Pressure sore risk perception and prevention control system based on electrical impedance tomography

Abstract

The invention relates to the technical field of intelligent nursing, in particular to an electrical impedance tomography-based pressure sore risk perception and prevention control system, which comprises an EIT signal acquisition module, an impedance trend grading module, a risk area judgment module, a bed body intervention control module and an intervention feedback regulation module. According to the invention, through an electrical impedance imaging principle, fine distinguishing of body surface and superficial tissue physiological states of a patient is realized, multi-region impedance signals are continuously collected and dynamically integrated, local tissue perfusion changes and abnormal stress trends are accurately mapped, high-risk regions are automatically identified and classified, bed body action intervention is triggered in real time, and an intervention effect is dynamically tracked; according to the method, intervention parameters are cooperatively adjusted, the response logic and the intervention rhythm of each region are gradually improved, intelligent risk judgment and closed-loop adaptive intervention are completed, individuation, initialization and process data traceability in a nursing scene are promoted, and adaptive response aiming at complex illness conditions and variable nursing requirements is supported.

Description

Technical Field

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

[0002] The field of intelligent nursing focuses on dynamic monitoring and intelligent intervention of patients' health status through means such as information perception and automatic control. It covers a variety of methods for improving nursing efficiency and safety, including the use of various sensor devices to collect patients' physiological and behavioral data, conduct risk analysis on this data, adjust patient position, adjust the environment, and push remote nursing advice. Among them, traditional pressure ulcer risk management refers to the problem of local tissue pressure injuries that are prone to long-term bedridden patients. It mainly involves placing pressure sensors in high-risk areas, periodically measuring local pressure data, and making risk assessments based on the pressure level and duration of force. Common solutions include manually adjusting the patient's position or activating a timed rollover device.

[0003] Existing technologies rely on fixed-point pressure monitoring and periodic position adjustment, and have the limitations of limited detection range and single information. It is difficult to identify internal perfusion changes and early damage trends of deep tissues. The definition of risk areas is affected by body shape and bed structure. The manual intervention process is fixed and the response is inflexible. Some danger signals are masked, and individual differences and stage changes are not fully identified, resulting in some risks being difficult to discover and deal with in a timely manner. Nursing measures are prone to deviations, affecting the scientificity and safety of pressure ulcer management. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a pressure sore 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: The EIT signal acquisition module is based on the electrode distribution array of the bed body. It analyzes the dynamic changes of impedance in each area, compares the signal distribution density and conductivity, classifies the fluctuation area, screens the key change segments, calculates the average amplitude and maximum change of the characteristic segment, and obtains the impedance fluctuation characteristic sequence; The impedance trend grading module analyzes the average amplitude and maximum amplitude in each time window based on the impedance fluctuation characteristic sequence, compares the fluctuation trend, and combines the mattress conductivity and tissue perfusion status to summarize the grading type and obtain the impedance trend distribution characteristics of the time period; The risk area identification module screens the EIT units involved in impedance mutation based on the impedance trend distribution characteristics of the time period, analyzes the correspondence between the number and the high-voltage risk area, determines the correlation between the continuous decline of the area and ischemia, and obtains the high-risk area identification result; Based on the high-risk area identification results, the bed intervention control module locates the high-risk partitions, analyzes the motion range requirements, optimizes the turning and air pressure intervention parameters, adjusts the motion range and control configuration, and integrates the parameters into the bed control system to obtain a motion intervention parameter set.

[0006] The improvements of the present invention are that the impedance fluctuation feature sequence includes signal timing segments, spatial mapping information, and abnormal change indexes; the time period impedance trend distribution characteristics include trend grading labels, response curve types, and reference comparison items; the high-risk area identification results include risk area distribution labels, priority intervention identifiers, and identification confidence parameters; and the action intervention parameter set includes action trigger instructions, execution mode types, and parameter configuration numbers.

[0007] The present invention is improved in that the EIT signal acquisition module includes: The electrode array acquisition submodule continuously monitors the impedance signal of each acquisition area based on the electrode distribution array of the bed. By marking each area and synchronously recording, the impedance acquisition process of each electrode is tracked throughout the entire process to obtain the impedance monitoring data set. The impedance fluctuation classification submodule compares the impedance changes of each spatial point based on the impedance monitoring data set, screens the continuous fluctuation areas, classifies the areas with key fluctuation amplitudes, and determines the relationship between the conductivity distribution and signal changes in each area to obtain the fluctuation classification distribution set; The feature quantity calculation submodule analyzes the impedance signal sequence of the involved areas based on the fluctuation classification distribution set, calculates the average amplitude and maximum fluctuation amplitude of each area, optimizes the feature sorting of each area, and obtains the impedance fluctuation feature sequence.

[0008] The present invention is improved in that the impedance trend grading module includes: The fluctuation feature extraction submodule identifies the average amplitude and maximum amplitude of the electrode acquisition signal in each time window based on the impedance fluctuation feature sequence, compares the fluctuation amplitude of the same electrode in each time period, determines the fluctuation frequency change characteristics, and obtains the amplitude fluctuation frequency characteristics; The trend change discrimination submodule is based on the amplitude fluctuation frequency characteristics. By comparing the data change direction and change gradient in adjacent time windows, it judges the trend continuity and direction consistency in the continuous change process of the signal, calculates the fluctuation of the change gradient, and obtains the trend direction change amplitude. The distribution feature induction submodule obtains the impedance space trend distribution width based on the trend direction change width, compares the spatial distribution correspondence, judges the consistency of the regional response and the conductivity change, and obtains the impedance trend distribution characteristics of the time period.

[0009] The present invention is improved in that the risk area identification module includes: The impedance mutation screening submodule analyzes the impedance change curves of each spatial position in the monitoring data based on the impedance trend distribution characteristics of the period, screens the areas where the impedance curve shows a decrease and the amplitude changes are critical, determines whether the area continues to have abnormal fluctuation characteristics, and obtains the impedance mutation positioning result; The electrode distribution analysis submodule analyzes the spatial position of the electrode numbers on the bed surface based on the impedance mutation positioning results, determines the overlap between the electrode numbers and the sacrococcygeal key monitoring areas, calculates the spatial overlap ratio and distribution density between the two, and obtains the sacrococcygeal electrode matching parameters; The risk status ranking submodule calculates the difference between the average impedance fluctuation amplitude and the average conductivity fluctuation amplitude of the target area in multiple time periods based on the sacral and coccyx electrode matching parameters, obtains the risk trend change amplitude, ranks each area, and obtains the high-risk area identification result.

[0010] The present invention is improved in that the bed intervention control module includes: The partition position determination submodule analyzes the regional distribution information and electrode numbers based on the high-risk area identification results, determines the spatial correspondence between 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 position mapping; The action demand calculation submodule extracts the impedance measurement data of the collection points in the area before and after the intervention 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 based on the support structure force area and linkage structure length to obtain the regional action level identification set; The control configuration generation submodule optimizes the action mode and execution order corresponding to each partition based on the regional action level identification set, screens the intervention priority of high-risk areas, adjusts the action type, amplitude and sequence of the bed control system, and obtains the action intervention parameter set.

[0011] The present invention is improved in that the system further comprises: The intervention feedback adjustment module calculates the impedance change rate before and after the intervention based on the action intervention parameter set, compares the impedance improvement trend, and combines the conductivity rebound of the support area to determine the relationship between the impedance change and the perfusion improvement, adjusts the action control configuration, and obtains the impedance response adjustment amplitude; The impedance response adjustment amplitude includes a response change amount, a feedback adjustment factor, and an effectiveness determination result.

[0012] The present invention is improved 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 change direction and continuous change characteristics of the signal curve, summarizes the response performance of the 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, determines the trend consistency of the conductivity curve and the impedance curve, selects the change segments with strong curve trend synchronization, summarizes the trend correlation between the two groups of signals, and obtains the perfusion trend consistency feature; Based on the consistent characteristics of the perfusion trend, the control configuration adjustment submodule screens the parameter configuration of the current intervention action and the synchronization performance of the conductivity and impedance curves, optimizes the action duration and amplitude distribution, adjusts the execution order to match the trend changes of the monitoring feedback, and obtains the impedance response adjustment amplitude.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the principle of electrical impedance imaging is used to achieve fine discrimination of the physiological state of the patient's body surface and shallow tissues, continuously collect and dynamically integrate multi-region impedance signals, accurately map local tissue perfusion changes and abnormal force trends, automatically identify and classify high-risk areas, trigger bed movement intervention in real time, dynamically track intervention effects, coordinately adjust intervention parameters, gradually improve the response logic and intervention rhythm of each region, complete intelligent risk judgment and closed-loop adaptive intervention, promote personalization, initiative and process data traceability in nursing scenarios, and support adaptive responses to complex conditions and changing nursing needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the EIT signal acquisition module in the present invention; Figure 3 This is a flow chart of the impedance trend grading module in the present invention; Figure 4 This is a flow chart of the risk area identification module of the present invention; Figure 5 This is a flow chart of the bed intervention control module in the present invention; Figure 6 This is a flow chart of the intervention feedback adjustment module in the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present 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 only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined. Example

[0017] See also Figure 1 The present invention provides a technical solution: a pressure sore risk perception and prevention control system based on electrical impedance imaging, comprising: The EIT signal acquisition module, based on the bed's electrode distribution array, analyzes impedance changes corresponding to the acquisition area, compares impedance fluctuations of each electrode during continuous acquisition, determines the distribution density and conductivity changes of continuous signals, classifies and analyzes impedance rises and falls in the fluctuation area, and screens fluctuation characteristics based on the local conductivity characteristics of the area. The average amplitude and maximum change of the characteristic segment are calculated to obtain the impedance fluctuation characteristic sequence; 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 the impedance trend within each time window, and determines the correlation between the impedance fluctuations and the perfusion of the subcutaneous soft tissue in the sacral coccyx. By combining the distribution of the conductive characteristics of the mattress support area with the changes in the impedance response during each time period, the impedance fluctuation type of each time period is summarized to obtain the impedance trend distribution characteristics of the time period. The risk area identification module screens the electrode numbers involved in local impedance mutations based on the impedance trend distribution characteristics of each period, analyzes the correspondence between the number distribution and the sacrococcygeal high-voltage risk area, determines the correspondence between the area of ​​continuous impedance decrease and the area of ​​subcutaneous tissue ischemia, classifies the risk status of each area, and ranks the risk status of each subarea to obtain the high-risk area identification results; Based on the high-risk area identification results, the bed intervention control module determines the location of high-risk zones on the bed, analyzes the motion range requirements of the corresponding zones and the bed linkage structure, optimizes the intervention parameters for turning and air pressure regulation, adjusts the motion range and control configuration of the high-risk zones, and integrates the parameters into the bed control system to obtain the motion intervention parameter set; The intervention feedback adjustment module calculates the impedance change rate before and after the intervention action in the high-risk area based on the action intervention parameter set, compares the impedance improvement trend during the action response, and combines the conductivity rebound of the mattress support area to determine the correspondence between the impedance change and the regional perfusion improvement. If the judgment criteria are not met, the action control configuration is adjusted to obtain the impedance response adjustment amplitude.

[0018] The impedance fluctuation feature sequence includes signal timing segments, spatial mapping information, and abnormal change indexes. The impedance trend distribution characteristics of the time period include trend classification labels, response curve types, and reference comparison items. The high-risk area identification results include risk area distribution labels, priority intervention identifiers, and identification confidence parameters. The action intervention parameter set includes action trigger instructions, execution mode types, and parameter configuration numbers. The impedance response adjustment amplitude includes response change amount, feedback adjustment factor, and effectiveness judgment result.

[0019] Local EIT sensing array: Deployed on the bed corresponding to high-risk areas such as the buttocks and back; Using a 16-32 electrode structure, local impedance distribution and change trends can be obtained in real time; Distinguish the decrease in conductivity caused by static pressure from the increase in impedance caused by restoration of perfusion after turning.

[0020] The core basis for identifying pressure ulcer risk is the rate of change of electrical impedance. By detecting changes in the electrical conductivity of the tissue, the tissue perfusion situation is assessed, and the pressure ulcer risk is then determined. The electrical impedance value (Z) will be affected by local pressure, time and tissue status.

[0021] Electrical Impedance Time Series Modeling: Setting: Use Indicates area In time The electrical impedance value when Calculate the relative rate of change of electrical impedance: ; in, is the electrical impedance value at the starting time, is the electrical impedance value at the current moment.

[0022] Detection of tissue perfusion changes: Determine the time-impedance relationship of the change in electrical impedance value. If the impedance of a certain local area drops by more than 10% within 30 minutes (i.e. ), indicating that the tissue perfusion state in this area is abnormal and is in an ischemic or compressed state.

[0023] Risk Assessment Rules: For each area, the risk level is determined based on the rate of change and duration of the electrical impedance change: And the duration is less than or equal to 30 minutes: low risk; and lasts for more than 30 minutes: medium risk; And lasts more than 30 minutes: High risk.

[0024] Based on indicators such as impedance change rate, regional asymmetry, and long-term low-impedance retention, the "poor perfusion" state of the tissue is judged and an intelligent risk threshold is set (such as a local impedance drop of more than 10% for ≥30 minutes).

[0025] The core of the bed linkage system is to automatically control bed movements (such as turning over, lifting, and adjusting mattress air pressure) based on the output of the risk identification unit (i.e. risk level) to reduce the risk of pressure ulcers.

[0026] Bed control formula: Automatic turning control: Generate turning motion instructions based on high-risk areas (such as buttocks and back), assuming the turning angle is ; The target angle of the turning movement is , the control target is the contralateral side of the patient’s body; If the risk level is high (risk_level='high'), automatic rollover is enabled: ; Air cushion adjustment: Control the air pressure of the mattress air cushion based on the feedback from the mattress pressure sensor Perform local pressure regulation; When the pressure in a certain area is higher than the set threshold, the air volume of the air cushion is increased: ; in, is the initial air pressure, is the boost value.

[0027] The system sends action commands to the bed control unit, such as automatic side rollover, raising the lower limbs, and adjusting the mattress air pressure. After the intervention, it continues to monitor the impedance to determine whether the intervention is effective, forming a closed-loop feedback control.

[0028] At the same time, the system optimizes the response strategy based on parameters such as the patient's weight, medical history, and autonomous mobility. If the impedance does not improve, it will push recommendations for manual turning or changing body position.

[0029] In the EIT signal acquisition module, the electrode distribution array refers to multiple EIT acquisition units installed on the surface of the bed (such as high-risk areas such as the buttocks and back), which are distributed in an array and are used to synchronously acquire tissue electrical impedance signals from multiple parts of the body; the acquisition area refers to the specific area covered and monitored on the surface of the bed where the EIT unit is located, such as the sacrum, back and other areas where long-term bedridden patients are prone to pressure sores; impedance fluctuation refers to the dynamic change of the electrical impedance measured in the same acquisition area over time during the acquisition cycle, reflecting the real-time changes in tissue conductivity and perfusion status; distribution density refers to the distribution of the electrical impedance signals collected at different spatial points (i.e., different EIT unit positions) ), that is, whether the signal changes in a certain area are more intensive and active; conductivity reflects the ability of tissue to conduct electric current, which is one of the basic physical quantities measured by EIT and indirectly reflects physiological states such as tissue moisture and blood perfusion; fluctuation area refers to the spatial area where frequent impedance changes or large amplitudes are detected, which is usually related to long-term compression and tissue ischemia; local conductivity refers to the actual electrical properties of the tissue at a specific acquisition point (under a certain EIT unit), such as conductivity and impedance response; average amplitude is the average absolute value of impedance fluctuations, and maximum change is the maximum change of the impedance signal in the same area during the acquisition period, which is used to quantify the strength of signal fluctuations.

[0030] In the impedance trend grading module, the average impedance amplitude and maximum impedance amplitude are the average and maximum absolute values ​​of the EIT signal fluctuation within a specific time window (such as a few minutes), which are used to distinguish tissue states in different regions or at different time periods. Impedance trend refers to the overall change direction and form of the impedance signal over time, such as continuous rise, fall, or stability, reflecting changes in tissue perfusion and pressure. The correlation of perfusion conditions refers to whether the impedance trend is intrinsically linked to the blood perfusion in the soft tissue. For example, a continuous decrease in impedance usually indicates poor perfusion and early ischemia. The conductivity distribution refers to the spatial distribution of conductivity in various areas of the mattress at the same time, which helps to identify support uniformity and local abnormal stress points. The impedance response change refers to the change in the impedance signal of a certain area in a specific time window due to external factors such as action and pressure. The impedance fluctuation type classifies the impedance state of each time period according to the manifestation of the impedance signal (such as a sharp drop or a slow rise).

[0031] In the risk area identification module, local impedance mutation refers to an obvious sudden drop or change in the EIT signal in the local area, which usually indicates an increased risk of early pressure ulcers; electrode numbering refers to the identification of the location and signal affiliation of each EIT unit, which facilitates the subsequent one-to-one correspondence with the spatial location of the risk area; high-pressure risk area refers to areas that are subjected to high pressure intensity for a long time and are most prone to pressure ulcers (such as the sacrum, under the scapula, etc.), which require key monitoring; the area of ​​continuous impedance decline refers to the local area where the EIT signal shows a downward trend for a long time, which is mostly related to tissue ischemia and perfusion disorders; correspondence refers to judging whether a certain change (such as a continuous drop in impedance) coincides with the actual clinical risk (such as subcutaneous tissue ischemia) in space and time; the risk status of each area is based on analysis to label different monitoring areas with risk labels, such as high risk, medium risk or low risk.

[0032] In the bed intervention control module, the bed linkage structure refers to the hardware mechanism on the bed used to perform automatic interventions such as turning, lifting, and air pressure adjustment, usually including motors, airbags, etc.; intervention parameters refer to intervention action parameters configured for different risk areas, such as turning angle, air pressure changes, action duration, and other settings; action amplitude refers to the amplitude of a specific intervention action, such as the turning angle range, lifting height, etc.; control configuration is the comprehensive parameter setting of the bed linkage system for each intervention action, including the action execution sequence, intensity, frequency, etc.; the bed control system is an intelligent bed operation platform used to integrate EIT monitoring, risk analysis and action control.

[0033] In the intervention feedback adjustment module, the impedance change rate before and after the intervention action refers to the comparison of the impedance change rate of the same high-risk area before and after the intervention action, which is used to evaluate the intervention effect; the impedance improvement trend refers to whether the impedance signal changes from a downward or abnormal trend to an upward or recovery after the intervention, reflecting whether the tissue perfusion has improved; the conductivity rebound situation refers to the situation where the regional tissue conductivity recovers from a low level after the intervention, indicating that local perfusion has recovered; the correspondence with the regional perfusion improvement refers to judging whether the impedance and conductivity rebound are consistent with the improvement trend of the actual tissue blood flow perfusion; the judgment standard refers to the indicator system that sets whether the intervention is considered effective (such as the impedance change reaches a certain level of improvement, the signal trend is reversed, etc.); the action control configuration refers to the readjustment or optimization of the intervention action parameters to achieve a better risk intervention closed loop.

[0034] See also Figure 2 , EIT signal acquisition module includes: The electrode array acquisition submodule continuously monitors the impedance signal of each acquisition area based on the electrode distribution array of the bed. By marking each area and synchronously recording, the impedance acquisition process of each electrode is tracked throughout the entire process to obtain the impedance monitoring data set. Multiple electrodes are installed on the surface of the bed in a preset array and densely arranged in key areas such as the sacrum and back. For example, 25 electrode nodes are set in 5 rows and 5 columns in the sacrum, and each electrode is assigned an independent number. By switching the excitation and receiving electrode pairs in sequence, impedance data is collected for the area where each electrode point is located. The sampling period is set to record the impedance value every 5 seconds. The entire collection period lasts for 5 minutes. Each electrode will generate 60 impedance data points. The data sampling timestamp and electrode spatial position information are synchronously recorded during the collection process, and independent identification numbers are set for different areas to support subsequent regional comparative analysis. For example, when recording electrode No. 13 in the central area of ​​the sacrum, its impedance value, position information (such as 25 cm horizontally and 15 cm vertically from the edge of the bed), time node (such as 30 seconds), collection current path information and corresponding received voltage value are simultaneously saved to construct a structured impedance monitoring data set.

[0035] The impedance fluctuation classification submodule compares the impedance changes of each spatial point based on the impedance monitoring data set, screens the continuous fluctuation areas, classifies the areas with key fluctuation amplitudes, and determines the relationship between the conductivity distribution and signal changes in each area to obtain the fluctuation classification distribution set; Impedance change records for each electrode point were extracted one by one. The difference between the maximum and minimum impedance values ​​at that point in the time series was first calculated and compared with a preset fluctuation threshold. If the difference exceeded the threshold, it was marked as a continuous fluctuation region. The absolute value of the difference between adjacent moments in each impedance sequence in the region was averaged to determine the intensity of the fluctuation amplitude. All regions were sorted from largest to smallest by the average fluctuation amplitude, and the top 20% of regions were selected as key fluctuation regions. The continuous change trend of the impedance values ​​over time was analyzed in each region, and the number of consecutive increases or decreases in the impedance value was counted. If a region showed at least three consecutive changes in the same direction in the sequence, it was determined to be a trend fluctuation region. Based on this, the conductivity data corresponding to the region was used to analyze the direction consistency with the impedance change trend. If the conductivity decreased when the impedance increased, or the conductivity increased when the impedance decreased, at multiple time points, the fluctuation in the region was considered to be closely related to the conductivity change. Finally, each fluctuation region was assigned a multi-label classification, such as high fluctuation, trend type, and conductivity correlation, and integrated into a fluctuation classification distribution set for subsequent feature extraction.

[0036] The characteristic quantity calculation submodule analyzes the impedance signal sequence of the involved areas based on the fluctuation classification distribution set, calculates the average amplitude and maximum fluctuation amplitude of each area, optimizes the feature sorting of each area, and obtains the impedance fluctuation characteristic sequence; According to the regional classification results of the fluctuation classification distribution concentration, 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 counted, and the difference is averaged to measure the average amplitude of the impedance fluctuation in the region. At the same time, the maximum impedance value and the minimum impedance value recorded in the region are extracted from the entire sequence, 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. The weight values ​​are set to 0.6 and 0.4. During the scoring process, the combined score values ​​of all regions are automatically sorted in descending order to identify the top 10 regions with the most significant impedance fluctuation characteristics. In the sorting results, each region records its electrode number, spatial position, average amplitude value, maximum fluctuation amplitude value and combined score value as part of the impedance fluctuation characteristic indicator sequence of the region. The output is the impedance fluctuation characteristic sequence, which serves as the input basis for subsequent trend identification and risk judgment.

[0037] See also Figure 3 , the impedance trend grading module includes: The fluctuation feature extraction submodule identifies the average amplitude and maximum amplitude of the electrode acquisition signal in each time window based on the impedance fluctuation feature sequence, compares the fluctuation amplitude of the same electrode in different time periods, determines the fluctuation frequency change characteristics, and obtains the amplitude fluctuation frequency characteristics; The impedance sequence of each electrode node in the set time window is intercepted, and the length of each window is set to 60 seconds. The absolute values ​​of all impedance differences in the electrode sequence in this time period are summed and the average value is taken as the average amplitude. At the same time, the difference between the maximum impedance value and the minimum impedance value in this time period is selected as the maximum amplitude. The two amplitude indicators of all electrodes in this time window are recorded and numbered with time tags to form a structured amplitude data table. Then, the average amplitude and maximum amplitude changes of the same electrode in multiple consecutive time windows are compared in chronological order. If the average amplitude in a certain interval rises continuously for more than two window cycles, it is determined to be a fluctuation enhancement trend. If the maximum amplitude in three adjacent windows is lower than If the static stability reference value is set to 1.0 ohm, the section is marked as the fluctuation convergence area. Then, the frequency analysis is performed on the fluctuation records of each electrode. The fluctuation frequency value is obtained by counting the number of times the fluctuation amplitude exceeds 1.5 ohms in five consecutive windows and dividing it by the total length of the time period. The fluctuation frequency division standard is set to 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 more than 0.5 times per minute. If an electrode fluctuates 7 times within 10 minutes, the fluctuation frequency is 0.7 times per minute, which is marked as a high-frequency fluctuation area. Finally, the amplitude fluctuation frequency characteristics of all electrodes are archived and stored, and used as input data for subsequent trend change judgment to obtain the amplitude fluctuation frequency characteristics.

[0038] The trend change identification submodule is based on the amplitude fluctuation frequency characteristics. By comparing the data change direction and change gradient in adjacent time windows, it determines the trend continuity and direction consistency in the continuous change process of the signal, calculates the fluctuation of the change gradient, and obtains the trend direction change amplitude. First, the difference between the fluctuation frequency values ​​of each electrode in two adjacent time windows was calculated, and the direction of change was recorded as positive or negative. If the fluctuation frequency of an electrode in the first window was 0.6 times per minute and 0.8 times per minute in the second window, the change direction was positive, indicating increased fluctuation. Next, the absolute difference between the fluctuation frequency changes in adjacent windows was calculated to record the change gradient. If the change direction was consistent for three consecutive times, the electrode was judged to have strong trend continuity within the segment and assigned a direction consistency label. If the direction changed continuously, it was marked as an unstable trend segment. At the same time, the standard deviation of the change gradient data for each electrode was calculated per time window to reflect the stability of the fluctuation gradient change. If the standard deviation of the fluctuation gradient in a certain area was greater than 1.2 ohms per minute in five consecutive windows, the segment was identified as a high volatility trend segment. Based on this, the trend continuity label, the number of consistent directions, and the gradient stability of each electrode in multiple time windows were summarized to classify and statistically analyze the trend direction change amplitude. This data table of trend direction change amplitude was used to support subsequent identification of high-risk change areas and obtain the trend direction change amplitude.

[0039] The distribution feature summary submodule is based on the trend direction change range and uses the formula: ; Obtain the impedance spatial trend distribution width, compare the spatial distribution correspondence, judge the consistency of regional response and conductivity change, and obtain the impedance trend distribution characteristics of the time period, among which, Indicates the The spatial trend distribution of regional impedance, Indicates the Region in time The trend direction change range, Indicates the Region in time The response curve of the offset amplitude, Indicates the Region in time The amplitude change expands the width, Indicates the The conductivity variation range of the region, Indicates the total number of time periods.

[0040] The impedance spatial trend distribution width is used to quantitatively describe the comprehensive performance characteristics of the tissue electrical impedance signal trend change at the spatial level in a specific area over a period of time. It reflects the amplitude characteristics, directionality, time series fluctuations of the impedance change in the area, and the coupling relationship with the conductivity change. It is used to quantitatively reflect the numerical comprehensive characteristics of the impedance signal trend change and abnormal width distribution in each partition of the bed over a period of time.

[0041] According to the trend direction change width of each monitoring area in a continuous time period ( ), response curve offset width ( ), amplitude change expansion width ( ) and conductivity variation range ( ), combined with the total number of observation periods ( ), calculate the impedance spatial trend distribution width ( ), the calculation process uses a combination of weighted summation and normalization to achieve the fusion evaluation of multi-dimensional features. First, take the area numbered 1 as an example. The trend direction change amplitude in three consecutive time periods is: , the original value of the response curve offset amplitude is 1.2, and after normalization it is , the original value of the amplitude change expansion amplitude is 0.9, and after normalization it is The original value of the conductivity change range is 0.15, and after normalization, it is , substitute into the formula: ; ; ; Then take the area numbered 2 as the main area, and its parameters are , , , , substitute into the formula: ; ; Finally, for region number 3, the parameters are , , , ,but: ; According to the above calculations, the impedance spatial trend distribution results for areas numbered 1, 2, and 3 are: , , ; Combined with the set reference interval of spatial trend distribution width, the distribution range of normal and stable state in historical monitoring samples can be taken as the comparison benchmark. If the reference interval is set to , then this interval reflects the typical characteristic distribution of the area without obvious trend deviation. The closer the value is to the upper limit, the higher the trend activity is but it is still normal. Exceeding this range indicates that the trend change tends to be more centralized or the signal deviation is obvious. The result shows that: the value of area No. 1 is within the benchmark interval, indicating that its overall trend change is stable, the spatial trend is relatively dispersed, and no outstanding features are shown; the values ​​of areas No. 2 and No. 3 exceed the upper limit of 0.55 respectively, indicating that the spatial concentration of their impedance trends is improved, reflecting that the trend fluctuation characteristics in this area are enhanced and the signal response structure is obvious. It is a trend active area and needs to be further combined with conductivity and pressure concentration data for spatial aggregation matching.

[0042] See also Figure 4 , the risk area identification module includes: The impedance mutation screening submodule analyzes the impedance change curves of each spatial location in the monitoring data based on the impedance trend distribution characteristics of the time period, screens the areas where the impedance curve shows a decrease and the amplitude changes are critical, determines whether the area continues to have abnormal fluctuation characteristics, and obtains the impedance mutation positioning results; The impedance data sequences of all electrodes in multiple consecutive time windows were extracted. The data of each electrode were classified according to the spatial number. In each electrode impedance sequence, the impedance difference between adjacent time windows was calculated one by one. By comparing the current value with the value of the previous window, it was identified whether there was an impedance decrease trend. When the decrease exceeded the set threshold, it was determined to be a mutation event. The threshold setting was based on the impedance decrease change of the typical precursor of tissue perfusion abnormality in the reference sample data and was set to 1.5 ohms. If a numbered electrode dropped from 12.3 ohms to 10.5 ohms within 5 minutes and there was no change in the process, the impedance of the electrode was detected. If a significant rebound occurs, it is determined to be a mutation area, and the average rate of impedance decrease during this period is recorded as 0.36 ohms per minute. Subsequently, all electrode data determined to be in a decreasing mutation area are analyzed again for continuity to check whether the subsequent time windows still maintain a downward trend or an unstable fluctuation state. If the impedance difference of an electrode is still negative or the oscillation change is greater than 0.5 ohms within the three time windows after the determination, it is determined that it continues to have abnormal fluctuation characteristics. At the same time, the spatial position of the electrode is marked, and the electrode number and its spatial coordinates are output as the impedance mutation positioning result.

[0043] The electrode distribution analysis submodule analyzes the spatial distribution of electrode numbers on the bed surface based on the impedance mutation positioning results, determines the overlap between the electrode numbers and the sacrococcygeal key monitoring areas, calculates the spatial overlap ratio and distribution density between the two, and obtains the sacrococcygeal electrode matching parameters; Extract the numbers of all mutant electrodes and their spatial position information on the bed. The bed coordinates corresponding to each numbered electrode have been uniformly calibrated during the modeling stage. Retrieve the regional coordinate boundary information of the sacrococcyx. This area is set to be within 30 cm to 60 cm from the end of the bed according to clinical standards, with a width of 20 cm on each side of the bed's central axis. Then compare the center coordinates of all mutant electrodes with the sacrococcyx regional boundary. If both the horizontal and vertical coordinates of the electrode center point fall within the sacrococcyx region, it is recorded as an overlap point. Count the number of all mutant electrodes located in the sacrococcyx region and compare it with the total number of mutant electrodes to obtain the overlap ratio. For example, identify There are 12 mutant electrodes, 9 of which are located in the sacrococcygeal region, so the overlap ratio is 75%. At the same time, the electrode distribution density in the sacrococcygeal region is further calculated, that is, the ratio of the number of mutant electrodes to the total number of electrodes per unit area in the region. Assuming that the area of ​​the sacrococcygeal region is 1200 square centimeters, there are 9 mutant electrodes, and the total number of electrodes is 25, then the mutant electrode density in the region is 0.75 electrodes per 100 square centimeters, and the overall electrode density is 2.08 electrodes per 100 square centimeters. Finally, the overlap ratio and density value are used as the sacrococcygeal electrode matching parameters for subsequent high-risk area identification calculations to obtain the sacrococcygeal electrode matching parameters.

[0044] The risk status ranking submodule calculates the difference between the average impedance fluctuation amplitude and the average conductivity fluctuation amplitude of the target area in multiple time periods based on the sacral and coccyx electrode matching parameters, using the formula: ; Obtain the magnitude of risk trend changes, sort each area, and obtain the high-risk area identification results, where: Indicates the The magnitude of changes in regional risk trends, Indicates the The average fluctuation amplitude of the impedance of the region, Indicates the The average fluctuation amplitude of the conductivity of the region, Indicates the The number of time periods in which the region participated in the risk trend assessment, Indicates the Region in the The impedance decrease rate within a time period is Indicates the Region in the The duration of the trend in the time period, Indicates the The sacrococcygeal electrode matching parameters of the region, Indicates the The number of densely distributed changes in the area.

[0045] The amplitude of risk trend change refers to the comprehensive change characteristics of key parameters such as impedance and conductivity for each monitoring area, combined with factors such as impedance fluctuations in multiple time periods, continuous decline rate, and spatial distribution matching, to quantitatively reflect the comprehensive degree of high-risk changes in the area in the recent period. The larger the amplitude of risk trend change, the more drastic the abnormal changes in physiological parameters such as impedance in the area in the recent period, and the closer the spatial distribution is to the high-risk area, which requires special attention and priority intervention. This value is an important comprehensive quantitative result that guides risk ranking and clinical response after simple normalization of complex and multi-dimensional monitoring data.

[0046] It represents the average impedance fluctuation amplitude of the target area in the selected time period. The acquisition cycle is once every 10 seconds. The absolute impedance change is recorded every time in 60 acquisitions. The average fluctuation amplitude is calculated to be 4.6Ω, which is set to 0.92 after normalization. It represents the average fluctuation amplitude of conductivity under the same period, which is converted into an equivalent impedance amplitude of 2.1Ω by back-calculation of the regional current response and is set to 0.58 after normalization. For the The impedance drop rates of the time periods were monitored to be 0.8Ω / h, 0.9Ω / h, 1.1Ω / h, and 1.0Ω / h, which were normalized to 0.6, 0.65, 0.8, and 0.7, respectively. is the trend duration of each time period, which are 3h, 2.5h, 2h, and 3.5h respectively, and the corresponding normalized values ​​are 0.75, 0.62, 0.5, and 0.88. is the sacral electrode matching parameter corresponding to the target area. The original value is 0.55 and is set to 0.61 after normalization. Indicates the change in number density distribution. The original value is 0.3, and it is set to 0.42 after normalization. Indicates that the total monitoring time period is 4 periods. Substitute into the formula for calculation: Calculate the molecular part: ; ; ; ; Calculate the denominator: ; The third step is to calculate the overall formula: ; The results show that the calculated risk trend changes 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 area). It can be judged that the impedance and conductivity of the target area show obvious abnormal differences. At the same time, the superposition effect of the trend fluctuation term is outside the system sensitive response range. It is comprehensively judged as a high-risk state that significantly deviates from the normal tissue state. The values ​​are sorted from high to low. Areas with sorted values ​​exceeding the upper limit of the identification interval will be directly marked as high-risk areas and output as high-risk area identification results. The numerical results are further sorted as follows: According to the set interval The values ​​are graded as follows: 0.00–1.2 is low risk; 1.21–2.2 is medium risk; Above 2.21 is high risk. The current regional value is 2.97, which corresponds to a high risk level. Therefore, this calculated value not only supports the sorting decision but also serves as a criterion for risk label classification.

[0047] See also Figure 5 , the bed intervention control module includes: The partition position determination submodule analyzes the regional distribution information and electrode numbers based on the high-risk area identification results, determines the spatial correspondence between 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 position mapping; Extract the electrode number set corresponding to each high-risk area, and call the coordinate correspondence between the electrode number and the bed partition defined in the bed structure mapping table. The spatial position corresponding to each electrode number is expressed as horizontal and vertical values ​​through the bed coordinates. In the spatial judgment process, the electrode center coordinates are compared one by one with the boundary coordinates of each bed control area to determine whether the electrode completely falls into a certain control area. When the horizontal and vertical range restrictions are met, the bed partition number to which it belongs is recorded. For example, electrode number E15 is between 30 and 40 cm horizontally and 70 to 90 cm vertically. If this range completely overlaps with the bed control area number Z3, E15 is mapped to the Z3 area. Then, the correspondence between all electrodes and bed control areas in each high-risk area is counted. The distribution should be such that if a high-risk area contains multiple electrodes that fall into different bed control zones, the main mapping position of the area is set according to the control zone with the largest number of electrodes. At the same time, the distribution of the remaining electrodes is recorded to determine the secondary control impact area. Further, according to the number of high-risk areas mapped by each bed control zone, a many-to-one mapping table is constructed, and the conflicting areas are adjusted in sequence according to the control priority weight. The weight setting method uses the ratio 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 main control mapping area and Z4 is the auxiliary control area. The main control bed control area number and the auxiliary control area number corresponding to each high-risk area are output to obtain the high-risk partition position mapping.

[0048] The action demand calculation submodule is based on the high-risk partition location mapping, extracts the impedance measurement data of the collection points in the area before and after the intervention, counts the number of collection points, selects the partition impedance benchmark, and combines the support structure force area and linkage structure length to use the formula: ; Calculate the action response adjustment amplitude and obtain the regional action level identification set, where Indicates the The amplitude of the action response adjustment in the high-risk area, Indicates the The number of collection points used for motion calculation in an area, Indicates the Region No. Impedance measurement before intervention at each collection point, Indicates the Region No. Impedance measurement after intervention at each collection point, Indicates the impedance reference used for action judgment, Indicates the The supporting structure bearing area of ​​each region, Indicates the The length of the linkage structure corresponding to each region; The action response adjustment amplitude refers to the For each high-risk area, before and after the intervention, the impedance changes of all relevant collection points in the area are normalized and statistically analyzed. This is combined with the force-bearing area of ​​the regional support structure and the length of the linkage structure to obtain a quantitative parameter that reflects the action response amplitude that needs to be adjusted or controlled in the area. This parameter is a comprehensive reflection of the impedance change intensity and spatial distribution characteristics combined with the support structure characteristics after the intervention action (such as turning over, bed adjustment, etc.) is implemented in the area. It is the core basis for subsequently determining the linkage action level and adjusting specific action parameters. Call all collection points in the area, and set the number of statistical collection points to , read the impedance measurement data before and after the intervention of each collection point, and record them as 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 impedance after intervention was 21Ω, 21.8Ω, 20.4Ω, 20.9Ω and 21.7Ω respectively. The absolute value of the impedance change at each point was calculated and obtained. They are 1.8Ω, 1.8Ω, 1.5Ω, 1.4Ω and 1.3Ω respectively, totaling 7.8Ω. The regional impedance reference is uniformly selected. Ω is used as the normalized reference, and the normalized impedance difference ratio is obtained as , the corresponding support structure bearing area is set to square meters, the length of the linkage structure is The normalized ratio of the two is 0.3929, which is substituted into the complete formula: ; This result shows that the calculated action response modulation amplitude Located in the middle section of the action level division interval, according to the response amplitude interval division standard, if It is judged as high level (level A), which is used to drive rapid linkage intervention. It is judged as medium level (level B), which is used to set up stable rhythm movement intervention. It is classified as low level (level C), suitable for periodic mild adjustment scheme; therefore, the value fall into The range indicates that the impedance response in this area has a moderate change, and the structural support and linkage adaptation are normal. The medium-level linkage configuration template can be matched according to this level. This result plays a bridging role in mapping from quantitative indicators to the action parameter configuration logic structure, promoting the execution of the decision logic generated by the subsequent control configuration.

[0049] The control configuration generation submodule optimizes the action mode and execution sequence corresponding to each partition based on the regional action level identification set, selects the intervention priority of high-risk areas, adjusts the action type, amplitude and sequence of the bed control system, and obtains the action intervention parameter set; Extract the level identification of each high-risk area. The identification adopts a three-level coding structure, including action type code, emergency level code and time window priority value code. For example, a certain area is coded as T2-E3-P1, which means that airbag boosting is required, it belongs to the highest emergency level, and it is the first priority area in the current time period. By parsing the identification set of each area, the action type associated with each control area is summarized and counted, and the emergency levels of the same type of action in different areas are compared. If there are multiple action types conflicting in the same area, the highest level is executed first. After classifying and sorting the action types of all areas, set the execution sequence number of each control action. The sequence number is arranged from small to large according to the priority value, and at the same time, each Each action is configured with an action amplitude value and execution duration. The amplitude value matches the set value according to the severity level of the area. Severity level 1 corresponds to lifting 10 cm or increasing the air pressure by 30 mmHg, level 2 corresponds to lifting 5 cm or increasing the air pressure by 15 mmHg, and level 3 is set to maintain the original position. The durations correspond to 120 seconds, 60 seconds, and 30 seconds respectively. All regional action data are combined and packaged to form an action control configuration list. If an area needs to perform T1 action and the level is 2, it is configured to lift 5 cm and last for 60 seconds, and set to the third execution order. All configurations are summarized and output as a structured parameter set, including the control area number, action type, execution order, action amplitude, duration and control number, to obtain the action intervention parameter set.

[0050] See also Figure 6 , the intervention feedback regulation 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 change direction and continuous change characteristics of the signal curve, summarizes the impedance response performance with the execution of the action, and obtains the impedance response change level; The intervention action type, start time, duration, and amplitude corresponding to each high-risk area were read, and the impedance sequence recorded by the corresponding electrodes in this time period was extracted. The impedance sequence 5 minutes before the intervention was used as the baseline segment, and the impedance sequence 5 minutes after the intervention was used as the comparison segment. The differences between the impedance values ​​at adjacent time points in these two time periods were averaged to obtain the average change rate of the two periods before and after the intervention. By comparing the two change rates, it was determined whether the impedance was increasing, decreasing, or oscillating. Several prognostic impedance changes with a positive direction and a rate greater than 1.5 times the pre-intervention rate were determined to be an improvement response. If the change direction was consistent but the rate increased by less than 50%, it was marked as a delayed response. If the change direction was reversed and the amplitude increased, it was marked as a worsening response. The impedance curve was also analyzed for whether there was a continuous unidirectional change before and after the intervention. For example, if the impedance continued to rise and exceeded 0.5 ohms within 1 minute after the start of the action, this period was recorded as a continuous improvement segment. A response rate ratio table and curve feature identifier set were generated according to the electrode number corresponding to each area to obtain the impedance response change level.

[0051] The response improvement trend analysis submodule calls the impedance response change level, compares the conductivity change curve of the mattress support area after intervention, determines the trend consistency of the conductivity curve and the impedance curve, selects the change segments with strong curve trend synchronization, summarizes the trend correlation between the two groups of signals, and obtains the perfusion trend consistency feature; The conductivity sequence of the mattress support area corresponding to each high-risk area electrode was retrieved, and the intervention action time point was used as the segmentation node. The conductivity curve data before and after the intervention was extracted to compare whether the conductivity showed a continuous increase, decrease or fluctuation after the intervention. Then, the direction consistency of the impedance response change curve and the conductivity curve was judged. The number of times the change direction of the two was consistent in three consecutive sampling points after the intervention was calculated. If the proportion of the number of consistent directions was greater than 70%, it was determined that the trend consistency of the area was strong. After performing this judgment on all areas, all trend consistency was marked as "synchronous" or "asynchronous". The areas marked as "synchronous" were further extracted for their conductivity change amplitude and impedance recovery speed within 3 minutes after the intervention, and trend synchronization segments were recorded in the segments where the two indicators rose simultaneously and the average change amplitude was greater than 0.2 ohms and 0.3 Siemens per meter. For example, after the intervention, the impedance of a certain area rose from 11.2 ohms to 11.9 ohms, and the conductivity rose from 0.35 Siemens per meter to 0.43 Siemens per meter, and the three sampling directions were consistent, then it was marked as a trend consistent segment. The number and distribution time period of the signal synchronization segments of all areas were summarized and output as the perfusion trend consistent feature.

[0052] The control configuration adjustment submodule screens the parameter configuration of the current intervention action based on the consistent characteristics of the perfusion trend and the synchronization performance of the conductivity and impedance curves, optimizes the action duration and amplitude distribution, adjusts the execution sequence to match the trend changes of the monitoring feedback, and obtains the impedance response adjustment amplitude; Identify the areas that are judged as "asynchronous" or "insufficiently responsive" in all intervention actions, extract their current corresponding control parameters, including action type, execution time, lifting amplitude or air pressure intensity, and then re-judge the time delay and amplitude change of the perfusion trend of the area. If the perfusion improvement lags for more than 2 minutes and the amplitude is lower than the set lifting baseline value of 0.3 ohms, it is determined that the original action configuration does not match, and the current action duration is increased by 20%, the lifting amplitude is increased by 10%, or the corresponding air pressure value is increased by 20%. For example, the original setting is air pressure 20 mmHg, duration If the impedance is increased, the conductivity will be increased by more than 0.5 Siemens per meter, which will give priority to the area with the greatest improvement potential.

[0053] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A pressure ulcer risk perception and prevention control system based on electrical impedance tomography, characterized in that: The system comprises: The EIT signal acquisition module is based on the electrode distribution array of the bed body. It analyzes the dynamic changes of impedance in each area, compares the signal distribution density and conductivity, classifies the fluctuation area, screens the key change segments, calculates the average amplitude and maximum change of the characteristic segment, and obtains the impedance fluctuation characteristic sequence; The impedance trend grading module analyzes the average amplitude and maximum amplitude in each time window based on the impedance fluctuation characteristic sequence, compares the fluctuation trend, and combines the mattress conductivity and tissue perfusion status to summarize the grading type and obtain the impedance trend distribution characteristics of the time period; The risk area identification module screens the EIT units involved in impedance mutation based on the impedance trend distribution characteristics of the time period, analyzes the correspondence between the number and the high-voltage risk area, determines the correlation between the continuous decline of the area and ischemia, and obtains the high-risk area identification result; Based on the high-risk area identification results, the bed intervention control module locates the high-risk partitions, analyzes the motion range requirements, optimizes the turning and air pressure intervention parameters, adjusts the motion range and control configuration, and integrates the parameters into the bed control system to obtain a motion intervention parameter set.

2. The pressure sore risk perception and prevention control system based on electrical impedance tomography according to claim 1 is characterized in that: The impedance fluctuation feature sequence includes signal timing segments, spatial mapping information, and abnormal change indexes; the time period impedance trend distribution features include trend classification labels, response curve types, and reference comparison items; the high-risk area identification results include risk area distribution labels, priority intervention identifiers, and identification confidence parameters; and the action intervention parameter set includes action trigger instructions, execution mode types, and parameter configuration numbers.

3. The pressure sore risk perception and prevention control system based on electrical impedance tomography according to claim 1 is characterized in that: The EIT signal acquisition module includes: The electrode array acquisition submodule continuously monitors the impedance signal of each acquisition area based on the electrode distribution array of the bed. By marking each area and synchronously recording, the impedance acquisition process of each electrode is tracked throughout the entire process to obtain the impedance monitoring data set. The impedance fluctuation classification submodule compares the impedance changes of each spatial point based on the impedance monitoring data set, screens the continuous fluctuation areas, classifies the areas with key fluctuation amplitudes, and determines the relationship between the conductivity distribution and signal changes in each area to obtain the fluctuation classification distribution set; The feature quantity calculation submodule analyzes the impedance signal sequence of the involved areas based on the fluctuation classification distribution set, calculates the average amplitude and maximum fluctuation amplitude of each area, optimizes the feature sorting of each area, and obtains the impedance fluctuation feature sequence.

4. The pressure sore risk perception and prevention control system based on electrical impedance tomography according to claim 1, characterized in that: The impedance trend grading module includes: The fluctuation feature extraction submodule identifies the average amplitude and maximum amplitude of the electrode acquisition signal in each time window based on the impedance fluctuation feature sequence, compares the fluctuation amplitude of the same electrode in each time period, determines the fluctuation frequency change characteristics, and obtains the amplitude fluctuation frequency characteristics; The trend change discrimination submodule is based on the amplitude fluctuation frequency characteristics. By comparing the data change direction and change gradient in adjacent time windows, it judges the trend continuity and direction consistency in the continuous change process of the signal, calculates the fluctuation of the change gradient, and obtains the trend direction change amplitude. The distribution feature induction submodule obtains the impedance space trend distribution width based on the trend direction change width, compares the spatial distribution correspondence, judges the consistency of the regional response and the conductivity change, and obtains the impedance trend distribution characteristics of the time period.

5. The pressure sore risk perception and prevention control system based on electrical impedance tomography according to claim 1 is characterized in that: The risk area identification module includes: The impedance mutation screening submodule analyzes the impedance change curves of each spatial position in the monitoring data based on the impedance trend distribution characteristics of the period, screens the areas where the impedance curve shows a decrease and the amplitude changes are critical, determines whether the area continues to have abnormal fluctuation characteristics, and obtains the impedance mutation positioning result; The electrode distribution analysis submodule analyzes the spatial position of the electrode numbers on the bed surface based on the impedance mutation positioning results, determines the overlap between the electrode numbers and the sacrococcygeal key monitoring areas, calculates the spatial overlap ratio and distribution density between the two, and obtains the sacrococcygeal electrode matching parameters; The risk status ranking submodule calculates the difference between the average impedance fluctuation amplitude and the average conductivity fluctuation amplitude of the target area in multiple time periods based on the sacral and coccyx electrode matching parameters, obtains the risk trend change amplitude, ranks each area, and obtains the high-risk area identification result.

6. The pressure sore risk perception and prevention control system based on electrical impedance tomography according to claim 1 is characterized in that: The bed intervention control module includes: The partition position determination submodule analyzes the regional distribution information and electrode numbers based on the high-risk area identification results, determines the spatial correspondence between 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 position mapping; The action demand calculation submodule extracts the impedance measurement data of the collection points in the area before and after the intervention 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 based on the support structure force area and linkage structure length to obtain the regional action level identification set; The control configuration generation submodule optimizes the action mode and execution order corresponding to each partition based on the regional action level identification set, screens the intervention priority of high-risk areas, adjusts the action type, amplitude and sequence of the bed control system, and obtains the action intervention parameter set.

7. The pressure sore risk perception and prevention control system based on electrical impedance tomography according to claim 1 is characterized in that: The system further comprises: The intervention feedback adjustment module calculates the impedance change rate before and after the intervention based on the action intervention parameter set, compares the impedance improvement trend, and combines the conductivity rebound of the support area to determine the relationship between the impedance change and the perfusion improvement, adjusts the action control configuration, and obtains the impedance response adjustment amplitude; The impedance response adjustment amplitude includes a response change amount, a feedback adjustment factor, and an effectiveness determination result.

8. The pressure sore risk perception and prevention control system based on electrical impedance tomography according to claim 7, 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 change direction and continuous change characteristics of the signal curve, summarizes the response performance of the 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, determines the trend consistency of the conductivity curve and the impedance curve, selects the change segments with strong curve trend synchronization, summarizes the trend correlation between the two groups of signals, and obtains the perfusion trend consistency feature; Based on the consistent characteristics of the perfusion trend, the control configuration adjustment submodule screens the parameter configuration of the current intervention action and the synchronization performance of the conductivity and impedance curves, optimizes the action duration and amplitude distribution, adjusts the execution order to match the trend changes of the monitoring feedback, and obtains the impedance response adjustment amplitude.

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