​​at each point is calculated in the time series and compared with a preset fluctuation judgment benchmark. If the difference exceeds the benchmark, it is marked as a continuous fluctuation region. Then, the absolute value of the difference between adjacent time points of each impedance sequence in the region is averaged to determine the intensity of the fluctuation. All regions are sorted from largest to smallest according to the average fluctuation amplitude, and the top 20% of regions are defined as key fluctuation regions. At the same time, the continuous change trend of impedance values ​​over time in the region is analyzed, and the number of consecutive increases or decreases in impedance values ​​is counted. If a region shows at least 3 consecutive changes in the same direction in the sequence, it is judged as a trend fluctuation region. Based on this, the conductivity data corresponding to the region is called and the direction consistency analysis is performed with the impedance change trend. If the proportion of the trend of impedance increase and conductivity decrease or impedance decrease and conductivity increase is higher than 80% at multiple time points, it is considered that the fluctuation of the region is closely related to the change in conductivity. Finally, each fluctuation region is assigned multiple labels for classification, such as high fluctuation, trend type, conductivity related, etc., and integrated into a fluctuation classification distribution set for subsequent feature extraction.

[0093] The feature calculation submodule analyzes the impedance signal sequence of the involved region based on the wave classification distribution set, calculates the average amplitude and maximum wave amplitude of each region, optimizes the feature sorting of each region, and obtains the impedance wave feature sequence.

[0094] Based on the regional classification results of the fluctuation distribution set, the impedance change sequence of each region is extracted one by one. The absolute value of the difference between all adjacent data points in the sequence is calculated and averaged to measure the average amplitude of impedance fluctuation in the region. At the same time, the maximum and minimum impedance values ​​recorded in the entire sequence are extracted for the region, and their difference is calculated to obtain the maximum fluctuation amplitude of the region. Then, the average amplitude and the maximum fluctuation amplitude are combined and scored according to the set weights, which are set to 0.6 and 0.4 respectively. During the scoring process, the combined score values ​​of all regions are automatically sorted in descending order, and the top 10 regions with the most significant impedance fluctuation characteristics are identified. In the sorting results, each region records its electrode number, spatial location, average amplitude value, maximum fluctuation amplitude value, and combined score value, which are used as components of the impedance fluctuation characteristic index sequence of the region. The output is the impedance fluctuation characteristic sequence, which serves as the input basis for subsequent trend identification and risk assessment.

[0095] Please see Figure 3 The impedance trend grading module includes:

[0096] The fluctuation feature extraction submodule identifies the average and maximum amplitude of the electrode acquisition signal within each time window based on the impedance fluctuation feature sequence, compares the fluctuation amplitude of the same electrode in each time period, judges the fluctuation frequency change characteristics, and obtains the amplitude fluctuation frequency characteristics.

[0097] The impedance sequence of each electrode node is extracted within a set time window, with each window length set to 60 seconds. The absolute values ​​of all impedance differences within this time period are summed, and the average value is taken as the average amplitude. Simultaneously, the difference between the maximum and minimum impedance values ​​within this time period is selected as the maximum amplitude. These two amplitude indicators for all electrodes within this time window are recorded and numbered with time tags to form a structured amplitude data table. Subsequently, the changes in the average amplitude and maximum amplitude of the same electrode in multiple consecutive time windows are compared in chronological order. If the average amplitude continuously increases for more than two window periods within a certain interval, it is determined to be an increasing fluctuation trend. If the maximum amplitude is lower than 1 in three adjacent windows, it is considered to be an increasing fluctuation trend. The static stability reference value is set at 1.0 ohms, and the segment is marked as the fluctuation convergence region. Then, frequency analysis is performed on the fluctuation records of each electrode. The fluctuation frequency value is obtained by counting the number of fluctuation amplitudes exceeding 1.5 ohms in five consecutive windows and dividing by the total duration of the time period. The fluctuation frequency is divided into three levels: low frequency is 0 to 0.2 times per minute, medium frequency is 0.2 to 0.5 times per minute, and high frequency is above 0.5 times per minute. If an electrode experiences 7 fluctuations within 10 minutes, the fluctuation frequency is 0.7 times per minute, and it is marked as a high-frequency fluctuation region. Finally, the amplitude fluctuation frequency characteristics of all electrodes are archived and stored, and used as input data for subsequent trend change discrimination to obtain amplitude fluctuation frequency characteristics.

[0098] The trend change discrimination submodule, based on the amplitude fluctuation frequency characteristics, compares the direction and gradient of data changes within adjacent time windows to determine the trend continuity and direction consistency during the continuous change of the signal, calculates the fluctuation of the change gradient, and obtains the trend direction change amplitude.

[0099] First, the difference in the fluctuation frequency values ​​of each electrode in two adjacent time windows is calculated, and the direction of change is recorded as positive or negative. If the fluctuation frequency of an electrode is 0.6 times per minute in the first window and 0.8 times per minute in the second window, the direction of change is positive, indicating that the fluctuation is strengthening. Next, the absolute value of the difference in fluctuation frequency changes between adjacent windows is calculated to record the change gradient. If the change direction is consistent for three consecutive times, it is determined that the electrode has a strong trend continuity in this segment and is assigned a consistent direction label. If the direction changes continuously, it is marked as an unstable trend segment. At the same time, the standard deviation of the change gradient data of each electrode is calculated according to the time window to reflect the stability of the fluctuation gradient change. If the standard deviation of the fluctuation gradient in a certain area is greater than 1.2 ohms per minute in five consecutive windows, the segment is determined to be a high-change trend segment. Based on this, by summarizing the trend continuity label, the number of consistent directions, and the gradient stability of each electrode in multiple time windows, the trend direction change amplitude is classified and statistically analyzed to form a trend direction change amplitude data table, which is used to support the subsequent identification operation of high-risk change areas and obtain the trend direction change amplitude.

[0100] The distribution characteristic summarization submodule uses the formula based on the magnitude of change in trend direction:

[0101] ;

[0102] The spatial trend distribution of impedance is obtained, the spatial distribution correspondence is compared, the consistency between regional response and conductivity changes is determined, and the characteristics of the impedance trend distribution over a time period are obtained. Indicates the first The spatial distribution of impedance trends in the region. Indicates the first Region in time The trend direction and range of change Indicates the first Region in time The response curve offset amplitude, Indicates the first Region in time The amplitude of the change expands the amplitude. Indicates the first The range of conductivity variation in the region This indicates the total number of time periods.

[0103] Impedance spatial trend distribution amplitude is used to quantitatively describe the comprehensive characteristics of the trend change of the organizational impedance signal in a specific region over a period of time at the spatial level. It reflects the amplitude characteristics, directionality, temporal fluctuations, and coupling relationship with conductivity changes in the region. It is used to quantitatively reflect the numerical comprehensive characteristics of the impedance signal trend change and abnormal amplitude distribution in each zone of the bed over a period of time.

[0104] Based on the trend direction and range of change in each monitoring area over a continuous time period ( ), response curve offset ( ), amplitude variation expansion area ( ) and conductivity variation range ( ), combined with the total number of observation periods ( ), calculate the spatial trend distribution area of ​​impedance ( This calculation process uses a combination of weighted summation and normalization to achieve the fusion evaluation of multi-dimensional features. Taking region number 1 as an example, the trend direction and magnitude of change over three consecutive time periods are analyzed. The original value of the response curve offset amplitude was 1.2, and after normalization it was... The original value of the amplitude variation expansion amplitude was 0.9, and after normalization it was... The original value of the conductivity variation range was 0.15, which was normalized to be... Substitute into the formula:

[0105] ;

[0106] ;

[0107] ;

[0108] Then, taking area number 2 as the main focus, its parameters are: , , , Substitute into the formula:

[0109] ;

[0110] ;

[0111] Finally, for region number 3, the parameter is... , , , ,but:

[0112] ;

[0113] Based on the above calculations, the impedance spatial trend distribution results for regions numbered 1, 2, and 3 are as follows:

[0114] , , ;

[0115] Based on the established spatial trend distribution range reference interval, the distribution range with a normal and stable state in historical monitoring samples can be used as a comparison benchmark. If the reference interval is set to... The range reflects the typical characteristics of a region without obvious trend shift. The closer the value is to the upper limit, the higher the trend activity, but it is still within the normal range. However, if the value exceeds the upper limit, it indicates that the trend change tends to be more concentrated or the signal shift is obvious. The results show that the value of region 1 is within the baseline range, indicating that its trend change is generally stable and the spatial trend is relatively dispersed, without showing any prominent features. The values ​​of regions 2 and 3 exceed the upper limit by 0.55, indicating that the spatial concentration of the impedance trend is increased, reflecting that the trend fluctuation characteristics in this region are enhanced and the signal response structure is obvious. It belongs to the trend active region and needs to be further combined with conductivity and pressure concentration data for spatial aggregation and matching.

[0116] Please see Figure 4 The risk area identification module includes:

[0117] The impedance change screening submodule analyzes the impedance change curves at various spatial locations in the monitoring data based on the impedance trend distribution characteristics over a time period. It screens areas where the impedance curve shows a decrease and the amplitude changes are critical, determines whether the area has continuous abnormal fluctuation characteristics, and obtains the impedance change location results.

[0118] Impedance data sequences of all electrodes were extracted over multiple consecutive time windows. Data for each electrode was categorized by spatial number. Within each electrode's impedance sequence, the impedance difference between adjacent time windows was calculated. By comparing the current value with the value of the previous window, an impedance decreasing trend was identified. A decrease exceeding a set threshold was considered a sudden event. The threshold was set at 1.5 ohms, referencing typical tissue perfusion abnormality precursor changes in the sample data. If an electrode with a specific number decreased from 12.3 ohms to 10.5 ohms within 5 minutes without any change in impedance, the event was considered a sudden event. If a significant rebound occurs, it is identified as a sudden change region, and the average rate of decrease in impedance during this period is recorded as 0.36 ohms per minute. Subsequently, the data of all electrodes identified as sudden change regions are subjected to continuous analysis again to check whether they continue to decrease in the same direction or fluctuate unstablely in subsequent time windows. If the impedance difference of an electrode is still negative or the oscillation change is greater than 0.5 ohms within 3 time windows after the identification, it is determined that it has a continuous abnormal fluctuation characteristic. At the same time, the spatial location of the electrode is marked, and the electrode number and its spatial coordinates are output as the impedance sudden change location result.

[0119] Based on the impedance abrupt change location results, the electrode distribution analysis submodule analyzes the spatial location of the electrode numbers on the bed surface, determines the overlap relationship between the electrode numbers and the key monitoring areas of the sacrococcygeal region, calculates the spatial overlap ratio and distribution density of the two, and obtains the electrode matching parameters of the sacrococcygeal region.

[0120] Extract the serial numbers and spatial locations of all mutated electrodes on the bed surface. The bed coordinates for each numbered electrode were standardized during the modeling phase. Retrieve the boundary coordinates of the sacrococcygeal region, defined by clinical standards as a range of 30-60 cm from the foot of the bed, with a width of 20 cm to each side of the bed's central axis. Then, compare the center coordinates of all mutated electrodes with the sacrococcygeal region boundary. If both the horizontal and vertical coordinates of the electrode's center point fall within the sacrococcygeal region, it is recorded as a coincidence point. Count the number of mutated electrodes located within the sacrococcygeal region and compare this to the total number of mutated electrodes to determine the overlap ratio. For example, identify... Of the 12 mutation electrodes, 9 are located in the sacrococcygeal region, resulting in an overlap ratio of 75%. Further calculations were made of the electrode distribution density within the sacrococcygeal region, which is the ratio of the number of mutation electrodes per unit area to the total number of electrodes. Assuming the sacrococcygeal region has an area of ​​1200 square centimeters, 9 mutation electrodes, and a total of 25 electrodes, the mutation electrode density in this region is 0.75 electrodes per 100 square centimeters, and the overall electrode density is 2.08 electrodes per 100 square centimeters. This overlap ratio and density value were then used as the sacrococcygeal electrode matching parameters for subsequent high-risk area identification calculations, thus obtaining the sacrococcygeal electrode matching parameters.

[0121] The risk status ranking submodule calculates the difference between the average impedance fluctuation and the average conductivity fluctuation of the target region over multiple time periods based on the sacrococcygeal electrode matching parameters, using the formula:

[0122] ;

[0123] The magnitude of risk trend changes is obtained, and each region is sorted to obtain the high-risk area identification results. Indicates the first The magnitude of changes in regional risk trends Indicates the first The average impedance fluctuation amplitude in the region Indicates the first The average fluctuation range of conductivity in the region. Indicates the first The number of time periods during which the region participated in risk trend assessment. Indicates the first The region in The rate of impedance decrease over a given time period Indicates the first The region in The duration of the trend within a given time period Indicates the first Sacrococcygeal electrode matching parameters for the region Indicates the first The area's numbering is densely distributed and varies.

[0124] The magnitude of risk trend change refers to the comprehensive change characteristics of key parameters such as impedance and conductivity for each monitoring area. It combines factors such as impedance fluctuations over multiple time periods, continuous rate of decline, and spatial distribution matching to quantitatively reflect the degree of recent high-risk changes in the area. The greater the magnitude of risk trend change, the more drastic the recent abnormal changes in physiological parameters such as impedance in the area, and the closer the spatial distribution is to high-risk areas, requiring key attention and priority intervention. This value is an important comprehensive quantitative result that guides risk ranking and clinical response after concise normalization of complex, multidimensional monitoring data.

[0125] This represents the average impedance fluctuation amplitude of the target area within a selected time period. The acquisition cycle is once every 10 seconds. The absolute impedance change is recorded for each of the 60 acquisitions, and the calculated average fluctuation amplitude is 4.6Ω. After normalization, this is set to 0.92. This represents the average conductivity fluctuation over the same period. Converted back to the equivalent impedance amplitude using the regional current response, it is 2.1Ω, and after normalization, it is set to 0.58. For the first The impedance decrease rates for the specified time periods were 0.8 Ω / h, 0.9 Ω / h, 1.1 Ω / h, and 1.0 Ω / h, respectively. After normalization, these rates were set to 0.6, 0.65, 0.8, and 0.7, respectively. The trend durations for each time period are 3 hours, 2.5 hours, 2 hours, and 3.5 hours, respectively, with normalized values ​​of 0.75, 0.62, 0.5, and 0.88. The electrode matching parameters for the sacrococcygeal region corresponding to the target area were set to 0.61 after normalization, with an original value of 0.55. This represents the change in the dense distribution of the serial numbers; the original value was 0.3, and after normalization, it was set to 0.42. This indicates that the total monitoring period is 4 segments. Substitute these segments into the formula for calculation:

[0126] Calculate the numerator:

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] Calculate the denominator:

[0132] ;

[0133] Step 3: Calculate the overall formula:

[0134] ;

[0135] The result indicates that the calculated risk trend change magnitude The value is significantly higher than the upper limit of the risk identification reference interval of 2.2 (this interval is set based on the normalized historical data fluctuation range of 0.00–2.2 in the normal region). Therefore, it can be determined that the impedance and conductivity of the target region exhibit significant abnormal differences. Simultaneously, the superposition effect of the trend fluctuation term is outside the system's sensitive response range. Overall, this is judged as a high-risk state significantly deviating from the normal tissue state. Based on all regions... The values ​​are sorted from highest to lowest. Areas with values ​​exceeding the upper limit of the recognition interval are directly marked as high-risk areas and output as high-risk area recognition results. The numerical results are further processed as follows:

[0136] All according to the set interval Values ​​are categorized into levels, such as:

[0137] A range of 0.00–1.2 indicates low risk.

[0138] 1.21–2.2 is considered medium risk;

[0139] A value of 2.21 or higher indicates high risk. The current regional value is 2.97, which corresponds to a high risk level. Therefore, this calculated value not only supports the ranking decision but also serves as a criterion for risk label classification.

[0140] Please see Figure 5 The bed intervention control module includes:

[0141] The partition location determination submodule analyzes the regional distribution information and electrode number based on the high-risk area identification results, determines the spatial correspondence of each number in the bed partition, optimizes the mapping method between each risk area and the bed control area, calculates the spatial distribution correspondence, and obtains the high-risk partition location mapping.

[0142] Extract the set of electrode numbers corresponding to each high-risk area, and call the coordinate correspondence between the electrode numbers and bed zoning areas defined in the bed structure mapping table. The spatial position corresponding to each electrode number is represented by horizontal and vertical coordinates on the bed surface. During the spatial judgment process, the center coordinates of the electrode are compared with the boundary coordinates of each bed control area to determine whether the electrode falls completely within a certain control area. When the horizontal and vertical range restrictions are met, its assigned bed zoning area number is recorded. For example, if electrode number E15 is located between 30 and 40 cm horizontally and 70 and 90 cm vertically, and this range completely overlaps with bed control area number Z3, then E15 is mapped to area Z3. Subsequently, the correspondence between all electrodes and bed control areas within each high-risk area is statistically analyzed. The distribution should be such that if a high-risk area contains multiple electrodes falling into different bed control zones, the primary mapping position of the area is set according to the control zone with the most electrodes. At the same time, the distribution of the remaining electrodes is recorded to determine the secondary control impact areas. Furthermore, based on the number of high-risk areas mapped to each bed control zone, a many-to-one mapping table is constructed, and the order of conflicting areas is adjusted according to the control priority weight. The weight setting method uses the proportion of the number of electrodes as the priority calculation basis. For example, if 80% of the electrodes in a certain area fall into control zone Z2 and the remaining 20% ​​fall into Z4, then Z2 is the primary control mapping zone and Z4 is the auxiliary control zone. The primary control bed control zone number and the auxiliary control zone number corresponding to each high-risk area are output to obtain the high-risk zone location mapping.

[0143] The action requirement calculation submodule, based on the high-risk zone location mapping, extracts impedance measurement data before and after intervention from the collection points within the area, counts the number of collection points, selects the zone impedance benchmark, and combines the force-bearing area of ​​the supporting structure and the length of the linkage structure, using the following formula:

[0144] ;

[0145] Calculate the motion response adjustment amplitude to obtain the regional motion level identifier set, where, Indicates the first The adjustment range of the action response in high-risk areas Indicates the first The number of data collection points used for motion calculation within a region. Indicates the first The first region Impedance measurement at each acquisition point before intervention; Indicates the first The first region Impedance measurement after intervention at each acquisition point This indicates the impedance reference used for motion determination. Indicates the first The stress-bearing area of ​​the supporting structure in each region Indicates the first The length of the linkage structure corresponding to each region;

[0146] The amplitude of motion response adjustment refers to the adjustment of the first... In high-risk areas, before and after intervention, the normalized statistics of impedance changes at all relevant collection points within the area, combined with the force-bearing area of ​​the supporting structure and the length of the linkage structure, are used to obtain quantitative parameters that reflect the action response amplitude that needs to be adjusted or controlled in the area. These parameters represent a comprehensive reflection of the impedance change intensity and spatial distribution characteristics of the area after intervention (such as turning over or adjusting the bed), combined with the characteristics of the supporting structure. This is the core basis for subsequently determining the linkage action level and adjusting specific action parameters.

[0147] Call all data collection points within the region, and set the number of data collection points to [value]. Read the impedance measurement data before and after intervention at each acquisition point, and record them as follows: and The area number is set to Collection point number The impedance measurement sequence before intervention was 22.8Ω, 23.6Ω, 21.9Ω, 22.3Ω, and 23Ω, and the impedances after intervention were 21Ω, 21.8Ω, 20.4Ω, 20.9Ω, and 21.7Ω, respectively. The absolute value of the impedance change at each point was calculated, and the results were obtained. The impedances are 1.8Ω, 1.8Ω, 1.5Ω, 1.4Ω, and 1.3Ω respectively, totaling 7.8Ω. A unified regional impedance reference is used. Using Ω as the normalization reference, the resulting normalized impedance difference ratio is: The corresponding area of ​​the supporting structure is set as square meters, the length of the linkage structure is The ratio of the two, after normalization, yields a result of 0.3929. Substituting this into the complete formula:

[0148] ;

[0149] The results indicate that the calculated action response adjustment amplitude Located in the middle of the action level division range, according to the response amplitude range division standard, if Determined to be of high level (Level A), used to drive rapid linkage intervention, if Determined to be of medium level (Level B), used for setting stable rhythmic movement interventions. It is then classified as a low-level (Level C) scheme, suitable for periodic mild adjustment; therefore, the numerical value fall into The range indicates that the impedance response in this region varies to a moderate degree, and the structural support and linkage adaptation are normal. The linkage configuration template of the medium level can be matched according to this level. This result plays a bridging role in mapping from quantitative indicators to the logical structure of action parameter configuration, and promotes the execution of the decision logic generated by subsequent control configuration.

[0150] The control configuration generation submodule optimizes the action mode and execution order of each partition based on the regional action level identifier set, filters the intervention priority of high-risk areas, and adjusts the action type, amplitude and sequence of the bed control system to obtain the action intervention parameter set.

[0151] Extract the level identifier for each high-risk area. This identifier uses a three-level coding structure, including an action type code, an emergency level code, and a time window priority value code. For example, an area coded as T2-E3-P1 indicates that airbag inflation is required, it belongs to the highest emergency level, and it is the first priority area in the current time period. By parsing the identifier set of each area, the action types associated with each control area are summarized and statistically analyzed. The emergency levels of the same type of action in different areas are compared. If multiple action types conflict in the same area, the highest level is prioritized. After classifying and sorting all area action types, an execution sequence number is assigned to each control action. The sequence number is arranged in ascending order according to the priority level value. Simultaneously, for each... Each action is configured with an amplitude value and execution duration. The amplitude value is matched to the set value according to the severity level of the area. Severity level 1 corresponds to a 10 cm elevation or a 30 mmHg increase in air pressure, level 2 corresponds to a 5 cm elevation or a 15 mmHg increase in air pressure, and level 3 is set to maintain the original position. The durations are 120 seconds, 60 seconds, and 30 seconds respectively. All area action data are combined and packaged to form an action control configuration list. If a certain area needs to perform action T1, which is level 2, it is configured to be an elevation of 5 cm for 60 seconds and set as the third execution order. All configurations are summarized and output as a structured parameter set, which includes the control area number, action type, execution order, action amplitude, duration, and control number, thus obtaining the action intervention parameter set.

[0152] Please see Figure 6 The intervention feedback adjustment module includes:

[0153] The impedance change rate calculation submodule analyzes the action type and the impedance signal of the corresponding high-risk area based on the action intervention parameter set, compares the change trend of the impedance signal curve before and after the intervention, judges the direction of change and continuous change characteristics of the signal curve, summarizes the response performance of impedance with the execution of the action, and obtains the impedance response change level.

[0154] The intervention action type, start time, execution duration, and amplitude value corresponding to each high-risk area are read, and the impedance sequence recorded by the corresponding electrode in that time period is extracted. Then, the impedance sequence of the first 5 minutes before the intervention is executed is used as the baseline segment, and the impedance sequence of the last 5 minutes after the intervention is executed is used as the comparison segment. The difference between the impedance values ​​at adjacent time points in these two time periods is averaged to obtain the average rate of change for the two periods before and after the intervention. By comparing the two rates of change, it is determined whether the impedance is rising, falling, or oscillating. Several prognostic impedance changes that are positive and whose rate is greater than 1.5 times the rate before the intervention are identified as an improvement response. If the change direction is the same but the rate increase is less than 50%, it is marked as a delayed response. If the change direction is reversed and the amplitude increases, it is marked as a deterioration response. At the same time, it is analyzed whether there is a continuous unidirectional change in the impedance curve before and after the intervention. For example, if the impedance continues to rise and exceeds 0.5 ohms within 1 minute after the start of the action, this time period is recorded as a continuous improvement segment. A response rate ratio table and a curve feature identifier set are formed according to the corresponding electrode number for each area to obtain the impedance response change level.

[0155] The response improvement trend analysis submodule calls the impedance response change level, compares the conductivity change curve of the mattress support area after intervention, judges the trend consistency between the conductivity curve and the impedance curve, filters the change segments with strong curve trend synchronization, summarizes the trend correlation between the two sets of signals, and obtains the consistent characteristics of the perfusion trend.

[0156] The conductivity sequence of the mattress support area corresponding to the electrode in each high-risk area was retrieved, and the intervention time point was used as the segmentation node. Conductivity curve data before and after the intervention were extracted, and the conductivity after intervention was compared to see if it showed a continuous increase, decrease, or fluctuation. Then, the directional consistency between the impedance response change curve and the conductivity curve was judged. The number of times the two curves changed in the same direction in three consecutive sampling points after the intervention was calculated. If the proportion of consistent direction was greater than 70%, the trend consistency of the area was determined to be strong. After performing this judgment on all areas, all trend consistency was marked as "synchronous" or "asynchronous". The regions marked as "synchronous" are further analyzed for their conductivity change magnitude and impedance recovery rate within 3 minutes after intervention. Trend synchronization segments are recorded in segments where both indicators rise simultaneously and the average change magnitude is greater than 0.2 ohms and 0.3 Siemens per meter, respectively. For example, if the impedance of a region increases from 11.2 ohms to 11.9 ohms and the conductivity increases from 0.35 Siemens per meter to 0.43 Siemens per meter after intervention, and the three sampling directions are consistent, then it is marked as a trend-consistent segment. The number and distribution time of signal synchronization segments of all regions are summarized, and the output is the perfusion trend-consistent feature.

[0157] Based on the consistent characteristics of the infusion trend, the control configuration adjustment submodule filters the parameter configuration of the current intervention action and the synchronous performance of the conductivity and impedance curves, optimizes the action duration and amplitude allocation, and adjusts the execution sequence to match the trend changes in the monitoring feedback, thereby obtaining the impedance response adjustment amplitude.

[0158] Identify areas deemed "out of sync" or "insufficiently responsive" among all intervention actions, extract their corresponding control parameters, including action type, execution time, elevation amplitude, or air pressure intensity. Then, reassess the time delay and amplitude changes in the perfusion trend of these areas. If perfusion improvement lags by more than 2 minutes and the amplitude is below the set elevation baseline of 0.3 ohms, the original action configuration is deemed mismatched. The current action duration is increased by 20%, the elevation amplitude by 10%, or the corresponding air pressure value is increased by 20%. For example, if the original setting is air pressure 20 mmHg and duration... If the time is 60 seconds, it will be optimized to 24 mmHg and 72 seconds. If multiple regions have asynchronous performance at the same time, the execution order of actions will be rearranged, and regions with greater potential for signal improvement will be prioritized. The judgment criteria are regions where the direction of the infusion trend change is opposite to the original impedance decrease trend and the conductivity is significantly improved. If the impedance turns to rise and the conductivity increases by more than 0.5 Siemens per meter, it will be assigned priority level 1. Finally, after all action configurations are updated, a new control parameter list is formed, which includes the updated action type, execution amplitude, duration and priority order number. The output is the impedance response adjustment amplitude.

[0159] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A pressure ulcer risk perception and prevention control system based on electrical impedance imaging, characterized in that, The system includes: The EIT signal acquisition module analyzes the dynamic changes in impedance in each region based on the electrode distribution array of the bed, compares the signal distribution density and conductivity, classifies the fluctuation regions, filters key change segments, calculates the average amplitude and maximum change of the characteristic segments, and obtains the impedance fluctuation characteristic sequence. The impedance fluctuation characteristic sequence includes signal time segments, spatial mapping information, and anomaly change index; The impedance trend grading module analyzes the average and maximum amplitudes within each time window based on the impedance fluctuation characteristic sequence, compares the fluctuation trends, and summarizes the grading types by combining the mattress conductivity and tissue perfusion status, thus obtaining the impedance trend distribution characteristics of the time period. The time-period impedance trend distribution characteristics include trend classification labels, response curve types, and reference comparison items; Based on the impedance trend distribution characteristics of the time period, the risk area identification module screens the EIT units involved in impedance abrupt changes, analyzes the correspondence between electrode numbers and high-voltage risk areas, determines the correlation between continuous regional decline and ischemia, and obtains the high-risk area identification results. The risk area identification module includes: The impedance change screening submodule analyzes the impedance change curves at each spatial location in the monitoring data based on the impedance trend distribution characteristics of the time period, screens areas where the impedance curve shows a decrease and the amplitude changes are critical, determines whether the area has continuous abnormal fluctuation characteristics, and obtains the impedance change location result. Based on the impedance change location results, the electrode distribution analysis submodule analyzes the spatial location of the electrode numbers on the bed surface, determines the overlap relationship between the electrode numbers and the key monitoring area of ​​the sacrococcygeal region, calculates the spatial overlap ratio and distribution density of the two, and obtains the electrode matching parameters of the sacrococcygeal region. The risk status ranking submodule calculates the difference between the average impedance fluctuation amplitude and the average conductivity fluctuation amplitude of the target area over multiple time periods based on the sacrococcygeal electrode matching parameters, obtains the risk trend change amplitude, ranks each area, and obtains the high-risk area identification result. The high-risk area identification results include risk area distribution labels, priority intervention indicators, and identification confidence parameters; Based on the high-risk area identification results, the bed intervention control module locates high-risk zones, analyzes the required range of motion, optimizes the turning and air pressure intervention parameters, adjusts the range of motion and control configuration, and integrates the parameters into the bed control system to obtain a set of motion intervention parameters. The action intervention parameter set includes action trigger command, execution mode type, and parameter configuration number.

2. The pressure ulcer risk perception and prevention control system based on electrical impedance imaging according to claim 1, characterized in that, The EIT signal acquisition module includes: The electrode array acquisition submodule is based on the electrode distribution array of the bed. It continuously monitors the impedance signal of each acquisition area. Through regional marking and synchronous recording, the impedance acquisition process of each electrode is tracked throughout the entire process to obtain the impedance monitoring dataset. The impedance fluctuation classification submodule compares the impedance changes at each spatial point based on the impedance monitoring dataset, filters out areas of continuous fluctuation, classifies areas with key fluctuation amplitudes, and determines the relationship between the conductivity distribution and signal changes in each area to obtain a fluctuation classification distribution set. The feature quantity calculation submodule analyzes the impedance signal sequence of the involved region based on the wave classification distribution set, calculates the average amplitude and maximum wave amplitude of each region, optimizes the feature sorting of each region, and obtains the impedance wave feature sequence.

3. The pressure ulcer risk perception and prevention control system based on electrical impedance imaging according to claim 1, characterized in that, The impedance trend grading module includes: Based on the impedance fluctuation feature sequence, the fluctuation feature extraction submodule identifies the average amplitude and maximum amplitude of the electrode acquisition signal within each time window, compares the fluctuation amplitude of the same electrode in each time period, judges the fluctuation frequency change characteristics, and obtains the amplitude fluctuation frequency characteristics. The trend change discrimination submodule, based on the amplitude fluctuation frequency characteristics, compares the direction and gradient of data changes within adjacent time windows to determine the trend continuity and direction consistency during the continuous change of the signal, calculates the fluctuation of the change gradient, and obtains the trend direction change amplitude. The distribution feature summarization submodule obtains the impedance spatial trend distribution area based on the trend direction change area, compares the spatial distribution correspondence, judges the consistency between regional response and conductivity change, and obtains the impedance trend distribution characteristics over a period of time.

4. The pressure ulcer risk perception and prevention control system based on electrical impedance imaging according to claim 1, characterized in that, The bed intervention control module includes: Based on the high-risk area identification results, the partition location determination submodule analyzes the area distribution information and electrode number, determines the spatial correspondence of each number in the bed partition, optimizes the mapping method between each risk area and the bed control area, calculates the spatial distribution correspondence, and obtains the high-risk partition location mapping. The action requirement calculation submodule extracts the impedance measurement data before and after intervention from the collection points in the area based on the high-risk zone location mapping, counts the number of collection points, selects the zone impedance benchmark, and calculates the action response adjustment amplitude by combining the force area of ​​the support structure and the length of the linkage structure to obtain the regional action level identifier set. The control configuration generation submodule optimizes the action mode and execution order corresponding to each partition based on the regional action level identifier set, filters the intervention priority of high-risk areas, and adjusts the action type, amplitude and sequence of the bed control system to obtain the action intervention parameter set.

5. The pressure ulcer risk perception and prevention control system based on electrical impedance imaging according to claim 1, characterized in that, The system also includes: Based on the set of action intervention parameters, the intervention feedback adjustment module calculates the rate of impedance change before and after intervention, compares the impedance improvement trend, and combines the rebound of conductivity in the support area to determine the relationship between impedance change and infusion improvement. It then adjusts the action control configuration to obtain the impedance response adjustment range. The impedance response adjustment range includes the response change, the feedback adjustment factor, and the effectiveness judgment result.

6. The pressure ulcer risk perception and prevention control system based on electrical impedance imaging according to claim 5, characterized in that, The intervention feedback adjustment module includes: The impedance change rate calculation submodule analyzes the action type and the impedance signal of the corresponding high-risk area based on the action intervention parameter set, compares the change trend of the impedance signal curve before and after the intervention, determines the direction of change and continuous change characteristics of the signal curve, summarizes the response performance of impedance with the execution of the action, and obtains the impedance response change level. The response improvement trend analysis submodule calls the impedance response change level, compares the conductivity change curve of the mattress support area after intervention, judges the trend consistency between the conductivity curve and the impedance curve, filters the change segments with strong curve trend synchronization, summarizes the trend correlation between the two sets of signals, and obtains the consistent characteristics of the perfusion trend. Based on the consistent characteristics of the infusion trend, the control configuration adjustment submodule filters the parameter configuration of the current intervention action and the synchronization performance of the conductivity and impedance curves, optimizes the action duration and amplitude allocation, adjusts the execution order to match the trend changes in the monitoring feedback, and obtains the impedance response adjustment amplitude.

Images